Evaluation of typical remote-sensing precipitation products in hydrological simulation
- Vol. 28, Issue 2, Pages: 398-413(2024)
Published： 07 February 2024
DOI： 10.11834/jrs.20222012
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摘要
遥感降水具备大尺度、近实时、高精度等优点，现已被广泛应用于流域水资源评估和生态环境保护的研究。但遥感降水产品众多，性能差异较大，不同产品在特定流域的适用性需要进行综合评估。本研究以渭河流域的4个子流域为研究区，以国家气象局（CMA）逐日降水格点数据集为标准，借助ABCD水文模型，评估了5种典型的遥感降水产品（CHIRPS v2.0，CMORPH v1.0，PERSIANN-CDR，TRMM，MSWEP v2.0）对降水等级和水文过程模拟的性能。（1）在捕捉降水的时空变化格局方面，TRMM产品表现出较好的性能；（2）在基本统计指标和分类统计指标方面，多源集成产品MSWEP明显优于其他4种产品；但各遥感产品对中雨和大雨，尤其是大雨的预测效果欠佳；（3）在径流模拟方面，以TRMM降水作为输入时，ABCD模型模拟径流的效果明显优于其他遥感产品，其NSE在渭河上游、泾河上游、马莲河和北洛河分别达到了0.66、0.46、0.56和0.55。TRMM产品在捕捉降水空间格局和径流模拟方面优越，而MSWEP的统计性能较为优越。本文的研究结果，可为半干旱半湿润区域水文和气象等应用研究，在遥感降水数据源的选择方面提供科学参考依据，还可为黄河流域生态保护和高质量发展提供数据支撑。
Abstract
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.
关键词
遥感降水渭河流域ABCD模型性能评估MSWEP
Keywords
remotely sensed precipitationWei river basinABCD modelperformance evaluationMSWEP
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