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Simulating consequences of land use change on hydrological landscape functions and sustainable crop production in Northwest Vietnam

Yohannes Zergaw Ayanu

Simulating consequences of land use change on hydrological landscape functions and sustainable crop production in

Northwest Vietnam


Master’s thesis submitted to the Institute of Plant Production and Agroecology in the Tropics and Subtropics for the requirements of the fulfillment of Master of Science in Agricultural Sciences in the Tropics and Subtropics with specialization in Rural Development Economics

October 2009

University of Hohenheim, Stuttgart, Germany

Supervisors

Prof. Dr. Georg Cadisch

(Institute of Plant Production and Agroecology in the Tropics and Subtropics) Garbenstrasse 13 D‐70599 Stuttgart Germany

Prof. Dr. rer. nat. Thilo Streck (Institute of Soil Science and Land Evaluation) D-70593 Stuttgart Germany

Dr. Carsten Marohn (Institute of Plant Production and Agroecology in the Tropics and Subtropics) Garbenstrasse 13 D‐70599 Stuttgart Germany

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Abstract

The Northern Mountainous Regions (NMR) of Vietnam have undergone rapid land use

changes in the past 50 years that basically shifted the traditional swiddening farming

system to less sustainable continuous annual monocropping systems. This study aimed at

assessing the booming expansion of maize monocropping and expected increase of

rubber (Hevea brasiliensis) plantations as a response to increasing market demand in

Northwest Vietnam based on the case study site of Tat hamlet watershed, Hoa Binh

province. Effects of land use change on water balance and crop production were

assessed over 20 years simulation period using the Land Use Change Impact

Assessment (LUCIA) tool (Marohn, 2008), a spatially explicit dynamic modeling tool

based on PCRaster. The model was parameterized using field data collected by the

Centre for Agricultural Research and Ecological Studies (CARES) from the study area

and calibrated for yield. The results were validated on independent datasets to verify

model plausibility. Two scenarios were defined and implemented in different model

simulation runs. The first scenario considered agricultural intensification as expansion of

maize in the uplands by clearing forests for maize monocropping. In the second scenario

introduction of rubber plantations in the uplands by converting forest lands was

implemented. A baseline scenario was defined based on the 2008 land use map of Tat

hamlet experimental watershed created from satellite images and field survey data

(Lippe, personal communication). Runoff, discharge and agronomic yield were

simulated at pixel level and watershed outflow. The results under each of the scenarios

were compared with the baseline situation. The findings of this research showed that

LUCIA was able to predict agronomic yield with acceptable accuracy. Simulations of

runoff and discharge were also found plausible though further calibration and validation

of these hydrological parameters is necessary. If agricultural expansion into forest areas

in the uplands of Northwest Vietnam is deemed unavoidable, then rubber plantations

appear better land use options than maize monocropping from a standpoint of runoff and

discharge generation in the area.

Key words: Land use change, Rubber, Maize, LUCIA, Runoff, Discharge, Yield

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Author’s Declaration

I, Yohannes Zergaw Ayanu, matriculation number 433648 at the University of

Hohenheim, hereby assert that I have written the thesis titled ‘Simulating consequences

of land use change on hydrological landscape functions and sustainable crop production

in Northwest Vietnam’, independently. This thesis is my own original work and has not

been presented for a degree or diploma in any other university.

This thesis was written as part of the Masters programme in Agricultural Sciences in the

Tropics and Subtropics (AgriTropics), and has not been submitted to any other

examination board. All authors quoted or mentioned in this manuscript have been

accredited and no work has been included without citing the author(s).

Signed: ________________________


Date: __________________________

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Approval

This thesis has been accredited for final submission with our approval as University

supervisors.

Signed: ________________________

Prof. Dr. Georg Cadisch

(Institute for Plant Production and Agroecology in the Tropics and Subtropics)

Date: __________________________

Signed: ________________________

Prof. Dr. rer. nat. Thilo Streck

(Institute of Soil Science and Land Evaluation) Date: __________________________

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Acknowledgments

Above all, glory to God, the father, in Jesus name Amen!

It is great opportunity for me to express my gratitude to my supervisors Professor Dr.

George Cadisch, Prof. Dr. rer. nat. Thilo Streck and Dr. Carsten Marohn for their

intellectual guidance with abundant patience and courage to help me accomplish this

research.

It is my pleasure to give my gratitude to Melvin Lippe for provision of some of the

necessary data for this study.

I would like to thank all friends in Stuttgart who contributed to my success until this end.

It is a pleasure to express my thanks to Kerstin Hoffbauer for her kind help in times of

difficulties during my study.

Last but not least, I would like to thank all the members of the institute of Plant

Production and Agroecology in the Tropics and Subtropics.

v

Dedication

To my parents

&

To my brothers and sisters

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Table of contents Abstract ............................................................................................................................... i Author’s Declaration.......................................................................................................... ii Approval ...........................................................................................................................iii Acknowledgments............................................................................................................. iv Dedication .......................................................................................................................... v List of Tables ..................................................................................................................viii List of Figures ................................................................................................................... ix 1. Introduction.................................................................................................................... 1

1.1 Background.............................................................................................................. 1 1.2 Traditional farming system in northern Vietnam..................................................... 2 1.3 Causes of land use change in northern Vietnam...................................................... 3 1.4 Hydrological functions and crop production ........................................................... 4 1.5 Effect of land use change on hydrology .................................................................. 7 1.6 Spatially-explicit modeling for watershed management.......................................... 9 1.7 Hypotheses............................................................................................................. 10 1.9 Objectives .............................................................................................................. 11

2. Materials and Methods................................................................................................. 12 2.1 The study site ......................................................................................................... 12 2.2 Data and software .................................................................................................. 14

2.2.1 Input maps....................................................................................................... 15 2.2.2 LUCIA parameterization ................................................................................ 18

2.4 Modeling approach ................................................................................................ 20 2.4.1 Runoff ............................................................................................................. 20 2.4.2 Total Discharge at the outflow point .............................................................. 23 2.4.3 Agronomic yield ............................................................................................. 24

2.5 Calibration and Validation of the LUCIA Model .................................................. 25 2.5.1 Graphical method............................................................................................ 25 2.5.2 Statistical measures......................................................................................... 26

3. Results.......................................................................................................................... 27 3.1 Calibration.............................................................................................................. 27 3.2 Validation............................................................................................................... 28

3.2.1 Agronomic yield ............................................................................................. 28 3.2.2 Discharge ........................................................................................................ 33

3.3 Sensitivity Analysis ............................................................................................... 34 3.4 Land use scenarios ................................................................................................. 37

3.4.1 Baseline........................................................................................................... 37 3.4.2 Alternative scenarios....................................................................................... 42

3.5 Runoff and discharge at pixel level ....................................................................... 46 3.5.1 Runoff at pixel level........................................................................................ 46 3.5.2 Discharge at pixel level................................................................................... 48

3.6 Runoff and discharge at the outflow point............................................................. 51 3.6.1 Runoff at the outflow point............................................................................. 51 3.6.2 Discharge at the outflow point........................................................................ 53

3.7 Water availability for plant growth........................................................................ 55

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4. Discussion .................................................................................................................... 57 4.1 Assessment of LUCIA ........................................................................................... 58

4.1.1 Runoff simulation ........................................................................................... 58 4.1.2 Discharge simulation ...................................................................................... 59 4.1.3 Agronomic yield prediction ............................................................................ 61

4.2 Runoff as surrogate for soil erosion risk assessment ............................................. 62 5. Conclusions.................................................................................................................. 65 6. References.................................................................................................................... 67 Annex I: Land use and Soil Parameterization...................................................................... Annex II: Local Drain Direction.......................................................................................... Annex III: Runoff under changing rainfall conditions ........................................................ Annex IV: Daily discharge for year 2002............................................................................ Annex V: Daily Runoff for the 20 years simulation period................................................. Annex VI: Daily discharge for the 20 years simulation period ........................................... Annex VII: Pattern of yearly runoff: scenarios.................................................................... Annex VIII: Pattern of yearly discharge: scenarios.............................................................

viii

List of Tables Table 1 Upland rice yield (Mgha-1) ................................................................................. 29 Table 2 Cassava yield (Mgha-1) ....................................................................................... 31 Table 3 Paddy rice yield (Mgha-1) ................................................................................... 32 Table 4 Actual rainfall amount and runoff with changing percentage of rainfall ........... 36 Table 5 Ranking land uses by effect on runoff ................................................................ 63 Table 6 Ranking scenarios by their effect on runoff........................................................ 64

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List of Figures Figure 1 Interaction between Hydrological processes, capillary rise not considered ....... 5 Figure 2 Location of Tat hamlet experimental site ......................................................... 12 Figure 3 Tat hamlet Experimental watershed .................................................................. 13 Figure 4 Overview of the modeling tools and procedure ................................................ 14 Figure 5 Elevation map (left) and LDD map (right) ....................................................... 17 Figure 6 Test points representing different land uses and outflow point of Tat hamlet .. 18 Figure 7 Plant development stage and harvest time......................................................... 24 Figure 8 Upland rice yield ............................................................................................... 29 Figure 9 Cassava yield ..................................................................................................... 30 Figure 10 Paddy yield ...................................................................................................... 32 Figure 11 Total daily discharge at the outflow point for year 2002 ................................ 33 Figure 12 Change in percent runoff as a result of change in percent rainfall amount..... 35 Figure 13 Land use plots of the study site ....................................................................... 38 Figure 14 Land use for the reference year . ..................................................................... 39 Figure 15 Cropping cycle in the baseline scenario; ........................................................ 40 Figure 16 Expansion of maize in Tat hamlet experimental watershed............................ 42 Figure 17 Cropping cycle in Maize and Rubber scenarios. ............................................. 43 Figure 18 Introduction of rubber plantation in Tat hamlet .............................................. 45 Figure 19 Runoff and Leaf Area Index............................................................................ 46 Figure 20 Simulated annual runoff under different land uses year 2002......................... 47 Figure 21 Simulated discharge in the days of a month (12/09/2002 to 12/10/2002)....... 49 Figure 22 Simulated annual discharge contribution at test points .................................. 50 Figure 23 Total yearly runoff comparison between land use scenarios........................... 52 Figure 24 Comparison of annual discharge between land use scenarios......................... 54 Figure 25 Plant available water and soil moisture ........................................................... 56

MSc Thesis by Yohannes Z . Ayanu October 2009

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1. Introduction 1.1 Background The mountainous regions of northern Vietnam have undergone rapid land use changes in

the past. Population growth coupled with changes in agricultural and economic situation

in the country since the defeat of the French in 1954 contributed to changes in the

farming systems (Lam et. al. 2004). Due to the increased population, fallow periods were

reduced tending more towards permanent farming systems. Recently emerging markets

for crops such as maize has led farmers in the area to engage in annual crops production

by clearing the upland forests. Hence, the replacement of upland forests with annual

crops has aggravated soil erosion problems in the watersheds putting more challenges on

the sustainability of crop production in the Northern Mountainous Region (NMR). Land

use change has impact on crop production since it directly affects soil fertility.

Moreover, water supply is largely affected by land use changes.

Land use decisions affect the sustainable use and stability of ecosystem services such as

crop production in a farming system. Selection of land use types as well as spatial

distribution of the land uses in a landscape largely determines the impact of land use

change in a watershed. Uplands and lowlands are interlinked in such a way that

decisions on land use in the upstream areas affect farmlands downstream. Thus, the inter

linkage between uplands and lowlands should be considered for a better understanding

of the impact due to land use changes. Land use types and their suitable location in a

landscape needs to be assessed to achieve the goals for sustainable resource use in

mountainous regions. Short-term productivity of crops needs to be seen in combination

with the sustainability of the production in the long-term. For instance, replacing upland

forests with maize could increase maize yield in the short-term. However, unless suitable

conservation measures are applied, this could result in soil erosion and nutrient loss in


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the uplands causing reduction in crop productivity in the long run. Therefore, identifying

land use options that minimize risk of land degradation while maintaining productivity is

crucial.

1.2 Traditional farming system in northern Vietnam The Northern Mountainous Region (NMR) of Vietnam is known for its traditional

farming system, “Composite swidden farming”. Swidden farming is a common

agricultural practice in Southeast Asia. It involves conversion of primary forests to

annual crops through slash and burn system. The land is left fallow for 10-12 years the

until it regenerates to secondary forest.(Schmidt-Vogt, 2001). Households

simultaneously cultivate both swidden fields on hilly slopes and paddy fields in the

valleys. Thus, composite swidden systems are combinations of swidden/fallow and

paddy rice systems. The fallow land is composed of small trees and bushes.

Tat hamlet watershed of Da Bac district in the Hoa Binh province is one of the NMR

hamlets known with its composite swiddening system (Lam et al. 2004). Several

components form the swidden farming in Tat hamlet such as wet rice fields, home

gardens, fishponds, livestock, tree plantations and cash crop swiddens. The wet rice

fields provide a relatively high rice yield compared to the swidden fields. This enables

supply of staple food to households in the area (Lam et. al., 2005). This integrated

farming system appears to be relatively sustainable (Lam et al. 2004). According to

Rambo, 1998 (cf. Leisz et. al., 2007), this system existed in the NMR for centuries and

has been practiced by the Da Bac Tay ethnic minority in Tat hamlet.


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1.3 Causes of land use change in northern Vietnam Multiple reasons have contributed to the land use change in the northern mountainous

region of Vietnam. Government policies had great impact on the farmer’s agricultural

practices and hence, the land use decision. Traditional farming systems in the NMR have

started to change due to the agrarian reform in the region. Agrarian reforms in the 1980s

returned means of production to individual farmers which in turn resulted in loss of huge

amount of forest cover in the mountainous region (Castella et al., 2004).

The relatively sustainable composite swiddening land use system has been affected by

the recent changes in agricultural and economic conditions in northern Vietnam.

Deforestation and land degradation are becoming threats to sustainable agricultural

production in the NMR. In previous times, farmers in Tat hamlet were entirely based on

traditional composite swiddening mainly for subsistence and hence, households were

economically undifferentiated. However, returning the management of agricultural

lands to individual households by the cooperatives introduced the farming system

differentiation. Nowadays, a more diversified and differentiated farming system is found

per individual household. Moreover, crop production has changed from subsistence to

commercial oriented production. Household income contribution of the composite

swiddening decreased over the course of time. In contrary, the contribution of livestock,

non-timber forest products and off-farm activities to the household income increased

(Lam et al., 2004).

According to Lam et al. (2004), increased population associated with government

policies on management of agricultural and forest lands triggered land use change. In

addition, policies in the macro-economic environment, improved infrastructure and

communication facilities as well as improved market situation contributed to the change

in farming system. Lam et al. (2004) remarked that the shortening of fallow periods and


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associated decline in soil fertility poses a serious challenge to the sustainability of the

composite swiddening system. Swiddening areas will continue to face degradation and

hence, their productivity will decline unless suitable conservation measures are

implemented. Castella et al. (2005) remarked that in the NMR land use change and

agricultural production have been influenced by the economic growth associated with

the agrarian reforms. Direct actors at local level are farmers involved in clearing of

forest for food crop production thereby changing the landscape. Nonetheless, the policies

encouraging privatization of agriculture and forest lands accelerated land use changes

(Ohlsson, 2009).

1.4 Hydrological functions and crop production Water is one of the main services that an ecosystem provides. Drinking water supply has

been a pressing issue in all parts of the world. Supply of water from uplands to lowlands

through runoff and discharge constitutes the main component for agriculture in

mountainous watersheds. Moreover, generation of electrical energy using hydropower

relies on the continuity of water resources supplied by ecosystems.

Management of landscapes influences the sustainability of these services from

ecosystems. Landscapes covered by natural forests have been regarded as the main

sources of water supply. However, nowadays, focus is also given to management of

agricultural landscapes for a sustainable supply of water. For instance, in Europe, in

addition to its primary purpose of producing food and raw materials, agriculture is being

under consideration for its impact on other ecosystem services such as water. Flows into

and out of agricultural fields have impact on agriculture itself and the surrounding

ecosystems and hence, supply of water resources. Agriculture has impact on water

resources through inducing of chemicals and pesticides that cause pollution. (Odoux et.

al., 2009).


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In a landscape, hydrological processes are affected by the interactions between different

components that influence inflows and outflows into and from the system (Figure 1).

Rainfall or precipitation (P) determines flows and storage in the landscape. Rainfall is

partly intercepted and evaporated from the leaves’ surface before reaching the soil. Of

the remaining throughfall, part is directly infiltrated into the topsoil or deep infiltrated

(bypass flow through macropores) to the subsoil. The part of the rainfall that does not

infiltrate in time is lost as surface flow (runoff) contributing to the river flow. Exceeding

field capacity, water in the topsoil can either pass to the subsoil through percolation or

be discharged from the soil (lateral interflow) causing soil quick flow that contributes to

river flow. With supply of water through deep infiltration and percolation, the ground

water stock becomes full and the excess water is lost as baseflow contributing to the total

river flow. Evaporation from the soil surface and transpiration through plants are

deducted from the top and subsoil (plant available water depends on rooted depth).

Figure 1 Interaction between Hydrological processes, capillary rise not considered (Source: Widodo et al. 2009)


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Therefore, precipitation (P) balances with the sum of evapotranspiration (E), River flow

(Q) and change in storage (∆S) expressed as: P = E + ∆S + Q. Soil discharge is soil

water exceeding field capacity of the soil.

Crop production depends on availability of soil water for plant growth, which in turn is

influenced by rainfall and infiltration into the soil. Surface flow such as runoff could

cause soil erosion on less vegetated upland areas contributing to nutrient loss from

uplands and soil material deposition in the lowlands. Nutrient transfer from uplands to

lowlands could reduce nutrient availability in the uplands and hence cause reduced crop

productivity. On the other hand, lowlands could benefit due to movement of nutrients to

the lowlands if the land is used for agricultural crop production as is the case for wetland

paddy rice cultivation. However, deposition of soil material in the lowlands during

heavy rain events may damage the paddy rice fields and affect crop productivity.

Moreover, through river flows, loss of nutrients out of the whole watershed is possible

causing degradation of the landscape and also affecting the nearby landscapes through

sediment deposition into valleys and lakes. Besides the impact on crop production,

chemical pollution from agriculture could affect the supply of pure water for human

beings as well as living beings in rivers and lakes. The effect on hydrological processes

and crop production depends on the land cover/use type in an area. Land use decision

under any circumstances could negatively or positively affect ecosystems and hence,

hydrological processes which in turn determine supply of water for crop production and

other uses for a living-kind.


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1.5 Effect of land use change on hydrology Hydrological cycles are highly influenced by changes in land use caused by human

disturbances as a result of expansion of agriculture, urbanization and industries (Barbaro

2007, cf Hong et al. 2009). Land use change affects hydrological functions at various

levels. Land use and hydrological processes undergo complex dynamic interactions.

Recently, various researches have been carried out by scientists to identify and describe

the linkage between land use/land cover change and hydrological processes in

watersheds. Different approaches have been used for assessing the catastrophic effects of

land use change on upland hydrology. For instance, Beeson et. al. (2001) used the

spatially explicit SPLASH model for simulating overland flow due to disturbance of a

landscape by fire. Consequently, overland flow and runoff were found increasing

considerably after burning. Discharge increased after burning and it varied over a longer

period of time. Chuan (2003) found that overland flow was lower in natural forests than

in disturbed landscapes due to influence of agricultural practices. Hence, runoff was

much higher in agricultural fields than under forests. This implies higher risk of erosion

in the agricultural fields. Chuan (2003) further remarked that natural landscape processes

such as processes in undisturbed natural forests had less negative on hydrological

functions. Rather, adverse effects on environment and hydrological functions occurred

due to human interference for expansion of agriculture, urbanization and

industrialization.

Bruijnzeel (2004) assessed the influence of forest cover change on hydrological

functions in Southeast Asia. Disturbance of forest had less effect on overland flows such

as runoff than complete conversion of forests to other land uses like grasslands.

According to Bruijnzeel (2004) clearing of forests in uplands increased annual water

yield reaching its maximum when the forest was completely cleared. However, influence

varied from place to place depending on the spatial distribution of rainfall. Under limited


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disturbance of the forest ecosystem, increase in water yield occurred due to increased

base flow due to ground water reserves recharged during the rainy season from

percolation of infiltrated soil water. Bruijnzeel (2004) further found that surface runoff

and erosion declined under well-developed forest cover but increased with clearing of

forests. Overland flows and hence catchment sediment yield were found increasing with

disturbance and conversion of forests to other land uses in Southeast Asia.

Marc and Robinson, 2007 stated that evaporation under forest lands is significantly

different from such under grassland fields. This in turn showed differences in water

balance between catchments dominated by forest and grasslands. Accordingly, stream

flow and runoff were found higher in grasslands than under forest lands. The forest lands

showed differences among each other due to variation in age of the forest in which

catchments with forest of older age experienced lower evaporation rates as compared to

such with younger forest. However, before canopy closure soil exposure to rainfall and

thus risk of runoff was higher than the matured forest stands.

Cao et al. (2008) used SWAT (Soil and Water Assessment Tool) to model impact of

land use/cover change on water resources. Total water yield, quick flow and base flow

were found affected by changing land uses resulting in change of overall water balance.

According to Cao et al. (2008), the hydrological cycle of catchments changed due to the

modifying effect of land use change on rainfall, evapotranspiration and runoff.

Hong et al. (2009) found that ecological disturbance due to change in land use has a

considerable effect on hydrological components such as base flow and surface runoff.

According to Hong et al. (2009), runoff and base flow are sensitive to change in forest

cover in such a way that decreasing area of forest cover in a certain watershed increases

runoff while it decreases the amount of base flow.


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1.6 Spatially-explicit modeling for watershed management Watershed management needs knowledge of location and distribution of resources in the

landscape. Land suitability assessments also depend on identification of proper location

of resources for sustainable use and management. Nowadays, using Geographic

Information systems (GIS), land use planning has been facilitated for guiding planning

and decision-makers. GIS and Remote Sensing provide the spatial location of events

occurring in a landscape in a given period of time. Moreover, distribution of resources

and area covered by resources can be identified and mapped.

Spatially explicit models are useful tools for predicting occurrence of events with its

location at spatial scale over a longer period. Using GIS, socio-economic events can be

integrated with their spatial location to provide useful information at a given time and

spatial scale. In mountainous landscapes such as northern Vietnam, uplands and

lowlands are inter-linked in such a way that changes in the uplands affect the lowland

area. Uplands are main suppliers of water through flows. Moreover, there is flow of

nutrients from uplands and deposition in the lowlands. Thus, these relationships can be

explained in a better way using spatially explicit models. So far, several modeling

approaches have been designed by geoscientists to assist decision-makers in land use

planning and watershed management. GIS has been widely used for quantifying the

impact of land use/land cover on assessing hydrological processes and watershed

delineation. Streams and river flows can be simulated and visualized using spatially

explicit models (Strager et al., 2009). In this study the recently developed Land Use Change Impact Assessment (LUCIA)

model was used to assess impact of land use change on selected hydrological functions

and crop production based on a case study site in Tat hamlet, North-West Vietnam.

LUCIA is developed as part of a research project at the Institute of Plant Production and


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Agro-ecology in the Tropics and Subtropics for the SFB 564 (The Uplands Program –

Sustainable Land use and Rural Development in Mountainous Regions of Southeast

Asia). The model is primarily meant to simulate watershed functions, soil fertility and

plant growth in small watersheds in the mountainous regions of Vietnam and Thailand,

though it is generic to be applied in other small watersheds elsewhere (Marohn, 2008).

LUCIA is a raster-based spatially explicit dynamic model with its algorithms written in

dynamic modeling language PCRaster code (Deursen et al., 1995). PCRaster allows the

propagation of material flow along a local drain direction map and enables modeling of

flows from high elevation points upstream area to low elevation points in downstream

areas. LUCIA operates on a daily time step for user-specified pixel size. Different

modules of the model are adapted from existing concepts of validated models.

Hydrological processes and water balance as well as dynamic soil properties are based

on the concept of GenRiver 1.1 (Noordwijk et al. 2005). Concepts of process-based crop

growth model- WOFOST (Supit, 2003) were adapted for radiation, plant growth,

evaporation, transpiration, crop assimilation and crop yield.

1.7 Hypotheses This study will be based on the following assumptions.

1. LUCIA is able to simulate upland-lowland linkages regarding the water balance

and effects on crop production determined by different land use scenarios.

2. Replacing the current forest cover in the upland areas with maize will increase

runoff and discharge

3. Compared with maize, replacing the upland forests with rubber plantations will

reduce runoff and discharge.


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1.9 Objectives The general objective of this research is to assess the impact of land use change on

hydrological watershed functions and crop production. The specific objectives are:

o To identify land use types and their spatial distribution in Tat hamlet watershed

based on existing remote sensing data

o To define the current baseline scenario

o To calibrate and validate LUCIA for agronomic yield

o To define and run alternative land use scenarios

o To evaluate different land uses in terms of impact on runoff and discharge


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2. Materials and Methods 2.1 The study site The study was based on a small experimental site in Tat hamlet watershed which is

situated in Hoa Binh province, Northwest Vietnam (Figure 2). Tat hamlet is located

between 105o11’92’’E longitude and 20o92’82’’ N latitude. The total area of the

experimental site is 3.54 ha and it is subdivided into upland (3.23ha) and paddy wet rice

fields (0.31ha) in the lowlands. The watershed topography is steep with slopes in the

uplands between 29.3 and 36.4°. Elevation of the experimental watershed (Figure 3)

ranges from 360m above sea level in the valley floor to 479 m at the mountain peaks

(Dung et al., 2008).

Figure 2 Location of Tat hamlet experimental site (Source: CARES report)


13

Flow

dire

ctio

n

Figure 3 Tat hamlet Experimental watershed

Two soil types are dominant in the Tat hamlet experimental site: Acrisols and Gleysols.

The larger part of the experimental site is the uplands area which entirely consists of

Ferralic Acrisols. On the other hand, paddy fields in the lowlands are entirely dominated

by Gleysols with relatively shallow rootable depth (Dung et al., 2008).


14

2.2 Data and software Two major components of data were used for this study: spatial and non-spatial datasets

were prepared following several steps and procedure (Figure 4).

Non-Spatial input (text files): parameter (.par) files, time series(.tss) files, look up table (.lut) files.

Step 1: Create Maps (ArcGIS)

Step 1: Parameterization

Step 2: Creating input files

Spatial input: PCRaster maps (.map) files

Step 2: Convert maps to ASCII (.asc, .txt files)

Step 3: PCRaster maps (.map)

PCRaster: Actual modeling

Figure 4 Overview of the modeling tools and procedure


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For this study ESRI ArcGIS 9.3 was used to process all the spatial input datasets.

Interoperability between ArcGIS and PCRaster was achieved by converting the ArcView

raster files into ASCII format. PCRaster has its own format for ratser maps used in the

modeling (.map files). Hence, the ASCII files were converted to PCRaster map files to

be used in the modeling. The non-spatial datasets comprised multiple excel input

worksheets for the model parameterization. Data from excel were exported as text files

of type parameterization (.par), time series (.tss), and lookup tables (.lut) files. Each of

these files contained inputs from one or more columns based on the requirements. The

whole non-spatial datasets were automatically generated used VBA macros after

parameterization of the model.

2.2.1 Input maps To fit the purpose of this study, an existing baseline land use map of 2008 (Lippe,

personal communication) was modified based on remote sensing data. Since the study

considered several scenarios, there was crop rotation in each of the land use plots

depending on the cropping cycle. For the purpose of 20 years simulation period, a total

of 60 land use maps representing rotational cropping cycle in the watershed were

produced and implemented in the model. Separate scenarios required 20 maps

corresponding to each year of the simulation period.

A Digital Elevation Model (DEM) was created from an existing contour map using

ArcGIS geostatistical interpolation techniques. Prior to interpolation, the contour map

was checked for discontinuities and errors were corrected by digitizing. There were no

available maps for the soil properties considered in the model. Hence, soil properties

maps were produced by interpolating the filed measured data using ArcGIS geostatistical

interpolation techniques.


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Soil properties such as bulk density, pH, total N, PBray and SOM from six representative

profiles attributed to each soil unit (Acrisol and Gleysol) collected from the study area

(Lippe personal communication) were used to produce the soil properties maps. The soil

type map classifying the total watershed into Acrisols and Gleysols was also created

using ArcGIS.

All the vector maps and interpolation results were converted to raster format and

extracted within the area limit of the experimental watershed using the area map as a

mask. ArcView vector or raster data cannot be directly used in PCRaster. Thus, all the

maps were converted to ASCII text files. The text files were converted to PCRaster maps

(.map files) using the PCRaster asc2map code.

The local drain direction map (LDD) was calculated from the digital elevation model by

connecting each pixel to its neighbor in the steepest of eight possible slope directions.

The LDD is a map representing flow direction from each cell to its neighboring down

slope cell (Annex II). The outflow point (pit cell) from the watershed was calculated

from the LDD. The outflow point is a cell with lowest pixel value (lowest elevation)

where all neighboring cells drain (Figure 5). It is the point where the catchment drains to

the neighboring catchment. Flows follow the LDD and finally drain into the outflow cell.

The outflow point was used to measure the overall effect of watershed processes in

terms of water balance including total amounts of runoff and discharge from the

watershed.


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Outflow cell

LDD

Figure 5 Elevation map (left) and LDD map (right, numerical values represent elevation per pixel). The lowest pixel value forms the outflow cell (Source: Deursen et. al., 2005)

In addition, test points were created representing each polygon covered by a certain land

use class (Figure 6). Runoff, discharge and agronomic yield were calculated at test

points in the land uses to compare the effect due to use of different land uses. These

points were used for the twenty years simulation period where the land uses were

changing periodically.


18

Figure 6 Test points representing different land uses and outflow point of Tat hamlet experimental watershed

2.2.2 LUCIA parameterization The parameterization data were prepared in a spreadsheet input file, which is part of the

LUCIA package. The input file consisted of several parameters under different

components. Each of the components was parameterized using data from CARES (Dung

et al. 2009), literature sources and existing validated models. Parameters related with the

land uses and soils were parameterized in the spreadsheet. Some of these parameters are

attached in Annex I.


19

Besides the land use and soil parameters, plant growth parameters were parameterized

independently. Partitioning of biomass into different components as allocation for root,

shoot and harvest depending growth stage were parameterized for each land use type.

Specific Leaf Area Index (SLA) and Maximum Assimilation Rates (AMD) were also

parameterized for the land uses.

In addition to the aforementioned parameters, the initial vegetation was also

parameterized. The initial biomass was parameterized for each land use considered in

this study. Moreover, plant nutrient concentration at different development (growth)

stages was parameterized for the land uses. N, P, K concentrations in leaf, root, stem and

harvestable plant biomass were parameterized for the plants at different development

stages. Soil field capacity parameters were also parameterized for the different soil

classes. Accordingly, field capacity of top and sub soil, pore volume of the sub and top

soils, and the stone content of the soils were parameterized.

Weather data such as Rainfall, Temperature, Evapotranspiration, and Radiation were

parameterized independently. Rainfall was parameterized using 4 years (2001 to 2004)

daily real data from the study area. Due to lack of real data for the 20 years (7300 days)

simulation period, the available 4 years rainfall data was looped every 4 years.

Fertilizer and manure application for the crop production was not considered in this

study. Hence, the related parameters were not parameterized and were switched off

during the modeling.


20

2.4 Modeling approach 2.4.1 Runoff Surface runoff was calculated at each sampling point (pixel) in the land uses in order to

compare land uses with their exposure to runoff. Total runoff was also calculated at the

outflow/discharge point to examine the overall effect of land use change on the

watershed. The risk of erosion due to change of different land uses and hence, the

sustainability of crop production in the area was assessed in terms of runoff.

In this version of LUCIA water that exceeds the soil water pool (saturation overflow)

was not considered. Hence, the surface water considered was water that cannot infiltrate

in time (Hortonian flow). Runoff is the water that has not reached the soil water stocks

and represents water which cannot infiltrate in time.

Runoff = f (Rainfall, Vegetation Cover, Soil Properties) --------------------- (1)

The amount and intensity of rainfall determine the magnitude of runoff from a patch of

land. The erosivity of rainfall depends on vegetation cover (land use) since cover

determines the amount of intercepted rainfall for later evaporation. Soil organic matter,

bulk density and texture influence the infiltration of rain water into the soil which in turn

determines surface flow. In LUCIA, surface runoff from each land use plot is expressed

as follows.

Surface runoff = (DailyRain-InterceptEvap-Infiltration-DeepInfiltration)--------- (2) Where, Surface Runoff = amount of daily runoff in mm/day

DailyRain = daily rainfall amount in mm/day InterceptEvap = amount of water intercepted and evaporated from plant leaves in mm/day Infiltration = amount of water infiltrated into soil in mm/day


21

DeepInfiltration = deep infiltration in mm/day Evaporation of intercepted water (IntercrptEvap) is estimated from the amount of daily

rainfall and storage capacity of the leaves to keep the intercepted rain water.

InterceptEvap = max(Storage*(1-exp(-DailyRain/Storage)),0) ------------------ (3)

Where,

Storage = capacity of plant leaves to store intercepted water expressed as Storage = LAItot*Cover*0.2 Where, LAItot = Total Leaf Area Index Cover = vegetation cover factor expressed as

Cover = 1-exp(-0.4*LAItot). 0.2 = thickness of water film in mm. The magnitude of water infiltrated into soil depends on the amount of the daily rainfall,

the water amount intercepted by plant leaves and evaporated.

Infiltration = f (Rainfall, Evaporation of intercepted water, pore volume of soil, amount

of water that is stored in soil, time required for infiltration) ------------------- (4)

The aforementioned relationship is expressed as:

Infiltration = min(DailyRain-InterceptEvap, max(0,TPVProfile-SoilWater), DailyMaxInf*Time4Infiltration/24)----------------------------------------------(5)

Where, TPVProfile = Total Pour Volume of the soil profile SoilWater = Soil Water DailyMaxInf = maximum infiltration in a day Time4Infiltration = Time needed for infiltration

The rainfall water intercepted and evaporated by plant leaves is not part of the water that

infiltrates. The water that reaches the soil surface (throughfall) is ready for infiltration.

However, this depends on total space available in the soil pores to store the water.


22

Infiltration is possible only if there is space in the soil. This in turn is affected by water

in the soil. Hence, the difference of total pore volume in the soil profile and soil water

describes the available space for extra water passing to the soil pores. The amount of

infiltration in a day depends also on the time required for the water to reach the soil

pores given the available space in the soil. This parameter, time available for infiltration,

is part of a simplified approach, which breaks down daily rainfall to hourly values using

a random distribution around user-defined mean and coefficient of variation. It accounts

for the fact that the infiltration process is determined by short-term dynamics of rainfall

and that rainfall events are not evenly distributed over a full day length.

Deep infiltration describes water bypassing the previous pathways and infiltrating

through macropores. It is limited by the same time for infiltration as infiltration itself.

DeepInfiltration = max(0,min(MaxInfSubSoil, DailyMaxInf*Time4Infiltration/24-TPVProfile+SoilWater, DailyRain-InterceptEvap-Infiltration, MaxGW-GWStock))----(6) Where,

GWStock = Ground water stock MaxInfSubSoil = maximum infiltration into the sub soil MaxGW = maximum initial ground water stock

Having obtained runoff per pixel in the abovementioned way, transport of runoff water

down the local drain direction net, i.e. routing, is implemented as shown in equation 7.

Total runoff

=accuthresholdflux(LDD,Surface_Flow,max(0,Space))*0.001*pixelsize/86400 ------- (7)

Where, Total runoff = daily cumulative runoff at the outflow point in m^3/s LDD =Local Drain Direction Surface_Flow = cover(Surface runoff, areaaverage(Surface runoff, AreaMap)) in m^3/pixel

Thus total runoff is the cumulative runoff from the different land uses draining at the outflow point.


23

2.4.2 Total Discharge at the outflow point Total discharge is the sum of total runoff, ground water discharge and discharge from

soil. Total ground water discharge is described as:

Total GWD =

accuthresholdflux(LDD,GW_Discharge,max(0,FCSub-

SubWat))*0.001*pixelsize/86400---------------------------------------------------(8)

Where,

Total GWD = Ground water discharge in m^3 s-1

GW_Discharge = Ground water discharge per pixel in m^3 per time step (day)

FCSub = Field capacity of subsoil in mm

SubWat = Stored water in the sub soil in mm

Total soil discharge is expressed as:

Total soil discharge = accuthresholdflux(LDD,Soil_Discharge,max(0,FCTop-

TopWat))*0.001*pixelsize/86400-----------------------------------------------------(9)

Where,

Total soil discharge = Sum of all discharge from soil in m^3/s

Soil_Discharge = Daily discharge from soil per pixel in m^3

FCTop = Field capacity of top soil in mm

TopWat = Stored water in top soil in mm

Therefore, total discharge is expressed as:

Total discharge = Total runoff + Total GWD + Total soil discharge


24

2.4.3 Agronomic yield The agronomic yield is the harvestable part of the plant at maturity expressed as:

Yield=if(D ge 2,Harvestable,0)---------------------------------------------------(10)

Where,

Yield = total production from a land use in Mgha-1 D = factor for plant development stage

D depends on degree days required to reach for flowering and harvest. Harvestable

biomass will be attained at maximum D value that is 2, while 0 is planting and 1 is

flowering (Figure 7).

Figure 7 Plant development stage and harvest time The maximum value of D for all plants is 2 at harvestable stage; for annual plants, this

value drops to 0 immediately after harvesting. The D value remains 0 until the next

planting and starts to increase slowly after planting reaching its maximum at a point

where next harvest is expected. This is the point where cassava yield is expected.

Time step


25

However, the value of D lies between 1 and 2 for forests since the trees are not removed,

but fall back into the vegetative phase after maturity of fruits.

2.5 Calibration and Validation of the LUCIA Model Lucia was manually calibrated for agronomic yield by considering cropping parameters

such as planting date and harvest time using data from the study area. To be able to

ensure that a calibrated model represents the actual real world phenomena, it has to be

tested and verified using an appropriate validation procedure. The aim of validation was

therefore, to ensure that all the parameters and conditions affecting the model results

were properly assessed by the calibrated model and to assess the model’s capacity to

predict measured independent field datasets for periods other than the calibration

(Donigian, 2002 cf. Bhardwaj and Kaushall, 2008). Accordingly, the simulated results of

the model were validated against independent data sets from Tat hamlet experimental

watershed. The model’s performance was evaluated using graphical (qualitative)

methods and statistical procedures based on Bhardwaj and Kaushal, 2008. The model

needed a warm-up phase until the simulation stabilizes to provide plausible results. This

was observed in the first year of the 20 years simulation period. Therefore, the first year

was excluded and only results after the second year were used for the validation.

2.5.1 Graphical method Graphical displays can be useful for showing trends, types of errors and distribution

patterns. At a specific site, graphical techniques that compare observed and predicted

values are useful to show whether the model over or under predicts the observed values

from the field (Loague & Green 1990). Preliminary assessment of model performance

can be illustrated using qualitative graphs of predicted and observed data (ASCE, 1993

cf Bhardwaj and Kaushall, 2008). In this study observed and predicted/simulated data


26

for agronomic yield were compared using simple scatter plots. Similarly, simulated

discharge was compared with the measured discharge at the outflow from the watershed.

2.5.2 Statistical measures Though graphs are useful tools to describe model performance, more objective testing of

the model is necessary. Thus, multiple statistical methods need to be applied to test

model performance. Statistical measures proposed by Bhardwaj Kaushall (2008) were

used to evaluate the performance of LUCIA. Bhardwaj Kaushall (2008) applied these

criteria for evaluating a runoff model for small agricultural watersheds and found

reliable results. The mathematical expression of these statistical measures is described

below.

PE = 100*⎥⎦

⎤⎢⎣

⎡ −

i

ii

OOS

-------------------------------------------------- (11)

AAE = n

En

ii∑

=1----------------------------------------------------------(12)

R2 =

⎥⎦

⎤⎢⎣

⎡−−

⎥⎦

⎤⎢⎣

⎡−−

∑ ∑

∑

= =

=

n

i

n

iaviavi

n

iaviavi

SSOO

SSOO

1 1

22

2

1

)()(

))((---------------------------------(13)

wR2 = 2* Rb for b≤ 1 --------------------------------(14)

= 21 * Rb − for b>1

NS-EF =

⎥⎦

⎤⎢⎣

⎡−

⎥⎦

⎤⎢⎣

⎡−−−

∑

∑ ∑

=

= =

n

iavi

n

i

n

iiiavi

OO

OSOO

1

2

1 1

22

)(

)()(---------------------------(15)


27

Where, PE = Percentage Error

AAE = Average Absolute Error R2 = The square of Pearson’s product-moment correlation coefficient and (wR2) its weighted form NS-EF = Nash and Sutcliffe model Efficiency

iO , iS = Observed and simulated values at each comparison point i respectively iE = the error in prediction

avO , avS = arithmetic means of the observed and simulated values b = the slope of the regression on which R2 is based

3. Results 3.1 Calibration Calibration of the model was carried out by adjusting model parameters through an

iterative process so that the model output estimated the field data. LUCIA was manually

calibrated for agronomic yield using available field data from Tat hamlet experimental

watershed. Three crops that are usually cultivated in the traditional farming system in the

NMR were considered for the calibration of the model. Upland rice and cassava yields

were calibrated with field data for the years 2001 and 2002, respectively. Similarly, yield

for paddy rice was calibrated for year 2001 by adjusting the model parameters that

influence yield output to fit the observed field data values for the same year. For the

selected calibration field data of the respective years, the simulated values were well

adjusted to fit the observed values. Besides calibration of agronomic yield, vegetation

period was also implicitly calibrated: Planting and harvesting dates were chosen

according to farmers’ practice and growth parameters adjusted in such a way that

agronomic yield within the resulting vegetation period fit measured yields. Time at

which plants flower was defined in terms of the degree days required for flowering and

harvesting time was defined in terms of degree days required for harvest. The manual

calibration of the model required repeated simulations. To fully calibrate the agronomic


28

yield for the three crop types 80 different simulations runs were made to fit the observed

data with the simulated data.

Calibration was an important step in which the model was adjusted to predict the real

filed situation for agronomic yield and related parameters. It also gave background for

validating the accuracy of model in predicting the actual field data.

3.2 Validation 3.2.1 Agronomic yield Following calibration, the model was validated using independent field datasets for

agronomic yield. The data used for the calibration was excluded from the datasets for

validating the model. The results are presented in graphs and tabular calculated statistical

measures for the validation. For upland rice there was yield data collected from several

plots from three different years (2001, 2002 and 2003). The plot in which yield data

existed in all the three years was used for calibration. Thus, the simulated data were

validated against yield data from the remaining plots independent of the calibration.

Figure 8 compares the simulated and observed yield from upland rice fields.


29

Figure 8 Upland rice yield For further validation of agronomic yield the statistical parameters described in section 2

above were calculated. These parameters were used for assessing the accuracy of model

simulation results. Table 1 presents the simulated and observed yield data and the

calculated validation criteria.

Table 1 Upland rice yield (Mgha-1)

Obs.Nr. Observed(Oi) Simulated(Si) PE(%)

1 0.74 0.72 32 0.29 0.21 173 0.50 0.61 224 0.32 0.24 255 0.58 0.59 2

SummaryR 2 wR 2

AAE NS-EF Average PE (%)0.91 0.80 0.006 0.85 14


30

From Table 1 it is noticeable that the simulation results were found closer to the

measured data for the upland rice. Percentage Error (PE) for upland rice was between 2

and 25% showing the differences in prediction at each sample point selected. The overall

average PE was 14%. The R2 and wR2 were 0.91 and 0.80 respectively. The Average

Absolute Error was 0.006. Nash and Sutcliffe model Efficiency (NS-EF) achieved was

0.85. Similarly, the simulated yield for cassava was validated against the observed field

data from Tat hamlet experimental watershed. The yield data from several cassava plots

were used to validate the simulated yield. The plot used for validation was excluded

from the validation. Since the observed data was in fresh weight, all data used for the

validation were converted to dry weight of cassava to compare with the simulated dry

weight. The scatter plot comparison between observed and simulated yield of cassava is

presented in Figure 9.

Figure 9 Cassava yield


31

The statistical parameters were also calculated to numerically validate the simulated

yield for cassava (Table 2).

Table 2 Cassava yield (Mgha-1)

Obs.Nr. Observed(Oi) Simulated(Si) PE(%)1 5.60 5.82 42 3.15 3.25 33 5.61 5.82 44 3.16 3.25 255 2.65 2.34 226 7.58 6.20 187 5.31 5.80 98 2.56 2.34 8

SummaryR 2 wR 2

AAE NS-EF Average PE (%)0.90 0.79 0.10 0.72 12

The Percentage Error (PE) ranges from 3 to 25% with an average of 12%. The R2 and

wR2 were 0.90 and 0.79 respectively. Average Absolute Error (AAE) was 0.10 and the

Nash and Sutcliffe model Efficiency (NS-EF) calculated was 0.72.

Following similar procedure as above, from the available field data for paddy rice one

plot was used to calibrate the model. The rest of the data was used to validate the model.

Observed and simulated data were compared graphically (Figure 10) and statistical

measures (Table 3). All the validation values lied below the regression line.


32

Figure 10 Paddy yield

The statistics calculated for the validation of the simulated yield of paddy wet rice field

against the observed data are shown in Table 3.

Table 3 Paddy rice yield (Mgha-1)

Obs.Nr. Observed(Oi) Simulated(Si) PE(%)1 5.58 4.75 152 5.54 4.75 143 5.29 4.48 154 5.01 4.76 5

SummaryR 2 wR 2

AAE NS-EF Average PE (%)0.02 0.07 0.67 -8.76 12

For paddy rice yield all the simulated values were found to be lower than the observed

values. R2 and wR2 values were 0.02 and 0.07 respectively. The Average Absolute Error


33

(AAE) was 0.67. Percentage Error (PE) ranges 5-15% with an average of 12%. The

Nash and Sutcliffe model Efficiency (NS-EF) achieved was -8.67.

3.2.2 Discharge Total discharge at the outflow point was evaluated for plausibility using measured daily

discharge at the outflow point from Tat hamlet experimental watershed (Figure 11).

Timestep (days)

565 366 465 665

01.01.02 10.04.02 19.07.02 27.10.02 31.12.02

730 Date

Figure 11 Total daily discharge at the outflow point for year 2002

The simulated discharge was lower than the measured discharge between days 366 and

465. However, the simulated discharge was higher than the observed discharge between

days 465 and 565 as well as between days 665 and 730. Between days 565 and 665 the

simulated discharge was close to 0 though observed discharge showed higher values.


34

3.3 Sensitivity Analysis Sensitivity analysis was carried out to evaluate land uses regarding the generation of

runoff under changing rainfall. Percent change in rainfall was calculated from the default

parameterization rainfall data. Using the calculated rainfall as percent change from the

default rainfall condition, 5 simulations (20-100%) for increasing rainfall scenario and 4

simulations (-20 to -80%) for decreasing rainfall were run independently. For

comparison a scenario with no change of rainfall (baseline) was also run to observe

impact of changing rainfall on runoff. To be able to compare response of the land uses,

growth stage at which all land uses have maximum Leaf Area Index (LAI) after planting

for the crops to achieve canopy closure was considered as starting point. Thus, a

reference rainfall (73mm) where all crops achieved canopy closure was considered. The

runoff generated by each land use under the reference rainfall was then simulated by the

model. Increment/decrement in rainfall was introduced as a percentage increase/decrease

from the reference rainfall for the selected time step (day). Runoff was simulated at

points where there was every 20% increase/decrease progressing until 100% increment

and -80% decrement in rainfall were reached. The percent change in runoff for the

percent change in rainfall was calculated as follows.

Percent change in Runoff = 100*)(

)()(

0

0⎟⎟⎠

⎞⎜⎜⎝

⎛ −RFRunoff

RFRunoffRFRunoff x ---------------------(16)

Where,

Runoff(RFx) = Runoff at point where rainfall changed by percent x

Runoff(RF0) = Runoff at reference rainfall point

The sensitivity analysis for different land uses with respect to percent change in rainfall

amount and the resulting percent change in runoff amount is shown in Figure 12. All


35

lines representing the land uses cross each other at a common intersection point labeled

as (0, 0) coordinate. This is the reference rainfall point with percent change in rainfall

and percent change in runoff was equal to 0. The lines representing land uses diverge

from one another away from the origin indicating change in percent rainfall and runoff.

Figure 12 Change in percent runoff as a result of change in percent rainfall amount

The change in percent rainfall amount from the reference rainfall showed a shift in the

runoff curves. In all the land uses decrease in percent rainfall amount shifted the curve

diagonally towards the left showing a decrease in runoff. Whereas, increasing the


36

percent rainfall amount shifted the curves diagonally towards the right showing a linear

increase in runoff as a result of increasing rainfall amount. In forest and agroforestry

fields, a decrease in rainfall amount by 60% resulted in 0 runoff. The lowest expected

rainfall amount (threshold) at which runoff occurred in forest and agroforestry fields was

between 15 and 29mm. Whereas, the threshold for upland rice and cassava was 15 mm

of rainfall (Table 4).

Table 4 Actual rainfall amount and runoff with changing percentage of rainfall

RF% Change Actual RF (mm/day) Forest Agroforestry Grassland Upland rice Cassava-80 14.55 0 0 1.14 3.27 2.57-60 29.11 1.84 3.22 4.67 6.8 6.1-40 43.66 5.37 6.75 8.2 10.33 9.63-20 58.21 8.9 10.28 11.73 13.87 13.17

0 72.76 12.43 13.82 15.26 17.39 16.6920 87.31 15.97 17.35 18.79 20.93 20.2340 101.87 19.5 20.9 22.33 24.46 23.7660 116.42 23.03 24.41 25.86 27.99 27.2980 130.97 26.56 27.94 29.39 31.53 30.83

100 145.52 30.09 31.48 32.92 35.06 34.36

Runoff in mm/day

The percent change in runoff as a result of decreasing or increasing percent rainfall

amount showed higher values for the aforementioned land uses as compared with

annuals such as upland rice and cassava. Runoff was higher for the annuals under the

reference rainfall amount (73 mm) as compared with forest and agroforestry fields.

Thus, the higher percent change in runoff in the forest and agroforestry field can be

related with the drastic shift from lower runoff to higher runoff and from lower runoff

amount to no-runoff phenomenon. In general, different land uses were found responding

differently to the changing rainfall amount with respect to runoff generation. The details

of the change in runoff with changing rainfall amount are presented in Annex III.


37

3.4 Land use scenarios After validating LUCIA for its applicability, two alternative land use scenarios were

implemented and tested to assess the impact of land use change on sustainable crop

production and natural resources management in the NMR. These scenarios were

compared with the baseline situation in the case study area. A twenty years simulation

period was run for each scenario to be able to assess long-term effects. The scenarios

defined and implemented are presented in the following sections.

3.4.1 Baseline The baseline scenario was defined according to the traditional land use system in the

area based on literature and data from previous studies. The land uses under the existing

traditional farming system were identified and mapped for the Tat hamlet experimental

watershed. For each year of a twenty years simulation period a new land use map was

then created. Shifting cultivation with fallow period of 12 years was considered for the

baseline situation. The entire watershed was divided into categories indicated as plots

with sequence of numbers in which different crops are grown periodically. The change

in land use was based on these plots considering the initial land use map as a reference

(Figure 13).


38

Figure 13 Land use plots of the study site The initial reference land use map used as starting year for the 20 years simulation

period was the 2008 land use map of Tat hamlet experimental watershed (Figure 9).

Since land is cultivated in rotation a change in land use after several years of cultivation

was introduced as change from the reference land use map (Figure 14) to other types of

land uses.


39

Figure 14 Land use for the reference year (Source: Lippe, personal communication).

The cropping cycle under the baseline scenario was defined for the twenty years

simulation period starting with the initial reference map (Figure 14). Situated in the

uplands, the swidden area is dominated by upland rice and cassava. The two crops are

grown in rotation periodically. After series of upland rice (2 years) and cassava (2 years)

cultivation, a fallow period (12 years) was used as a means for regenerating the

agricultural lands to maintain soil fertility (Figure 15 plots_2 and 7).


40

Plots_1

Plots_2

Plots_3

Plots_4

Plots_5

Plots_6

Plots_7

Years 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Figure 15 Cropping cycle in the baseline scenario; fallow can develop from bush into pioneer, secondary and primary forest.


41

In the upland area of Tat hamlet experimental watershed, agroforestry is a common

practice. Agroforestry fields comprise Melia azedarach and rice. In this study,

intercropping of Melia trees with rice was considered for two consecutive years.

Following rice, cassava is intercropped with Melia for another two years. The field is

later left fallow for 12 consecutive years. The fallow field is largely dominated by Melia

trees. Melia trees take 7 to 8 years to reach maturity for timber production (Thanh,

2003). After the twelve years fallow period the field is slashed and cultivated for

replanting with rice and cassava (Figure 15 Plots_1). Secondary forest fields are

converted to upland rice fields after slash and burn. After two years upland rice

cultivation, the fields are replaced by cassava, which is cultivated for 2 consecutive

years. Following cassava, the land is left fallow for 12 years to enable regeneration of

secondary forest (Figure 15 Plots_4).

The lowlands of Tat hamlet watershed are entirely dominated by paddy rice, which is the

main staple food in the NMR. Paddy rice is grown in all years using irrigation with only

a few months of fallow during the transition between seasons. The cropping cycle for

each of the farm plots (Figure 13) in the 20 years simulation period is illustrated in

Figure 15 Plots_6.


42

3.4.2 Alternative scenarios

a) Scenario one: Expansion of maize in the uplands With increasing market opportunities for maize to supply feed for pig farming in the

NMR, it is expected that farmers will tend towards producing more maize. This

increases the demand for land and hence, it is apparent that part of the forest in the

uplands will be cleared for maize production (Figure 16).

Figure 16 Expansion of maize in Tat hamlet experimental watershed

Maize will be produced in addition to the other crops starting from year 2 in the 20 years

simulation period. Crop fields are left fallow after consecutive maize and cassava

production (Figure 17).


43

Forest

Maize

Rubber

Years 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Figure 17 Cropping cycle in Maize and Rubber scenarios.


44

The fallow period is reduced to only 2 years unlike the traditional 12 years fallow

period. Production from new maize fields as well as the existing crop lands was assessed

in order to evaluate the effects of deforestation on crop production and water balance in

the watershed. Maize is grown for two years and is replaced by cassava which is also

grown for another two consecutive years. The field is left fallow after two years cassava

cropping (Figure 17). Maize and cassava are grown in rotation with the less exigent

cassava replacing maize once yields begin to decline (Figure 17). Part of the upland area

is expected to be left as forest since cultivation of the very steep areas for maize may not

be possible. The impact of introduction of maize in Tat hamlet watershed on runoff and

discharge was assessed and compared with the baseline and an alternative scenario in

which rubber is introduced.

b) Scenario two: Introduction of rubber tree plantations The shift from subsistence production to commercial crop production calls for

alternative crops as an option to increase farmers’ income. Relying on maize crop alone

for commercial production in the NMR may not be sustainable in the long run.

Therefore, introduction of more sustainable systems is needed. Rubber tree plantations

are one potential source of income for the farmers in the NMR of Vietnam. Thus, this

scenario assesses the impact of introduction of rubber tree plantations in the NMR based

on Tat hamlet watershed. Effects of introduction of rubber plantations on the remaining

crop lands as well as the entire watershed were assessed. Rubber trees were assumed to

be planted by clearing part of the forest in the area (Figure 18).


45

Figure 18 Introduction of rubber plantation in Tat hamlet

The rubber plantations were introduced as cash crops for the 20 years simulation period.

The impact of introducing rubber trees on runoff and discharge in Tat hamlet watershed

was assessed. The results were compared with that of introducing maize in the area.


46

3.5 Runoff and discharge at pixel level 3.5.1 Runoff at pixel level Land uses were compared in their response to rainfall by simulating runoff at test points

placed in each respective land use. Runoff generation at the source by different land uses

was predicted at pixel level without considering material transport along the local drain

direction. The influence of Leaf Area Index (LAI) was reflected in the runoff from the

land uses (Figure 19).

Figure 19 Runoff and Leaf Area Index


47

LAI of perennials such as agroforestry fields was higher than that of annuals such as

cassava and upland rice. Hence, runoff from annuals was found higher than that

generated on agroforestry fields. Comparing the annuals, LAI for cassava was higher

than that of upland rice around day 656 where both land uses are covered with plants.

Therefore, runoff for cassava was lower than that of upland rice at this period. LAI for

upland rice drops to 0 after day 658 and remains the same because upland rice was not

yet planted in the field. Hence, during this period runoff from upland rice fields was

much higher than cassava fields which were covered with plants.

The main advantage of perennial fields over the annuals fields is the fact that perennial

fields remain always covered with vegetation but annual fields expose bare soil after

harvest. Therefore, during the time after harvest of annual crops the upcoming rainfall

could cause huge amount of runoff. However, though annual fields are covered with

crops, higher runoff was found from these fields compared with the perennial fields. To

be able to assess the overall effect of different land uses on runoff, the annual runoff

contribution from the different fields was evaluated (Figure 20).

0

100

200

300

400

500

600

700

Run

off (

mm

/yea

r)

Forest

Rubber

Agroforestry

Grassla

nd

BushFall

ow

UplandRice

Cassav

aMaiz

e

Figure 20 Simulated annual runoff under different land uses year 2002


48

The highest annual runoff was observed in maize and upland rice fields followed by

cassava. The lowest annual runoff was from forest and rubber followed by agroforestry

fields. Bush fallows and grasslands were intermediate in generating runoff. In general,

after canopy closure, the perennials were found less vulnerable fields compared with the

annuals due to higher protection of the soil without exposing the ground. For instance,

from the total annual rainfall of 2595 mm in 2002 only 10% of the rainfall was lost as

runoff under the forest land uses whereas, 23% of the rainfall was lost as runoff from the

maize fields. The rain water thus, was evaporated and transpired or slowly infiltrated

rather than causing higher amount of runoff. Unlike the perennials such as forest,

annuals intercept less amount of the upcoming rainfall and heavy rain could easily reach

the ground causing higher runoff. Moreover, between harvest and replanting until the

crops reach canopy closure, there is higher runoff due to less interception of the rainfall.

3.5.2 Discharge at pixel level Land uses were compared also with respect to the amount of discharge they contributed

to the watershed corresponding to the daily rainfall amount (Figure 21). In LUCIA, total

discharge is composed of runoff, soil discharge (lateral flow) and ground water

discharge expressed as:

Discharge = Runoff + Soil Discharge + Ground Water Discharge

The pattern of discharge followed the rainfall pattern with high peaks of discharge

corresponding to high peaks of rainfall amount. Lowest discharge values were from

forest and rubber, followed by agroforestry fields. Grasslands and bush fallows showed

relatively higher discharge as compared with forest and rubber, although the discharge

from these fields was lower than from annual crop fields such as upland rice, maize and

cassava.


49

Figure 21 Simulated discharge in the days of a month (12/09/2002 to 12/10/2002)


50

The detailed view provided in Fig. 21 shows that, due to the small area of the catchment,

there was no lag phase for discharge, which reacted immediately to stronger rainfall.

Discharge did not drop to 0 during any time step and there was discharge even during

dry periods without rainfall (days 645-649) due to the lateral flow from soil and

discharge from groundwater (baseflow). A more detailed view for the above time step is

presented in Annex IV.

Yearly discharge amount was calculated for the purpose of assessing the annual

contribution of discharge by different land uses (Figure 22).

0200400600800

10001200140016001800

Dis

char

ge (m

m/y

ear)

Forest

Rubber

Agrofo

restry

Grass

land

BushFall

ow

Upland

rice

Cassa

vaMaiz

e

Figure 22 Simulated annual discharge contribution at test points by different land uses year 2002

Highest discharge contribution was from the maize and upland fields followed by

cassava. Rubber, forest and bush fallow showed the lowest annual discharge

contribution. Agroforestry and grasslands showed comparable annual discharge

contribution. The discharge from these land uses was lower than that of the annual crops

such as upland rice, maize and cassava. Surface runoff comprised the main share of total


51

discharge and hence, the annual discharge for the land uses followed a similar sequence

as runoff with discharge being higher than runoff due to the additional components,

ground water discharge and soil discharge. The percentage was lower for perennials. For

instance, 40% of discharge in maize fields was from runoff while runoff contribution to

discharge from forests was 25%.

3.6 Runoff and discharge at the outflow point Simulation results at the pixel level showed only the runoff and discharge generated by

different land uses at the source and flow from uplands to lowlands was not predicted.

Hence, to assess the overall effect of land uses at the watershed level, runoff and

discharge were simulated at the watershed outflow point. The findings under the two

alternative scenarios defined in section 3.4: expansion of maize monocropping and

rubber plantations in the uplands are presented in the following sections.

3.6.1 Runoff at the outflow point Total runoff from the Tat hamlet experimental watershed was simulated at the watershed

outflow on a daily time step for 7300 days (Annex V) for maize and rubber scenarios as

well as the baseline. For the purpose of clarity, the annual runoff was calculated by

summing up the daily runoff.

Total yearly runoff = Sum (total daily runoff at the outflow during one year)

The annual runoff under the given scenarios is presented in Figure 23. The pattern of

yearly runoff followed the rainfall pattern (data not shown). As rainfall data were looped

every 4 years, runoff pattern, too, was repeated after the 4 years (see years 6-10, 10-14).

However, due to differences in the land use types under the different scenarios the

magnitude of annual runoff varied under the same rainfall amount.


52

0

5000

10000

15000

20000

25000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Year

Run

off (

m^3

/yea

r)

Baseline Maize scenario Rubber scenario

Figure 23 Total yearly runoff comparison between land use scenarios


53

Over the 20 years (7300 days) simulation period, the maize scenario showed the highest

runoff at the outflow point compared to the baseline and rubber scenarios (Figure 23).

However, between years 5 and 7 the curve for runoff at the outflow for maize scenario

lied below that of the baseline scenario (Annex VI). During this period, maize fields

were left fallow for 2 years after rotation with cassava (Figure 17). Whereas, in the

baseline scenario, a larger area of the watershed was planted to cassava which may have

contributed to higher runoff during years 5 to 7 as compared with the maize scenario.

Runoff under the rubber scenario decreased as rubber trees reached canopy closure about

4 years after planting. Once canopy closure was reached, the total runoff amount

followed the rainfall pattern without showing much decrement in the remaining years.

3.6.2 Discharge at the outflow point Total yearly discharge at the outflow under the scenarios considered was calculated as

the sum of total yearly runoff, soil discharge and ground water discharge. The total

yearly runoff, soil discharge and ground discharge were calculated by summing up the

daily simulations. The results were plotted against rainfall (Annex VII). The annual

discharge for the considered land use scenarios is shown in Figure 24. The results

showed that the yearly discharge was the same as the yearly total runoff except in the

first 2-3 years. This was because the value for ground water discharge at the outflow was

0 in all the 20 years period and the soil discharge occurred only during the first years of

the simulation.


54

0

5000

10000

15000

20000

25000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Year

Dis

char

ge (m

^3/y

ear)

Baseline Maize scenario Rubber scenario

Figure 24 Comparison of annual discharge between land use scenarios

The highest discharge under all the scenarios was found during the first year

immediately after replacing forest with other land uses such as maize in case of maize

scenario, rubber in case of rubber scenario and upland rice in case of the baseline (Figure

24 years 0 to 3). The lowest annual discharge occurred under the rubber scenario and the

curve lies below the curves for the maize scenario and baseline. Nonetheless, between

years 5 and 7 discharge was lower for the maize scenario than the baseline scenario. As

for runoff, this was due to relatively higher values in the baseline scenario which

resulted from a large area of cassava in contrast to the maize scenario where most of the

watershed area was under fallow during the same period. The pattern of discharge

followed the pattern of rainfall during the 20 years simulation period with rainfall being

looped every 4 years (Annex VIII).


55

3.7 Water availability for plant growth Under the given weather conditions of the Tat hamlet experimental watershed, water was

not constraining. Thus, for the considered crop types a direct effect of water stress on

crop yield was not observed. However, the relationship between plant available water

and the soil water under varying rainfall was predicted at pixel level. In LUCIA plant

available water is defined depending on:

• Soil moisture in the respective horizon, which is assumed to be homogeneously

distributed within the horizon

• Actual rooted depth, implying that plant available water at constant soil water

contents increases as the plant grows and that deep-rooting plants can reach

higher levels of potential plant available water

Therefore,

PlantAvWat = 10*SoilWater*(min(RootingDepth,TopSDepth)/SoilDepth*avFCTop/TPVTop+ if(RootingDepth gt TopSDepth,(RootingDepth-TopSDepth)*avFCSub/TPVSub,0));

with avFC being available field capacity and TPV total pore volume. Top and Sub refer

to the respective soil horizons.

Figure 25 shows the change in plant available water and soil water for some of the land

uses with changing rainfall amount. The pattern of plant available water and soil water

followed the pattern of rainfall. Soil moisture decreased with decreasing rainfall. The

plant available water also decreased with decreasing rainfall amount. With increasing

rainfall the soil moisture and plant available water increased. The value for soil moisture

was found close to 0 for agroforestry during dry period (day 725). However, the plant

available water was not close to 0 since the plants could pull water from the sub soil.

Soil moisture rose up immediately after rainfall.


56

Figure 25 Plant available water and soil moisture


57

Paddy rice fields showed the highest soil water and the highest plant available water.

This may be due to the supply of water from the uplands to the lowland paddy rice fields

keeping the soil moisture of these fields higher during all periods. However, during

heavy rainfall events the paddy rice fields could be oversaturated and also too much

deposition of soil material could hamper the productivity of the lowlands. This could

potentially be addressed in the erosion module for LUCIA which has not been

implemented yet. Plant available water for upland rice was lower than that of cassava

even if the soil moisture for upland rice was higher. This may be due to the influence of

rooting depth on plant water availability. The (user-defined) maximum rooting depth and

consequently actual rooting depth for cassava were higher than upland rice which gave

cassava an advantage to pull water from the subsoil.

4. Discussion The main aim of this research was to assess the impact of land use change on

hydrological watershed functions and crop production based on a modeling approach –

LUCIA. The research aimed also at testing the performance of the model with respect to

prediction of hydrological parameters such as runoff and discharge, and agronomic

yield. In this section the findings of the research will be explained in detail under three

major sub sections. In the first sub section, LUCIA model performance will be discussed

based on the findings. In the second sub section, importance of using runoff as a proxy

for erosion risk assessment will be elaborated. In the third sub section the findings under

different land use scenarios will be discussed to assess the land use options for

sustainable resource management in the Northern Mountainous Regions of Vietnam.


58

4.1 Assessment of LUCIA The version of LUCIA used for this study was based on the concepts of GenRiver and

did not have a detailed calibration for hydrograph separation to differentiate parts that

come from runoff, soil discharge (lateral interflow) and ground water discharge

(baseflow). Hydrograph separation can only be estimated visually based on curve

patterns in which smooth curves indicate high share of ground water whereas, abrupt

changes in the trend of hydrograph indicate high influence of runoff. However, recent

version has shifted from GenRiver to KINEROS concept and improvements have been

made in infiltration and redistribution of water inside the soil profile. For separate

calibration of hydrological components such as runoff and ground water, field data are

required. The assessment of the model was made based on the findings by taking into

account the limitations of the version used in this study.

4.1.1 Runoff simulation The findings in this research demonstrated that LUCIA was able to simulate runoff as

affected by changing weather conditions. The sensitivity analysis results showed that the

model responds well to changing rainfall conditions with respect to runoff. Though

runoff and rainfall showed direct relationship, there was difference in threshold at which

runoff occurred depending on the land use types. For instance, runoff occurred at a

rainfall amount of 15 mm in cassava but it was 0 at this point for forests.

At pixel level, the differences between land uses in runoff amount generated at source in

response to changing rainfall amount were depicted well by the model. Thus, for a given

pixel size indicating an area of a specific land use in a watershed, runoff generated from

the plot can be estimated by LUCIA to assess the differences in land uses at their growth

stage (and LAI) for proper choice of land use types in the upland areas in order to reduce

runoff.


59

At the watershed outlet, the cumulative effect of runoff from land uses in the upland

areas was well depicted by the model. Runoff from land uses in the uplands adds up and

the cumulative effect on the watershed is estimated at the discharge point at the lowest

elevation point in the lowlands. On the other hand, run on can infiltrate in downstream

cells along the LDD if soil water is less than the field capacity, so that there is space

available in the topsoil. The effect of changing land uses in the uplands on total runoff

was depicted well by the model. For instance, the increased runoff due to replacing

forest areas by maize was well predicted by the model.

4.1.2 Discharge simulation At pixel level, discharge was predicted well showing the differences between land uses

in response to prevailing rainfall amount by the crop types considered. Trends in

discharge over the simulation period went parallel to runoff corresponding to each of the

land uses with discharge being higher than the runoff. Discharge was calculated as the

sum of runoff, soil discharge and ground water discharge. Therefore, the higher values

for discharge came due to the extra water for discharge from through lateral flows from

the soil in the form of soil discharge and ground water discharge.

At the watershed outflow, discharge showed similar patterns with rainfall showing the

capacity of the model to predict the impact of decreasing or increasing rainfall on

discharge from the watershed. The values of discharge were more close to the values of

runoff at the watershed level. Main differences in pattern and magnitude between total

discharge and total runoff at the outflow were observed during the initial periods of the

simulation. This may have resulted from the initialization of the model for the soil water

content. As described in chapter 3, in LUCIA total discharge at the watershed outflow

point is expressed as:

Total Discharge = total Runoff + total Soil Discharge + total Ground Water discharge


60

From the theoretical background expressed by the above equation, the findings in

chapter 3 illustrated that most of the contribution to discharge at the outflow was through

runoff from the different land uses in the uplands. There was some soil discharge during

the first 2 to 3 years which resulted in increased total discharge during this period. The

soil discharge during the first 2 to 3 years may have been due to the initialization of the

model which gave sufficient soil water to be available for discharge. However, in the

longer time during the simulation period of the 20 years there was not sufficient soil

water available for discharge. On the other hand, there was no ground water discharge

for all days in the simulation period. Hence, the reduced amount of discharge at the

outflow point came from the lower amount of soil discharge and missing ground water

discharge. In the LUCIA version used for this study flow along LDD is expected to pass

to the lowest cell (outflow) only if the ground water stock of the neighboring upstream

cells is fully saturated. Hence, ground water discharge from the upstream cells may not

reach the outflow point and stays within the system with transfer as baseflow from one

upslope cell to the neighboring downslope cell. Moreover, lack of knowledge of the

initial amounts of soil water and ground water made the simulation of discharge

difficult. Field data on hydrograph separation were not available at this stage. Assuming

that sharp peaks of total discharge at the outlet were due to runoff is a common practice,

but may be questionable for very small watersheds like Tat hamlet. This also contributed

to the difficulty in modeling the infiltration and redistribution inside the soil profile.

The validation result for discharge at the outflow showed plausibility in terms of the

relationship between rainfall and discharge with higher discharge values corresponding

to higher rainfall and vice versa. Both the simulated and observed discharge values were

lower than the rainfall amount throughout the simulation period due to interception, soil

evaporation and plant transpiration. However, the predicted values appeared far from the

measured discharge values at the outflow point. There was discharge simulated at the


61

pixel level and this was expected to add up to cause discharge at the watershed outlet.

Thus, the discrepancies in the simulation of discharge at the outlet may have resulted

from the algorithm used to predict discharge along the LDD. The higher depth of the soil

may have contributed for the flow to stay in the system rather than passing to the

downslope area. Therefore, further investigation is necessary to identify the problem in

discharge simulation at the watershed outflow point.

4.1.3 Agronomic yield prediction The graphical representations of observed yield against simulated yield in chapter 3

show that LUCIA was able to predict agronomic yield with acceptable accuracy. Further

analyses of the results done using statistical methods have shown the capacity of the

model to predict yield from different land uses under unconstrained water availability.

Several scholars have suggested the ranges of the statistical measures to evaluate model

efficiency and accuracy. For a hydrological watershed model Percentage Error (PE)

expected is described as very good if PE < 10%; good if PE = 10-15%; and fair if PE

=15-25% (Love and Donigian, 2002 cf Bhardwaj and Kaushall, 2008). Nash and

Sutcliffe model Efficiency (NS-EF) > 0.4, R2 > 0.6 and Average Absolute Error (AAE)

value close to zero are recommended for a hydrological model predictive capacity

evaluation (Wang et al., 2006 cf Bhardwaj and Kaushall, 2008).

For upland rice yield the PE was 14% which shows that the model was good in

simulating agronomic yield. The NS-EF value was 0.85 that shows that the model

efficiency was 85% in predicting agronomic yield. An R2 value of 0.91 and wR2 value of

0.80 were achieved for upland rice which was a relatively high value to accept the

simulated values for the model. The AAE was 0.006 which is quite close to 0 that makes

the model simulation results acceptable. For cassava, an R2 value of 0.90, wR2 of 0.79

were obtained, which are clearly above the minimum requirement of 0.6. AAE was 0.10


62

which is close to 0. The PE value was 12% ranking the model as good according to the

criterion. The model efficiency (NS-EF) was 0.72 (72%), which is quite high above the

requirement of 0.4 for a watershed model. Therefore, for upland rice and cassava yield

prediction, LUCIA performed well fulfilling all the statistical measures applied for

testing the model accuracy.

For paddy rice yield prediction, PE was 12%, which classifies the prediction as good.

However, the R2 and wR2 were very low with values 0.02 and 0.07 respectively showing

poor performance in predicting paddy rice yield. The AAE value was 0.67, which is far

from 0. NS-EF value calculated was -8.76 which again shows that the model did not

depict the real measured values well. Hence, from the multi-criteria of statistical

measures applied, it can be deduced that paddy rice was under predicted. In Tat hamlet

paddy rice is grown using chemical and organic fertilizers. However, in this study

fertilizer application was not considered which may have resulted in lower yields of the

paddy rice. In addition, the low paddy rice yield may also be due to the missing linkage

between upland-lowland nutrient flow and deposition which is not yet implemented at

this version (Marohn, 2008) of the model. Since paddy rice fields are located in the

lower valley bottoms of the watershed, the nutrient flow and deposition in these fields

influences yields (Dung et al., 2008). Thus, higher could be expected as a result of

nutrient flow from the upland to the lowland paddy fields. Nonetheless, the concept of

upland-lowland nutrient flow needs to be verified with an appropriate erosion and

deposition algorithm.

4.2 Runoff as surrogate for soil erosion risk assessment Under bare soil conditions runoff could directly cause erosion where there is intensive

rainfall, especially in upland sloping areas (Francke et al. 2008). Therefore, higher

runoff would result in higher water erosion in the uplands and deposition in the

lowlands. Vegetation cover determines the erodibility of soil and hence is important in


63

controlling water erosion. In the short term, the influence of vegetation on erosion results

from interception of rainfall water that would have caused runoff (Bochet et al., 2006).

The impact of splash erosion, which is influenced by drop height, is not yet implemented

in the current version of the model. According to Bochet et al. (2006), the long term

influence of vegetation on erosion originates from the fact that vegetation influences

water fluxes and sediments through improving infiltration capacity and aggregate

stability of soils. An according mechanism adjusting bulk density to land use specific

values of rooting intensity is implemented in the model and updated yearly. The findings

of this research demonstrated that different vegetation types have different impact on

runoff. Given the same weather conditions and plant growth stage after reaching canopy

closure, land uses were ranked according to their impact on runoff (Table 5).

Table 5 Ranking land uses by effect on runoff

Land use Rank (based on runoff

amount)

Use type in the NMR

Maize 1 Commercial (for pig production) Upland rice 2 Home food consumption Cassava 3 Home food consumption Agroforestry 4 Mixed (Home food + Commercial) Rubber 5 Commercial Forest 6 Conservation

The findings from the different land use scenarios also indicated the cumulative impact

of land use change on runoff at watershed outflow. The scenarios considered in this

study were also ranked in their effect on runoff and erosion risk (Table 6).


64

Table 6 Ranking scenarios by their effect on runoff

Scenarios Rank (based on runoff

amount)

Use type in the NMR

Fallow period

Expansion of Maize

1 Commercial (for pig production)

2

Baseline 2 Home food consumption

12

Introduction of Rubber

3 Commercial -

In all existing theories on water erosion, runoff is considered one of the crucial impact

factors. The most common approaches used for modeling such as USLE, MUSLE,

RUSLE, Rose, use runoff as one main predictor for erosion. Using runoff as proxy for

erosion risk, the booming expansion of maize over a larger area of the forest indicated

risk of erosion and soil degradation. The introduction of rubber plantations as

commercial crops showed less risk of erosion and soil degradation. The lowest risk of

erosion and soil degradation was found under forest lands, which could be left as

protection forests. As slope length is another relevant factor for erosion, strategic

positioning of such protection forests is of importance. Annual crops such as upland rice

and cassava indicated high risk of erosion and soil degradation. Agroforestry showed

less risk of erosion while providing multiple uses for home consumption and market.

The baseline situation showed less risk of erosion and degradation compared with maize

cropping, probably because it involved longer fallow periods of 12 years. However, this

study was intended to evaluate the impact of land uses on runoff and did not consider the

erosion and deposition modeling. Hence, verification of the aforementioned concepts of

runoff and erosion risk is necessary with further investigation by implementing erosion

and deposition modules in LUCIA.


65

5. Conclusions Spatially explicit models are useful tools for predicting the impact of land use change on

hydrological functions and crop production. LUCIA was able to simulate runoff at pixel

level and the watershed outflow. Discharge at pixel level was also predicted well by the

model. However, further investigation is needed for the prediction of discharge at the

watershed outflow. LUCIA was able to predict also agronomic yield with acceptable

accuracy. Therefore, the model can be applied for small watersheds to estimate

hydrological and crop parameters at landscape level. However, for better assessment of

the upland-lowland linkages in terms of nutrient flow and deposition in the lowlands, an

erosion and deposition module needs to be incorporated in the model. Manual calibration

of the model was found a time consuming task. Thus, developing a technique for

automatic calibration of LUCIA could facilitate the modelling.

There are growing tradeoffs between resource conservation and agricultural crop

production in the NMR of Vietnam. The traditional farming system has been a relatively

sustainable system in which crop production for home consumption was maintained with

reduced risk of erosion and soil degradation. However, since this system involves longer

fallow periods (12 years) due to increasing population pressure the system would not be

practical any more. Moreover, there are competing interests over land use for

commercial crop production and production for home consumption. Thus, systems that

enable optimum crop production with less soil erosion and degradation under shorter

fallow periods are needed.

If crop production for home consumption is sought, agroforestry systems may be the

most sustainable systems that provide multiple uses while maintaining the fertility of soil

by reducing soil erosion and degradation. Though monocropping could provide high

productivity over a shorter period, in the long term there will be high risk of erosion and


66

soil degradation. For the commercial crop production, if agricultural expansion into

forest areas in the uplands of Northwest Vietnam is deemed unavoidable, then rubber

plantations are better land use options for sustainable resource management in the area

than maize monocropping. However, because monocropping of annuals may not be

ignored due to the benefits farmers get for food consumption needs using suitable

conservation measures could reduce the risk of erosion in the area. For instance,

protection measures such as soil mulching and planting hedgerows could be useful in

reducing runoff due to seasonal heavy rainfalls. Keeping the soil covered between

cropping times through use of green manure, rely cropping or cover crops could thus

minimize the soil erosion problem in the area. In cases where there is focus on market-

oriented production such as rubber plantations, intensification of the lowland paddy rice

is necessary to supply food to the increasing human population in the NMR. Research is

necessary also for maximizing the revenue of farmers from rubber products to enable the

farmers purchase food during deficit times. Rubber needs up to 7 years until the start of

latex production. Therefore, research is needed to identify crops for intercropping with

rubber during the early stages and/or after canopy closure intensive use of land.


67

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Study of Tay Community in Northwest Vietnam. Hanoi Agricultural University,

Vietnam.

Widodo R.H., Farida A., van Noordwijk M., Lusiana B., Suyamto D. and Khasanah N.

2009. Gen River – Generic River Flow Model, User Manual Version 1.1. World

Agroforestry Centre (ICRAF), Bogor, Indonesia.

Annex I: Land use and Soil Parameterization Parameterization of land use parameters

Category Land use I_LandCoverID Annual? Terrestrial? Planting_day Planting_day2 I_TDbase I_TDmax1 I_TDmax2 DD2Flower unit [] [] [] [] [] [°C] [°C] [°C] [°C d] LU1 Forest 2 0 1 1 0 8 35 42 1200 LU2 Agroforestry 3 0 1 55 0 8 35 42 1200 LU3 Grassland 4 0 1 1 0 8 35 42 1350 LU4 Bush_fallow 5 0 1 1 0 8 35 42 1200 LU5 Paddy_rice 6 1 1 60 220 10 35 42 700 LU6 Uprice_low 7 1 1 151 0 8 35 42 875 LU7 Cassava 8 1 1 55 0 10 32 45 1100 LU8 Maize 9 1 1 151 0 8 35 42 900 LU9 Rubber 9 0 1 1 0 8 35 42 1300

Land use parameters (continued)

Category Land use DD2Harvest I_MaintResp Rootmax I_CovEff StartFlower EndFlower AlbedoMaxPlant LightExtinction

coefficient f_RWD Unit [°C d] kgco2/d/ha [cm] [] [d] [d] [] [] [Mg/m3] LU1 Forest 2000 0.02 300 0.8 1 365 0.1 1 0.007 LU2 Agroforestry 2000 0.02 150 0.6 1 365 0.12 1 0.007 LU3 Grassland 2350 0.02 125 0.5 1 365 0.2 1 0.003 LU4 Bush_fallow 2000 0.02 300 0.8 1 365 0.1 1 0.007 LU5 Paddy_rice 1050 0.02 80 0.35 1 365 0.2 1 0.004 LU6 Uprice_low 1400 0.02 80 0.35 1 365 0.15 1 0.004 LU7 Cassava 2700 0.02 100 0.4 1 365 0.13 1 0.003 LU8 Maize 1500 0.02 800 0.35 1 365 0.15 1 0.004 LU9 Rubber 2100 0.02 300 0.8 1 365 0.1 1 0.007

Land use parameters (continued)

Category Land use I_MaxLAI NFixRate I_WUE

IBDBDRefVeg

FodderIndex_lv

FodderIndex_st

Day1stFlower KC_Coeff CropDrought I_LUE

Unit [] [kg/ha/d] [l/kg] [] [] [] [] [] [] [] LU1 Forest 12 0.05 250 1 0 0 1 1 4 0.625 LU2 Agroforestry 9 0.15 300 1 0 0 1 1 3 0.625 LU3 Grassland 6 0.07 500 1 0.3 0 1 1 4.5 0.315 LU4 Bush_fallow 12 0.05 250 1 0 0 1 1 4 0.625 LU5 Paddy_rice 6 0.01 900 1 0 0 1 1 3 0.315 LU6 Uprice_low 6 0 700 1 0 0 1 1 3.5 0.315 LU7 Cassava 7 0 700 1 0 0 1 1 4.5 0.315 LU8 Maize 7 0 700 1 0.2 0.2 1 1 4.5 0.315 LU9 Rubber 12 0 250 1 0 0 1 1 4 0.625

Parameterization of soil parameters Soil type

Soil name SoilID TopSoilDepth SubSoilDepth StonesTop StonesSubsoil TextureTop TextureSub

Unit [cm] [cm] [dec %] [] [code] [code] Soil1 Gleysol 2 20 70 0 0.05 6 9 Soil2 Acrisol 3 8 90 0 0 3 11

Soil parameters (continued) Soil type

Soil name SoilID SandTop SandSub ClayTop CorgSub PBrayTop PBraySub pHTop pHSub

Unit [dec %] [dec %] [dec %] [%] [mg/kg] [mg/kg] [] []

Soil1 Gleysol 2 0.49 0.5 0.2 0.7 64 33 5.8 5 Soil2 Acrisol 3 0.53 0.35 0.18 0.7 8 3 4.8 5

Soil parameters (continued) Soil type

Soil name SoilID

BulkDensityTop BulkDensitySub I_KsatParent Corg Nt Pbray K_av

Unit [g/cm3] [g/cm3] [mm/d] [g/kg] [g/kg] [mg/kg] [g/kg] Soil1 Gleysol 2 1.1 1.35 1 15 0.85 430 1.03 Soil2 Acrisol 3 1.05 1.25 8 7 0.96 160 0.95

Annex II: Local Drain Direction

Annex III: Runoff under changing rainfall conditions

RF% Change Actual RF (mm/day) Forest Agroforestry Grassland Upland rice Cassava-80 14.55 0 0 1.14 3.27 2.57-60 29.11 1.84 3.22 4.67 6.8 6.1-40 43.66 5.37 6.75 8.2 10.33 9.63-20 58.21 8.9 10.28 11.73 13.87 13.17

0 72.76 12.43 13.82 15.26 17.39 16.6920 87.31 15.97 17.35 18.79 20.93 20.2340 101.87 19.5 20.9 22.33 24.46 23.7660 116.42 23.03 24.41 25.86 27.99 27.2980 130.97 26.56 27.94 29.39 31.53 30.83

100 145.52 30.09 31.48 32.92 35.06 34.36

Percent change in RunoffRF% Change Actual RF (mm/day) Forest Agroforestry Grassland Upland rice Cassava

-80 14.55 -100.00 -100.00 -92.53 -81.20 -84.60-60 29.11 -85.20 -76.70 -69.40 -60.90 -63.45-40 43.66 -56.80 -51.16 -46.26 -40.60 -42.30-20 58.21 -28.40 -25.62 -23.13 -20.24 -21.09

0 72.76 0 0 0 0 020 87.31 28.50 25.54 23.13 20.36 21.2140 101.87 56.88 51.23 46.33 40.66 42.3660 116.42 85.28 76.62 69.46 60.95 63.5180 130.97 113.68 102.17 92.59 81.31 84.72

100 145.52 142.08 127.79 115.73 101.61 105.87

percent change in soil waterRF% Change Actual RF (mm/day) Forest Agroforestry Grassland Upland rice Cassava

-80 14.55 -97.00 -96.66 -96.77 -90.69 -96.43-60 29.11 -91.04 -91.34 -90.56 -79.51 -86.24-40 43.66 -77.25 -76.65 -76.22 -59.70 -70.79-20 58.21 -62.16 -53.58 -44.73 0.00 -0.45

0 72.76 0.00 0.00 0.00 0.00 0.0020 87.31 -0.05 -0.02 0.00 0.00 0.0040 101.87 -0.09 -0.04 -0.01 0.00 0.0060 116.42 0.00 0.00 0.00 0.00 0.0080 130.97 -0.13 -0.05 -0.01 0.00 0.00

100 145.52 -0.14 -0.06 -0.01 0.00 0.00

Runoff in mm/day

Annex IV: Daily discharge for year 2002

0

5

10

15

20

25

30

620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650

T imestep (days)

Disch

arge (m

m/day

)

Ag roforestry Upland rice C assava Maiz e Rubber

Annex V: Daily Runoff for the 20 years simulation period

Note: the daily runoff was simulated in m^3/s. The results were converted to m^3/day

Annex VI: Daily discharge for the 20 years simulation period

Discharge was calculated after converting the runoff from m^3/s to m^3/day and also the soil discharge and ground water discharge were converted to m^3/day. Hence, the final unit is in m^3/day.

Annex VII: Pattern of yearly runoff: scenarios

Annex VIII: Pattern of yearly discharge: scenarios

simulating consequences of land use change on hydrological ...€¦ · 1.2 traditional farming...

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