Enhancing Sustainable Groundwater Use in Hout Catchment, South Africa

1. Supporting Sustainable Agricultural Groundwater Use in a Hard Rock Catchment, Hout, Limpopo Province, South Africa – Application of an Integrated Hydrogeological Model Girma Y Ebrahim, Karen G. Villholth, Maurice Boulos

2. Outline • Introduction • Objectives • Study area • Modelling approach (data scarce conditions) • Results • Summary and conclusions • Model limitations

3. Introduction • Hout river catchment/Dendron aquifer area is one of the most agricultural productive regions in South Africa • Irrigated agriculture is solely supplied from groundwater • Eefficient use of groundwater resources is becoming increasingly important for sustainable food production and economic development in the region • Groundwater modelling is proposed as a tool to study the aquifer dynamics and thereby to develop appropriate management schemes

4. Objective • To develop a model that is relevant for understanding the groundwater recharge and flow processes, water use and its dynamics in the Hout river catchment/Dendron aquifer area • Specific objectives • Develop water balance for the catchment area • Obtain better understanding of the interrelationship of the different hydrological process • Describe the recharge and discharge process and their dynamics • Gain understanding how human activities (irrigation pumping) affect the hydrological response • Identify data gaps in the in the modelling and provide recommendation for future monitoring • With future monitoring and data gap filling the model can be used in the future to assess the effect of change in land use and climate change.

5. Study area Sand catchment =3x Hout

6. Land use

7. Catchment characteristics • Catchment area of 2478 km2. Elevation [840-1739 mamsl] • Hout catchment is comprise of three quaternary catchments (A71E, A71F and A71G) • The climate is semi-arid with annual precipitation of 300-580 mm/a. • The Hout River Gneiss occupies the largest portion of the study area • Dikes dissect the aquifer into a series of poorly connected blocks • The area is known for its high potato production. • 9% of the catchment area is equipped for irrigation • Actually irrigated area is about 0.9% of the catchment area Mean annual rainfall of coefficient and annual ET calculated using Hargreaves Method

8. Modelling Approach • The groundwater flow system was modelled with MODFLOW-OWHM using the ModelMuse graphic user interface (USGS). • The main reason for the selection of MODFLOW-OWHM is because it allow indirect estimation of irrigation groundwater pumping from crop consumptive use (the main driver for the development of the model) • To constrain the simulated runoff by the MODFLOW-OWHM, a rainfall-runoff model was developed for the combined Hout and Sand catchments using Precipitation Runoff Modelling System (PRMS) • Groundwater flow in the weathered and fractured rock aquifer was simulated assuming an Equivalent Porous Medium approach (EPM).

9. Model description • One-Water Hydrologic Flow Model (MODFLOW-OWHM), which is a coupled MODFLOW and Farm process package (USGS) • It provides coupled simulation of the groundwater, unsaturated zoneand surface water.

10. Conceptual model • The active model domain covers an area of 2478 km2 • Flow into the model domain (direct recharge from rainfall and focused recharge from river leakage) • Flow out of the model domain (domestic pumping, irrigation pumping, evapotranspiration and GHB).

11. Schematic weathered profile above crystalline basement rocks, source Wright (1992) Source Holland (2011) Source: Rushton and Weller (1985) A generalized conceptual model of granite aquifers Dewandelet al. (2006)

12. Model grid and layering • The finite-difference mesh consists of 1036 rows and 482 columns with a uniform grid size of 100 m x 100 m • Hence, in the vertical directions, the model is divided into two layers

13. Determining irrigation pumping • Irrigation pumping is estimated indirectly from crop water requirements • To identify irrigated areas, we applied a method based on NDVI • To determine period of peak irrigation, we used MODIS NDVI time series data, as these data have more temporal coverage than Landsat • Landsat (with higher spatial resolution) was then used to pinpoint irrigated areas in wet and dry season for each year NDVI Landsat 5TM, May 21, 2007

14. Landsat images used for irrigated area delineation The map dates with bold numbers are not from the simulated year, but the previous year as there was no map in that year available to capture the agriculture peak

15. Evapotranspiration • Crop transpiration and soil evaporation are assumed to occur in vegetated and non-vegetated area. • ET is the actual evapotranspiration (m/d), • kc crop coefficient (-) • ETO is the potential evapotranspiration rate (m/d). Transpiratory (Kt) and Evaporative (Ke) Fractions of Consumptive Use Source FMP1 user manual (Schmidet al., 2006)

16. Surface runoff and Net Recharge • Rn = Net Recharge • I= applied irrigation • CIR= crop irrigation requirements • P= precipitation • ETp = actual evaporation and transpiration from precipitation • IESWI= fraction of excess irrigation that becomes surface runoff • IESWP= fraction of excess precipitation that becomes surface runoff

17. Precipitation – Runoff modeling system (PRMS)-Rainfall- Runoff Hydrological response units (HRUs) for Sand River catchment Conceptual diagram of the PRMS model

18. PRMS-calibration and validation • The PRMS model was calibrated using observed streamflow (A7H010) for period 2004-2012, and validated using streamflow data 2013-2015 • The Nash-Sutcliff Efficiency (NSE) between the observed and simulated streamflow during the calibration and validation period were 0.72 and 0.53, respectively. Observed and simulated monthly streamflow hydrographs for the Sand catchment

19. Hydrogeological model calibration • The model was calibrated using observed water level data from 10 monitoring wells measured from 2008 till 2012and validated [2013-2015]. • The transient model was set up with a monthly stress period (72 stress periods) and weekly time step • The calibration was performed automatically, using PEST code • The MAE and RMSE between the observed and simulated water levels for the calibration period were 2.10 and 3.09 m respectively.

20. Hydrogeological model calibration Composite-scaled parameter sensitivities We used pilot point calibration for SS2 and assumed spatially uniform for others.

21. Results Mean annual water budget for hydrologic year [2008-2015] (mm/a) Head distribution FEB 2015

22. Water balance • Recharge rate varies considerably between wet and dry years. • Estimated recharge rates during the simulation period ranges from 3.7- 99.6 mm/a. • The mean annual recharge is 40.1 ± 37.6 mm/a. • Recharge as percentage of annual rainfall ranges from 1.1-18.9%, with mean of 9.3 ± 5.9%. • Evapotranspiration from groundwater is a significant component of the groundwater budget of the study area. • Mean annual evapotranspiration from groundwater is 7.7 ± 0.6 mm/a. • The mean annual simulated irrigation pumping for the period 2008-2015 was 6.3 mm/a. The mean annual irrigation licenced volume (aggregated for all licensed farmers) for the same period obtained from DWS was 11.3 mm/a. Almost twice of the simulated.

23. Recharge and actual ET simulated by the PRMS compare reasonably well with MODFLOW-OWHM results.

24. Previous studies recharge estimates Source: Vegter 1995 SADC : 2 - 20 mm/a Vegter (1995): 8 mm/a du Toit (2001): 2-15% MAP Holland (2009): 0.5% MAP Source: SADC recharge map

25. Summary and conclusions • The results of the transient simulation indicate that groundwater recharge is a small component of the water balance (8.9 ± 6.1% of annual rainfall), while associated with high temporal variability, partly due to variability in rainfall. • Estimated recharge rates compared well with estimates from the PRMS, but showed significant variability compared to previous estimates • With regard to the licensing, farmers are abstracting less than their allotted amount. • The model can be used in the future to assess the effect of water management scenarios such as improving irrigation efficiency, the effect of change in land use and climate change.

26. Sources of uncertainties • Estimation of cropping area and their temporal and spatial variability using NDVI. • Lack of observation well data across spatial and temporal scales for model calibration and validation. • Lack of short-term, episodic event representation, due to monthly time scale used in the model. • Lack of streamflow and groundwater level data close to the river, which would enable estimation of focused recharge from the river. • Lack of soil moisture storage change in MODFLOW-OWHM • Local scale heterogeneities that cannot be simulated using EPM approach e.g. the role of enhanced fracture zones associated to dike intrusions. • Lack of unsaturated zone process, direct recharge without land is assumed

27. Acknowledgments GroFutures - the Groundwater Futures in Sub-Saharan Africa project, funded by the Natural Environment Research Council (NERC) and the Economic and Social Research Council (ESRC) (UPGro No. NE/M008932/1) The CGIAR Research Program on Water, Land and Ecosystems (WLE) GRECHLIM project, funded by USAID-Southern Africa, and National Research Foundation (NRF), South Africa.

28. Thank you!!