Abstract
Abstract. Accurate and consistent satellite-based precipitation estimates blended with rain gauge data are important for regional precipitation monitoring and hydrological applications, especially in regions with limited rain gauges. However, the existing fusion precipitation estimates often have large uncertainties over mountainous areas with complex topography and sparse rain gauges, and most of the existing data blending algorithms are not good at removing the day-by-day errors. Therefore, the development of effective methods for high-accuracy precipitation estimates over complex terrain and at a daily scale is of vital importance for mountainous hydrological applications. This study aims to offer a novel approach for blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP; daily, 0.05∘) satellite-derived precipitation developed by UC Santa Barbara over the Jinsha River basin from 1994 to 2014. This method is called the Wuhan University Satellite and Gauge precipitation Collaborated Correction (WHU-SGCC). The results show that the WHU-SGCC method is effective for liquid precipitation bias adjustments from points to surfaces as evaluated by multiple error statistics and from different perspectives. Compared with CHIRP and CHIRP with station data (CHIRPS), the precipitation adjusted by the WHU-SGCC method has greater accuracy, with overall average improvements of the Pearson correlation coefficient (PCC) by 0.0082–0.2232 and 0.0612–0.3243, respectively, and decreases in the root mean square error (RMSE) by 0.0922–0.65 and 0.2249–2.9525 mm, respectively. In addition, the Nash–Sutcliffe efficiency coefficient (NSE) of the WHU-SGCC provides more substantial improvements than CHIRP and CHIRPS, which reached 0.2836, 0.2944, and 0.1853 in the spring, autumn, and winter. Daily accuracy evaluations indicate that the WHU-SGCC method has the best ability to reduce precipitation bias, with average reductions of 21.68 % and 31.44 % compared to CHIRP and CHIRPS, respectively. Moreover, the accuracy of the spatial distribution of the precipitation estimates derived from the WHU-SGCC method is related to the complexity of the topography. The validation also verifies that the proposed approach is effective at detecting major precipitation events within the Jinsha River basin. In spite of the correction, the uncertainties in the seasonal precipitation forecasts in the summer and winter are still large, which might be due to the homogenization attenuating the extreme rain event estimates. However, the WHU-SGCC approach may serve as a promising tool to monitor daily precipitation over the Jinsha River basin, which contains complicated mountainous terrain with sparse rain gauge data, based on the spatial correlation and the historical precipitation characteristics. The daily precipitation estimations at the 0.05∘ resolution over the Jinsha River basin during all four seasons from 1990 to 2014, derived from WHU-SGCC, are available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.org/10.1594/PANGAEA.905376, Shen et al., 2019).
Highlights
Accurate and consistent estimates of precipitation are vital for hydrological modelling, flood forecasting, and climatological studies in support of better planning and decision making (Agutu et al, 2017; Cattani et al, 2018; Roy et al, 2017)
As for the results of the WHU-SGCC, the assessments of probability of detection (POD) and critical success index (CSI) are the best in the summer, followed by the autumn, spring, and winter, which are related to the seasonal rainfall pattern of more rain in the summer and less in the winter
A case study of the Jinsha River basin was conducted to verify the effectiveness of the WHUSGCC approach during all four seasons from 1990 to 2014, and the adjusted precipitation estimates were compared to Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP with station data (CHIRPS)
Summary
Accurate and consistent estimates of precipitation are vital for hydrological modelling, flood forecasting, and climatological studies in support of better planning and decision making (Agutu et al, 2017; Cattani et al, 2018; Roy et al, 2017). The sparse distribution and point measurements limit the accurate estimation of spatially gridded rainfall (Martens et al, 2013). Due to the sparseness and uneven spatial distribution of rain gauges and the high proportion of missing data, satellitederived precipitation data are an attractive supplement offering the advantage of plentiful information with high spatiotemporal resolution over widespread regions, over oceans, high-elevation mountainous regions, and other remote regions where gauge networks are difficult to deploy. Satellite estimates are susceptible to systematic biases that can influence hydrological modelling, and the retrieval algorithms are relatively insensitive to light rainfall events, especially in complex terrain, resulting in underestimations of the magnitudes of precipitation events (Behrangi et al, 2014; Thiemig et al, 2013; Yang et al, 2017). Inaccurate satellite-based precipitation estimates will lead to unreliable assessments of risk and reliability (AghaKouchak et al, 2011)
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