Changes in the long-term (1948–2016) rainfall and evapotranspiration over Mpologoma catchment were analysed using gridded (0.25° × 0.25°) Princeton Global Forcing data. Trend and variability were assessed using a nonparametric approach based on the cumulative sum of the difference between exceedance and nonexceedance counts of data. Annual and March-May (MAM) rainfall displayed a positive trend (p<0.05), whereas October-December (OND) and June-September rainfall exhibited negative trends with p>0.05 and p<0.05, respectively. Positive subtrends in rainfall occurred in the 1950s and from the mid-2000s till 2016; however, negative subtrends existed between 1960 till around 2005. Seasonal evapotranspiration exhibited a positive trend (p>0.05). For the entire period (1948–2016), there was no negative subtrend in the OND and MAM evapotranspiration. Rainfall and evapotranspiration trends and oscillatory variation in subtrends over multidecadal time scales indicate the need for careful planning of predictive adaptation to the impacts of climate variability on environmental applications which depend on water balance in the Mpologoma catchment. It is recommended that future studies quantify possible contributions of human factors on the variability of rainfall and evapotranspiration. Furthermore, climate change impacts on rainfall and evapotranspiration across the study area should be investigated.

This study analysed long-term (1948-2016) changes in gridded (0.25°× 0.25°) Princeton Global Forcing (PGF) precipitation and potential evapotranspiration (PET) data over Lokok and Lokere catchments. PGF-based and station datasets were compared. Trend and variability were analysed using a nonparametric technique based on the cumulative sum of the difference between exceedance and non-exceedance counts of data.

Daily River Malaba flows recorded from 1999 to 2016 were modelled using seven lumped conceptual rainfall–runoff models including AWBM, SACRAMENTO, TANK, IHACRES, SIMHYD, SMAR and HMSV. Optimal parameters of each model were obtained using an automatic calibration strategy. Mismatches between observed and modelled flows were assessed using a total of nine “goodness-of-fit” metrics. Capacity of the models to reproduce historical hydrological extremes was assessed through comparison of amplitude–duration–frequency (ADF) relationships or curves constructed based on observed and modelled flow quantiles. Generally, most of the hydrological models performed better for high than low flows. ADF curves of both high and low flows for various return periods from 5 to 100 years were well reproduced by AWBM, SAC, TANK and HMSV. ADF curves for high and low flows were poorly reproduced by SIMHYD and SMAR, respectively. Overall, AWBM performed slightly better than other models if both high and low flows are to be considered simultaneously. The deviations of these models were larger for high than low return periods. It was found that the choice of a “goodness-of-fit” metric affects how model performance can be judged. Results from this study also show that when focusing on hydrological extremes, uncertainty due to the choice of a particular model should be taken into consideration. Insights from this study provide relevant information for planning of risk-based water resources applications.