The use of CHIRPS satellite rainfall estimates for Pitman hydrological modelling in South Africa

Abstract

The Pitman model is widely used in South Africa for hydrological modelling and water resource management. For the model to assist with ongoing water management, it needs to use the most recent observed rainfall data, which has proved challenging over the past decade due to data scarcity. This research aimed at developing a CHIRPS (Climate Hazards Group Infrared Precipitation)–based Pitman model framework that instead uses satellite-derived rainfall estimates for the simulation of stream flows. The framework was developed and tested in Catchments G, B, V, and L, as case study catchments representative of the diverse hydroclimatic regions of South Africa. CHIRPS estimates (1981–2019), downloaded at a quaternary catchment scale for the study catchments, demonstrated a generally strong monthly correlation (R² > 0.7) with the WR2012 rainfall data. The satellite rainfall data (CHIRPS) were adjusted to correspond to the WR2012 rainfall data, and the Pitman model was set up and calibrated for the period 1981–2009, using the satellite rainfall data. Validation was done for the period 2010–2019. Calibration and validation were performed for 351 quaternary catchments in evaluating the suitability of using satellite data in modelling hydrological catchment responses. The simulated CHIRPS-based flows illustrated good similarity (∆ ≤ 4%) to observed flows. Further, a goodness-of-fit assessment of CHIRPS-based flows using 8 hydrological indices at a ±15% threshold of acceptable error was performed. The results demonstrated 78%, 73%, and 80% suitability of simulated CHIRPS-based flows for Catchments B, V and G, respectively. Catchment L had ‘suspect’ results, with indices illustrating inadequate correspondence between observed and CHIRPS-based flows. Based on satisfactory performance of the developed CHIRPS-based Pitman model framework, complementary application of CHIRPS rainfall estimates with the declining available observed rainfall data for the simulation of observed flows in data-scarce South African catchments is recommended.