Abstract

Reliable climate data are essential for hydroclimatic assessment and water-resources management in data-scarce regions. This study evaluated the performance of the WorldClim 2.1 historical weather dataset (WC2.1– CRU-TS v4.09), downscaled and bias-corrected from CRU-TS v4.0 using WorldClim 2.1 climatology, against observed meteorological records within the semi-arid C5 Secondary Drainage Region (C5 SDR; comprising the Riet and Modder River catchments) in central South Africa for the period 1950–2023. Precipitation, maximum temperature (TMAX), and minimum temperature (TMIN) were assessed using statistical performance evaluation metrics, scatter and residual analyses, Innovative Trend Analysis (ITA), Rescaled Adjusted Partial Sums (RAPS), and extreme-event evaluation based on the 95th-percentile threshold. The results showed strong agreement between observed and gridded precipitation records, with correlation coefficients ranging (R) from 0.78 to 0.90 and Nash–Sutcliffe Efficiency (NSE) values between 0.61 and 0.90. Temperature datasets exhibited similarly good performance, with TMAX showing stronger agreement than TMIN. ITA and RAPS analyses demonstrated that the dataset successfully reproduced long-term climatic trends, hydroclimatic regime shifts, and interannual variability observed in station records. Performance varied spatially, with the strongest agreement occurring at lower-elevation stations and comparatively lower performance at stations influenced by localized convective rainfall and topographic variability. Extreme-event analysis revealed that although the dataset effectively reproduced the timing and occurrence of high-rainfall years (R2 = 0.974–0.997), it systematically underestimated the magnitude of extreme precipitation events, with percent bias values ranging from −5.5% to −21.0%. In contrast, extreme temperature events were reproduced with very high accuracy and minimal bias. Overall, the WC2.1– CRU-TS v4.09 dataset provides a reliable climatic baseline for hydroclimatic assessments in the C5 SDR. However, caution is required when applying the dataset to analyses sensitive to localized precipitation extremes. The results provide confidence in the use of this dataset for climate characterization, drought assessment, hydrological modeling, ecosystem service evaluation, and future climate-change impact investigations in data-scarce semi-arid environments.

IPC Classification

Keywords