Seasonal Hydrological Forecasting in the Berg Water Management Area of South Africa
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1 Seasonal Hydrological Forecasting in the Berg Water Management Area of South Africa Trevor LUMSDEN and Roland SCHULZE University of KwaZulu-Natal, South Africa
2 OUTLINE Introduction Objectives Study Area Methodology Results Conclusions Future work
3 INTRODUCTION South Africa (SA) experiences high interannual variability in rainfall Added threat of climate change Western Cape province (location of study area) frequent droughts IPCC assessments consistently suggest reduction in rainfall
4 Kilometres Study Area
5 INTRODUCTION Study area characterised by: competition between urban and agricultural sectors for water water shortages => need for demand management Seasonal hydrological forecasting as a management tool
6 OBJECTIVE To develop seasonal hydrological forecasts for the Berg Water Management Area
7 APPLICATION Assist in decision-making related to: water demand management in the urban and agricultural sectors meeting compulsory environmental flow requirements
8 Kilometres STUDY AREA
9 Inter-catchment transfers
10 Land use includes intensive irrigated agriculture (e.g. wine/table grapes, deciduous fruits),...
11 the demand of the City of Cape Town and other urban areas.
12 plantation forestry.
13 METHODOLOGY Ghile & Schulze (2008) developed an agrohydrological forecasting tool:
14 Forecasted Rainfall/Temp Short, Medium, Long Term Observed Rainfall Forecast Process - Spatial downscaling - Temporal downscaling - Format converting - Extracting Satellite, Radar Reporting Sta. Daily Synoptic Stations Updates Monthly GIS Crop Models e.g. CERES-Maize Q/QCDB Update & Self- Correction Output Process AgHy. Forecasts - Soil moisture, Streamflow - Irrigation demand, Reservoir status - Crop yield status
15 METHODOLOGY Applied the forecasting tool to forecast seasonal runoff in the study area First season forecasted: Aug-Sep-Oct 2009 Utilized South African Weather Service (SAWS) seasonal climate forecasts Selected the ACRU hydrological model assumed natural vegetation and no water resources infrastructure
16 METHODOLOGY
17 Seasonal Forecasting
18 Seasonal Forecasting
19 RESULTS The 20 daily runoff sequences (ensemble members) were summed to obtain seasonal totals These members were categorized as being either below, near or above normal in terms of the long term (50 year) simulated runoff record (determined in a separate exercise) Calculate probability of below, near and above normal runoff
20 RESULTS
21 RESULTS
22 RESULTS
23 RESULTS
24 RESULTS: Comparison with SAWS seasonal streamflow forecast South African Weather Service Probability of Above Normal Streamflow: Aug-Sep-Oct 2009
25 RESULTS: Comparison with SAWS seasonal streamflow forecast South African Weather Service Probability of Above Normal Streamflow: Aug-Sep-Oct 2009
26 How good are the forecasts? The Mgeni Catchment
27 CONCLUSIONS Relatively simple methodology that produces a probabilistic forecast representing uncertainty in seasonal climate Need to assess forecast quality over a number of seasons A work in progress many enhancements planned
28 FUTURE WORK Use actual observed weather data in the leadup to generating a forecast (instead of average conditions) Generate hindcasts & assess their quality against simulations performed using observed data Introduce the influence of land use, water resources infrastructure and abstractions produce dam level forecasts
29 ACKNOWLEDGEMENTS SA Water Research Commission and the International Development Research Centre for funding the research The Riversymposium organisers & AusAID for sponsoring attendance at the Riversymposium University of KwaZulu-Natal for top-up funding Mr D Matten for some data processing tasks
30 THANK YOU
31 Content Slide
32 .and plant species found nowhere else in the world (fynbos).
33 ECHAM, CCA Lead time 6 Months 3 Months Streamflow Reservoir status Irrigation demand Soil moisture Crop yield Monthly 1 Month ACRU Update Gauges Radar Satellite Merged Daily NCEP C-CAM UM 14 Days 4 Days 2 Days Crop Model Phenological development Crop yield 12 Hours In Future Radar, NWP Satellite, AWS Hourly 6 Hours 1 Hour Sub-daily Hydrological Model Flood
34 The ACRU Model
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