Jk Kibii

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.

Long-term rainfall data with good spatial and temporal distribution is essential for all climate-related analyses. The availability of observed rainfall data has become increasingly problematic over the years due to a limited and deteriorating rainfall station network, occasioned by limited reporting and/or quality control of rainfall and, in some cases, closure of these stations. Remotely sensed satellite-based rainfall data sets offer an alternative source of information. In this study, daily and monthly rainfall data derived from Climate Hazards Group InfraRed Precipitation (CHIRPS) is compared with observed rainfall data from 46 stations evenly distributed across South Africa. Various metrics, based on a pairwise comparison between the observed and CHIRPS data, were applied to evaluate CHIRPS performance in the estimation of daily and monthly rainfall. The results show that CHIRPS data correlate well with observed monthly rainfall data for all stations used, having an average coefficient of determination of 0.6 and bias of 0.95. This study concludes that monthly CHIRPS data corresponds well, with good precision and relatively little bias when compared to observed monthly rainfall data, and can therefore be considered for use in conjunction with observed rainfall data where no or limited data is available in South Africa for hydrological analysis.

Water is life. It is an important element of the social and economic well-being of society. Kenya is a water-scarce country, ranked as 21st globally for the worst levels of water accessibility. The town of Eldoret is currently experiencing rapid population growth, resulting in ever-growing water demand. On the other hand, climate variability, land cover, and land use changes have altered the hydrologic response of the Kaptagat catchment, one of the major sources of water for Eldoret. This study uses the SWAT model in seeking to evaluate the impact of land use change and climate variability on the catchment yield, resulting in high variations in river flows and storage reservoir levels, and suggests possible mitigation measures to improve the yield. The model was customized for the study area, calibrated, and validated, and simulations were done to establish the changes in yield and river flow over time. This study observes that with time, land use changed due to increased settlement in the catchment, resulting in a decrease in forest cover (natural and planted) from approximately 37% in 1989 to 26% in 2019. Rainfall events also decreased but became more intense. The results of the changing land use and climate variability were changes in the catchment hydrologic response, occasioned by increased surface runoff and decreased baseflow and groundwater recharge, hence the high variations in water levels at the Elegirini and Two Rivers dams in the catchment during the dry and wet seasons, as modeled. The modeling of the catchment management scenarios indicates groundwater recharge increased by 17% and surface runoff decreased by 9%. Therefore, if the ongoing afforestation, reafforestation, and terracing practices by farmers (although small-scale) increasing vegetation cover in the catchment are adhered to, the catchment response regime will improve significantly with time, despite the increasing climatic variability.