At-site and Multisite Probabilistic Forecasting of Streamflow

Abstract

Streamflow forecasts are very useful for a variety of applications such as flood warning, reservoir operation and water resources planning and management, especially in countries like India where streamflow can be highly variable. Methods available for streamflow forecasting can be broadly classified as process-driven and data-driven methods. Forecasts always have uncertainty associated with them due to limitations in modelling complex processes in the hydrologic system, and factors such as scarcity of data and measurement errors. It is important to quantify the forecast uncertainty for making informed decisions. Hydrologic Ensemble Prediction Systems (HEPS), which use ensembles in process-driven approach for generating probabilistic forecasts to quantify uncertainty are gaining popularity in the world. However, there is dearth of studies on application of HEPS for forecasting streamflows in Indian rivers. Recently, United States (US) National Weather Service developed a HEPS called Hydrologic Ensemble Forecast Service (HEFS) to generate seamless probabilistic hydrologic forecasts from short to long lead times. The first objective of this thesis is to investigate the potential of HEFS in generating skilful streamflow forecasts for an Indian river, as there is no prior application of HEFS outside US. Tel river, which is one of the tributaries of Mahanadi river (which is frequently prone to floods) was chosen for case study. Forecasts of meteorological variables (precipitation and temperature) required as input to HEFS were obtained from Global Ensemble Forecast System (GEFS). The HEFS consists of three main components - (i) Meteorological Ensemble Forecast Processor (MEFP), (ii) Hydrologic Processor and (iii) Hydrologic Ensemble Postprocessor (EnsPost). MEFP accounts for meteorological uncertainty by generating bias corrected ensemble meteorological forecast which is subsequently propagated through the Hydrologic Processor initialised with basin conditions. The resulting hydrologic ensemble forecast is input to EnsPost to generate postprocessed hydrologic ensemble forecast which reflects the total forecast uncertainty accounting for both meteorological and hydrologic uncertainties. A lumped rainfall-runoff model called GR4J was used as the Hydrologic Processor. Verification of retrospective daily streamflow forecasts generated using HEFS for Tel river against corresponding observations indicated that the forecasts have fairly good skill at short lead times (1 to 3 days). The forecasts were found to have higher skill compared to climatological forecasts and forecasts generated by an ARIMA model. Statistical methods are widely used operationally for forecasting streamflow at coarser time scales such as seasonal. For some applications (e.g., coordinated operation of a system of reservoirs), contemporaneous streamflow forecasts may be required at many sites in a basin. Forecasts generated using separate statistical models for each site may not preserve spatial correlation structure between flows at different sites. The second objective of this thesis is to explore the potential of regularised Multivariate Multiple Linear Regression (MMLR) models in generating skilful multisite streamflow forecasts. Three regularisation methods namely ridge regression, lasso and MRCE (Multivariate Regression with Covariance Estimation) were considered. The potential of the regularised MMLR models was examined through a case study on seasonal streamflow forecasting in upper Colorado river basin of US. Performance of the models was compared with that of four other multisite forecasting methods based on (i) Schaake Shuffle, (ii) Principal Component Analysis, (iii) disaggregation and (iv) k-nearest neighbour resampling, which were available in literature. Considering both forecast skill and ability to preserve inter-site correlations, the method based on MMLR and ridge regression was found to perform better than the other methods considered.