Performance of merged multi-satellite precipitation data products based on the ensemble model output statistic (EMOS):A case study in the middle Yellow River
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
The error in the precipitation estimation will bring great uncertainty to hydrological process simulation and climate change prediction research.A series of remote sensing precipitation products have provided the possibility to accurately estimate largescale precipitation.However,the remote sensing products from different sources are quite inconsistent.It is urgent to obtain highresolution,highprecision precipitation data sets through multisource data fusion technology,with the purpose of reducing the error of single remotesensing precipitation products and improve the accuracy of precipitation estimation.The middle reaches of the Yellow River is located in the semiarid and semihumid transition zone of China,which is a crucial area for the ecological environment restoration projects.Precipitation is the main water resources for this region,while an accurate precipitation estimation is of great singificance to the sustainable development of the regional ecological environment and the stability of economic life. Based on the background,the middle reaches of the Yellow River are chosed as the research area,which is extremely sensitive to the climate change and human activities.The methods of ensemble model output statistical (EMOS),quantile mapping (QM),and simple mean to the four productions (MME),while the indexes of four basic statistical indicators of correlation coefficient,relative deviation,root mean square error,and KlingGupta coefficient,as well as three category indexes of detection rate (POD),false alarm rate (FAR),and critical success index (CSI),to quantitatively evaluate the performance of four precipitation products (CHIRPS,CMORPH,PERSIANNCDR and TMPA 3B42),and then compare and analyze the performance of different data fusion data sets. The results show that:(1) The fusion data set of EMOS can significantly improve the index of bias,root mean square error,and KGE,with the values of -3.4%,13.1 mm,γKGE=0.233,respectively,compared with MME and individual product.The stations with better performance of QM were mainly located in the southern region,while the better performance for EMOS was observed in the eastern region.(2) Both the QM and EMOS methods have significantly improved the performance of POD,with the average values reaching 0.44 and 0.38,respectively,but the spatial distribution POD calucated from EMOS data set is more concentrated.The critical success index (CSI) of QM and EMOS methods reached 0.30 and 0.28,respectively,increasing approximately 12% compared with MME.The QM method did not significantly improve the POD,but the POD is doubled with a value of 0.33 by the EMOS method. In summary,the data fusion results obtained by the EMOS method have significant advantages over other methods,and the performance improvements are spatially different.The precipitation data set obtained by the EMOS method significantly improves the detection ability for moderate and heavy rain,indicating better performance for detecting the highintensity precipitation events.The precipitation data set fused with EMOS can well provide data support for the study of the relationship between extreme precipitation and sediment transport in typical ecological restoration areas in the middle reaches of the Yellow River,and provide a scientific basis for the rapid restoration of vegetation on precipitation and other climate feedback.
Keywords:
Project Supported:
Clc Number:
Mobile website
® 2024 All Rights Reserved
冀公网安备13010502002656号
冀公网安备13010502002656号