Evaluation of typical remote-sensing precipitation products in hydrological simulation
We comprehensively evaluated various Remote Sensing Precipitation Estimates(RSPEs)to identify the ones that can better capture the precipitation pattern in the Weihe River Basin.Our findings can serve as an important scientific reference for the evaluation and management of water resources in the basin and provide favorable support for the implementation of environmental protection and high-quality development planning in the Yellow River basin.Based on the gridded precipitation data of CMA and the five popular RSPEs(including CHIRPS v2.0,CMORPH v1.0,PERSIANN-CDR,TRMM 3B42,and MSWEP v2.0),we comprehensively evaluated the basic skill of precipitation products by using the four statistical metrics,namely,Pearson correlation coefficient(CORR),bias,Root-Mean-Square Error(RMSE),and Kling-Gupta Efficient(KGE).We further used three categorical metrics,namely,Probability Of Detection(POD),False Alarm Ratio(FAR),and Critical Success Index(CSI).Then,the hydrological simulation skill of RSPEs was also assessed by the traditional lumped hydrological model of ABCD and nash efficiency coefficient(NSE).All five RMPEs can capture the spatial distribution of precipitation.Among them,MSWEP,based on multi-source weighted-ensemble precipitation,can better capture the spatial heterogeneous of precipitation with superior performance.The spatial distribution of PERSIANN smoothly performed and was underestimated in most areas,resulting in lower performance.At the interannual level,all RMPEs generally performed well in the upper Weihe River,and TRMM was excellent,followed by MSWEP,whereas PERSIANN performed poorly.MSWEP products performed well for basic statistical skills,with lower RMSE,higher CORR,and KGE.However,the CHIRPS and PERSIANN had poor basic statistical skills.For the categorical skill of precipitation,the PERSIANN exhibited good skill for light rain,followed by MSWEP.Additionally,all RMPEs performed better than the other three basins in the upstream of Weihe River.To detect moderate rain and heavy rain,the performance of the PERSIANN was degraded.The MSWEP product had a better POD for these two types of rainfall,but its FAR was also higher.In terms of hydrological simulation performance,the hydrological simulation performance of the TRMM was the best,indicating that the retrieval algorithm of RMPEs based on active microwave had a high hydrological application prospect,followed by the MSWEP and COMRPH.The poor performance was the infrared/near-infrared-based CHIRPS and PERSIANN products,whose retrieval algorithms required further improvement in the climate-sensitive transition region.TRMM products performed better in capturing the temporal and spatial patterns of precipitation.The multi-source integrated product of MSWEP was significantly better than the other four products.The predictability of each precipitation estimate to moderate and heavy rain was unsatisfactory and was especially poor for the latter one.Using TRMM precipitation as the input of ABCD model,the performance of simulating runoff was significantly better than other remote-sensing products in the four sub-basins of the Weihe River,followed by MSWEP and COMRPH.
remotely sensed precipitationWei river basinABCD modelperformance evaluationMSWEP
NETL
NSTL
万方数据
