Scenario approach for the seasonal forecast of Kharif flows from the Upper Indus Basin
- 1Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Munich, Germany
- 2Department of Civil Engineering, Koblenz University of Applied Sciences, Koblenz, Germany
Abstract. Snow and glacial melt runoff are the major sources of water contribution from the high mountainous terrain of the Indus River upstream of the Tarbela reservoir. A reliable forecast of seasonal water availability for the Kharif cropping season (April–September) can pave the way towards better water management and a subsequent boost in the agro-economy of Pakistan. The use of degree-day models in conjunction with satellite-based remote-sensing data for the forecasting of seasonal snow and ice melt runoff has proved to be a suitable approach for data-scarce regions. In the present research, the Snowmelt Runoff Model (SRM) has not only been enhanced by incorporating the
glacier (G) component but also applied for the forecast of seasonal water availability from the Upper Indus Basin (UIB). Excel-based SRM+G takes account of separate degree-day factors for snow and glacier melt processes. All-year simulation runs with SRM+G for the period 2003–2014 result in an average flow component distribution of 53, 21, and 26 % for snow, glacier, and rain, respectively. The UIB has been divided into Upper and Lower parts because of the different climatic conditions in the Tibetan Plateau. The scenario approach for seasonal forecasting, which like the Ensemble Streamflow Prediction method uses historic meteorology as model forcings, has proven to be adequate for long-term water availability forecasts. The accuracy of the forecast with a mean absolute percentage error (MAPE) of 9.5 % could be slightly improved compared to two existing operational forecasts for the UIB, and the bias could be reduced to −2.0 %. However, the association between forecasts and observations as well as the skill in predicting extreme conditions is rather weak for all three models, which motivates further research on the selection of a subset of ensemble members according to forecasted seasonal anomalies.