Comparing the performance of high‐resolution global precipitation products across topographic and climatic gradients of Central Asia

Abstract

Accurate and reliable precipitation data with high spatial and temporal resolution are essential in studying climate variability, water resources management, and hydrological forecasting. A range of global precipitation data are available to this end, but how well these capture actual precipitation remains unknown, particularly for mountain regions where ground stations are sparse. We examined the performance of three global high‐resolution precipitation products for capturing precipitation over Central Asia, a hotspot of climate change, where reliable precipitation data are particularly scarce. Specifically, we evaluated MSWEP, CHIRPS, and GSMAP against independent gauging stations for the period 1985–2015. Our results show that MSWEP and CHIRPS outperformed GSMAP for wetter periods (i.e., winter and spring) and wetter locations (150–600 mm·year−1), lowlands, and mid‐altitudes (0–3,000 m), and regions dominated by winter and spring precipitation. MSWEP performed best in representing temporal precipitation dynamics and CHIRPS excelled in capturing the volume and distribution of precipitation. All precipitation products poorly estimated precipitation at higher elevations (>3,000 m), in drier areas (<150 mm), and in regions characterized by summer precipitation. All products accurately detected dry spells, but their performance decreased for wet spells with increasing precipitation intensity. In sum, we find that CHIRPS and MSWEP provide the most reliable high‐resolution precipitation estimates for Central Asia. However, the high spatial and temporal heterogeneity of the performance call for a careful selection of a suitable product for local applications considering the prevailing precipitation dynamics, climatic, and topographic conditions.

CA-discharge: Geo-Located Discharge Time Series for Mountainous Rivers in Central Asia

“…We thus have to rely on publicly available gridded data products. We calculate average annual precipitation for each basin as mean over 7 gridded precipitation products (P mean ): CHELSA v2.1 (P CHELSA ), WorldClim v21 32 , CHPclim 34 , CRU 49 , GPM IMERG 50 , CHIRPS 51 , and APHRODITE 52 which all have shown acceptable performance in Central Asia [53][54][55][56][57] . Depending on the basin, the difference in precipitation can be up to 1'000 mm/a from one precipitation product to another.…”

Integrating Open-Source Datasets to Analyze the Transboundary Water–Food–Energy–Climate Nexus in Central Asia

“…Several precipitation and temperature datasets covering Central Asia are available in the literature. We selected the products reported to perform well in Central Asia [54][55][56][57]. A thorough comparison of all precipitation products is unavailable but would be extremely helpful for practitioners.…”

Catchment characterization: current descriptors, knowledge gaps and future opportunities

“…Since catchment descriptors are derived from geospatial datasets and time series, they inherit the uncertainties of their source datasets that can be associated with the measurement or sensor errors or with insufficient resolution for deriving the descriptor of interest (e.g., using coarse digital elevation model to derive density of the stream network). Although such uncertainties are difficult to quantify, there is sufficient evidence, particularly from differences between various digital elevation models (Hawker et al, 2018, derived catchment areas (Addor et al, 2017 and datasets of hydrometeorological variables (Peña-Guerrero et al, 2022), that they might be considerable.…”