1. Hydro-Economic decision support to enhance catchment management B ENNIE G ROVÉ D EPARTMENT OF A GRICULTURAL E CONOMICS

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 MIKE BASINOPTIMISATION

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

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

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

13. 65% : ROE >= 7.66% 6% : 0 <= ROE < 7.66%

14. PROFITABILITY ANALYSIS

15. 0.9 vs 1.2

16. No vs Present

17. Class C - all farms infeasible

18. Montrose

19. Present vs Class C

21. LIVELIHOOD ANALYSIS

23. 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

24. THANK YOU B ENNIE G ROVÉ D EPARTMENT OF A GRICULTURAL E CONOMICS