Yan Liu , Yong Chang , Ingo Haag , Julia Krumm , Visakh Sivaprasad , Dirk Aigner , Harry Vereecken , Harrie-Jan Hendricks Franssen
{"title":"遥感土壤水分的历史记忆可增强德国山洪暴发的源头流域建模能力","authors":"Yan Liu , Yong Chang , Ingo Haag , Julia Krumm , Visakh Sivaprasad , Dirk Aigner , Harry Vereecken , Harrie-Jan Hendricks Franssen","doi":"10.1016/j.jhydrol.2024.132395","DOIUrl":null,"url":null,"abstract":"<div><div>The wetness precondition of a catchment affects available soil water storage capacity and infiltration rate, thus influences flash flood generation. Remotely sensed (RS) soil moisture (SM) can provide valuable information on catchment wetness, but typically only represents the top 5 cm of the land surface. However, hydrological models for flash flood simulation need to consider deeper layers to calculate the total soil water storage. Therefore, a key challenge is to link RS SM to total soil water storage and assimilate RS SM into flash flood models to correctly describe initial catchment wetness. In this study, we developed an approach to combine present and antecedent RS SM to infer present soil water storage based on four regression models. The inferred soil water storage from SMAP (soil moisture active passive) SM was assimilated into the operational LARSIM (Large Area Runoff Simulation Model) hydrological model. We tested this new approach with 12 events in the headwater catchments Körsch, Adenauer Bach and Fischbach in Germany. Results show that random forest regression performs the best among the four regression models. The BIC (Bayesian Information Criterion) score suggests that regressions considering antecedent RS SM can well infer soil water storage, resulting in R<sup>2</sup> of 0.85, 0.94 and 0.93 for the Körsch, Adenauer Bach and Fischbach catchments, respectively. Compared to the open loop (without data assimilation) simulations, our approach enhanced the general performance of event simulations with average KGE increases of 0.09, 0.24 and 0.33 for the Körsch, Adenauer Bach and Fischbach, respectively; and the mean error in the 12 simulated event peaks is reduced 15 %. Moreover, the simulation uncertainty is reduced, too. The transferability of the proposed approach to other RS products is also discussed. Although assimilating RS SM can enhance flash flood modeling, it is primarily affected by the uncertainty in precipitation. In the future, the proposed approach should be tested with more catchments and events to verify its general validity.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132395"},"PeriodicalIF":5.9000,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Historical memory in remotely sensed soil moisture can enhance flash flood modeling for headwater catchments in Germany\",\"authors\":\"Yan Liu , Yong Chang , Ingo Haag , Julia Krumm , Visakh Sivaprasad , Dirk Aigner , Harry Vereecken , Harrie-Jan Hendricks Franssen\",\"doi\":\"10.1016/j.jhydrol.2024.132395\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The wetness precondition of a catchment affects available soil water storage capacity and infiltration rate, thus influences flash flood generation. Remotely sensed (RS) soil moisture (SM) can provide valuable information on catchment wetness, but typically only represents the top 5 cm of the land surface. However, hydrological models for flash flood simulation need to consider deeper layers to calculate the total soil water storage. Therefore, a key challenge is to link RS SM to total soil water storage and assimilate RS SM into flash flood models to correctly describe initial catchment wetness. In this study, we developed an approach to combine present and antecedent RS SM to infer present soil water storage based on four regression models. The inferred soil water storage from SMAP (soil moisture active passive) SM was assimilated into the operational LARSIM (Large Area Runoff Simulation Model) hydrological model. We tested this new approach with 12 events in the headwater catchments Körsch, Adenauer Bach and Fischbach in Germany. Results show that random forest regression performs the best among the four regression models. The BIC (Bayesian Information Criterion) score suggests that regressions considering antecedent RS SM can well infer soil water storage, resulting in R<sup>2</sup> of 0.85, 0.94 and 0.93 for the Körsch, Adenauer Bach and Fischbach catchments, respectively. Compared to the open loop (without data assimilation) simulations, our approach enhanced the general performance of event simulations with average KGE increases of 0.09, 0.24 and 0.33 for the Körsch, Adenauer Bach and Fischbach, respectively; and the mean error in the 12 simulated event peaks is reduced 15 %. Moreover, the simulation uncertainty is reduced, too. The transferability of the proposed approach to other RS products is also discussed. Although assimilating RS SM can enhance flash flood modeling, it is primarily affected by the uncertainty in precipitation. In the future, the proposed approach should be tested with more catchments and events to verify its general validity.</div></div>\",\"PeriodicalId\":362,\"journal\":{\"name\":\"Journal of Hydrology\",\"volume\":\"648 \",\"pages\":\"Article 132395\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hydrology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022169424017918\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrology","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022169424017918","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Historical memory in remotely sensed soil moisture can enhance flash flood modeling for headwater catchments in Germany
The wetness precondition of a catchment affects available soil water storage capacity and infiltration rate, thus influences flash flood generation. Remotely sensed (RS) soil moisture (SM) can provide valuable information on catchment wetness, but typically only represents the top 5 cm of the land surface. However, hydrological models for flash flood simulation need to consider deeper layers to calculate the total soil water storage. Therefore, a key challenge is to link RS SM to total soil water storage and assimilate RS SM into flash flood models to correctly describe initial catchment wetness. In this study, we developed an approach to combine present and antecedent RS SM to infer present soil water storage based on four regression models. The inferred soil water storage from SMAP (soil moisture active passive) SM was assimilated into the operational LARSIM (Large Area Runoff Simulation Model) hydrological model. We tested this new approach with 12 events in the headwater catchments Körsch, Adenauer Bach and Fischbach in Germany. Results show that random forest regression performs the best among the four regression models. The BIC (Bayesian Information Criterion) score suggests that regressions considering antecedent RS SM can well infer soil water storage, resulting in R2 of 0.85, 0.94 and 0.93 for the Körsch, Adenauer Bach and Fischbach catchments, respectively. Compared to the open loop (without data assimilation) simulations, our approach enhanced the general performance of event simulations with average KGE increases of 0.09, 0.24 and 0.33 for the Körsch, Adenauer Bach and Fischbach, respectively; and the mean error in the 12 simulated event peaks is reduced 15 %. Moreover, the simulation uncertainty is reduced, too. The transferability of the proposed approach to other RS products is also discussed. Although assimilating RS SM can enhance flash flood modeling, it is primarily affected by the uncertainty in precipitation. In the future, the proposed approach should be tested with more catchments and events to verify its general validity.
期刊介绍:
The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.