Implementation and evaluation of the Pitman model in seasonal hydrological forecasting mode using the Kraai River catchment in Eastern Cape South Africa as a case study - Fikileni, Sesethu (Climate Systems Analysis Group, 2021)
Seasonal hydrologic extremes such as drought and floods have devastating impacts on human and natural systems (e.g. 2015-2017 Western Cape drought). Sentence has been reworded to: Therefore, the need for a reliable seasonal hydrologic forecast is significant and becoming even more urgent under future climate, as the assimilation of seasonal forecast information in decision making. Hence, SHF becomes part of the short and long-term climate change adaptation strategies in a range of contexts such as energy supply, water supply and management, rural-urban, agriculture, infrastructure and disaster preparedness and relief. This work deals with implementation and evaluation of the Pitman/Water Resources Simulation Model 2012 model (WR2012) in seasonal hydrological forecasting mode. The aim of the study is to improve the understanding of seasonal hydrological forecasting by evaluating the performance of a hydrological model (Pitman Model) in the seasonal forecast mode in Kraai River tertiary catchment (D13) as a case study and the objectives are: To determine steps to be undertaken to implement integration of Pitman in WR2012 configuration with climate forecast to generate seasonal hydrological forecast and to evaluate the performance of the model forced by climate model data in the simulation and forecast mode. Pitman model in the WR2012 version works with a specific rainfall dataset spanning the period of 1920-2009. Operationalizing the seasonal hydrological forecast with Pitman model requires, therefore, updating of the WR2012 rainfall so that it extends to-date. To achieve that, two datasets were evaluated: Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), which is a satellite-based gridded rainfall dataset, and rain gauge-based dataset from South African Weather Service (SAWS). The analyses revealed that CHIRPS rainfall data had better correlation and lower bias with respect to the WR2012 data when compared with SAWS rainfall data for the overlap period 1981-2009. The CHIRPS data showed no significant difference from the WR2012 in all the three rainfall zones of the Kraai River catchment. Therefore, CHIRPS data were used to extend the WR2012 data and were used as input to set up Pitman model/WR2012 in the seasonal hydrological forecasting mode. The Pitman/WR2012 model was forced with 10 ensemble seasonal climate forecast from Climate Forecast Systems v.2 which is downscaled using the Principal Components Regression (PCR) approach. The generated seasonal hydrological forecast focused on the summer season, in particular on the Dec-Jan-Feb (DJF) period, which is the rainy season in the catchment. The hydrological forecast showed skills more especially in Dec and Feb (assessed through ROC and RPSS forecast verification methods) with Jan having a poor skill. Importantly, the skill of streamflow forecast was better than that of rainfall forecast, which likely results from the influence of initial conditions of the hydrological model. In conclusion Pitman/WR2012 model can perform realistically when implemented in seasonal hydrological forecasts mode, and it is important that in that model, the model is run with near real time rainfall data in order to achieve good initial conditions. However, the results in terms of forecast skill are specific to the studied catchment and analysed forecast, and skill of forecast in any other catchment has to be investigated separately.