water quality amelioration...>40 monitoring stations. ethekwinini municipality. umgeni catchment....
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Water quality amelioration
Jane Turpie University of Cape Town & Anchor Environmental Consultants
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Service & Benefits
1. Reduced impacts on water treatment costs or health costs
2. Reduced impacts on other downstream aquatic ecosystem services
AgricNatural
Downstream ecosystem/ reservoir
Urban
+
+-
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final service
intermediate service
1. Limiting the area of polluting land uses
2. Assimilating some of the loads generated
the passive service
the active service
1/disservice of degradation
+ = generates excess nutrients- = removes excess nutrients
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Nagle Dam Inanda
Dam
Durban case study (eThekwini Municipality)• Included active (nutrient assimilation) and passive (no LULC change) aspects; • Included final (treatment cost) and intermediate (estuary ES) values
These ecosystems save on water treatment costs
These ecosystems save prevent damages to
downstream rivers and estuaries
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Understanding of treatment costs avoided
Land cover & use
River WQ
Reservoir WQ
Chemical use, backwashing & sludge removal at WTW
Water treatment costs
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Two-stage valuation process
• Physical modelling • Change in nutrient load at each point of
interest (m3/year)• Mapping the removal back to service areas
(m3/ha/year) based on model coefficients
• Valuation • Avoided treatment costs at each treatment
plant based on empirical model – treatment costs = f(N, P entering dams)
• Map value to source areas based on removal rate ($/ha/year)
Land cover & use
River WQ
Reservoir WQ
Chemical use, backwashing & sludge removal at WTW
Water treatment costs
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Physical model
• Whole catchment area modelled using PC-SWMM
• Production and uptake rates of nutrients for each land cover type based on literature
• Coupled with modelled flows to estimate the concentration and total load of N & P at relevant points
• Calibrated with WQ data from >40 monitoring stations.
eThekwininiMunicipality
uMgenicatchment
Mkomazicatchment
Mdloticatchment
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Valuation model
• Treatment cost vs reservoir WQ: model had good fit
• However, physical model predicts inflowing river water quality
• (Too complex to model reservoir response)
• So used slightly weaker model of cost against river water quality
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Model of cost vs reservoir water quality
Model of cost vs inflowing river water quality
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Defining the baseline• Two scenarios were used to estimate service
provided:
1: removal of uptake capacity of natural habitats (a hypothetical construct)
2: replacing natural habitats with dense rural settlement as a likely alternative land use
• These provided lower and upper bound estimates of the magnitude of the service, depending on what is considered as the service
quantifies the active + passive services
quantifies the active service
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Results• Big increases in P loads entering
dams • 193%-319% for Inanda Dam, • 193%-968% for Hazelmere and • 200%-509% for Nungwane Dam
• Estimated water treatment cost savings of R1 - R8.7 million per annum
• Large range of uncertainty, mainly from physical modelling and service definition
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Empirical valuation based on land cover/use• Few studies directly relate catchment land
cover to water treatment costs• Many anecdotal reports, but little empirical
evidence • Some early studies have been criticized
• Vincent et al. 2015 analysed effect of forest cover on water treatment cost
• Rigorous approach using fixed effects and instrumental variable (IV) models
• 1% increase in virgin forest reduces costs by 1.16%
• ESAforD study used this approach to value water quality amelioration in South Africa, Sweden, Costa Rica, China, Kenya, Ethiopia & Tanzania
Land cover & use
River WQ
Reservoir WQ
Chemical use, backwashing & sludge removal at WTW
Water treatment costs
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RSA study • Panel data analysis 2008-2014• Cost and output data from 14 treatment plants (TPs) • Climate and land cover data extracted each catchment
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Model resultsVariables Random
EffectsFixedEffects Mundlak
Constant 740,405 -6.339e+07*** -1.273e+07***TP_capacity 15,690*** 15,395***Volume treated 0.156* 0.235*** 0.421***Rainfall 5,243* -521.1 300.9Rain sqr -8.5** 2.075 0.602Settlmnt -309.3 -18,543*** -450.6**Settlmnt sqr 0.00218 0.772*** 0.00335Subsistence 2,011*** 14,901*** 2,626***Orchards 659.5** 53,021*** 1,098***Plantations -350.9*** -8,599*** -622.2***Grasslands 5.882 1,215** 65.02*Natural_forest -2,830*** -39,942** -4,937***Woodland -1,252 -9,200*** 463.2Wetland -936.3** -4,133*** -2,243***Observations 92 92 92Number of TP 14 14 14R-squared 0.757
• Co-efficients translate to cost savings in R/ha/year
• Sensitive to model choice
• Some unexpected signs• Lack of data on
ecosystem condition
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Discussion• Improving confidence
• More empirical modelling needed to refine modeling assumptions
• Needs larger datasets, more detailed land use• Empirical modelling may better isolate the active from
the overall service• Two/multi-stage empirical approach may be more
practical, achieve higher confidence
• Defining service • Should only assign active service value to ecosystems, • but should also assign negative (disservice) value to
polluting land uses (when added to production, this internalizes the externality)
Land cover & use
River WQ
Reservoir WQ
Chemical use, backwashing & sludge removal at WTW
Water treatment costs
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Discussion
• Double counting?• run-of-river water – provisioning - maybe• treatment costs – sedimentation – no• if extrapolated to areas not serving users – yes – ie. Check demand
• Possibility of scaling up• Yes, but rigorous modelling will take time; must take spatial variation in
demand into account.• Once set up, highly repeatable.
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• Thank you!
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WQ amelioration by ecosystems• In landscape before reaching river systems, especially riparian zone• Within drainage system, particularly in wetlands