A Statistical Vertically Mixed Runoff Model for Regions Featured by Complex Runoff Generation Process
Hydrological models for regions characterized by complex runoff generation process been suffer from a great weakness. A delicate hydrological balance triggered by prolonged wet or dry underlying condition and variable extreme rainfall makes the rainfall-runoff process difficult to simulate with trad...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/9/2324 |
| _version_ | 1797557018971602944 |
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| author | Peng Lin Pengfei Shi Tao Yang Chong-Yu Xu Zhenya Li Xiaoyan Wang |
| author_facet | Peng Lin Pengfei Shi Tao Yang Chong-Yu Xu Zhenya Li Xiaoyan Wang |
| author_sort | Peng Lin |
| collection | DOAJ |
| description | Hydrological models for regions characterized by complex runoff generation process been suffer from a great weakness. A delicate hydrological balance triggered by prolonged wet or dry underlying condition and variable extreme rainfall makes the rainfall-runoff process difficult to simulate with traditional models. To this end, this study develops a novel vertically mixed model for complex runoff estimation that considers both the runoff generation in excess of infiltration at soil surface and that on excess of storage capacity at subsurface. Different from traditional models, the model is first coupled through a statistical approach proposed in this study, which considers the spatial heterogeneity of water transport and runoff generation. The model has the advantage of distributed model to describe spatial heterogeneity and the merits of lumped conceptual model to conveniently and accurately forecast flood. The model is tested through comparison with other four models in three catchments in China. The Nash–Sutcliffe efficiency coefficient and the ratio of qualified results increase obviously. Results show that the model performs well in simulating various floods, providing a beneficial means to simulate floods in regions with complex runoff generation process. |
| first_indexed | 2024-03-10T17:11:17Z |
| format | Article |
| id | doaj.art-a28bb2805fff47d4962670b7b2346daa |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T17:11:17Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-a28bb2805fff47d4962670b7b2346daa2023-11-20T10:39:04ZengMDPI AGWater2073-44412020-08-01129232410.3390/w12092324A Statistical Vertically Mixed Runoff Model for Regions Featured by Complex Runoff Generation ProcessPeng Lin0Pengfei Shi1Tao Yang2Chong-Yu Xu3Zhenya Li4Xiaoyan Wang5State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaDepartment of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, NorwayState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaHydrological models for regions characterized by complex runoff generation process been suffer from a great weakness. A delicate hydrological balance triggered by prolonged wet or dry underlying condition and variable extreme rainfall makes the rainfall-runoff process difficult to simulate with traditional models. To this end, this study develops a novel vertically mixed model for complex runoff estimation that considers both the runoff generation in excess of infiltration at soil surface and that on excess of storage capacity at subsurface. Different from traditional models, the model is first coupled through a statistical approach proposed in this study, which considers the spatial heterogeneity of water transport and runoff generation. The model has the advantage of distributed model to describe spatial heterogeneity and the merits of lumped conceptual model to conveniently and accurately forecast flood. The model is tested through comparison with other four models in three catchments in China. The Nash–Sutcliffe efficiency coefficient and the ratio of qualified results increase obviously. Results show that the model performs well in simulating various floods, providing a beneficial means to simulate floods in regions with complex runoff generation process.https://www.mdpi.com/2073-4441/12/9/2324regions characterized by complex runoff generation processhydrological modelvertically mixed structureprobabilistic approachreal time flood forecasting |
| spellingShingle | Peng Lin Pengfei Shi Tao Yang Chong-Yu Xu Zhenya Li Xiaoyan Wang A Statistical Vertically Mixed Runoff Model for Regions Featured by Complex Runoff Generation Process Water regions characterized by complex runoff generation process hydrological model vertically mixed structure probabilistic approach real time flood forecasting |
| title | A Statistical Vertically Mixed Runoff Model for Regions Featured by Complex Runoff Generation Process |
| title_full | A Statistical Vertically Mixed Runoff Model for Regions Featured by Complex Runoff Generation Process |
| title_fullStr | A Statistical Vertically Mixed Runoff Model for Regions Featured by Complex Runoff Generation Process |
| title_full_unstemmed | A Statistical Vertically Mixed Runoff Model for Regions Featured by Complex Runoff Generation Process |
| title_short | A Statistical Vertically Mixed Runoff Model for Regions Featured by Complex Runoff Generation Process |
| title_sort | statistical vertically mixed runoff model for regions featured by complex runoff generation process |
| topic | regions characterized by complex runoff generation process hydrological model vertically mixed structure probabilistic approach real time flood forecasting |
| url | https://www.mdpi.com/2073-4441/12/9/2324 |
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