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Hydro-Economic decision support to enhance catchment management

Hydro-Economic decision support to enhance catchment management. Bennie Grové Department of Agricultural Economics. Introduction. South Africa is a water scarce country National Water Act (1998) Ecological Water Requirements (EWR) National Water Resources Strategy

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Hydro-Economic decision support to enhance catchment management

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  1. Hydro-Economic decision support to enhance catchment management Bennie Grové Department of Agricultural Economics

  2. Introduction • South Africa is a water scarce country • National Water Act (1998) • Ecological Water Requirements (EWR) • National Water Resources Strategy • a number of the South African catchments to be in situation of being over-allocated • Before issuing water licenses to address imbalances, water managers have to reconsider • catchment scale operating rules, • water conservation and demand management options, • water augmentations alternatives and • the level and necessity of water curtailments to determine the most viable option.

  3. Objectives • The main objective of this research is to develop a Decision Support System (DSS) to help water managers test various catchment scale water management scenarios impact on irrigation farming profitability and livelihoods. • Achieving the object requires an integrated hydro-economic modelling framework.

  4. Research area • Crocodile East catchment South Africa • Highly over-allocated • Instream flow requirement • Ecology • International flows to Mozambique • Water needs to be re-allocated

  5. Over allocation in South Africa Crocodile Catchment is in The Nkomati WMA

  6. Ecological sensitive area: Kruger National Park

  7. Integrated set of models • MIKE-BASIN • reconcile irrigation water demand with catchment water availability • for given catchment operating rules • Daily input requirements • Catchment hydrology • Water demand • Optimisation model • Maximises total farm gross margins • Water availability • Operating rules • Dated production functions (water use optimisation) • Weekly • State contingent • Irrigation technology specific (Distribution unifromity) • Multiple fields • Results are used to evaluate • Profitability (REO) • Livelihood (ability to generate cashflows) • MIKE BASIN Irrigation • Information to generate irrigation technology specific dated production functions (daily)

  8. Modelling difficulty

  9. MIKE BASIN IrrigationModel Daily Irrigation Outputs (ET, ES, EOP, DP, RO, AI) Weekly Irrigation inputs to SKELETON (ET, ES, EOP, DP, RO, AI) Weekly water available limit Optimsation Model Catchment water availability and water available to the farm from all sources Optimised Weekly Farm Demand Profile MIKE BASIN without the irrigation model, Demand node representing farm Disaggregate to Daily Farm Demand Profile

  10. REsults • Profitability • ROE > ROA • Financial sustainability • Indicates profitable employment of foreign capital • Do not need to use own capital to meet interest payments • Reported as probability to achieve financial sustainability • Livelihood objective • Determine whether enough cash is generated to cover living expenses

  11. 65% : ROE >= 7.66% 6% : 0 <= ROE < 7.66%

  12. 65% : ROE >= 7.66% 6% : 0 <= ROE < 7.66%

  13. Profitability analysis

  14. 0.9 vs 1.2

  15. No vs Present

  16. Class C - all farms infeasible

  17. Montrose

  18. Present vs Class C

  19. Present vs Class C

  20. Livelihood analysis

  21. Conclusions • MVD greatest potential • Cost of dam not included • Class C is a no go scenario • Investigate enforcing EWR based on present flow regime • Stimulate dialogue

  22. THANK YOU Bennie Grové Department of Agricultural Economics

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