application of multihazard drought risk management...
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Application of multihazard drought risk management tools
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 2
Report - Application of multihazard drought risk
management tools
3
Deliverable n°11.3
Deliverable Application of multihazard drought risk management tools
Related Work Package: 11
Deliverable lead: S. Groot
Author(s): Chapter 1: all
Chapter 2: Joaquín Andreu, Abel Solera, Sara Suárez-Almiñana (UPV)
Chapter 3: Susanne Groot, Carolien Wegman (HKV)
Chapter 4: Femke Schasfoort (Deltares)
Chapter 5: and 6: Femke Schasfoort and Susanne Groot
Contact for queries [email protected]
Grant Agreement Number: n° 641811
Instrument: HORIZON 2020
Start date of the project: 01.10.2015
Duration of the project: 48 months
Website: www.imprex.eu
Abstract This deliverable reports on the application of a multihazard drought risk
management tool in three pilot studies. One of the pilots is located in
Spain and two of the pilots are located in The Netherlands. The methods
used in the two countries differ. A description of the applied methods
and the results are given. The risk profiles and conclusions that can be
drawn from them are described. A comparison of the pilot studies is
made, in which the differences and the implications of the differences
are described.
The drought risk approach has not yet proven its use in management or
operational advice. Based on the results of the three case studies, the
method is promising.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 4
Dissemination level of this document
X PU Public
PP Restricted to other programme participants (including the Commission Services)
RE Restricted to a group specified by the consortium (including the European Commission
Services)
CO Confidential, only for members of the consortium (including the European Commission
Services)
Versioning and Contribution History
Version Date Modified by Modification reasons
v.01 16-07-2018 S. Groot and other
authors
Full draft for first review by J Hunink (FW)
v.02 07-09-2018 S. Groot and other
authors
Adjusted after review
v.03 14-09-2018 B vd Hurk review
v.04 28-09-2018 S. Groot and other
authors
Adjusted after review by B vd Hurk (KNMI)
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Deliverable n°11.3
Table of Contents
Introduction ................................................................................................................................................... 7 1
Aim ........................................................................................................................................................ 7 1.1
Method ................................................................................................................................................. 8 1.2
Pilot areas ............................................................................................................................................ 10 1.3
Júcar basin ................................................................................................................................................... 17 2
Introduction ......................................................................................................................................... 17 2.1
Method ................................................................................................................................................ 18 2.2
Results ................................................................................................................................................ 24 2.3
From concept to user ........................................................................................................................... 38 2.4
Discussion and conclusions .................................................................................................................. 39 2.5
Next steps .......................................................................................................................................... 44 2.6
Berkel .......................................................................................................................................................... 45 3
Introduction ......................................................................................................................................... 45 3.1
Method ............................................................................................................................................... 48 3.2
Results ................................................................................................................................................. 50 3.3
Risk approach ...................................................................................................................................... 54 3.4
Discussion and conclusions .................................................................................................................. 58 3.5
From concept to user .......................................................................................................................... 60 3.6
Next steps ........................................................................................................................................... 61 3.7
ARC-NSC ..................................................................................................................................................... 63 4
Introduction ......................................................................................................................................... 63 4.1
Method ............................................................................................................................................... 66 4.2
Results ................................................................................................................................................ 69 4.3
Risk profile ........................................................................................................................................... 79 4.4
Discussion and conclusions .................................................................................................................. 79 4.5
From concept to user .......................................................................................................................... 80 4.6
Next steps .......................................................................................................................................... 80 4.7
Comparing the case study results ................................................................................................................. 81 5
Implications ................................................................................................................................................. 85 6
References ........................................................................................................................................................... 87
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Deliverable n°11.3
Introduction 1
Aim 1.1
Already in the present situation, delta regions world-wide encounter problems resulting from fresh water scarcity.
Climate change and socio-economic developments make delta societies even more susceptible to consequences
arising from drought events. The need for fresh water will increase, whereas more and longer periods of extreme
drought are expected. The resulting risks, for present day and for future situations, exhibit a wide range of
uncertainty. Management decisions on water resources are therefore difficult to make. Decision making in water
resources management and especially making appointments for fresh water assignment rules is becoming
increasingly more complex. There is a strong wish to make uncertainty in water shortage and drought related
risks more explicit. The inters
In WP5.3, multihazard drought risk management instruments are developed. These instruments combine
probability of occurrence of droughts with the effect of this drought on different land-uses, amongst which
agriculture. In other words, drought risk is determined as a probabilistic assessment of economic drought
impacts. In WP11.3 these instruments are applied in several case studies. The aim of application of a drought risk
management approach is to facilitate proactive drought risk management. A systematic process to prevent,
mitigate and prepare for drought-induced disaster is suggested by UNISDR (2009) as a more sustainable solution
than reactive emergency management. This will allow analysis of the effects of climate change, prediction of
drought damage under certain conditions and assessment of the effectiveness of management options and
interventions.
By means of the drought risk management instruments, the impact of climate variability on drought related risks
for the agricultural sector is evaluated. The cost-efficiency of measures to better cope with water scarcity is
assessed. The resulting risk profiles serve as a starting point for new management strategies for risk adaptation
and mitigation (explored in WP13).
This deliverable reports on the application of the instruments in case studies. The results so far are described, as
well as the findings in bringing the concept to (future) users. Ongoing work and future steps to improve the
application of the tools are illustrated.
To test different implementations of a drought risk management approach, three case studies are chosen. One is
the Júcar River Basin in Spain, the second is the Berkel catchment area in The Netherlands and the third is the
Amsterdam-Rhine Canal-North Sea Canal system in The Netherlands. Both pilot areas in The Netherlands are
part of the Rhine-Meuse estuary. All three areas are introduced in paragraph 1.3. The methods used for the
drought risk management approach also differ per area and are described in paragraph 1.2. In these case studies,
stakeholders are explicitly involved. Although economic optimization would often lead to other risk management
practices, legal and psycho-sociological aspects are often dominant in the real world's decision making. The input
of stakeholders is required to define what the demands are for the risk method to be adopted for policy and
management decisions.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 8
Example of the need for a risk based approach in The Netherlands
In 2010, the Netherlands initiated a national Delta Programme that was designed to protect the Netherlands against flooding
and to secure freshwater supplies under a changing climate. As temperatures are rising, summers are expected to get hotter
and drier, which could lead to a lack of freshwater for farming, industry and nature. To mitigate these potential impacts, the
Delta Programme presented in 2016 a set of ‘fresh water’ measures and regulations, such as the enlargement of water supply
channels. The plans were supported by a cost-benefit analysis, which used characteristic drought years (return period of 1/10
and 1/100) to assess the benefits of measures (Stratelligence, 2014). Since the benefits of these measures highly depend on the
progression of a drought event over the year, and could be substantially influenced by extreme drought events, there is a
distinctive need to develop a drought risk approach, in which risk is based on multiple drought events with different return
periods (CPB, 2015). This approach could support the ability to make a well-founded decision on the implementation of new
measures in the next phase of the Delta Programme.
Method 1.2
The approach includes a conceptual framework for drought risk analysis. The drought risk-management
instruments (developed in WP5.3) consider:
1. the probabilities of the joint occurrence of low river discharges, salt-water intrusion and limited water
storage in the surface and ground water system,
2. the impact on water shortage and the consequences for the agricultural sector (using damage functions,
often depending on the frequency, duration and season in which shortage occurs),
3. the consequences of water shortage for other relevant functions.
Figure 1 A schematic overview of a drought risk assessment framework.
Different drought risk-management instruments are developed for both regions, the Rhine-Meuse estuary and
the Júcar River Basin. For the three pilot studies, different methods are applied. These methods are shortly
introduced here, and described in more detail in the following chapters per pilot study.
Method developed for Júcar basin in Spain
The tool used in the Júcar basin is a risk management model that allows for the risk assessment for different
decision alternatives. The tool used is SIMRISK (Andreu and Solera, 2006), which is one of the modules of the
Aquatool software (Andreu et al., 1996). Aquatool is the decision support toolbox that is used by the water
resources authorities to analyse the management of the Júcar River Basin.
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Deliverable n°11.3
SIMRISK assesses the risk of reduced water availability in the future and allows evaluating how this risk is
modified when a given set of measures are applied. SIMRISK requires a high number of streamflow series and a
stochastic approach to generate enough synthetic series to carry out the analysis. The current method used for
the risk assessment in the JRB begins with the use of a Multivariate Autoregressive-moving-average (ARMA)
model (Salas et al., 1980), or Autoregressive-moving average with exogenous terms (in this case precipitation)
model: ARMAX, for the generation of synthetic series of stream flows.
Results from various simulations with ARMA/ARMAX model can be inserted in the SIMRISK module to simulate
the management of the system and assess the risk in a certain period. Afterwards, any given set of management
measures may be incorporated and the risk modifications can be assessed. This can lead to advice on water
management to reduce the risk of drought.
It has to be clarified that risk assessment is performed in terms of probabilities of hydrological variables and
deficits, since these types of results are the ones expected by decision-makers and users because they are easy to
understand, and they relate them quite well with any other consequences (including their economic interests),
and the modification of risks due to the implementation of any set of mitigation measures is also easy to perceive.
So far, economic analysis, which can be produced with the drought risk management tools available in JRB, has
not contributed to practical decision making. The main reasons may be that participatory water allocation process
in JRB responds more to water rights, priorities, and equity, rather than to economic objectives, and also that the
economic results have at least one order of magnitude higher of uncertainty than the above-mentioned
indicators, and therefore, are not perceived as robust basis for decision making.
Method developed for Rhine-Meuse estuary in The Netherlands
The proposed framework, which has been developed in cooperation with regional water authorities, The
Directorate General for Public Works and Water Management, the Delta Programme, HKV and other research
partners, translates hydrological time series into the drought risk. Figure 2 illustrates the general steps of the
framework, in which hydrological impacts are converted into physical impacts on drought prone sectors, the
impact of droughts on national welfare and subsequently into risk, which is defined as the probability of having a
certain impact on welfare due to droughts.
Figure 2 Four graphs that are inter-connected conceptually show the drought risk assessment framework
(derived from Deltares et al, 2015).
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 10
The observed meteorological and hydrological time series are typically limited to 50-100 years, and thus these
series do not include information on extreme events with return periods of more than 50-100 years. Therefore,
also the use of synthetic time series is considered alike the Jucar case study, as these can be more representative
for the longer return periods than observed series. As such they can assess the impacts of more intense or
extreme events and support the assessment of the robustness of the existing system and potential benefits of
mitigating measures. Time series from RACMO and ARMA model are considered. The quality of these time series
is verified by using measured data of precipitation, evaporation and Rhine discharge as well as results from other
models. This work is still on-going and will be reported in the final deliverable of WP 5.
Input for the drought risk management tool are time series of drought damage per sector. In the case studies,
these are derived from the combination of a hydrological model (covering the surface water, groundwater and its
interactions), a dose-impact relation (relationship between water shortage and a physical effect) and a model
translating the physical effect of water shortage into a probabilistic welfare effect (or in other words the drought
risk). The drought risk is defined as the probability of occurrence of the drought, multiplied by the welfare effect it
causes. By collecting many realisations within a range of probabilities and magnitudes of welfare effect, a drought
risk curve can be derived. From this curve, an average drought risk per year, or for example per 10 years, can be
calculated.
Pilot areas 1.3
Júcar Basin
In the Deliverable 11.1 an exhaustive description of the Júcar River Basin (JRB) and its exploitation system was
made, including the current problems with water scarcity and long drought periods, as well as the management
options at short, medium and long term, depending on the users’ needs and the demands of stakeholders. A short
introduction to this basin and its main characteristics, problems and management strategies are stated below.
Figure 3 Iberian Peninsula and the Júcar River Basin District (JRBD).
The Júcar River Basin is the main exploitation system of the Júcar River Basin District (JRBD), which is located in
the east of the Iberian Peninsula (Figure 3). It has an extension of 22,186 km2 and an average volume of water
resources of around 1,605 hm3/year. The irrigated agriculture accounts for nearly 80% of water demand, while
other sectors (including urban supply) account for 20%. To cope with these demands, the water exploitation
system has several reservoirs, the more important ones are Alarcón (1,118 hm3), Contreras (852 hm
3) and Tous
(378 hm3), as can be seen in the next figure. In addition, based on the position of these reservoirs and the
hydrological characteristics of the area, this basin is divided in five sub-basins (Figure 4).
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Deliverable n°11.3
Figure 4 Júcar River Basin and the sub-basins division.
The upper (inland) part of the JRB is a mountainous area, and then, the middle basin is a relatively flat area that
supports about 100,000 hectares of relatively recently installed irrigated agriculture. The lower basin lies in the
coastal plain, which support 35,000 hectares of traditionally irrigated agricultural areas and around 25,000
hectares of relatively recent irrigated areas. In these areas are permeable materials that allow the infiltration of
the rainfall to the aquifers of La Mancha Oriental (middle part of the basin) and La Plana de Valencia (lower basin),
which at the same time allow the water abstraction for irrigation purposes. In addition, in the coastal area there is
an important wetland called La Albufera de Valencia that has an extension of 21,120 hectares including a vast
extension of rice crops.
Due to the influence of the Mediterranean climate, this basin is characterised by the semi-aridity of the climate
and the high intra-annual and inter-annual hydrological variability (low flows in summer, autumn floods, etc.) that
lead to recurrent multiannual droughts. These hydrological features forced to adaptation by different
management strategies (water storage infrastructures, conjunctive use of surface and ground waters, institutional
and legal developments, etcetera to cope with drought periods. The most considerable drought events over the
last 50 years were the periods 1981-1986, 1992-1995, and 2005/2006-2008, with less extreme droughts in
between. Currently, this system has been in drought state since 2013.
To cope with these extreme situations, a proactive approach was introduced in all Spanish basins, developing a
Special Drought Management Plan (PES in Spanish) approved in 2007 (Estrela and Vargas, 2012), which includes
drought monitoring by means of a compounded drought operative index (CDOI) (Ortega et al, 2015), used to
define drought scenarios. These scenarios are normality, pre-alert, alert and emergency, which are related to
specific measures as water saving practices, reclaimed wastewater direct reuse, conjunctive use (surface and
groundwater) and water rights purchase for environmental protection, among others. In addition, CDOI maps are
updated monthly (Figure 3) and displayed in the web page of the Júcar River Basin Partnership (CHJ in Spanish),
so these maps serve as a first step early warning system to trigger predefined anticipation and mitigation
measures attached to each scenario in order to decrease vulnerability and increase resiliency.
The CHJ is the public-private participatory institution that manages water resources since 1936, and allocates
them among urban, agricultural, hydropower and industrial users, taking into account environmental protection,
and predefined water rights and priorities. Water allocation decisions are taken at different time scales and under
different scenarios. In the long term, water allocation is made in the framework of JRBD Water Plans, designed by
the CHJ since 1998, and updated regularly (last update in 2015). At the seasonal and short term, water allocation
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 12
is decided at the Water Allocation Boards of every exploitation system in JRBD taking into account the actual
situation of water storages in reservoirs and aquifers. But, in alert and emergency scenarios, water allocation
decisions are taken in the sessions of the Permanent Drought Committee, taking into account the actual situation
and following the guidelines about mitigation measures included in the PES in order to find an equitable
allocation among users minimizing environmental impacts..
This institution uses a Decision Support System (DSS) to simulate the water resources system performance and
extract the CDOI, based on historical values. It is also possible to assess the risk and the effectiveness of the
selected measures to mitigate the effects of the drought on both the established uses and on the environment by
means of simulations with the DSS. More details about this procedure are described later.
Figure 5 Drought scenario by exploitation system in the Júcar River Basin District. Modified from the Monitoring
of Drought Indicators Report in the area of the CHJ (May, 2018).
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Deliverable n°11.3
Berkel
The Berkel catchment is located in the eastern part of The Netherlands, above mean sea level. Most of the
catchment drains freely. In the downstream part it is possible to let in water from the Twenthe Canal.
The choice for the Berkel catchment as a pilot area is made in consultation with the cooperation of stakeholders in
drought water management ZON (‘Zoetwatervoorziening Oost-Nederland’). This catchment is representative for
free draining catchments in the higher areas with sandy subsoils. Also, due to the different use functions for water
and the range of possible measures to prevent drought, such as measures relating to inlets, the restructuring of
waterways and water retention in the subsurface, makes this a suitable pilot case for application of the drought
risk management tool.
Figure 6 shows the location of the Berkel catchment. Figure 7 depicts the water uses.
Figure 6 Location of the Berkel catchment.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 14
Figure 7 Water consumers in the Berkel catchment.
ARC-NSC
Also the case studie Amsterdam-Rhine Canal and the North Sea Canal (ARC-NSC) illustrates the applicability and
the added value of the risk approach. This completely managed polder system is expected to give specific insight
in the applicability of the risk method, related to the high level of management of the canals and importance of
the context specific management decisions during droughts. The frequency of occurrence of drought and the
welfare effects are depending on management decisions on water distribution. The question is if and how a risk
approach is applicable in such a managed system. Since large parts of The Netherlands are completely managed,
and many more systems also in other countries are managed up to a certain degree, this is a relevant question to
answer.
A specific reason to choose the ARC-NSC region as a pilot, is the other work that is carried out in the region. The
national water authority, Rijkswaterstaat, is improving the management of the canals by means of ‘Smart water
management’. Furthermore, a new sea-sluice is built, which will influence the salt intrusion and thereby the water
demand. There is a demand for knowledge on drought situations, their frequency of occurrence, effects on the
use functions and expected welfare effects of shortages. The canal system is part of the main water system in The
Netherlands, of which management decisions have an effect on other parts of the country as well.
The ARC-NSC is a controlled canal system in the western part of The Netherlands. The canals have a discharge,
water supply and navigation function. Water is let in from the rivers Lek and, during droughts, the river Waal. A
main use for water inlet in the canals is reducing salt water intrusion at the sea-sluice in IJmuiden. Besides, the
water from the canals is used for different functions. The operational water management in the area is dedicated
to maintaining surface water levels in and around the polder areas, and to prevent salinization of the surface
water. One source of salinization is the sea-sluice, others are more diffuse sources as saline seepage. A draft of
the system and use-functions is shown in Figure 8. All the water authorities surrounding the canals have a
dependency on water supply from the canals, albeit relatively small in the northernmost water board.
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Deliverable n°11.3
Figure 8 Location of the ARK-NZK and water demands.
There are several water use functions:
- Agriculture (flushing for water quality and irrigation)
- Navigation
- Drinking water
- Nature (flushing to reduce salinity)
- Safety (stability of dikes)
- Recreation, fishing
- Infrastructure (effects of soil subsidence in case of low groundwater levels)
- Industry.
All these functions experience potential negative effects of droughts, or water shortages.
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Deliverable n°11.3
Júcar basin 2
Introduction 2.1
As it was said above, in the Júcar River Basin the irrigated agriculture accounts for about 80% of water demand,
but agriculture is a second rank priority use in the operation rules of this water exploitation system. This means
that it is the most affected sector in periods of water scarcity, as it has happened in many past drought events.
Consequences were, for example, restrictions in water supply, reduced yields, loss of annual crops, and persistent
damage to permanent crops during the long drought event of 1992-1995. On the other hand, the impacts were
low-ranked in the 2005/2006-2008 drought event, due to the improvements in drought preparedness and
management in the JRB (Andreu et al, 2013). In this case, there also were water restrictions but they resulted in
minor reductions of yields. Despite this, it was complicated in many areas to meet full irrigation water
requirements and the management turned out to be more expensive.
Despite the improvements and measures taken, reduced yields resulted into huge economic losses. In the last
period, investments in emergency measures accounted for more than 75 million €. Besides, there was a reduction
in rain fed agriculture yields, which means a huge reduction in the GDP of this sector. The biggest reduction was in
2006 (respect to 2004) which was 411 million € in total.
These experiences demonstrate the importance of having a proactive drought planning and management
approach in order to cope with extreme droughts. Here is where the IMPREX project can help with forecasts,
projections and effective tools to improve resiliency and decrease vulnerability in front of droughts in the JRB.
In this work package (WP) devoted to agriculture, several lines of work related to WP5 are being developed. In
WP5, tasks 5.3 and 5.4, the tools are fitted to use them in sectorial work packages, and also in WP13. Thus, an
improvement of the risk assessment methodology is presented in this deliverable. This tool allows knowing the
deficits of water supply in all agricultural demands by the estimation of the risk of drought at short and long term,
and also analyses different scenarios derived from the application of measures in order to avoid damages and
economic loses for a certain period. With this improvement, it is expected that the methodology will allow a
better and quicker response to drought impacts, integrating climate and seasonal forecasts from different
institutions in their decision making. In the case of seasonal forecasts, these will produce better early warning
systems, and in the case of climate change predictions, these will produce better anticipation to water problems
related to droughts in the future.
In order to achieve this improvements in the early warning systems, the use of an Autoregressive-moving average
with exogenous terms (ARMAX) model was proposed. Outcomes from this model (river flows) will be used to run
the management model of the JRB to estimate the probabilistic risk of droughts at short or long term, depending
on the situation. In this case, short term is related to management at seasonal scale to know the potential deficits
of the water allocation system and also the evolution of these deficits when implementing measures. Long term is
related to planning in order to know the overall vulnerability of the system, from 6-12 to 24 years (planning
horizons in JRB). The tool allows the implementation of measures and several changes in the management of the
system and then estimates its response in terms of probabilities. Thus, the effectiveness of proposed measures
can be assessed, and changes in reliability, vulnerability and resilience. All this means that it can predict the
probabilities of having a better or worse scenario depending on the measures applied at each time scale.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 18
The ARMAX results will be compared with results from the current methodology used in the JRB, which is based
on the application of an autoregressive model (AR(1)) based on the historical river flows in natural regime. In the
following section both are explained in more detail.
An alternative method previously mentioned, which will be employed for the long term risk assessment, is the use
of a hydrological model (HBV) that is able to process and extract river flows from provided precipitation and
temperature. Also these streamflows will be integrated in the risk management model and will be compared with
the other methods to know their predictive capabilities.
Also, once the effects of precipitation projections will be known, the idea is to include the change in temperatures
in the analysis in order to have a multihazard approach. The effect of the temperature increase will be noted in the
increment of demands, which may aggravate the lack of water availability. Thus, the decrease of precipitations
and the increase on temperatures will be jointly analysed in a second part of the analysis. These lines of work are
ongoing and will be reported in deliverables 13.4 and/or 11.4, as results are available.
The information of this deliverable is based on the progress so far with the drought risk management tool using
river flows data from the ARMAX model, which is forced with seasonal meteorological forecasts. Then, this tool is
run with river flows from the model currently used in the basin. At the end, probabilistic results from both
procedures are compared in order to assess their drought predictive capacity. In addition, it should be noted that
this tool is suitable for all uses of the system, whether urban, agricultural, hydropower or environmental since
they are related, but agriculture and environment uses are the most affected in extreme situations, so the
assessment of results will be related to them, as it is the objective of this WP.
Method 2.2
The tool proposed is a risk management model that allows the integration of different decision alternatives to
know how the system evolves in front of different situations. It is able to statistically process the management
results from various scenarios and to express them in form of probabilities, related to deficits and reservoir
storage, among others. In order to use it, it is necessary to generate multiple scenarios, or ensemble members, of
river flows from some hydrological or stochastic model, which needs to be properly calibrated in order to
represent the characteristics of the basin in an appropriate way. At the same time, these models need local
historical data of river flows, temperature and precipitation in order to be calibrated and validated. Within
IMPREX there are some providers of seasonal forecasts that are very helpful in order to increase or improve the
predictive capacity of the tools used in this basin, the main purpose of this analysis. The source of predictability is
partly coming from these climate model outputs. A first step therefore is to comparing these with local data, as
some ability is required to integrate them into the models and minimize uncertainty at the end of the process.
Some of these comparisons were presented in previous deliverables (D4.2, D5.2 and D11.1).
In this section the above process is explained step by step. First of all, the tool is introduced, it is called SIMRISK
(Andreu and Solera, 2006) and it is one of the modules of Aquatool software (Andreu et al., 1996), which has been
already mentioned and depicted in several deliverables (D5.2, D11.1) and in more detail in D13.3. The software
Aquatool is the DSS currently used in order to simulate the management of the JRB. This software is used in most
Spanish basins and also in other countries due to its user-friendly interface for water resources planning and
management, and its design oriented to assess reliability and vulnerability in drought prone areas. In addition, it
has been evolving over time, incrementing the number of modules associated in order to cover the major
problems related to water management, and also in order to facilitate the related work in the same tool. In Figure
9 the different modules of this software are shown, which are related between them and depend on each other in
some way. This assures the robustness of the modelling system and its flexibility to model a wide variety of water
resource systems. The relations are shown by the lines in Figure 9: solid lines mean that modules are inside the
same software or program, while dotted lines indicate that outputs of one module can be used as the inputs of the
other despite not being integrated in the same “window”.
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Deliverable n°11.3
Figure 9 Scheme of AQUATOOL modules where EH is EVALHID (hydrological module), OG is OPTIGES
(optimisation module), SG is SIMGES (water allocation module), SR is SIMRISK (risk management
module), GC is GESCAL (water quality module), CE is CAUDECO (module for environmental flows), and
MW is MASHWIN (stochastic module to generate synthetic series). Reference:
https://aquatool.webs.upv.es
The modules used to perform the work reported in this deliverable are:
- SIMGES: is a general water allocation model for river basin or complex water resources systems
management where there are storage elements both superficial and subterranean, and elements for collection,
transport, use and/or consumption, and artificial recharge of water. In this interface is where all the elements of
the exploitation system are located schematically in their place, providing a graphic overview of the system. The
simulation is done at a monthly scale and can reproduce the water flow through the system at any spatial scale. It
also considers the return flows to the surface system, the infiltration, the relations between river and aquifer, the
evaporation and infiltration loses from reservoirs and the relationship between surface water and groundwater.
The water resources management is simulated using reservoir operation rules which aim to maintain a similar
filling level in pre-defined reservoir zone curves. It is allowed to define environmental flows as well as different
water use priorities. The simulation and management of the surface system are done at the same time using a
network flows optimization algorithm. This algorithm determines the flows through the system trying to satisfy
the multiple objectives of deficit minimization, and maximum adaptation to the reservoir objective volume
curves. Moreover, this optimization is improved with an iterative process for the solution of the network, which
allows the simulation of non-linear processes such as infiltrations, evaporations and surface and groundwater
relationships.
- SIMRISK: a risk assessment module designed for its use in river basin management in the short, medium
and long terms to evaluate risks, mainly related to drought. It runs a simulation of the management of the system
from an initial condition of levels in reservoirs and aquifers, and uses as data multiple future hydrological inflows
scenarios of one or more years. Probability calculations are done for each month of the simulated period from the
results of all the scenarios. These can be used as probable estimation of the final situation of the system at the
end of the present campaign, or after two or more hydrological years. This module was designed to be used in
water resources system models calibrated with SIMGES, so each simulation is made in the same way than in
SIMGES.
- MASHWIN: is used to analyse historical hydrological inflow series and to facilitate the formulation of
stochastic models for generating synthetic series. Its principal utility is as complement of the SIMRISK module,
generating the different series of future scenarios for hydrological inflows.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 20
As stated previously, the process of this analysis is focused on the use of SIMRISK, which anticipates the risk of
having problems with future resources availability and allows evaluating the risk modification if a given set of
measures were applied. This helps to identifying the most appropriate mitigation measures (Haro, 2014). It is
based on the Monte-Carlo analysis approach, which consists of generating multiple, future and equiprobable
natural streamflow scenarios, and simulating the management of the system according to the criteria defined
previously for each scenario. Each simulation will provide different management results, which are statistically
treated in order to obtain the probable estimation or the risk level at which the system will be at the selected
period.
As SIMRISK requires a high number of streamflow series, it is necessary to use some kind of stochastic model
from which is possible to generate as many synthetic series as necessary to carry out the analysis.
The current method used for the risk assessment in the JRB begins with the use of a Multivariate Autoregressive-
moving-average (ARMA) model (Salas et al., 1980). Using MASHWIN, a multivariate ARMA (1,0) monthly model,
also called AR(1), is calibrated using the historical monthly streamflows to generate multiple equiprobable time
series in order to know the risk of droughts in terms of probabilities within SIMRISK.
The basic equation for multivariate ARMA (1,0) models is:
(1)
Where Xt and Xt-1 are the variables, ϕ1 is an autocorrelation matrix, θ0 is an matrix of coefficients that multiplies
the random N(0,1) values vector represented by ε.
With this type of modelling, the generated series will reproduce not only the basic statistics of the historic
streamflow values, but also the cross-correlation between the 5 catchment sites over the basin, and the time
auto-correlations. The preservation of the historical drought characteristics is not explicit in the ARMA
formulation, but it is always checked in order to ensure that the main drought characteristics are reproduced.
On the other hand, the methodology proposed for this deliverable has the same process structure of the current
methodology but replacing the AR(1) model by the new development and the implementation of an
Autoregressive-moving average with exogenous terms (ARMAX) model (Salas et. Al, 1980), which takes into
account the influence of an exogenous time series on the process, as its name suggests. In this case, the external
variable is precipitation. In this way, the results from ARMAX model will be fit into SIMRISK module for risk
management instead of those coming from the AR(1).
The basic equation of the ARMAX model is:
∑ ∑ ∑
(2)
where Yt is the random variable "output" (or the variable to be modeled) at time t, Xt is an exogenous random
variable, whose information is used to explain the variable Yt, t is a white gaussian noise (WGN) and i, j and k
are the parameters of the model. p, q and r are called model orders. The parameter p represents the number of
self-regression terms to be considered for the output series (i.e. their dependence on their past values), q
represents the number of exogenous terms to be entered as information, and r the number of moving average
terms.
21
Deliverable n°11.3
In this case, as we work with 5 sub-basins (Figure 4), this approach was extended to a multivariate case,
considering Yt and Xt, vectors of random variables and i, j, k parameter matrices. In this way, the spatial
dependence between sub-basins is considered and preserved in the analysis.
In order to have a correct representation of the basin features, the calibration is the most important process to be
carried out. In addition, it is a complicated procedure that takes a lot of time and many analysis to get the most
appropriate orders of the model. The specifications about ARMAX’s calibration will be explained in the
corresponding deliverable of WP5 at the end of the project, but a short introduction is written below and also in
the results section.
First of all, the characteristics of the historical series have to be known in order to reproduce them with the new
model. In this case, the interest is focused in the biased periodicity of the series, the temporal and spatial
dependence of the flows and the dependence between the variables flow and precipitation. To study these
properties, it is important to perform a statistical analysis.
In order to facilitate the process, a first step is necessary to obtain normalized and standardized series from the
historical series, both in terms of flow rates and rainfall. After that, it is important to test and fit the model
parameters taking into account the fit rates, as the lowest values of the Akaike’s Information Criterion (AIC) and
the Bayesian Information Criteria (BIC) (Salas et. Al., 1980). Once an initial set of best models is selected, the
generation of series and the extraction of its statistical properties is performed in order to compare them with the
historical ones and make the final selection of the best model for this basin.
Once the calibration of the model is ready, it is possible to generate new series of river flows at short or long term
using other series of precipitation (seasonal forecasts, RCMs…) as exogenous variables using the module
MASHWIN. In this way, results from various simulations with ARMAX model can be inserted in the SIMRISK
module to perform the management of the system and assess the risk in a certain period. Afterwards, any given
set of management measures may be incorporated and the risk modifications can be assessed using SIMRISK.
In conclusion, the comparison of the different model chain options could be decisive for the methodological
approach to be used in practice for future planning and management of this basin in case that the option with the
ARMAX model results to be better in terms of drought predictive capacity than the one currently employed.
In Figure 10 the different model chain options related to seasonal forecasts can be seen, where the processes
developed for this deliverable are highlighted in blue. In red is the data that was discarded in this process due to
the low skill in the JRB area, or due to the finding of a better option. The first case is related to the river flows
provided from the E-HYPE model (SMHI), and in the second case, meteorological data from ECMWF are replaced
by bias corrected data provided from SMHI, which seems to be more adequate for this area. In the line of using
the seasonal forecast provided from MetOffice, a preliminary analysis of data was performed and, as a result,
their use was discarded. The main reasons for this were the short lag time provided (up to 3 months) and the fairly
experimental rather than (pre-) operational state of results. This means that these data are too short for the
purpose of this study, which considers all seasons of the year. In addition, it was very hard to download the heavy
files and manage the huge number of variables provided, which resulted in significant time consumption. On the
other hand, the hydrological model HBV within the module EVALHID of Aquatool DSS will be further included in
the process, as already commented in the introduction section. Thus, all these models fed with different data will
be used to generate series for multiple scenarios and the risk management model (SIMRISK) will treat the
multiple management results in a statistical way in order to compare all possible options and choose the method
with the best predictive capacity. In this deliverable, results from the model chain highlighted in blue are
developed in the next section.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 22
Figure 10 Working lines related to seasonal forecasts where elements in red were discarded, elements in grey will
be tested and elements in blue were performed in this analysis.
Indicators for drought risk assessment
UPV has a fluid relationship with the main stakeholder of the basin (CHJ) and all users that this institution
represents. As a consequence, many meetings and workshops happen in order to exchange information and
discuss about how problems have been solved, and, in addition, to how improve water resources management.
Moreover, in D11.1 the results of the survey related to the needs of the users of the JRB were presented, with
many questions related to climate services, forecasts and projections, time scales windows, etc.
As an example of this relationship, some meetings already reported in the progress reports of the project, can be
mentioned:
- Multilateral: 11/11/2015 (Júcar Basin Drought Seminars); 14/03/2016 (EDgE project); 15/06/2016 (JBDS);
07/10/2016 (EDgE project) and 21/03/2017 (Climateurope preparation). 29/01/2018 (Workshop about
update of JRB Drought Plan).
- Bilateral with EMIVASA (water provider of the city of Valencia): 29/10/2015; 04/02/2016; 11/04/2016;
21/10/2016.
- Bilateral with CHJ: 11/01/2016; 20/05/2016; 20/10/2016; 15/03/2017; 30/03/2017; 19/02/2018; 03/05/2018;
20/06/2018; 12/07/2018.
- Bilateral with USUJ (Jucar’s irrigation association): 26/01/2018; 23/03/2018; 26/04/2018; 15/06/2018;
19/07/2018 and 21/09/2018.
Thus, the above mentioned methodology was developed based on the stakeholders needs, the proposed
improvements for risk tools (by stakeholders) and the problems to be faced in this basin. In particular, it is
important to highlight that, besides any results that the risk management tools can provide, risk perception in JRB
in times of drought management is accomplished in two different levels:
- Collective risk perception, as a general idea of the situation of the basin.
- Sectoral and/or individual risk perception.
Sectoral and/or individual risk perception is attained by means of probabilistic results about expected deficits for
the sector/association/individual user in every month of the remaining of the campaign (i.e. time horizon
analyzed, generally, from the moment of the analysis until the end of next September), as it will be shown later.
23
Deliverable n°11.3
Collective perception of risk is gained through probabilistic results about the storage level of the reservoirs during
the time horizon analyzed, and particularly, at the end of the campaign (end of September).
These types of results are the ones expected by decision-makers and users because they are easy to understand,
and they relate them quite well with any other consequences (including their economic interests), and the
modification of risks due to the implementation of any set of mitigation measures is also easy to perceive. So far,
economic analysis, which can be produced with the drought risk management tools available in JRB, has not
contributed to practical decision making. The main reasons may be that participatory water allocation process in
JRB responds more to water rights, priorities, and equity, rather than to economic objectives, and also that the
economic results have at least one order of magnitude higher of uncertainty than the above-mentioned
indicators, and therefore, are not perceived as robust basis for decision making.
Moreover, the Drought Index (Figure 11) of the Drought Special Plan (PES) used for drought monitoring, early
warning, and guidance for mitigation, is largely influenced by the volume of water stored in the reservoirs. Instead
of using a complex model, users can relate the probabilities of the storage level with the Drought Index and each
user can relate these results to the measures stipulated for each level of risk.
Figure 11 Evolution of the JRB compounded drought index the period October 2001 to October 2009 (green colour
is for normal situation, yellow is for pre-alert, orange for alert and red for emergency) (source: self-
elaboration with data provided by CHJ).
For all the above-mentioned reasons, in this deliverable, results related to drought risk assessment and
management will be shown by means of reservoirs storage forecasts, and sector/group/individual deficit
forecasts. Nevertheless, if time allows, economic assessments of risks will be produced in a later phase, and
reported in following deliverables in WP11 and WP13.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 24
Results 2.3
The JRB water exploitation system was developed, calibrated and tested in the SIMGES interface with all
elements of the system, reservoirs, demands, operation and priority rules. The schematic diagram is shown in
Figure 12 (for more information see D13.3).
Figure 12 Júcar River exploitation system in SIMGES.
As explained before, this program needs as input series of river flows in order to simulate the management of the
system. In this case, these inputs will come from the ARMAX model, which is already calibrated. All details from
calibration will be shown at the end of the project in the corresponding deliverable of WP5, but a summary of it is
informed in this deliverable.
ARMAX calibration
In the process of calibration, the general statistics of the streamflow historical series (1971-2006) in natural regime
were obtained (Figure 13) in order to take them into account when assembling the ARMAX model. As exogenous
variable, the series of historical precipitation were selected for the same period of 37 years (1971-2006). Both river
flows and precipitation series need to be treated and changed in order to work better with them, so the
normalisation and the standardisation of the series were carried out.
25
Deliverable n°11.3
Figure 13 Statistics of historical series (1971-2007) from the five sub-basins of the JRB: the annual mean of
streamflows (top left), the standard deviation (top right), t he variation coefficient (bottom left) and the
correlation coefficient (bottom right).
Once all inputs of the model were selected and treated, the parameters of the model and the dependency orders
come into play. The dependency orders are 4 and there are stated the self-regression dependence of the flow
variable, the dependence of the external variable precipitation, the moving average order and the time lag
between the external variable (precipitation) and the simulated variable (flows). The possible combinations for
these orders are huge, so it was decided to not use dependency orders bigger than 4. It is known that the higher
the order, the less parsimonious is the model, which is an inconvenience. Thus, all possible combinations of the 4
dependency orders resulted in 180 ARMAX models. In order to choose the best fitted models, the AIC and BIC
indexes from all models were obtained and those with the lower rates were selected (40 models).
Then, streamflow series for 37 years were generated with each selected model and its statistical properties were
extracted. In addition, fit indicators as the RMSE were obtained from the comparison between the mean values of
historical and generated series, in order to choose the best-fitted model. Then, after visual and error analysis, the
best-fitted model was chosen.
Once the ARMAX model is calibrated and ready to use, the next step is to choose the precipitation data that will
act as an exogenous variable. The idea was to use seasonal forecast precipitation from ECMWF and/or MetOffice
institutions for short term, and then use projections from different Regional Climate Models (RCMs) for long term.
These projections can come from the CORDEX dataset or other projects with more adapted and processed data
as it is SWICCA. In addition, other variables related to streamflows may be considered as exogenous, as could be
the temperature.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 26
Data used as exogenous variable
At this stage of the analysis, precipitation seasonal forecasts from System 4 (ECMWF) and its bias corrected
version provided by the SMHI were compared with regional data (Spain02) in order to know which one represents
better the characteristics of the basin. The SMHI corrected the data with the WDEI dataset, which statistics
characteristics are similar to those of Spain02. In the following figure can be seen the case of Alarcón sub-basin as
an example of the better fit from corrected data. However, there is a mismatch in Molinar and Tous sub-basins,
but the tendency is the same, so tests are being conducted to know which is the best and simplest method is to
correct them.
Figure 14 Mean year average for seven month accumulated precipitation of 15 scenarios (E1 -E15) from bias
corrected System 4 seasonal data (ECMWF) compared to Spain02 historical data in the Alarcón sub -
basin.
Thus, precipitation data from bias corrected seasonal forecasts were used to generate series of river flows with
the ARMAX (2,2,0,1) model.
Once series are simulated with the ARMAX model, they are integrated in the management model (Figure 12) and
SIMRISK is run to treat conjointly and statistically the results of the different management scenarios simulated
(Monte-Carlo method). The aggregation of these scenarios provides shortages at consumptive demands, aquifers
extractions, probability distributions for reservoirs storage and the status of drought indicators.
Results from the Risk management tool SIMRISK
In order to illustrate de methodology, the analysis will be performed for two forecast periods of 7 months for two
different years: a normal year (2003/2004) and for a dry year (2005/2006). For both years, the first forecast is done
in October, and corresponds to a 7-month from the start of the hydrological year (October) until the beginning of
the irrigation season (June). The second forecast is done in March, and corresponds to 7-month period which
includes the entire irrigation season.
27
Deliverable n°11.3
Forecasts are produced by means of SIMRISK module, as previously mentioned, using 15 inflow series, one for
each one of the 15 ensembles of precipitation provided in the ECMWF forecasts. Then, the results are treated
statistically in order to obtain expected values, probabilities, etc.
For the purpose of this deliverable, we will focus on two types of results:
- The probabilistic evolution of water storage in reservoirs (collective risk perception)
- The value of the deficit with exceedance probability of 10% of all agricultural demands in the irrigation season
(sectoral/association/individual risk perception).
Results are produced using the inflows obtained by means of the ARMAX model forecast (IMPREX proposed
methodology) and also using the inflows obtained by means of the AR model forecast (methodology currently in
use in JRB, which does not include precipitation forecast information). Then, the results are compared and
discussed.
Probabilistic evolution of water storage in reservoirs
a) using ARMAX model for inflow generation
In Figure 15, the October 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous
reservoirs of the (normal) year 2003/2004, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1)
model can be seen. For result displaying purposes, the total capacity of each reservoir has been divided into 10
equal intervals, and the probability of being in each interval is displayed. Alarcón and Contreras are located in the
headwaters, and Tous in the middle course of the river. Due to the operation rules, water from Tous reservoir is
discharged in the first months of this period in order to achieve a low storage to prevent damages from the typical
flash floods of this season; in other words, for safety. Then, water from headwater reservoirs is released to store it
in Tous in order to meet the demands of the irrigation season. That is the reason for the increase in probability of
lower storage from January to April in Alarcón and Contreras and the contrary in the case of Tous, which
probability of storing more than 72 hm3 is around 80% in these months.
Similarly, the March 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous
reservoirs of the (normal) year 2003/2004, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1)
model can be seen in Figure 16.
On the other hand, results corresponding to the October forecast and the March forecast for the dry year
2005/2006 (which corresponds with the starting of a 4-years drought period) can be seen in Figure 17 and Figure
18, respectively. The figures show higher probabilities of having lower level of storage in the three reservoirs.
Alarcón is the reservoir with higher storage capacity and the probability of being in the first (lower) zone is about
100% during all the period. In Contreras, the season starts with an 80% of probability of being in the second zone,
while probabilities of being in a higher zone are increasing towards the end of the season until 80%. In the case of
Tous, the probabilities of being in the third zone or higher are high during almost the entire period.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 28
Figure 15 October 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous reservoirs
of the (normal) year 2003/2004, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1)
model.
Figure 16 March 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous reservoirs of
the (normal) year 2003/2004, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1) model.
29
Deliverable n°11.3
Figure 17 October 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous reservoirs
of the (dry) year 2005/2006, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1) model.
Figure 18 March 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous reservoirs of
the (dry) year 2005/2006, given by SIMRISK using inflows provided by the ARMAX (2,2,0,1) model.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 30
In order to get a more aggregated figure, and as it is currently done in the management of JRB, we can consider
the sum of the storages in these three reservoirs, providing an indicator of the evolution of the total storage of the
system. This results in a unique figure that represents the entire system in an easy and understandable way to
facilitate the decision making. Figure 19 shows the October 7-month forecast for the evolution of this indicator for
the normal and dry year, where differences are quite clear. In 2003/2004 the probabilities of being in the third
storage zone is about 100% for the first two months, then in December and January there are the same
probabilities (50%) of being in second and third zone. For the last months, the probabilities of being in zones
higher than the third is about 70%, showing a recovery tendency. On the other hand, for the year 2005/2006 the
situation is more complicated, and the probabilities of being in the first zone is around 70% for almost all period.
Figure 19 October 7-months forecast for the evolution of the indicator of total storage in the JRB system of the
(normal) year 2003/2004 and the (dry) year 2005/2006, given by SIMRISK using inflows provided by the
ARMAX (2,2,0,1) model.
Similarly, Figure 20 shows the March 7-months forecast for the evolution of this indicator for the normal and dry
year, where differences are quite clear. In both cases, the evolution is towards higher probabilities for lower
reservoir zones, as expected in the irrigation season. Obviously, for the dry year 2005/2006 the situation is more
complicated, and the probabilities of being in a zone lower than the third zone is around 90% for the last three
months.
31
Deliverable n°11.3
Figure 20 March 7-months forecast for the evolution of the indicator of total storage in the JRB system of the
(normal) year 2003/2004 and the(dry) year 2005/2006, given by SIMRISK using inflows provided by the
ARMAX (2,2,0,1) model.
b) using AR model for inflow generation
As was said before, once previous results were extracted, they were compared with those using the current
methodology (AR(1) for river flows forecast in order to know if the added value of the forecasts is meaningful to
predict drought events improving the tool that is implemented in the JRB for this purpose.
In this case, an AR(1) model was implemented and calibrated using MASHWIN module and generating 15 series of
river flows based on the historical ones. Figures 21 to 24 show the results for October and March forecasts, and for
the normal and dry years, and for each of the three reservoirs, corresponding to the same cases already
commented previously when using the ARMAX approach. In general, we can say that the results obtained with
the AR approach are more optimistic than the ones obtained with the ARMAX approach. No more comments will
be made, since it is preferable to focus on the indicator of the total storage in the system, rather than on the
individual reservoir storages.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 32
Figure 21 October 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous reservoirs
of the (normal) year 2003/2004, given by SIMRISK using inflows provided by the AR(1) model .
Figure 22 March 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous reservoirs of
the (normal) year 2003/2004, given by SIMRISK using inflows provided by the AR(1) model .
33
Deliverable n°11.3
Figure 23 October 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous reservoirs
of the (dry) year 2005/2006, given by SIMRISK using inflows provided by the AR(1) model .
Figure 24 March 7-months probabilistic forecast for storage evolution at Alarcón, Contreras and Tous reservoirs of
the (dry) year 2005/2006, given by SIMRISK using inflows provided by the AR(1) model .
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 34
Figure 25 and Figure 26 show the results for October and March forecasts, respectively, of the indicator of total
storage in the JRB system for the normal and dry years, corresponding to the same cases already commented
previously when using the ARMAX approach. Again, and in general, we can say that the results obtained with the
AR approach are more optimistic than the ones obtained with the ARMAX approach. However, more research
with the ARMAX model is needed to define whether ARMAX is better than the AR approach. Both indicators have
the same tendency of recovering at the end of the season. But, in the dry year the probabilities of having a lower
volume of water resources are higher using the ARMAX approach.
Figure 25 October 7-months forecast for the evolution of the indicator of total storage in the JRB system of the
(normal) year 2003/2004 and the (dry) year 2005/2006, given by SIMRISK using inflows provided by the
AR(1) model..
35
Deliverable n°11.3
Figure 26 March 7-months forecast for the evolution of the indicator of total storage in the JRB system from
March to September of the (normal) year 2003/2004 and the (dry) year 2005/2006, given by SIMRISK
using inflows provided by the AR(1) model.
In the context of WP11, the attention is focussed on the probability of experiencing deficits by the agricultural
sector during the irrigation season. As expected, the results obtained with SIMRISK show notable differences
between a normal and a dry year. In the graphs of Figure 27, the deficits with a 10% probability of exceedance are
depicted for each agricultural demand, and also for the entire agricultural sector. Figures are given for every
month, and also as cumulative values. Even though 10% may seem a low probability, in this way the maximum
values that can be reached in a normal and a dry year are more differentiated and the worst situation can be
observed in both cases. The results presented in Figure 27 correspond to the use of the ARMAX approach.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 36
Figure 27 March forecast of deficits with 10% probability of exceedance for each agricultural demand in the
normal (2003/2004) and dry (2005/2006) years coming from the ARMAX modelling approach.
In the normal year, the month with higher probability of having deficits is August (Figure 27 top left) and the most
affected crops are the citrus trees and orchards (grey colour). In the dry year (top right), the higher deficits are
brought forward by one month and remain high until September but with less intensity, in this case, the rice is
also affected (orange colour) to a large extent. These changes are due to the operation rules of the management
system regarding the priorities of the different agricultural demands in these situations. It is also evident that the
cumulative deficit at the end of the irrigation season (September) is much higher in a dry year (Figure 27, bottom
right), with a difference of about 120 hm3.
In Figure 28 the same type of results are presented corresponding to the use of the AR approach. If we compare
previously mentioned results with those coming from the AR(1) modelling approach (Figure 28, upper part),
deficits during the normal year are very small in the AR case compared with the ARMAX case. In addition, they are
more spread among the demands and reaching the highest deficits in July and August, while in the dry year, the
highest deficits are reached in August and the most affected crop seems to be citrus and orchards (grey colour). In
this case, the differences between cumulative deficits (Figure 28, bottom part) at the end of the irrigation season
(September) are lower, about 67 hm3.
37
Deliverable n°11.3
Figure 28 March forecast of deficits with 10% probability of exceedance for each agricultural demand in the
normal (2003/2004) and dry (2005/2006) years coming from the AR modelling approach .
In Figure 29 the differences between both modelling approaches can be seen, for normal and dry years. In both
cases, the cumulative deficits during the irrigation season are represented. In both years, the estimated deficit
with the ARMAX option results to be greater than those from AR(1) model.
Figure 29 March forecasts of cumulative deficits with 10% probability of exceedance for all agricultural demands
in the normal (2003/2004) and dry (2005/2006) years, coming from the ARMAX and the AR(1) modelling
approaches.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 38
From concept to user 2.4
The main stakeholder interested in this analysis is the CHJ, which manages and allocates water resources of the
basin since 1936. In Figure 30 is showed clearly what the importance of SIMRISK process is in this study and where
stakeholders and decision makers are involved.
Figure 30 Scheme of the SIMRISK process within the decision making process.
Currently, water managers responsible for this system use AR(1) to generate multiple series of streamflows, and
to assess the risk of the basin to be in a drought situation given the current management and also to evaluate
impacts. This helps the decision makers drawing the different alternatives of management to minimize possible
impacts. With this methodology, it is also possible to analyse new management alternatives or mitigation
measures to select the most effective ones at reducing the risk. The methodology is an interesting tool to use with
and by stakeholders during public participation processes addressed to find the best management practices for
drought risk minimization as seen in Andreu et al (2009).
In this way, SIMRISK can show these results in graphical form, highlighting the evolution of probabilities and
percentiles for water demands and for reservoir storages. Cumulative distribution functions of any state or quality
variable at any time can be obtained. If the estimated risks are acceptably low, then there is no need to undertake
measures. However, if the estimated risks are unacceptably high, then some measures must be applied. In that
case, alternatives with sets of measures are formulated, and the modification of risks and the efficiency of
measures are assessed again with this module. This iterative procedure can be continued until an acceptable value
of risk is reached and the process ends. The approach provides a complete vision of the consequences of
decisions, either concerning management or infrastructure (Andreu et al, 2013).
As an example of this process, a reduction of the 30% in water delivery to agriculture demands (e.g. by growing
different crops and accepting lower yields) was imposed for the dry year 2005/2006 (Figure 31, bottom) in order to
see the differences in storage level evolution and judge if this could be a good measure for this dry situation. In
Figure 31 it can be observed how the probabilities of being in reservoir zone 2 increase for the entire forecast
period. The situation improves slightly, and consequently, additional measures will have to be taken in order to
maintain the system operative.
39
Deliverable n°11.3
Figure 31 October 7-months forecast for the evolution of the indicator of total storage in the JRB system of the
(normal) year 2003/2004 and the(dry) year 2005/2006, given by SIMRISK using inflows provided by the
ARMAX (2,2,0,1) model and modified probabilities after a reduction of the agricultural demands by
30%.
From the interaction with stakeholders it became clear that they will only integrate the results in the Water and
Drought Management Plans in case the meteorological forecast and the climate change predictions demonstrate
enough skill to reduce the uncertainty existing in the current management tools. If not, it will only introduce a
new source of uncertainty in the currently used methodologies for drought planning and management in JRB and
not be useful for the targeted end-users. That stresses the importance of comparing the new tools with the
current one.
Discussion and conclusions 2.5
In addition to the comments and discussion about the results presented in previous paragraphs, and based on
those results, let’s think about two different teams that are competing in producing forecasts for decision
making., Team A uses the ARMAX approach, while team B uses the AR approach. And let’s concentrate only on
the 7-months forecasts of total water storage in JRB system.
In Table 1 and Table 2, it can be seen that for the normal year 2003/2004, the October and March respectively,
probabilistic forecasts of team B seem more accurate than the ones produced by team A for all months of the
forecast period (an “x” is placed in the storage zone corresponding to the observed value, and the probability
value for each approach is the value depicted in the graphs previously presented).
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 40
Probabilities of storage
Table 1 Probabilities per storage interval for the (normal) year 2003/2004 from the October 7 -months forecast coming
from the ARMAX and the AR(1) modelling approaches, and historic observations’ marks.
Table 2 Probabilities per storage interval for the (normal) year 2003/2004 from the March 7-months forecast coming from
the ARMAX and the AR(1) modelling approaches, and historic observations’ marks.
Moreover, when we go to the dry year 2005/2006, it can be seen in Table 3 that team B would appear again more
accurate than team A for the October forecast. But, by the contrary, team A would appear as more accurate for
the March forecast than team B, as it can be seen in Table 4.
41
Deliverable n°11.3
Table 3 Probabilities per storage interval for the (dry) year 2005/2006 from the October 7-months forecast coming from
the ARMAX and the AR(1) modelling approaches, and historic observations’ marks.
Table 4 Probabilities per storage interval for the (dry) year 2005/2006 from the March 7-months forecast coming from the
ARMAX and the AR(1) modelling approaches, and historic observations’ marks.
It has to be noticed that, for a practical application in Júcar River Basin in drought management, it is desirable to
be accurate in the March forecast, because it corresponds to the irrigation season, which is the period in which
most water is used, and therefore, the most critical period for drought management.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 42
In order to further illustrate the discussion, in Figure 32 and Figure 33, the October and March 7-months forecast,
respectively, of the expected values of the total storage in JRB system for the normal year 2003/2004 are
presented. Also, in this type of results, team B (AR approach) appears as more accurate than team A (ARMAX
approach). Tables 1 to 4 summarize the decision making based on the forecast probabilities, and its comparison
with the historical values. The figures below provide a continuous comparison of the historical evolution with the
expected values of the forecast.
Figure 32 Observed storage level and expected values from AR and ARMMAX options in Alarcón, Contreras and
Tous reservoirs integrated in some total capacity thresholds (10%-50%) for volumes reached in the first
7 months of the hydrological years 2003/2004 (normal year).
Figure 33 Observed storage level and expected values from AR and ARMMAX options in Alarcón, Contreras and
Tous reservoirs integrated in some total capacity thresholds (10%-50%) for volumes reached from
March to September of the hydrological years 2003/2004 (normal year) .
43
Deliverable n°11.3
But, in the October 7-months forecast of the expected values of the total storage in JRB system for the dry year
2005/2006 depicted Figure 34, it can be seen that team B forecast is over the observed result, while team A is
below the observed result. Both are at similar distance from the observation (each one in a different side),
although team A is a little bit more distant than team B. Furthermore, a similar situation can be found in the
March 7-months forecast of the expected values of the total storage in JRB system for the dry year 2005/2006
depicted in Figure 35, it can be seen again that team B forecast is over the observed result, while team A is below
the observed result, even though team A is a little bit more distant than team B. So, for the dry year, the accuracy
is about the same in terms of forecasted expected values, but team B tends to be overoptimistic, which for
practical application to drought management in JRB is unsecure.
Figure 34 Observed storage level and October 7-months forecast of expected values from AR and ARMAX
approaches of total storage in JRB system for the hydrological year 2005/2006 (dry year).
Figure 35 Observed storage level and March 7-months forecast of expected values from AR and ARMAX
approaches of total storage in JRB system for the hydrological year 200 5/2006 (dry year).
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 44
So, as general conclusions from this limited analysis, AR approach seems more accurate for normal years, while
ARMAX approach seems to be more adequate for dry years, which are the critical periods for drought
management. This last conclusion gives reason to expect that the inclusion of the improved forecasts of
precipitation from WP3 of IMPREX can improve drought management by means of the proposed ARMAX
approach.
However, this is an analysis of only two years and results are not conclusive, but they are encouraging in terms of
the use of ARMAX with seasonal forecasts as opposed to the use of AR, with flows alone.
On the other hand, we also expect that, in future times, better skilled seasonal forecasts and better schemes to
link forecasts to local hydrological conditions will be produced. This would decrease the uncertainties attached to
the currently used in the generation scheme. In this way, if better seasonal forecasts were available, they could be
incorporated in the probabilistic risk assessment, reducing uncertainty in the probabilistic impacts forecast that
are used for decision making in design and approval of seasonal action plans and mitigation measures. Better
short term and seasonal forecasts could also help farmers to design their crop selection and irrigation strategies
for every growing season.
Thus, this tool is an option to improve drought management. In the end, results obtained are an easy
understandable representation of a possible future that allows stakeholders to make decisions. Moreover, this
methodology can be extended to any system with a proper calibration adapted to that area.
Next steps 2.6
As said before, this is an analysis of only two years and results are not conclusive. In the following months UPV
team will work in extending the analysis to more years. Also, the number of series used in SIMRISK simulation is
15, i.e., one for each ensemble provided in the 7-month forecast from ECMWF. It would be desirable that the
ARMAX approach would be used to generate let’s say 70 series from each ensemble, which will render around
1000 series to be used by SIMRISK, in which case the statistical and probabilistic approach would have more
significance, which is desirable. It has not be done so far because the connection between the ARMAX analyser
and generator (developed using Mathlab) had no automatic connection with SIMRISK inputs, and the transfer was
done manually. Therefore, UPV team will work in the following months in order to get an automatic transfer,
which will facilitate the use of more time series.
In addition, other areas of work are ongoing, as the one using the hydrological model HBV in EVALHID, as
mentioned in the introduction.
Besides, an evaluation of the effect of temperatures on demands will be considered as a second part of the
analysis, looking at multiple hazards simultaneously. Moreover, if there is enough time, the economic impacts
derived from water shortages will be estimated despite the fact that economic indicators are not perceived as
robust basis for decision making, as explained above.
Finally, the approach will be used in an integrated analysis in WP13 including results for other sectors (Urban
supply, hydroelectricity, etcetera).
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Deliverable n°11.3
Berkel 3
Introduction 3.1
The aim of the IMPREX Berkel case study is to investigate the feasibility of a risk approach to decision-making
about freshwater management in a semi-natural area, where several sectors are depending on the amount of
water and water quality. The feasibility of the method is investigated by deriving the present day integral drought
risk, in which all relevant sectors are incorporated. This will lead to insight in which sector has the highest drought
risk, or, where the (scarce) water can have most added value by reducing the drought risk. At the moment, such
considerations are not made, because the damage for different functions is simply not known. Using a risk
approach might give a more objective and solid ground for management decisions.
In the east of the Netherlands, a range of organisations are collaborating on the Eastern Netherlands Freshwater
Supplies (ZON1) program to ensure that there will be adequate supplies of good water for use in the future. The
organisations involved need to establish a picture of the consequences of agreements relating to the availability
of water and possible water shortages. In addition, information is needed about possible action, together with
sound arguments as a basis for decisions and measures. In consultation with ZON, it was decided to adopt the
Berkel catchment as a pilot area.
Characteristics of the Berkel catchment
The Berkel catchment drains freely, with water entering the downstream section from the Twenthe Canal. Water
is used in a range of ways (urban area, agriculture, drinking water extraction, nature, leisure) and there is a range
of possible measures to prevent drought, such as measures relating to inlets, the restructuring of waterways and
water retention in the subsurface.
The Berkel River begins in Germany and the river flows into the IJssel in the Netherlands. It is fed by various
streams and by water from the Twenthe Canal. Figure 36 provides an overview of the main watercourses and the
in the Berkel catchment. The Berkel provides part of the catchment with water. Water is diverted from the main
watercourses of the Berkel to lateral waterways by means of small inlet structures in several locations. These
inlets are used, for example, to flush urban water systems or to supply water for livestock or arable farming.
Almost half of the Berkel catchment is located in Germany and so discharge rates in the catchment are heavily
dependent on amounts of water coming in from Germany. It is not known how much water extracted from the
Berkel at inlets, to be directed to agricultural areas (for uses like watering the crops). Nor is it always clear why
water is let in and how effective these interventions are. At the same time, sustaining the current structure of the
water system requires management and maintenance. An understanding of the benefits of water distribution
through inlets and the costs of the management and maintenance of the water system could help to make a
proper assessment of the structure, and management and maintenance, of the water system, the distribution of
water and any decisions about possible measures.
Water availability is limited in the Berkel during the summer. When there are shortages, the emergency plan and
the water agreement for drought (Waterakkoord Twenthekanalen / Overijsselsche Vecht (2011), Calamiteitenplan
Waterschap Rijn en IJssel (2015)) come into operation. There is a priority sequence for water distribution
(“verdringingsreeks”) in which urban water quality has the highest priority and agriculture (a ban on overhead
irrigation using surface water) the lowest. Agriculture is still allowed to extract groundwater when there is a ban
1 Cooperation partners are the Provinces Overijssel, Drenthe and Gelderland, water authorities Vechtstromen, Drents Overijsselse Delta, Rijn
en IJssel and Vallei en Veluwe, Rijkswaterstaat Oost-Nederland, inbound municipalities and drinking water company Vitens
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 46
on overhead irrigation, except in locations close to nature areas. These plans mainly provide solutions for drought
conditions that occur regularly, with repeat periods of up to approximately ten years. These plans are not based
on a risk assessment, but on a perception of acceptable and less acceptable drought damage.
Figure 36 Berkel catchment with main watercourses and inlets, and it location in The Netherl ands.
The summer of 2018 (the moment of writing) turned out to be a very dry one. The precipitation shortage at the
beginning of August is very close to the highest ever measured in The Netherlands (in a period of more than 100
years), and the discharges of the important rivers are very low (Rhine) and low (Meuse). This led to several
interventions by the Water board in the Berkel catchment:
- All inlets to neighboring catchments and agricultural areas are closed. The only inlet that is maintained is
the inlet to the area where the groundwater extraction by Vitens is located.
- Closing of the inlet together with the high precipitation shortage, has led to a reduction in crop yield or to
extra costs for irrigation by means of groundwater extraction. These effects are not quantified yet.
- Extra water from the Twenthe canal is pumped into the Berkel to maintain minimum water levels in the
Berkel.
- The Verdringingsreeks (priority sequence for water distributions) was close to becoming active.
- Up to September there a no major issues regarding water quality.
It is interesting to note that the Verdringingsreeks has not been activated in this exceptionally dry period.
Apparently, the system and the water management is more robust than foreseen.
The aim of the risk approach in this area is:
- to establish an understanding of the occurrence of shortages in present and future climate conditions;
- to establish an understanding of the welfare effect that occurs as a result of these shortages;
- to be able to make a well-founded assessment of water distribution during drought and possible
investments to mitigate impacts in the water system.
- learn from current hydrological extremes to anticipate future conditions.
47
Deliverable n°11.3
Water consumers
The main water consumers in the area are depicted in Figure 37, without ranking. Not all water users are water
consumers. Urban water, for example, benefits from enough water of reasonable quality. The same amount of
water will provide for leisure.
Figure 37 Relevant use functions (sectors depending on water) in the Berkel catchment.
Arable agriculture depends primarily on the groundwater level, and the availability and quality of surface water.
Water depths for shipping and water quality are important for leisure purposes. Water quality is also important for
urban water in order to minimise smells and health risks. Drinking water and industry mainly use deeper
groundwater where supplies are adequate. During droughts, extraction for drinking water companies and
industry does contribute to the drying out of the area.
The Natura2000 areas depend on groundwater levels, soil moisture levels, and water depths and discharge
dynamics in watercourses. In streams, stagnation/near-stagnation and the drying up of the streams have an
adverse effect on aquatic flora and fauna. And the drying up of fish passages, for example, impacts fish stocks
negatively.
Groundwater levels may decline on the long term as a result of the extraction of groundwater and climate change.
Groundwater extraction can also have a major impact during the course of a single season. In the current climate,
groundwater stocks are replenished adequately in the winter. Lower groundwater levels reduce the availability of
water for agriculture and nature, and for mitigating land subsidence in areas with peat soils.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 48
Method 3.2
General
The method applied here was developed in WP5 and it is described in deliverables 5.2 and 5.5 (as planned). The
primary focus here is on the description of the specific application of the method to this case.
The following information was required for the implementation of the drought-risk approach:
- Information about hydrology: the statistics for the hydrological parameters that play a role in the
physical effect of a given use function.
- Information about the dose-effect relationship: the relationship between hydrological parameters and
the physical effect of a given use function.
- Information about the welfare effect (damage): the expression of the physical effect as the welfare
effect.
Effects by use function
The relevant hydrological parameters and the physical effects of drought are identified for each use function. The
hydrological parameters and the physical effects are transferred into welfare effects. They can be expressed as
monetary and non-monetary values.
In this case study, reduced crop yields as a result of drought in agriculture are expressed as monetary values.
Problems in the urban area caused by reduced water quality are expressed as non-monetary values. Problems
with the drying out of terrestrial nature can lead to a reduction in natural variety and food availability, and a
knock-on effect on the intrinsic (non-monetary) value of nature, and possibly on welfare as well (monetary). The
effects are elaborated for each use function on the basis of existing local information and key figures. Information
about the hydrology, and therefore the probability of drought, is derived from model results and measurements.
Hydrological models and measurement data
This study drew on the National Hydrological Model (LHM), which schematises both the groundwater and surface
water systems for almost all of the Netherlands, including interaction. Figure 38 shows the components of the
national model and how they are interrelated. At the moment of this research, calculations are available for this
analysis for a period of twenty years (1960-1980) (this period will be extended, but the run-time of the model is
long). Groundwater and soil moisture levels were calculated with a spatial resolution of 250 by 250 m. This is a
practical resolution for this regional scale. In terms of surface water, the Berkel has been schematised in the LHM
as a single water body, with a single total balance and one water level. This is not enough detail for the
elaboration of use functions that depend on the surface water. It is therefore decided to use (limited) series of
measurements of water depth and water temperature at various locations in the Berkel catchment for these use
functions. An overview of the measurement locations used for water depth and water temperature can be found
in Figure 39. A drawback of this approach is that by using the limited series of measurements, it is not possible to
arrive at any conclusions about future developments in risk as a result of climate change and socio-economic
developments. This drawback will be addressed in paragraph 3.5, the discussion. In future work (within IMPREX),
risks will also be derived for future scenarios.
49
Deliverable n°11.3
Figure 38 Schematic representation of the National Hydrological Model with the four model components .
Figure 39 Measurement locations used for water depth and water temperature in the surface water system .
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 50
Workshops
Stakeholders are actively involved in this study. Stakeholders supply the local knowledge and information needed
to define the use functions and possible effects. In addition, their input is essential to achieve a practical result
that fits in with the realities of water management. There were therefore several consultations with stakeholders,
and two workshops were organised, one at the start and one more towards the end of the work presented here:
1. One workshop looked at the use functions and welfare effects, and discussed the following questions with the
stakeholders:
- Who are the main water consumers in a normal year and in a very dry year?
- What is the main consequential welfare effect caused by water shortages in a dry year and in a very dry
year?
- What considerations are made in a dry year and in a very dry year?
2. The other workshop reviewed the results, and put a number of questions to the stakeholders:
- What could you use the results for?
- What is missing for a clearer picture?
- What criteria other than monetary effects are important in the public domain?
- How could you quantify these criteria or assess them qualitatively?
Results 3.3
Agriculture
Agriculture in the Berkel catchment consists mainly of grassland and maize. It is not clear whether the areas which
are supplied with water from the Berkel contain different types of vegetation than the areas where no water is let
in. Table 5 shows the crop types in the area, with the number of hectares.
Table 5 Overview of the number of hectares per crop type in the Berkel catchment
Crop type Area in hectares Crop type Area in hectares
Grassland 22,281 Cereals 946
Maize 8,447 Miscellaneous 341
Potatoes 481 Trees 31
Sugar beet 150 Total 32,678
Figure 40 shows how hydrological parameters are translated into physical effects and expressed as welfare effects
for each type of crop. The physical effects of drought on agriculture are expressed as a reduction in crop yields.
The yields are dependent on adequate soil water availability (which is a hydrological parameter). To define the
reduction in the crop yield due to soil moisture shortage, the agricultural yield model AGRICOM is used. The
reduction in the yield has been defined as the difference between the potential and the actual agricultural yields
(kg/crop/area). Since there are rarely any years where the yield is equal to the potential, this means that also
under non-drought conditions some yield reduction will apply. Wageningen University and Research Centre has
developed a price tool in which changes in agricultural yields are expressed as a welfare effect taking price
elasticities and imports/exports in the agricultural sector into account.
Figure 40 Effect chain for agriculture: from probability of hydrological effects to physical effects and welfare
effects
51
Deliverable n°11.3
Recreational shipping with 'Berkelzomp' boats
Recreational shipping with 'Berkelzomp' boats is an important tourist attraction in the Berkel catchment. The
negative effects of drought are that, if the water is not deep enough for the boats, they will not sail and there will
be no revenue. To determine the impact of drought, the required sailing depth and the average revenue during
the season were determined. The model results were used to calculate the number of days per year when the
depth in the Berkel was lower than required.
Figure 41 shows the steps from hydrological effect to welfare effect. The physical effect is the number of trips per
year that did not take place because the water was not deep enough. The welfare effect for the company is the
number of trips per year that do not take place because the water is not deep enough, multiplied by the number
of people per trip and the ticket price per person. Because we have no information about the fixed and variable
costs of the Berkelzompen company, the welfare effect has not been corrected for the ratio between them. In
addition to direct effects, indirect effects are also likely to occur. For example, the revenue of local catering
outlets will also be affected if sailing is not possible. For the time being, this factor has not been considered.
Figure 41 Effect chain for the Recreational Shipping user function with the Berkelzomp boats: from probability of
hydrological effects to physical effects and welfare effects
In addition to the loss of revenue for the Berkelzompen company, the welfare effect at the regional and national
levels can also be taken into consideration. A substitution effect occurs in the case of tourists from the
Netherlands. These tourists may engage in different activities in their own country and so there will be no welfare
loss at the regional and national levels. The lost income from foreign tourists does have a national welfare effect.
The loss of revenue is equal to the number of trips per year that do not take place because the water is not deep
enough, multiplied by the expenditure of the number of foreign tourists.
Urban water quality
Urban areas can have problems during dry periods because of poorer water quality. This can cause nuisance, for
example in the form of smells, reduced amenity value, and negative health effects (welfare effects). Because it is
difficult to express these effects as monetary values, it was decided, when looking at urban water quality, to
consider the extra maintenance of watercourses with the aim of preventing the negative effects listed here. This
is not the actual welfare effect of water shortage, but a means to prevent negative welfare effects, and can
therefore be seen as the ‘economical damage’ of water shortages in urban areas.
To determine the extra maintenance work needed on watercourses to prevent negative effects such as smells,
reduced amenity value and negative effects on health, it has been assumed that more mowing and dredging is
required as a result of faster plant growth at a water temperature of more than 20°C and that more maintenance
is required in the transition from oligotrophic to eurotrophic water. The costs of maintenance increase from € 1.40
per m2/year to € 5.67 per m2/year (Kamsma, 2014).
Measurement series for water temperature are available for a limited number of locations. The measurement
frequency varies between eight times a year and monthly measurements. The number of times the water
temperature exceeds the threshold value of 20°C was determined on that basis. Table 6 shows the surface area
covered by urban water.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 52
Table 6 Overview of surface water in the urban areas in the Berkel catchment (Zutphen, Lochem, Borculo and
Eibergen)
Town Area covered by water in urban areas [m2]
Zutphen 697,500
Lochem 335,000
Borculo 130,000
Eibergen 93,750
Figure 42 shows the procedure for working up hydrological parameters into physical effects and stating them as
additional maintenance costs for the management of the water area in urban areas.
Figure 42 Effect chain for the Urban Water Quality use function: from probability of hydrological effects to
physical effects and welfare effects
Nature
The Nature use function involves a distinction between aquatic and terrestrial nature. The physical effects for
nature are expressed as intrinsic values such as the loss of species or the reduction of numbers per species. In
order to determine the species loss in aquatic nature during droughts and to value that loss, information about
the water depth and the flow rate of the waterways in the area is required, as well as the limit values for different
species at which species loss occurs. The duration of the exceedance of the limit values (in the case of aquatic
nature, this is usually when watercourses fall dry) is also a determinant of species loss. This information can be
obtained from the hydrological model. In addition, drought can also lead to species loss in aquatic nature as a
result of eutrophication. Parameters such as temperature, seepage, drainage and nutrients are important here.
The consequences of drought for terrestrial nature depend on the soil moisture level. Here also, it is important to
know at which level of soil moisture species loss occurs, and what the limit values are for the different species in
the Berkel catchment. As well as species being lost, new species may be found in aquatic and terrestrial nature
and these may be seen as assets. This factor has not been taken into consideration.
In the ”‘verdringingsreeks”, which determines the priority of water consumers during water shortages, nature that
would suffer unrepairable damage is the highest priority. This means all other water using sectors can be limited
in their water use to secure water availability for nature as much as possible. This does not imply there will be no
shortages and thus negative effects for nature, but it shows nature is considered as important. However, the
effects on the Nature function are difficult to express in monetary values. One way to do this involves looking at
the effect of changed nature on ecosystem services or estimating the recovery costs of nature (see also Deltares,
Stratelligence and LEI, 2016). To determine these indicators, more information is needed about the limit values at
which species are lost as a result of drought. Data were not available as the study was being conducted and so the
effects on nature have not been considered further in this risk analysis. Figure 43 does set out the steps needed to
determine the effect on nature.
A future possibility for the aquatic nature could be to use a method derived for the Water Framework Directive
(WFD-Explorer method), in which tolerance levels for different species are compared with environmental factors
in the water body (Wortelboer, in prep.). For the ARC-NSC case study this method is already applied (described in
paragraph 0). Another option to determine the welfare effect for water shortages for nature could be the loss of
nature areas for inhabitants and tourism. This could partly be an economical effect, as a reduction of income from
53
Deliverable n°11.3
tourism. A larger effect would be more qualitative: the presence of attractive areas and biodiversity. An attempt is
made to determine this effect, but this has not yet lead to a applicable method.
Figure 43 Effect chain for the Aquatic Nature (top two chains) and Terrestrial Nature (bottom chain) use
functions: from probability of hydrological effects to physical effects and welfare effects .
Industry
The main industry in the Berkel catchment is Friesland Campina (dairy processing), which uses water as process
water and cooling water. Process water is not highly dependent on the temperature; it depends mainly on the
water supply. Both temperature and quantity are important for cooling water. The surface water must not warm
up too much as a result of the discharge of cooling water. It is therefore important to know to what extent
Friesland Campina uses process water and cooling water in the production process.
Friesland Campina extracts the water from the deeper groundwater. It does not discharge process and cooling
water into the surface water. Any discharges go directly to the waste water treatment plant. As a result, the
surface water does not suffer any adverse effects from the heated discharge water.
Friesland Campina is authorised to extract groundwater at all times. There are no circumstances in which a ban on
groundwater extraction applies. As a result, the company never suffers from a shortage of water in dry conditions.
It is not foreseen that this will change in the future.
The effect chain for the Industry use function is therefore not relevant here and so it has not been elaborated
further. Groundwater extraction may have an effect on agriculture or nature by causing the drying out of the area.
The extent to which this is a factor and to which compensation measures are applied is not known. In is possible
that, when there is a certain balance in the present situation, in the future this balance gets disturbed when there
is less groundwater recharge in summer, and higher extraction during the year. This is a topic for further research.
Drinking water
The drinking water company Vitens is one of the most important water consumers in the area. Like Friesland
Campina, Vitens extracts water from the deeper groundwater. There are no circumstances in which a ban on
groundwater extraction applies. As a result, the drinking water company itself is not affected by dry conditions.
The effect chain for the Drinking Water use function has therefore not been elaborated further. As with the
Industry use function, there may be indirect effects on other use functions because water extraction does affect
the water levels in watercourses, groundwater levels and soil moisture levels in the affected area. For 2040, an
increase in drinking water demand of 30% is foreseen. Vitens is exploring possibilities to provide in this extra
demand without negative effects for the groundwater levels, or example by actively increasing the water storage
in the neighbouring higher area and using this as a buffer.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 54
Risk approach 3.4
The results of the application of the first version of the risk tool will be presented and discussed in this chapter.
They provide a provisional picture of the Berkel risk profile for the current situation (present climate without
climate change). This application looks at two situations:
1. the current situation,
2. the situation without a water inlet from the Twenthe Canal to the Berkel.
First of all, the risk for one sector – agriculture – was analysed separately. Recreational shipping and urban water
quality were then also analysed in brief and compared with the agriculture sector.
The figures presented in this chapter are indicative and they are intended to test the method for the risk approach
and show stakeholders the concept. The welfare effect is expressed as the expected annual level of drought
damage. There has not yet been any assessment of the reliability of the outcomes (by means of a comparison
with occurred loss of crop yield, for example) and so they cannot yet be used for a genuine social cost-benefit
analysis, for example.
Risk without intervention
Agriculture
In the absence of a regional model, The National Hydrological Model (LHM) and the AGRICOM agricultural effect
model are used to establish a picture of the risk of drought for agriculture in the entire Berkel area. The LHM is
used to model the groundwater and surface water system in the current situation for a 20-year time series. This
means that current land use is simulated in combination with the historical hydrological and meteorological
conditions for the years 1961-1980 (actual precipitation, evapotranspiration and discharges). A more recent
simulation is not available at the start of this research. The agricultural yield loss is calculated for each year using
AGRICOM and broken down into the different crop categories and surface areas used by AGRICOM. AGRICOM
found that the total surface area used for agriculture for which the agricultural yield loss was calculated was
34,000 ha. Agriculture in the Berkel catchment consists mainly of grassland and maize. Together, these crops also
incur most of the calculated drought damage.
Figure 44 shows the loss of agricultural yield in the period of 20 years (1961-1980) considered. The figure shows
that the welfare effect due to drought is highest in the year 1976. This was also, climatologically, the driest year in
the time series. The average probability of the climate conditions of 1976 in the Netherlands is considered as once
every 100 years. The year 2018 will probably become the second-driest year (see Figure 45), but is not included in
the analysis. In welfare effect, 1976 is followed at some distance by five other relatively dry years, including 1973
and 1975. There is hardly any welfare effect owing to drought in the other years.
Figure 44 The welfare effect of drought over time for each crop type (without taking the price effect into account)
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Deliverable n°11.3
Figure 45 Precipitation deficit in The Netherlands in 2018 and dry reference year 1976.
The annual expected loss of income from agriculture for the Berkel (based on the average for the 20-year time
series) is approximately € 2.92 million (see Figure 46). The expected actual annual agricultural yield is
approximately € 55 million, so the annual expected welfare effect of drought is about 5% of the annual expected
yield. The pie chart shows once again that grassland and maize are the main crops.
Figure 46 Distribution of the expected annual welfare effect for agriculture for each crop type (without taking the
price effect into account).
The relatively large welfare effects for the crop types grassland and maize are mainly caused by the production of
grass and maize, which affects the production of cattle feed: lower roughage production on the farm and in the
region can be offset by the purchase of concentrated feed. The extra costs involved have been included in the
calculation of the welfare effects for the consumers of grass and maize.
Recreational shipping with 'Berkelzomp' boats
The annual expected welfare effect is also calculated for recreational shipping using the 'chain' described in
Section 3.3 and historical measurement series of water levels covering 1969 through to 1994. Water levels were
measured daily during this period. It can be seen from Figure 47 that from 1982 the welfare effect due to drought
is much higher than in the years before. Probably a change in water management, for example a different weir
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 56
regime, is implemented in 1982. For the years 1972, 1973, 1974 and 1976 measurements are only available for one
location. In this location, water levels in summer are always relatively high, probably due to inlet of water.
Therefore, no welfare effect is calculated for these years, whereas we do suspect that there will be a welfare effect
on other locations. The year 1976 is known in the Netherlands as a very dry year. The absence of this year from
the measurement series therefore has an impact on the calculated welfare effect. The average expected annual
welfare effect is € 5900. The average annual revenue is equal to the number of recreational vessels multiplied by
the number of people a year and the ticket price. This amounts to approximately € 178,000. The annual expected
welfare effect of drought is about 3% of the annual revenue. This percentage is similar to the effect for agriculture
(5%). However, the annual expected welfare effect in euro’s is significantly lower for recreational shipping than
the welfare effect for agriculture.
To investigate the effect if 1976 would be taken into account, it is assumed that for most of July, August and
September (based on precipitation shortages that period) water levels have been too low sail with the
Berkelzomp. This leads to a welfare effect of € 17,400 for 1976. The average annual welfare effect including this
value for 1976 would be € 6550. The average annual revenue would be € 200,000. This is still significantly lower
than the welfare effect for agriculture.
Figure 47 The welfare effect over time for recreational shipping with the 'Berkelzompen' (there are no estimates
for 1972, 1973, 1974 and 1976 for the effect of drought on recreational shipping).
Urban water quality
The welfare effect for the urban area is based on the mowing regime for urban waters. The aim of extra
maintenance on the watercourses is to prevent the negative effects such as smells, other disruptive effects,
impaired amenity value, and negative effects on health. The annual expected value of additional management
and maintenance is determined for the four largest urban centres with urban water, in other words Zutphen,
Lochem, Borculo and Eibergen. The cost for additional management is estimated at almost € 360,000 (see Figure
48). This cost is a maximum value for a dry year. Zutphen accounts for the largest share of this amount. With its
canals around the inner city and other waters in residential areas, this city has most urban water. The additional
amount needed for management and maintenance must be assessed in relation to the total municipal
expenditure on the maintenance of watercourses. No information is available about the annual amount of this
expenditure, since it is not known which years extra maintenance is necessary. However, in relation to the annual
water tax this will be negligible.
The costs for extra management and maintenance of watercourses are not the actual welfare effect. Welfare
effects of drought in urban areas include the effects of poor water quality on property prices (because waterside
houses may become less attractive properties) and the additional costs for health care (caused by health
complaints). These however are not taken into account, due to a lack of data on these costs.
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Deliverable n°11.3
Figure 48 Distribution of the annually expected welfare effect for the Urban Water Quality function for Zutphe n,
Lochem, Borculo and Eibergen.
Agriculture, recreational shipping and urban water quality combined
Finally, the welfare effect in euro for the three user functions for which the welfare effect is calculated are
compared with each other (see Figure 49). This comparison has clear limitations because the time period, the
damage figures, model data and measurement data etcetera on which the various welfare effects are based differ
significantly from each other. As a result, the baseline situations differ for each use function. Nevertheless, based
on the results it is expected that the annual welfare effect will be largest for the agricultural sector by comparison
with recreational shipping and urban water quality. The effect per sector as percentage of the annual revenue is
equal for agriculture and recreational shipping (with 5 and 3 %) and negligible for urban water quality.
In practice, other criteria will be taken into account in the administrative context or management strategy than
the welfare effects considered here, such as the cultural value of sailing with the Berkelzompen boats and related
spending for hotels and catering. In addition, the water authority or the municipality will, as more water bodies
are created in new urban areas ('living on the waterside'), attach more importance to deteriorating water quality
as it is experienced by inhabitants. These matters are difficult to express in monetary terms (data as house prices
of the house on the water side, compared to other houses, could be used) but they are taken into consideration
when deciding about measures to be taken and priorities for the distribution or redistribution of the available
water.
Figure 49 Distribution of the annual expected welfare effect for the functions agriculture, recreational shipping
with Berkelzompen and urban water quality for Zutphen, Lochem, Borculo and Eibergen .
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 58
Risk with measure (shutting down the inlet of water from the Twenthe Canal)
Shutting down the inlet of water from the Twenthe Canal to the Berkel catchment has been explored as an
alternative management strategy for the Berkel catchment.
In the National Hydrological Model, the Twenthe Canal is modelled separately as a watercourse, but the Berkel is
not. Nevertheless, it is possible to obtain a very broad indication of the effect of the measure, in other words when
the inlet from the Twenthe Canal at Lochem into the Berkel of 1.3 m3/s is reduced to 0 m
3/s. The effects of
stopping the inlet of water on the loss of agricultural yield were also considered. The model showed that the
measure mainly affects the GLG (mean lowest water table, as calculated over the time series of 1960-1980). In the
area between Lochem and Zutphen, the water table fell by a maximum of approximately 10 cm. This is shown in
Figure 50, where the area in red is the area where there was a sharp fall in the water table. The dark red shows a
fall of 10 cm; the lighter colour indicates a smaller fall. The annual expected welfare effect for agriculture was
recalculated. The results show that the measure has hardly any effect: the annual expected welfare effect
increases by € 50,000, which is less than 2% of the annual expected effect (€ 2.92 million).
Figure 50 Impact of stopping the inlet from the Twenthe Canal (from 1.3 m3/s to 0 m3/s) on the mean lowest
water table as calculated with the National Hydrological Model .
Discussion and conclusions 3.5
Discussion
The aim of the IMPREX Berkel case study is to investigate the feasibility of a risk approach to decision-making
about freshwater management in a freely drained area like the Berkel. Therefore the drought risk in a baseline
situation is compared with the situation involving an intervention. The annual drought damage for the sectors
explored is not very high (ranging between 0 to 5% of the annual turnover). The intervention that is investigated
(stopping the water supply from the Twenthe Canal) has a very small effect on annual drought damage.
The intervention reduces water availability for agriculture, in areas where in the basic situation surface water level
are kept at a certain level with inlet of water from the Twenthe Canal. The expected effect of this measure is
reduced soil moisture availability in these areas, and therefore reduced crop yields. The water from the Twenthe
Canal that is not used here, can be used for agriculture or other functions like nature elsewhere. This intervention
is not a drought mitigation measure for the agricultural area, since it only reduces the water availability for
agriculture. Measures that reduce drought risk for the agriculture can also be investigated. Examples of such
measures are increasing the bottom level of the ditches (and thereby reducing the draining function of these
ditches and increasing the groundwater storage) and increasing the inlet capacity. The risk profile with these
kinds of measures is investigated in further research.
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Deliverable n°11.3
The results provide a first impression of the current risk profile, with a limited number of use functions (water consumers) being considered. The future development of the risk as a result of climate change and socio-economic developments is not yet taken into account. The risk is expressed as an expected annual welfare effect. In the risk approach for the Berkel, use functions of agriculture, urban water quality and recreational shipping are considered. The risk approach is not applied to other relevant use functions such as nature, industry and drinking water. This was not yet possible for nature because the effect chain (hydrology-dose effect-welfare effect) was not complete. Interviews with the drinking water company Vitens and with Friesland Campina have shown that there are no circumstances in which a drought can affect their groundwater extraction (no ban will be imposed). As a result, Friesland Campina and the drinking water company Vitens are not affected by drought. However, there may be indirect effects due to lower water and groundwater levels in the area of extraction. This will result in an indirect welfare effect on functions that depend on water levels in watercourses, groundwater levels and soil moisture levels. The Industry and Drinking Water use functions were not considered, nor were the indirect effects of extraction. In future work (ongoing at the moment of writing), interviews will be held with Vitens to learn more about these indirect effects. This is incorporated in the next phase, in which also the future situation with climate change and socio-economic changes is considered. For the Berkel water system no appropriate regional water system model is available, that adequately describes the interaction between the groundwater and surface water systems. For further research, especially to define the risk profile for the future situation, a more detailed groundwater model is needed. For this purpose, a detailed model is built for part of the Berkel catchment. This is not done within the IMPREX project, but will be used in the next step for Imprex (ongoing work). For now, in order to establish a risk for the situation without climate change and socio-economic development, model results from the National Hydrological Model Instruments (LHM) are used for the agriculture use function. Measurement series of water levels, water temperature and discharge rates are used for the other use functions. The MetaSWAP model component in the LHM contains information about soil moisture levels with a resolution of 250x250 m. This information about soil moisture levels is used for agriculture in combination with the effect module for agriculture (AGRICOM) and the WER price tool (to determine the effect of price elasticity). The LHM (version 3.0.1) provides hardly any model results for the surface water in the Berkel catchment and it provides information only about the total surface water balance in the Berkel (water demand and supply), in other words information at the 'district level'. Local measurement points in the Berkel, with information about water levels, water depths and flow velocities in time, are lacking. As a result, the LHM outcomes cannot be used for the risk assessment relating to use functions such as urban water quality and recreational shipping, while a picture of the time-dependent development of hydrological variables at multiple locations in the surface water is needed to determine physical effects. For the urban water quality and recreational shipping use functions, measurement series (some of which were not continuous) are used in combination with simple assumptions and key figures about costs and benefits taken from various sources, including the literature. An initial assessment of the drought risk was established. The outcomes of the risk approach are preliminary and they should not be interpreted as absolute numbers. They still include a broad margin of uncertainty. However, in spite of the limitations of the current version of the Drought Risk Tool for the Berkel, it has become clear that the agricultural sector will have the largest annual expected welfare effect (however not very large) relative to recreational shipping and urban water quality. To demonstrate the added value for the consideration of measures, the effect of a measure on the risk for
agriculture is examined. The effect of stopping the inlet of water from the Twenthe Canal to the Berkel (in other
words, 0 m3/s instead of max. 1.3 m
3/s (the usual inlet rate)) on the risk is determined. The analysis showed that
the measure has hardly any effect: the effect is mainly visible in the area between Zutphen and Lochem (in the
form of a limited fall in the average lowest water level). The welfare effect of the measure is very limited. To
establish an idea of the magnitude of the effect, the costs of the measure can be compared with the welfare
effect.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 60
Conclusions
The annual expected value of the welfare effect of drought for the Berkel area is determined with limited
accuracy. This does however give insight on which of the use functions has a higher expected welfare effect, for
the current situation and for a different water management strategy (no inlet from Twenthe Canal). It occurs that
not all drought effects on use functions can be expressed in welfare effects with the same accuracy. For example,
the value that is attached to nature or to water quality in an inhabited area, is difficult to capture. This leads to a
denial or an underestimation of certain effects of drought in the risk calculation.
The extremely dry summer of 2018 shows that the water system and management practises are robust. This
drought did not lead to activating the Verdringingreeks (priority sequence in water distribution). This is in line
with the findings of the study that annual average drought effects on welfare are 0-5% of the annual turnover (for
the use functions agriculture, recreational shipping and urban water quality).
For stakeholders it is not yet clear how the results of a risk approach can contribute to decision-making or
decisions relating to the assessment of specific measures, tipping-point analyses on the regional and national
scales, arriving at agreements about water availability and/or contributions to the Decision-Support Information
Profile (BOI) of the Freshwater Delta Programme. The results are too uncertain to see their use. To improve this
uncertainty, analyses are being made with a more detailed regional model. The results of these calculations are
however not yet available (but will become available within IMPREX). These uncertainties are related to the
spatial scale and related expected accuracy of the pilot study.
The stakeholders stated that they wish to keep things simple. Clear and understandable arguments are
important. This is a relevant remark, since a risk analysis and the demanded accuracy easily lead to complex
assumptions and calculations. Also, a detailed model is needed to arrive at recognisable calculation results.
In the Berkel area drought is not experienced as a problem by stakeholders. There are very few complaints from
farmers about inadequate water supplies. The water authority is mainly held responsible for damage caused by
too much water. If dry periods become more frequent in the future or if they last longer, there will be a higher
demand for groundwater use for irrigation.
Since the effects on nature a not considered in the risk profile, the total drought risk is not yet known.
Groundwater use for irrigation, and groundwater use by industry and the drinking water company, might result in
decreasing groundwater levels in summer and therefore in shortages for nature. The future situation (2050) is
considered in ongoing work and will be reported later in IMPREX (Deliverable 5.5). However, also in this work
there will be no welfare effect calculated for nature. It is taken into account in a qualitative manner only.
From concept to user 3.6
As mentioned, the work is conducted in close collaboration with a number of end users. The method developed in
WP5 is developed with close involvement of The Dutch Ministry for Infrastructure and Environment, the
Freshwater Supply Programme Office, the Dutch governmental organisation responsible for water management
(Rijkswaterstaat), the Foundation for Applied Water Research, (STOWA, knowledge centre of the water boards)
and a number of water boards. The application of the method in the Berkel area is carried out in cooperation with
a range of organisations are collaborating on the Eastern Netherlands Freshwater Supplies, ZON. These include
water boards and the Province of Gelderland, and STOWA is closely involved as well. Also the water users (e.g.
agricultural cooperation and the drinking water company) are involved. The involved parties are stakeholders and
knowledge parties. They are involved in several work-shops, meetings and interviews. Discussions are had over
the use of the method. Here it became clear that working on a relatively small spatial scale as the Berkel river
basin, involving the water users directly, leads to a demand for high detail and high accuracy outcome.
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Deliverable n°11.3
As yet, the risk profiles have not been used for management decisions on this local scale. For the local scale more
detailed analysis is needed to convince the stakeholders, since in the present analysis, the results of the model
simulations that form the basis of the risk analysis, are criticized. Secondly, by not being able to address all effects
(agriculture, recreational shipping, water quality, nature) at the same level, it is difficult to obtain a full drought
risk-profile. Work is done on the risk calculation for present day as well as future scenario’s and with a more
detailed hydrological model, leading to more realistic results. The method is promising in providing valuable
information for long-term planning. Water authorities can use the risk profile to define whether or not measures
are needed in the water supply system, on a management level. Based on the risk profiles, insight can be given on
water availability for specific water users. These users can subsequently decide on specific local measures.
Next steps 3.7
To determine the future development of the drought risk, calculations with future climate and socio-economic
developments are made. Also analyses are being made with a more detailed regional model to reduce the
uncertainties in the present analysis. For both analyses, the results are not yet available. These results will be
reported in Deliverable 5.5.
Some aspects of the method need more work:
- Whether to summarise the overall risks for all user functions as a total risk or to consider the use
functions individually (since effects on all functions cannot equally be defined in a monetarily value). And,
in the latter case, whether to state the effects as monetary values or as other units, like loss of species for
nature, number of affected hectares for nature, value of houses, etcetera. An option would be to use
monetary and individual risk functions, and accept to need to do more bold assumptions, for example on
nature.
- The approach to the quantification of all kinds of less uniform welfare effects, such as effects of drought
in urban areas, such as stating the effect in terms of property prices (because waterside properties
become less attractive due to drought).
- What should be the spatial scale of the risks of the use functions, the regional system or the national
system? The focus may vary depending on the different parties involved.
- Determine whether the risk approach leads to new insight in the spending limit for measures to improve
water distribution and allocation.
- Determine whether the risk approach indeed leads to a different water allocation scheme compared to
using a water-availability approach. In other words, are the differences in the economic damage between
sectors giving rise to a different prioritization of water supply?
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Deliverable n°11.3
ARC-NSC 4
Introduction 4.1
The area to be considered in the third case study consists of the Amsterdam-Rhine Canal and the North Sea Canal
(ARC-NSC) and the areas that depend on water coming from this system. The aim of the IMPREX ARC-NSC case
study is to investigate the feasibility of a risk approach to decision-making about freshwater management in the
ARC-NSC area on the basis of a case in which the drought risk in a baseline situation is compared with the
situation involving an intervention. The selected measure in the case study is 'high-priority flushing for the
Amsterdam-Rhine Canal'. This intervention reduces salt intrusion into the Amsterdam-Rhine Canal from the
North Sea Canal and it requires water that cannot therefore be used elsewhere. The reduced salt intrusion is
expected to diminish damage to agriculture, nature and drinking water in the case study area during dry
circumstances. The risk approach will be applied to all these functions in order to assess the overall risk. The
agricultural risk will be compared with the results of a risk assessment based on characteristic years. This will
provide insight in the added value of using the proposed risk approach over the current method.
In order to be able to assess the impact of an intervention reducing salt intrusion, an algorithm for salt has been
developed, making it possible to establish a relationship between the effects of a lower discharge and the chloride
concentration in the ARC-NSC. This provides for stating this effect in terms of the impact on nature and drinking
water supplies. For the effect on agriculture, a comparison is made between different calculation methods
(potential impact, actual impact or long-term impact) for the welfare effect.
Characteristics of the ARC/NSC system
The Amsterdam-Rhine Canal takes in fresh water from the Lower Rhine/Lek or through the Merwede Canal from
the Waal (see Figure 51). An adequate flow of fresh water is important to prevent salt intrusion from the sea locks
at IJmuiden. The water in the ARC-NSC can be let in by the neighbouring water authorities to their belt canal
systems and be used for various functions such as irrigation in agriculture and level maintenance for the
protection of peat dikes. If the ARC-NSC water contains too much chloride, agriculture can be adversely affected.
Higher salt concentrations can also have an effect on nature and drinking water supplies. Operational water
management in the ARC-NSC area is designed to maintain water levels in the polders and in the belt canal
systems, and to prevent excessively high chloride concentrations. The high chloride concentrations are limited by
flushing the system. The salt load from the polders is ultimately discharged into the ARC, NSC or other open
waters (North Sea, Markermeer lake).
The Hoogheemraadschap Stichtse Rijnlanden and Amstel Gooi en Vechtstreek water authorities (both of which
are located on the eastern side of the area) take in water directly from the ARC. Usually, the Hoogheemraadschap
van Rijnland water authority takes fresh water from the Hollandse IJssel near Gouda during periods of drought. If
the chloride concentration at Gouda becomes too high to let water in, the Small-Scale Water Input (KWA) system
goes into operation and fresh water is brought in from the Amsterdam-Rhine Canal to the area managed by the
Hoogheemraadschap van Rijnland water authority. This system operated during the drought of the 2003 and
2018.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 64
Figure 51 Schematisation of the Amsterdam Rhine Canal-North Sea Canal water system, with in red the
discharges in m3/s at the different water ways.
An overview has been made together with the water management authorities and other stakeholders of the main
water consumers in times of drought and the effects that can occur during dry periods. That has resulted in to an
overview of the functions in the case study area exposed to the highest risk (see Figure 52). Agriculture emerged
as the function with the highest risk of drought damage, followed by shipping, nature, flood risk management and
drinking water.
In the current situation, droughts have a limited effect on the system, partly owing to the implementation of
measures such as the Small-Scale Water Input (KWA) system2. Climate and socio-economic change may result in
more frequent water shortages in the future, possibly leading to damage to agriculture and drinking water
because there is not enough water or because the available water is too salty. The focus of the regional
stakeholders, such as regional water authorities, is on preventing future water shortages and its impacts. That
implies making freshwater supplies more robust and allocating water according to economic principles. However,
to be able to prevent future shortage the current drought risk should be firstly understood.
The ARC-NSC is a preeminent example of a system where the water can be controlled in many ways. Therefore,
there are several steps that can be taken that have an effect on water allocation, not only in the main water
system, but also at regional management authorities and by users. There are therefore several windows of
opportunity to prevent water shortages in the future. The economic benefits of freshwater measures and the
alternative management of water have, in the past, only been identified to a limited extent. To facilitate sound
decisions about possible measures, the water authorities and Rijkswaterstaat wish to establish more robust
economic support for measures and to make the risks of drought transparent.
2
The small-scale water supply system increases the water supply to the western part of the Netherlands in times of droughts. This is done by
additional supply of water from the river Lek and Amsterdam-Rhine Canal.
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Deliverable n°11.3
Figure 52 Overview of sectors and functions with a high risk of drought (without ranking) (outcome of working
session on 10 May 2016).
The risk approach for freshwater supplies was developed in response. The approach considers both the
probability of drought-related hazard events, as well as the probability of socio-economic and environmental
consequences of droughts per end-users/sectors. The risk approach starts with estimating the hydrological effects
of droughts using -in this case study- the Dutch National Hydrological Model, which consists of models for the
unsaturated zone (MODFLOW), the saturated zone (MetaSWAP), the regional distribution of surface water
(MOZART) and the national distribution of surface water (SOBEK). The results include the allocation of water in
the case study area, taking into account priorities of water allocation and operational management. Model results
for 50 years are produced to be able to define return periods for water shortage. The output of the hydrological
models serve as an input for the available impact modules, which produces physical impacts for the respective
time series. Physical impacts include amongst others agricultural crop yield reduction and drought (depth)
limitations for navigation. This case study uses the crop yield model ‘AGRICOM’, which is shortly described in the
coming chapters. The last step is to translate the physical impacts in economic impacts using damage functions,
resulting in welfare effects of droughts. As welfare effects are estimated for 50 to 100 years, exceedance
probabilities of welfare effects can be obtained. The expected value of all welfare effects together is seen as the
drought risks. The drought risk can be calculated for the different use functions of water and for different spatial
scales. The ARC-NSC area is a test case for the application of the risk approach to level-controlled areas. The
results of this case study can be used to determine the wider applicability of the approach.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 66
This chapter describes the results of the application of the risk approach to the ARC-NSC case study. First a
description is given of the applied methods Then the hydrological results are described, followed by the
calculation of the welfare effects on agriculture, nature, drinking water and shipping. The chapter ends with a
discussion of the added value of the risk approach and issues relating to application.
Method 4.2
The risk approach, as described in the introduction, is applied. With this approach, the current and future
economic risk of water shortages in the area can be assessed. Furthermore, the economic benefits of measures
that increase water availability can be estimated based on a reduction in risk as a consequence of a particular
measure. The risk approach is demonstrated in this case study and compared with a characteristic year approach.
Furthermore, some additional methodological tests were done in order to further develop the approach. The case
study goals were determined together with local stakeholders.
A workshop was organised with the water authorities concerned from the ARC-NSC area, during which it was
decided to apply the risk approach first to the current situation and current climate. The measure under
consideration was the granting of a high priority to the flushing of the Amsterdam-Rhine Canal, which is expected
to reduce salt concentrations. The underlying idea was to make an appraisal of the trade-off between water
quality problems affecting the use functions agriculture, nature, drinking water, and industry on the one hand
and, on the other hand, problems caused by impaired water quantity with an effect on the agriculture, shipping
and infrastructure use functions. The hydrological calculations draw on the National Hydrological Model (LHM) as
this is the only available model in the area that can assess water shortages, and the model is already linked to
various relevant effect models. As the hydrological models does not produce satisfactory results regarding salt
concentrations, an additional analysis has been conducted in order to be able to analyse the impact of higher salt
concentrations.. The water authorities were actively involved during the study phase in elaborating the risk
approach and giving feedback on the results.
Determination of chloride concentrations
Chloride can result in damage to a number of functions in the ARC-NSC area: agriculture, nature, drinking water
and industry. Therefore, assessment of chloride concentration in the Amsterdam-Rhine Canal and the North Sea
Canal is important to determine the full welfare effect of water shortages. The National Hydrological Model
(LHM) does not provide the chloride concentration in the ARC-NSC as model output. Arcadis (2016) derived a
relationship between discharge rates (and the associated duration) and salt intrusion on the basis of a 3D salt
model. These results were used to convert the LHM discharges into chloride concentrations.
Arcadis (2016) established a picture of time-dependent salt intrusion for different discharge rates for the salinity
contours 0.27 PSU (Cl level 150 mg/l) and 0.9 PSU (Cl level 500 mg/l), see Figure 53. The salt intrusion for these
contours in relation to discharge and the duration of the shortfall are shown. Contour plots were established for
three discharge rates: 9, 16 and 23 m3/s. A chloride concentration of 150 mg/l is the maximum salinity for the
intake of surface water for drinking water production. The chloride concentration of 500 mg/l is the boundary
between relatively sweet brackish and relatively salt brackish water and it is therefore an important limit value in
nature management. This information was adopted as the starting point for the quantification of welfare effects
for nature and drinking water.
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Deliverable n°11.3
Figure 53 Intrusion of the salt tongue in the ARC [in km] for chloride concentrations 150 and 500 mg/l in the
scenario 'New Lock with selective extraction' and three discharge rates in the ARC (9, 16 and 23 m3/s).
The horizontal axis shows the number of days with a discharge of 9 m3/s it takes before the 500 mg/l
contour is reached for Nigtevecht (5 days, line 1) and Nieuwersluis (52 days, line 2).
Chloride concentrations were calculated for two locations on the Amsterdam-Rhine Canal that are important for
the drinking water and nature functions. The Nieuwersluis location is important for drinking water supplies
because there is an intake point here for drinking water extraction. Intake is shut down when the chloride
concentration of 150 mg/l is exceeded, with possible welfare effects as a result. The effects on the nature function
were considered for the Nieuwersluis and Nigtevecht locations, which are important because there is a direct
connection here with nature areas that are susceptible to high salt concentrations.
The hydrological calculations with the LHM produced a time series of 50 years for the discharge rate of the
Amsterdam-Rhine Canal at Weesp for the baseline situation, as well as for the measure high priority for flushing.
The average discharges per decade from the LHM were converted into daily values and then into a running
average discharge for time windows ranging from 1 to 7 weeks, with steps of 1 week. The minimum discharge per
time window was then determined for each year on that basis. The discharges associated with the baseline
situation and the measure are depicted in figure 55. With high-priority flushing, the discharge is at least 23 m3/s.
There are no extreme years in which discharge is lower because the model grants the highest priority to the
flushing demand, and this amount of water is available. When flushing is given a low priority, the minimum
weekly average discharge is 12 m3/s in the most extreme year. This information is the basis for assessing the salt
concentrations on the ARC/NSC.
Figure 54 The relationship between the return period and the average discharge for different time windows for the
baseline situation (flushing salt has a low priority) (left) and high-priority flushing (right).
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 68
An indication was given for each time window and year of whether the limit concentrations are exceeded. This
was done by interpolating the data as presented in Figure 54. The normative shortfall period was then selected for
each return period. The results are shown in Figure 55.
A chloride concentration of 150 mg/l was exceeded at Nieuwersluis in none of the scenarios. In the baseline
situation also (low-priority flushing), the discharge is still adequate to stop the salt tongue extending to the
Nieuwersluis location. There are therefore no effects on drinking water extraction and nature reserves that are
directly linked to this location.
There are no exceedances at Nigtevecht in the high-priority flushing scenario. In the baseline situation (low-
priority flushing), the discharge is inadequate to prevent salt intrusion.
Figure 55 Chloride exceedance duration at Nieuwersluis and Nigtevecht with high- and low-priority flushing. The
dotted lines represent the salt concentration without planned measures to reduce salt intrusion at the
sluice of IJmuiden.
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Deliverable n°11.3
Results 4.3
The multi-year hydrological results are the basis to estimate the welfare effects of drought for the most important
users of freshwater such as agriculture, shipping, drinking water and nature. These welfare effects are described
for each user of water. Furthermore, the impact of the intervention (lower priority for flushing) is estimated. The
welfare effect together with the probability exceedance will give the drought risk.
Agriculture
The impact on Dutch agriculture of drought and a lower priority for flushing was calculated using the AGRICOM
model (Mulder en Veldhuizen, 2016). This effect was then stated as the national welfare effect using the
agricultural pricing tool (Peerlings et al., 2017). AGRICOM calculates the drought and salt damage to crops and
the resulting loss of yield in kilos on the basis of the evapotranspiration deficit determined with the LHM (not
enough water available for maximum evaporation). AGRICOM values the loss of revenue using fixed crop prices in
order to determine the loss in terms of euros. The Agriculture price tool calculates the price effects on the various
crops depending on whether crops have a large international market share or are only traded in their own region
(see inset).
The welfare effect for agriculture has been estimated for the alternatives 'highest priority for flushing' and 'lowest
priority for flushing' (see Section 4.2). The welfare effect can be calculated in various ways, for example with or
without the effects of yield loss on the price, as calculated in the price tool. The calculation method has an effect
on the size of the welfare effect. This chapter first discusses the assumptions before presenting the results for the
high-priority alternative using different calculation methods. Finally, the results for the alternative examined will
be discussed in brief.
Price effects in agriculture
Reduced yields due to drought can affect the market price of a product. The size of this effect depends on how large the
change in production is by comparison with the market as a whole. If it is small, prices will not be affected. This is the case, for
example, when less Dutch cereals are produced due to drought. Dutch cereals account for a small proportion of the world
market and so the price will not change. If the relative change is large, for example in the case of bulbs, the price will change
when Dutch yields fall.
In the other case, the market is local and production in the region is equal to demand in the region. This will be the case with a
local product that is not traded outside the region, cattle feed being one example. Demand will not change a great deal if the
price changes since cattle must eat and cattle feed is not imported from outside the region.
In order to calculate the impact on the national economy due to the effects of drought on agriculture, a number of
assumptions were made. In all situations, supply was assumed to be price-insensitive. In other words, crops go on to the
market regardless of the prevailing price. The assumption was that production is fully determined by the price in the previous
period. Furthermore, it was assumed that variable costs have already been occurred before the start of a drought. It was
assumed for the international market that production does not change over the years.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 70
A change in national welfare was defined as a change in the consumer surplus and producer surplus. The national welfare
effect of drought can be described with the following demand and supply curve in which p = price, q = supply, o = old and n =
new (see Figure below). The demand curve shows that demand rises as the price falls. In the case of the supply curve, supply
remains constant when the price rises: supply is price-insensitive. When supply falls due to drought, the price - in this example
- rises. The farmer loses the income BC, but receives the income AB in return because of the higher price. The new producer
surplus is therefore a 0. As a result of the higher price, fewer consumers will be willing to pay for the product, as a result of
which the consumer surplus will fall. Without price change and drought, the consumer surplus is: C. As a result of drought, this
will decline to: A. Part of the consumer surplus returns as income for the farmer: AB. The other component is welfare loss:
ABC. When a product is exported, a higher price has an effect on foreign consumers, but not on Dutch consumers. Since only
changes in national welfare are taken into account, the loss of consumer surplus outside the Netherlands should be deducted
from the total welfare loss to obtain loss of Dutch welfare. If there is no price effect, the consumer surplus will not change : the
price remains the same and therefore the same number of consumers is prepared to pay for the product. The welfare loss BC
will then be entirely for the account of the farmer.
It was assumed for the following crops that the price will change when production changes:
- arboriculture and bulbs (large national and international market share)
- grass and maize (local products not traded outside the region)
In the case of cereals and sugar beets, drought in the Netherlands will never affect the price because the price is determined on
the international market and the Dutch share of this market is small. In the case of the other products (potatoes, fruit, and
'other'), it is less clear what the effect of drought in the Netherlands will be on the price but, for the time being, it has been
assumed that the price will not change if Dutch production changes (Peerlings et al., 2017).
In order to investigate how different assumptions influence the welfare effect of drought and the alternatives, a
range of calculation methods were used. For example, the welfare effect was calculated both with and without a
price effect. In addition, loss of yield was calculated in two different ways. The usual way is to define the
difference between the potential yield and the actual yield as affected by water shortage. The potential yield is
defined in AGRICOM as the yield in a given year without water shortages, in other words including losses that
cannot be attributed to water shortages such as crop losses, storage, disease, etc. The actual yield is the
calculated yield in a given year, including losses due to water shortages. This calculation approach is in line with
the maximum yield that can be achieved if there had been enough water. The second method is more in line with
farmers' perceptions. The actual yield is compared with the long-term average historical yield. For farmers, the
years with damage are the years in which yields are lower than expected because of drought. Accordingly, this
calculation approach matches farmers' perceptions more closely than an approach calculating the difference
between the potential and actual yields.
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Deliverable n°11.3
We calculated the welfare effect of drought and the two alternatives in the following ways:
1. No price effect, the welfare effect is based on the difference between potential and actual crop
yields;
2. No price effect, the welfare effect is based on the difference between the actual crop yield and the
long-term average actual crop yield, only yields below average are considered to represent the
welfare effect of drought;
3. Price effect, the welfare effect is based on the difference between potential and actual crop yields
and the associated prices which are determined by the yield.
In the case of the variant in which a price effect is included, it should be noted that no correction was made for
exports. As a result, it was assumed that the entire welfare loss due to the loss of income is confined to the
Netherlands.
Other assumptions were:
- The calculation of the welfare effect included the entire area used for agriculture: both the area in which
irrigation may be used and the entire rain-dependent area.
- Irrigation costs are included in the welfare effect in all variants.
Results for the welfare effect in the current situation
Figure 56 shows the welfare effect for the three alternative calculation variants for the baseline alternative. The
largest welfare effect was found for the extremely dry year 1976 in all variants but the absolute size of the effect
varied widely. The largest welfare effect was found in variant 3 (with a price effect and the difference between the
potential and current yields), with a welfare loss of M€ 1200. It was followed by variant 1 (without a price effect
and the difference between potential and actual yields), with a welfare loss of M€ 700. Finally, the welfare loss in
variant 2 (without a price effect and compared to the multi-year average) was smallest at just under M€ 300. The
welfare effect on average for the multi-year series also varied considerably between the different variants. The
welfare effect was highest in variant 3 with an average of M€ 317/year and lowest in variant 2 at M€ 38/year. The
welfare effect in variant 1 was, at M€ 270/year, close to the welfare effect in variant 3.
The low welfare effect in variant 2 was in line with expectations since the approach looks only at the negative
outliers with respect to the average. In average years crop yield is lower than potential yield, as soil moisture
conditions are never optimal in all areas.
The high welfare effect in variant 3 can be explained as follows. The higher price will, depending on the assumed
price elasticity, compensate for some of the damage suffered by farmers because of the reduction in the crop
yield. The higher price means that fewer consumers will be prepared to pay for the product. Depending on the
details of the supply and demand curves for the product, the consumer may suffer a negative effect owing to the
price increase that is larger than the benefit for the producer. As a result, the welfare effect can be higher in a
situation in which the price effect is included than in a situation in which a price effect is excluded.
However, it should be pointed out here that the consumer component of the national welfare effect must be
compensated for by the export share (the export share of bulbs, for example, is approximately 70%). That results
in a reduction in the welfare effect of variant 3. Variants 1 and 2 implicitly assume that the entire welfare loss is for
the account of farmers and so there is no way to correct for exports in these variants.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 72
Figure 56 Distribution of the welfare effect by year for the three variants.
Figure 57 shows the exceedance probability of the annual welfare effect for variants 1, 2 and 3. Variants 1 and 3
always result in a welfare effect: the potential yield is not achieved everywhere in any year. The welfare effect
does fall to zero in variant 2 because the yield is higher than average at a given moment. Once again here, it can
be seen that the welfare effect in variant 3 is higher than in variant 1. The difference is mainly seen in the years
with a small exceedance probability. This was as expected because the price will react sharply in the years with a
very high yield loss (in which there is a low probability of exceedance). If the negative effect on the consumer is
already larger than the benefit for the producer as a result of the price increase, the perceived negative effect will
become increasingly large in relative terms as the price increases further. This resulted in the ever larger
differences in welfare effect between variants 1 and 3 in the years with low exceedance probabilities.
Figure 57 Exceedance probability of annual welfare effect for the three variants.
Before long hydrological time series were available, characteristic dry years were used in the Netherlands to
estimate the risk of droughts. For the Dutch situation three characteristic years were used. The year 1976 is
characterized as a dry year with a probability of 1/100, the year 1989 represents a year with a drought probability
of 1/10 and the year 1967 represents an average dry year. In order to assess the risk of droughts the economic
impacts in these years were assessed, after which the results were interpolation to be able to produce a drought
risk curve. When comparing this method with a risk-based method, which considers the economic impacts of
droughts in multiple years, an indication of the added value of the risk based method is obtained.
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Deliverable n°11.3
Figure 58 illustrates the difference between the risk based approach and the ‘traditional’ approach using
characteristic years with a stated return period. In previous analyses, a certain year was associated with a certain
return period. Interpolation between the economic impacts associated with these years provided an estimation of
the risk. For example, the year 1976 was characterized as a year with a return period of 1/100, while 1989 is seen as
an average year (return period ½). The expected value of the welfare effect on agriculture that was estimated
based on the difference between potential and actual crop yields shows that characteristic years overestimate3
the risk on the agricultural sector. The differences are, on the one hand, caused by the interpolation between the
years and on the other hand by not assigning a for agriculture accurate return period to a year. When using the
risk approach the expected value (i.e. the average of the distribution) of the welfare effect is 270 Meuro per year,
while the expected value is 331 Meuro per year when using characteristic years. This difference is significant,
especially when used in a cost benefit analysis, which will sum the expected value of multiple years. If the welfare
effect is estimated based on the difference between the long-term average crop yield the actual crop yield, the
expected value using a risk approach is higher (38 Meuro) then using the characteristic years (18 Meuro). This
shows that the risk approach improves risk estimations in comparison with the ‘traditional’ method, this will help
policy makers to better assess risks and benefits of fresh water measures.
Figure 58 Illustration of the impact of using characteristic years and the risk approach on the final risk calculation .
Impact of the intervention
The effects of the baseline in comparison with the high flushing (or salt avoidance scenario) are limited. The
hydrological calculation results show negligible differences in the study area. Water availability and the salt
concentration for agriculture hardly change at all and this is reflected in the welfare effects: there are hardly any
differences between the baseline scenario and salt avoidance scenario (high priority flushing). Figure 59 and
Figure 60 show that, in variant 3, the average benefit of high priority flushing is € 16,000 per year. The differences
are even smaller in the other variants.
3 As a more precise estimation of the risk shows lower total risk values than using characteristic years.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 74
Figure 59 Welfare effect in millions of euros per year for the baseline alternative (Low-priority flushing) and
alternative 1 (High-priority flushing). The differences between the welfare effects for the two
alternatives are very small.
Figure 60 Difference between the expected value of the welfare effect (drought risk expressed in welfare
indicators) for high-priority flushing and low-priority flushing in the five areas managed by water
authorities in the ARC-NSC. The effect of low-priority flushing is slightly negative (€21,600) for the
HHNK water authority (there is a more negative welfare effect); for the other wate r authorities, there is
no change or a slight positive effect.
Nature
Reduced water availability and increased chloride concentrations can have an effect on nature: a loss of intrinsic
natural value, in other words the value of nature for nature itself. This can be expressed as species loss or as a
reduction in numbers per species, which cannot be stated in monetary terms. In addition to the loss of intrinsic
value, a change in the biodiversity of aquatic nature can also have an effect on the economic value of nature. This
factor was not included in the analysis here.
In the ARC area, aquatic nature in particular is sensitive to higher chloride concentrations. Salt intrusion in the
ARC primarily affects the nature areas in the area managed by the Amstel Gooi en Vecht water authority because
these areas are directly connected to the northern section of the Amsterdam-Rhine Canal, where high chloride
concentrations are possible. Areas originally replenished by fresh water (seepage or rainwater) are most sensitive
to chloride. These are the seeps on the edge of the Gooi area, Loosdrechtse Plassen and Noorderpark. Brackish
seepage is found in the Groot Mijdrecht polder and further to the west around Ankeveen, Horstermeer and the
Nieuwe Keverdijkse polder. Here, less water would result in less flushing and therefore damage due to salt. The
most critical locations are the Noorderpark and the Loosdrechtse Plassen, where there are local species with low
chloride tolerance that cannot survive at higher chloride levels [correspondence Bart Specken, Waternet].
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Deliverable n°11.3
Figure 61 Locations considered for the nature function.
The intake point for water from the ARC for the Loosdrecht Plassen is at Nieuwersluis (see Figure 61). Another
important location is Nigtevecht, where the ARC is connected directly to the Vecht river. The Ankeveense
Plassen, among other bodies of water, obtain water from the Vecht system. The effects on nature were estimated
using the WFD-Explorer method, in which tolerance levels for different species are compared with environmental
factors in the water body (Wortelboer, in prep.) for the quality elements 'other water flora' and 'macro-fauna'. The
results provided indications of the change in the capacity to accommodate WFD reference species. Here, we
assumed that any reduction in that capacity would be associated with a fall in the intrinsic value of nature. When
determining the effects on nature, it was assumed that higher chloride concentrations occur in the summer
season (the growing season for aquatic plants). It was also assumed that the chloride concentrations at the inlet
points are representative for the concentrations in the entire area covered by the lakes (the buffering of
freshwater in the area was therefore disregarded). At present, these results cannot yet be compared directly with
the results of the other water consumers, such as agriculture and shipping, because these results are not stated in
monetary terms4. It may be possible, in time, to state the results as ‘nature points’
5, which is a method to estimate
impacts on biodiversity, and, on that basis, to determine a risk in terms of ‘nature points’.
4
Intrinsic value cannot be expressed in monetary terms as it represents the value of nature for nature itself. 5
Nature points are estimated by multiplying the surface of nature times the nature quality times a weighting factor (PBL, 2014).
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 76
Impact of the intervention
No exceedance was found of the chloride concentration at the Nieuwersluis inlet (Figure 61) in the ARC. The
model results therefore indicate that there is no risk of negative effects in the Loosdrechtse Plassen due to the
intrusion of water into the ARC from the North Sea Canal for either aquatic plants or macro-fauna.
At the Nigtevecht inlet (Figure 61), no exceedance of the chloride concentration was found in the high-priority
flushing variant and no effect on nature is therefore to be expected. In the case of the baseline scenario, an
exceedance of the chloride concentrations was found and an effect on nature is therefore possible.
In the baseline scenario (low-priority flushing), the exceedance duration for the different chloride levels increases
with the return period. An exceedance duration of 2 weeks occurs once every 2-3 years at a concentration of 150
mg/l, once every 4-9 years for a concentration of 300 mg/l and once in about 25 years for a concentration of 500
mg/l. A period of two weeks in which the chloride concentration is exceeded is probably too short to eliminate the
sensitive species of aquatic plants entirely. In addition, 25 years is a long enough time for aquatic vegetation to
fully recover (Wegen naar Natuurdoeltypen 2). A recurrent increased concentration of 300 mg/l once every 4-9
years will lead to micro-succession (Noordhuis, 2016), in which there are fluctuations over time in the density of
species but in which vegetation as a whole does not undergo any significant change. There will be more
physiological damage and some species of aquatic plants will be lost temporarily in the case of the exceedance of
a chlorine concentration of 300 mg/l lasting more than 3 weeks, which occurs once every 10 years. Because of the
seed bank in place, these species will be able to recover in a few years. Even in this situation, the effects are likely
to remain negligible. A period of 2 weeks with a concentration of 150 mg/l every two or three years would not
seem to represent a problem for aquatic plants.
The data in the WEW database indicates a limited susceptibility of reference species of macrofauna to increases in
the chloride content. Above 1000 mg/l, there is a clear decline in the potential number of species present. 1000
mg/l was not used as a signal value in this study but given the prolonged salt intrusion at Nigtevecht, this level
may be exceeded, possibly resulting in damage to organisms (and particularly to sensitive species).
Drinking water
There may be welfare effects in the drinking water sector as a result of high chloride concentrations. At a
concentration higher than 150 mg/l, the intake of surface water for drinking water production will stop. Welfare
effects may result in various ways:
- additional costs for drinking water supplies using alternative routes;
- additional investment costs for supplementary water treatment;
- costs for interventions to change consumer behaviour (campaigns to reduce drinking water use).
It has been concluded that there will be no limitation of supplies of drinking water because the drinking water
sector always has enough alternative sources of drinking water in the short term and because it will take adequate
measures, such as increasing their buffer capacity, in the long term to prevent any limitation of supplies. The
welfare effect therefore consists of the costs of the investments that drinking-water companies have to make in
order to adapt to changing circumstances. This is a similar approach as used in the Berkel case.
If the salt concentration at the location of the intake point at the Nieuwersluis drinking water extraction location
exceeds 150 mg/l in the future, it will be necessary to invest in additional water treatment (such as fast and slow
sand filtration and reverse osmosis). This investment amounts to approximately € 15 to 20 million (Sikma, 2015).
In the long term, the Nieuwersluis drinking water extraction location may also be used for normal intake purposes
in order to deliver the amount of drinking water required for Amsterdam and its surrounding areas and possibly
for supplies to other drinking water companies. It is therefore expected that the importance of emergency intake
will increase in the future. In combination with increasing salt intrusion in the Amsterdam-Rhine Canal as a result
of climate change, the new sea locks and increasing movement through the locks, this makes a sound analysis of
the salt-related risks important.
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Deliverable n°11.3
Impact of the intervention
It was assumed in this study that drinking water producers will not accept an annual probability of a failure of
supplies of more than 1%. If this probability increases, drinking water companies will invest in additional backup
capacity so that they can cope with the consequences of a possible fall in the availability of fresh water. RO
(reverse osmosis) as a treatment technology is one of the conceivable measures that can almost always be used.
On the basis of the additional costs for investment and production relating to RO, Ecorys has drafted a damage
function (KWR, 2017; Ecorys, 2017). The damage function for the drinking water company in this region
(Waternet) shows that the current backup arrangements provide a capacity of approximately 6 million m3/year.
Should this capacity prove inadequate, it will be necessary to turn to RO plants or other, cheaper, options (Ecorys,
2017).
Hydrological calculations with the National Hydrological Model (LHM) indicate that the current exceedance
probability for the standard of 150 mg/l is 0 (zero) for the Nieuwersluis inlet point, based on a 100 year time series.
Potentially the standard could be exceeded at lower return periods. This means that there are no welfare effects
for drinking water. Welfare effects may be found if climate and socio-economic scenarios are taken into account,
which will be analysed later in the context of IMPREX .
Shipping
The welfare effects for shipping in the ARC-NSC study area are caused by limitations on movements through a
number of locks. By contrast with the large rivers outside the study area, there are no limitations on navigation
depth here. To quantify the welfare effects, the method and the numbers from (IMPREX, 2016) were used. In an
IMPREX working session with water management authorities in April 2017, it was agreed that the welfare effects
for shipping are related to the use of the Small-Scale Water Input (KWA) system. Less lock capacity is available for
shipping during these periods. It is possible that the 'extra flushing' has an additional effect on the available
capacity for shipping but the relationship between inlet quantity and limitations on movements through the locks
has not been elaborated at this level of detail and it cannot therefore be applied in this case. The damage for
shipping in the baseline situation is therefore equal to the damage associated with the 'high-priority flushing'
measure.
Impact of the intervention
The welfare effects for Spaarndam and the Princess Irene Locks are both related to the deployment of the Small-
Scale Water Input (KWA) system. On the basis of assumptions about the increase in the waiting time, the costs
per hour spent waiting at the lock and the number of shipping movements (see Table 7), it has been estimated
that the welfare effects for the Princess Irene Locks amount to € 31,000 per day of KWA and € 6200 per day of
KWA for Spaarndam. These amounts are based on 2008 price levels. For 2017, the total damage per day
associated with KWA is € 44,400.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 78
Table 7 The number of extra hours per day spent on waiting when there are restrictions on shipping movements through
locks and when a ship must wait for the duration of one lock movement on the basis of passages through locks in the case
study area (Table 8) and own calculations.
Number of extra hours spent waiting on a day
with restrictions on shipping movements through
locks* for the baseline year 2008 IJmuiden
Sea Lock Irene Lock Beatrix Lock
Large lock in
Spaarndam
container vessels 1 0 0 0
non-container vessels 55 0 0 0
all professional vessels 0 49 64 10
ferries 0.5 0 0 0
cruise vessels 0.5 0 0 0
recreational vessels 0 0 6 20
*The calculations are based on 365 days per year, even though cost-benefit analyses for travel time losses usually take
holidays etc. into account, in other words times when there is less traffic. Calculations for road traffic are based on 342 days
per year. This number of days is not known for shipping. Furthermore, no information is available about the days on which
movements through the locks are limited.
The 50-year hydrological calculation with the LHM includes four periods during which the KWA was activated.
Table 8 shows the welfare effects in these years. It should be noted here that, in practice, water inlet at the
Princess Irene Locks is usually at night, which will probably result in less disruption for shipping.
Table 8 Welfare effects for shipping in the period 1961-2010.
Year Number of days KWA Welfare effect (€)
1964 20 880,000
1976 30 1,320,000
1991 10 440,000
2003 10 440,000
The 'high-priority flushing' measure has no effect on the implementation frequency of the KWA. There is
therefore no difference in the welfare effects for shipping between the current situation and the situation in which
the measure is implemented. In a risk-based consideration of the implementation of this measure, the shipping
effects in the current situation do not play a role in the economic assessment. It is possible that 'more flushing' will
result in more damage for shipping because the lock capacity for shipping will be reduced further. However, the
welfare effects cannot yet be calculated in such detail.
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Deliverable n°11.3
Risk profile 4.4
In order to get an overview of the risks, a risk profile can be obtained combining the different risks for the use
functions of water. The differences between the risk profile for the high priority flushing and baseline scenario
(low priority flushing) can be seen as benefits. As there is (almost) no difference between the risk with and without
the intervention, the same risk profile can be used to assess the risk (see Figure 62). The risk profile shows that
agriculture risk is dominant in this region. Although the shipping sector faces some risk of droughts, the expected
value of this risk is only 60,000 euro per year. In comparison with agricultural risk this is insignificant. No risk for
the drinking water sector and industry is assumed under the current socio-economic circumstances and climate
and the risk for nature is not expressed in monetary values and therefore not included in the risk profile. There can
be concluded that in this region the current economic risk (excluding nature) is limited to the agricultural sector.
Figure 62 Exceedance probability of the annual welfare effect.
Discussion and conclusions 4.5
The aim of the IMPREX ARC-NSC case study is to investigate the feasibility of a risk approach to decision-making
about freshwater management in the ARC-NSC area on the basis of a case in which the drought risk in a baseline
situation is compared with the situation involving an intervention. The measure used in the case study was 'high-
priority flushing for the Amsterdam-Rhine Canal'. This intervention reduces salt intrusion into the Amsterdam-
Rhine Canal from the North Sea Canal and it requires water that cannot therefore be used elsewhere.
By drawing up an algorithm for salt, it is possible to establish a picture in a practical way of the effects of a lower
discharge on the chloride concentration in the ARC-NSC. It is then possible to state this effect in terms of the
impact on nature and drinking water supplies. For the effect on agriculture, a comparison is made between
different calculation methods (potential, actual or long-term) for the welfare effect.
The current risk has been calculated for shipping and agriculture. The comparison shows that, in this area, the risk
for agriculture is much higher than for shipping. Including the effects outside the study area could alter this
conclusion. Water from outside the study area is required to give flushing a high priority. Although the extraction
of water from the rivers Lek and Waal are limited, during drought periods this could have adverse effects on
shipping and drinking water supply outside the study area. For shipping no welfare effects are expected in the
current situation and so there is no risk. Adverse effects can be expected for nature. Although it is possible to
estimate the effect on nature with the WFD Explorer these cannot be quantified in such way that the results can
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 80
easily be included in the risk approach. By expressing the results of the WFD Explorer, possibly accompanied by
quality factors (such as the presence of amphibians), as nature points, risk can be expressed in nature points.
However, these points will not be directly comparable with the other risks.
The model calculations show that the intervention 'more flushing' has no negative effect on the agricultural yield
in the study area because the extra water required can be supplied from the Lek and Waal rivers. In the study area,
the intervention influences nature only, and in a positive way. The negative effects of flushing are found outside
the area in the rivers Waal and Lek. Bringing in additional water from the Waal may have adverse effects on ship-
ping due to the reduction of the navigating depth and the associated need to limit the size of cargoes. In addition
to the effects on shipping, a reduced discharge in the Lek or Waal can lead to higher salt intrusion in the down-
stream sections of the rivers. This can also have an indirect reverse effect on the ARC-NSC area if the chloride
concentration in the Hollandse IJssel increases and the KWA has to be implemented more often. These effects
outside the study area are not investigated, as these considerations go beyond the boundaries of the study area.
On the basis of the risk approach implemented here, limited to the ARC-NSC study area, the risk approach could
not be fully demonstrated. Given the very limited effects of the intervention in the study area considered, it is
difficult to demonstrate whether the risk approach provides added value. The added value of the risk approach
over the ‘traditional’ approach has been shown. The risk method leads to a more reliable annual welfare effect.
From concept to user 4.6
As mentioned, the work is conducted in close collaboration with a number of end users. The Dutch Ministry for
Infrastructure and Environment, the Freshwater Supply Programme Office, the Dutch governmental organisation
responsible for water management (Rijkswaterstaat), the Foundation for Applied Water Research, (STOWA,
knowledge centre of the water boards) and a number of water boards are involved as stakeholders in the
research. We follow the concept learning by doing, and use the outcomes of demonstrations of the method for
further development and refinements of the generic risk assessment framework and tool. For this learning and
development of the tool, we closely cooperate with the end users. This is done in practice by organizing several
workshops with the main stakeholders to discuss the main assumptions and present the approach. In addition,
stakeholders are involved in collecting data and selecting the focus of the study.
The users consider the approach as promising, which is demonstrated by a current application of the approach in
the Dutch Delta Programme Fresh Water that aims to adapt to (future) droughts. Lessons learned from the case
studies are used to draw general assumptions for nationwide analysis based on risk expressed in monetary terms.
This analysis includes assessment of the current and future risk as well as differences in risk of potential fresh
water measures that are defined by the national and regional governments. Final decisions on fresh water
measures (supply, demand and measures to reduce vulnerability) should be made by 2021.
Next steps 4.7
Work on the ARC-NSC case study within IMPREX will continue in order to improve the risk approach and
demonstrate the added value. As the current risk is limited, the risk approach will be applied to assess future risk
in the ARC-NSC region, including climate change and socio-economic scenarios. Furthermore, the measure ‘low
priority flushing’ will be assessed; with the difference that water supply from the river Waal will be limited. The
expectation is that the risk will increase under these assumptions. Besides the use of 50 year time series, extreme
dry years will be determined based on RACMO and probabilistic ‘ARMA’ time series both developed in work
package 5 of IMPREX. The impact of these extreme years will be analysed to assess the added value of including
more extreme years in the risk approach. Finally, the impact of (model) uncertainty on the risk profile will be
examined by unravelling the individual uncertainty factors with help of representatives of regional water
authorities.
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Deliverable n°11.3
Comparing the case study results 5
In all three case studies a drought risk is determined, as a probabilistic assessment of economic drought impacts.
The exact definition differs for the Spanish and Dutch case studies. In the Júcar case study, drought risk is defined
as the probability reservoir storage levels, or, for this work package, of experiencing water deficits by the
agricultural sector. In the case studies Berkel and ARC-NSC, drought risk is defined as the probability of
occurrence of the drought, times the welfare effect it causes. This drought risk can be expressed as a yearly
expected damage value or an exceedance probability of the annual welfare effect.
The case studies demonstrate that probabilistic drought risk assessments provide valuable information for
operational water management as well as for long-term planning. In the Júcar basin the concept of drought risk is
used to produce seasonal forecasts of reservoir water levels. The stochastic ARMAX model has been used to
generate probabilistic river flows for multiple years. River flows serve as input in the SIMRISK model simulating
the management of the system, which results in exceedance probability of reservoir water levels. Reservoir levels
are an important indicator for water managers to take decisions and determine amongst others agriculture
productivity loss and loss in hydropower. Next steps include defining long term drought risk for usage in planning
and translation of the results in economic damage. In the Berkel and ARC-NSC case studies, drought risk is
defined as the yearly expected value of the welfare effects of droughts or as the exceedance probability of the
annual welfare effect. Historical time series of precipitation, evaporation and river discharges6 with a length of 20
to 50 years, serve as input in a series of hydrological models for the surface water system, groundwater system
and unsaturated zone. The AGRICOM model is applied in order to assess the impact on the agricultural sector.
Impact on the other relevant sectors (use functions) are assessed based on simple dose-effect relationships. These
have been further translated into welfare effects. The drought risk is presented as a risk curve for the different use
functions of water. The information on drought risk can be used to determine if measures should be taken to
reduce the impacts of droughts. By comparing the welfare effect for the use functions, it can be determined in
which sector the available water is of most economic value (note: economic value will not always be the only
defining factor). Furthermore, the benefits – described as a reduction in risk (either a reduction in negative effect
or a reduction in probability of occurrence) – can be assessed to economically evaluate the impacts of measures
that increase water availability or reduce vulnerability of actors to droughts.
The largest difference between the drought risk approaches as applied in the Spanish and the Dutch case studies
is the focus on either seasonal forecasts or long-term planning. Especially in regions that already face extensive
droughts and who have the ability to manage the allocation of fresh water resources, seasonal forecasts are
valuable. The Júcar basin is an example of such a region. In the Netherlands, the current droughts are less
prolonged and there are no reservoirs that can be managed to reduce the impact of droughts. The most short-
term drought measures, which can profit from seasonal forecasts, are relatively small. Dutch policy makers are
especially interested in future drought risk to be able to decide whether or not additional measures should be
taken. Although not demonstrated in the case studies yet, the developed drought risk approach can answer this
question by using climate enforced long-term time series and socio-economic scenarios. This analysis is currently
carried out and will be reported in deliverable 5.5. Furthermore, the Júcar case study will also estimate long-term
drought risk for planning based on the ARMAX model. Also these results will be reported in deliverable 5.5. The
Dutch case studies have worked with time series of 20 to 50 years which are used to determine return period of
6 In order to use these series for a risk evaluation in the present day climate, it has been be explored if the historical time series of discharges,
precipitation and evaporation historical time series are representative for the current climate. There is a positive trend in annual precipitation
over the years (Stowa, 2015), however, the precipitation trend in the ‘summer half year’ is not significant meaning that there is no significant
impact of this trend on dry periods. Furthermore, there is no significant trend in river discharge of the Rhine and Meuse over the years.
Therefore it has been decided to take the historical time series as representative for the current climate.
IMPREX has received funding from the European Union Horizon 2020 Research and Innovation
Programme under Grant agreement N° 641811 82
certain impacts. The Júcar case study uses stochastic time series based on the ARMAX model. The advantages of
using deterministic (historical) time series instead of probabilistic series is that – if available - they are relatively
straightforward, easily understandable and no probabilistic model has to be developed. These time series allow
for statistical analysis and determination of probabilities as well, but for a more limited range of return periods.
These time series do not include information on the extent of an extreme event or on return period of more than
about 10 to 50 or - when extended - 50 to 100 years. This information is especially valuable if there is an indication
that the extreme events can lead to disruptive situations, for example problems with drinking water supply or
bankruptcy of a large part of the agricultural businesses. As this probability is expected to be higher in the Júcar
basin, it is especially important to include extreme events with smaller return periods in the drought risk in this
basin. The probabilistic ‘ARMAX’ time series based on precipitation and discharge developed provide valuable
information on the extreme droughts with high return periods (e.g. 1000 years).
In The Netherlands, the most extreme drought in the last 100 years was not disruptive, indicating that the impacts
of drought with a small (1/50 to 1/150 years) return period are limited. However, the impact in an extreme climate
change scenario or at very small return periods can still be substantial. Therefore, the contribution of extreme
events to drought risk is currently tested in the ARC-NSC case study. A purely stochastic method is being used,
which combines an autoregressive modelling approach (ARMA) and a copula method for incorporating
dependences between a precipitation and river discharge time series (see the deliverables of work package 5 for a
comprehensive description of the model). Besides an ARMA model, GCM/RACMO time series were used to derive
synthetic time series of Rhine River discharges. However, it was observed that the performance of the
hydrological model for drought periods was poor; low flow discharges of the Rhine River are systematically
underestimated. For that reason, it was decided to implement the ARMA model to assess the impact of extreme
droughts on the drought risk.
Another difference is the expression of the risk in economic terms. Dutch stakeholders ask for expression of the
impacts in economic terms (impact on the welfare in The Netherlands) in order to include these impacts in a social
cost benefit analysis, while the Spanish stakeholders prefer expression of the impacts in physical terms (reservoir
levels). The case studies show that a risk approach is able to respond to both needs.
A third difference is the size of the pilot area, and therefore the spatial scale on which the risk is determined. In
the Júcar case study, the whole Júcar River Basin is involved. In the Berkel case study only a small river basin is
studied, and the ARC-NSC pilot area covers the canals and especially the regions depending on water supply from
the canals. The Dutch case studies are relatively small-case compared to the Júcar case study. This difference in
scale leads to a difference in expected detail and accuracy of the results. In the Dutch case studies it is observed
that the demanded accuracy is high, leading to the conclusion that the National model that is used, does not
provide enough detail on all demanded output. This goes especially for the Berkel case study. Therefore, a
drought risk analysis on an even smaller (sub-basin of the Berkel) scale is carried out with a much more detailed
hydrological model. Results of both approaches will be compared in deliverable 5.5.
In summary, although the approaches differ, both approaches are using information on drought risks to inform
policy makers on drought management and planning. The case studies give a valuable overview of the different
possibilities and methods of using drought risk. Both studies are not completely finished and will provide new
results in the near future, which will be reported in deliverable 5.5. The new results will give a more profound
overview of the added value for future planning.
In Table 9 a comparison between the case studies is made. From this, the applicability of each method for other
areas can be derived.
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Deliverable n°11.3
Table 9 Comparison of the case studies on key-elements.
Júcar basin Berkel and ARC-NSC
Aim Improve drought forecasts on the short
and medium term in order to improve
water system management and assess
the effects of new measures.
In future work, and especially in WP13,
long term forecasts including climate
change will be used to obtain risk
assessment, in order to define the
program of measures for river basin
plans
Give more insight in current and future drought
risk to assess the (economic) impacts of
measures. Facilitate management decisions on
water distribution, water demand measures
and measures that reduce vulnerability to
droughts.
Definition of risk Risk perception from Júcar River Basin
Stakeholders, and therefore, decision
making, is more based on the
information related to future reservoir
storage (collective risk perception as
implemented in the Drought Plans), and
to future deficits (sectoral and individual
risk perception) than on expected
economic losses.
Nevertheless, in IMPREX project,
estimates of expected economic losses,
and exceedance probabilities will also be
obtained, and the results will be
discussed with stakeholders.
Probability of occurrence of the drought, times
the welfare effect it causes. This drought risk
can be expressed as a yearly expected damage
value or an exceedance probability of the
annual welfare effect.
Time frame Seasonal (in this deliverable), and long
term (in future work to be reported in
D5.5 and D13.4)
2014 and 2050
Risk model Hydrological series are produced with
ARMAX model for seasonal forecasts,
and ARMA model for long term
analysis. The risk assessment model is
SIMRISK model in both cases. SIMRIK
performs a Monte-Carlo analysis using
hundreds Of hydrological time series
generated with ARMA(X) model.
50 year deterministic time series, use of a
model network including water allocation and
impact models (agriculture, shipping and
nature), in the future phase an ARMA model
will be tested.
Effects considered
Agriculture, hydroelectricity, urban
supply, environmental requirements
Agriculture, Shipping, Drinking Water, Nature
Spatial scale Large: river basin of 22,186.61 km2 Current application Small: maximum several
hundreds of km2. Also applicable on larger
spatial scales.
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Deliverable n°11.3
Implications 6
The management instrument will help to identify opportunities and measures and to quantify their subsequent
consequences. By combining this with probability of occurrence we can quantify drought-related risks in present
and future conditions (under the influence of climate and socio-economic change) associated with various
measures (which may include controlling water flows in major waterways, local infrastructural solutions to
supplement water and local adaptations by the end users). This also allows the economic comparison of various
interventions, which helps to prioritize operational and strategic actions and investments. We follow the concept
learning by doing, and use the outcomes of demonstrations of the method for further development and
refinements of the generic risk assessment framework and tool. The case study applications so are considered as
a proof of concept.
Given the very limited effects of the investigated measures and the relatively low drought risks in the Dutch study
areas considered, it is difficult to demonstrate whether the risk approach provides added value. Applying the
method in a highly managed and complex water system, such as the ARC-NSC system is challenging, as the
responses to drought highly depend on the type of event. Differences in the management and operation of the
system during droughts have a direct impact on the (long term) drought risk. In the case of ARC-NSC not all
management and operation rules are well presented in the model, which is affecting the estimation of the
drought risk. However, the ARC-NSC case study also indicated that the approach can improve the current
assessment of the impacts of droughts and therefore also the estimation of costs and benefits of drought
measures. The method is found to be promising and will therefore be used in the Dutch Delta Programme Fresh
Water, which aims to prepare the Netherlands on the impacts of climate change. The developed risk approach will
be used to estimate the current and future risk (under changing climatic and socio-economic circumstances) of
droughts and the impacts of fresh water measures. For the Júcar case study, the risk assessment methodology
provides useful information for first level early warning and action against drought, as well as for risk perception
by the public. In order to manage droughts, a more elaborated and detailed information system is needed. The
drought risk method is an option to improve drought management. Moreover, this methodology can be extended
to any system with a proper calibration adapted to that area.
The approach is expected to be applicable in other countries. A (simple) water allocation model, time series (or
stochastic time series) and damage functions are required to obtain a risk profile. Better insight in current and
future drought risk around the world will help policy makers to decide on the need to implement drought
(mitigating/adaptation) measures. Furthermore, this approach can help to select drought measures that are most
beneficial from a societal perspective.
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Deliverable n°11.3
References
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Websites
CHJ (May, 2018): https://www.chj.es/es-es/medioambiente/gestionsequia/Documents/Informes%20Seguimiento/InformeSequia.pdf
https://aquatool.webs.upv.es
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Grant Agreement N° 641811