Can Dominant Runoff Generation Mechanisms Be Disentangled Through Hypothesis Testing? Insights From Integrated Hydrological‐Hydrodynamic Modeling
Abstract Identifying flood‐inducing processes remains a challenge in catchment hydrology due to the complex runoff dynamics, particularly in semi‐arid regions where surface and subsurface mechanisms alternatively drive streamflow across seasons. Tracer data can help identify hydrograph sources, but...
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Wiley
2025-04-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR039394 |
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| author | Pasquale Perrini Vito Iacobellis Andrea Gioia Luis Cea Hubert H. G. Savenije Fabrizio Fenicia |
| author_facet | Pasquale Perrini Vito Iacobellis Andrea Gioia Luis Cea Hubert H. G. Savenije Fabrizio Fenicia |
| author_sort | Pasquale Perrini |
| collection | DOAJ |
| description | Abstract Identifying flood‐inducing processes remains a challenge in catchment hydrology due to the complex runoff dynamics, particularly in semi‐arid regions where surface and subsurface mechanisms alternatively drive streamflow across seasons. Tracer data can help identify hydrograph sources, but they are often unavailable or lack sufficient temporal resolution. To aid process identification at the event‐scale, we developed an integrated hydrological‐hydrodynamic framework and compared multiple model hypotheses informed by hydrological signatures. We systematically tested these hypotheses through falsification, meta‐evaluation, spatial validation, and posterior diagnostics, using the semi‐arid Salsola nested catchment in southern Italy as case study. While all model structures performed well on common calibration metrics, differences emerged in spatial transferability tests and alternative diagnostic assessments. Some models, despite strong performance, exhibited inconsistent representations of internal runoff mechanisms, indicating that they achieved good results for the wrong reasons. Furthermore, the choice of routing schemes significantly influenced high‐peak estimations and overall model performance, particularly when Horton‐type overland flow was considered. This underscores the need to treat routing methods as a key component in event‐scale modeling. Our findings reveal that during consecutive storm events in the study catchment, surface processes dominate the initial stages, whereas subsurface processes become more influential in later events, providing valuable insights that may be applicable to similar semi‐arid regions. Overall, we emphasize the importance of hypothesis testing in runoff process identification, which can compensate for the absence of hydrochemical data for hydrograph separation. Additionally, our results highlight the value of a landscape‐based modeling approach for distinguishing alternative runoff generation processes. |
| format | Article |
| id | doaj-art-fa87a7fa727d4fc28c02e1c41b199b80 |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-fa87a7fa727d4fc28c02e1c41b199b802025-08-20T02:09:26ZengWileyWater Resources Research0043-13971944-79732025-04-01614n/an/a10.1029/2024WR039394Can Dominant Runoff Generation Mechanisms Be Disentangled Through Hypothesis Testing? Insights From Integrated Hydrological‐Hydrodynamic ModelingPasquale Perrini0Vito Iacobellis1Andrea Gioia2Luis Cea3Hubert H. G. Savenije4Fabrizio Fenicia5Department of Soil Plant and Food Science University of Bari Aldo Moro Bari ItalyDepartment of Civil, Environmental, Land, Building Engineering and Chemistry Polytechnic University of Bari Bari ItalyDepartment of Civil, Environmental, Land, Building Engineering and Chemistry Polytechnic University of Bari Bari ItalyWater and Environmental Engineering Group Center for Technological Innovation in Construction and Civil Engineering (CITEEC) University of A Coruña A Coruña SpainWater Resources Section Faculty of Civil Engineering and Geosciences Delft University of Technology CN Delft The NetherlandsEawag: Swiss Federal Institute of Aquatic Science and Technology Dübendorf SwitzerlandAbstract Identifying flood‐inducing processes remains a challenge in catchment hydrology due to the complex runoff dynamics, particularly in semi‐arid regions where surface and subsurface mechanisms alternatively drive streamflow across seasons. Tracer data can help identify hydrograph sources, but they are often unavailable or lack sufficient temporal resolution. To aid process identification at the event‐scale, we developed an integrated hydrological‐hydrodynamic framework and compared multiple model hypotheses informed by hydrological signatures. We systematically tested these hypotheses through falsification, meta‐evaluation, spatial validation, and posterior diagnostics, using the semi‐arid Salsola nested catchment in southern Italy as case study. While all model structures performed well on common calibration metrics, differences emerged in spatial transferability tests and alternative diagnostic assessments. Some models, despite strong performance, exhibited inconsistent representations of internal runoff mechanisms, indicating that they achieved good results for the wrong reasons. Furthermore, the choice of routing schemes significantly influenced high‐peak estimations and overall model performance, particularly when Horton‐type overland flow was considered. This underscores the need to treat routing methods as a key component in event‐scale modeling. Our findings reveal that during consecutive storm events in the study catchment, surface processes dominate the initial stages, whereas subsurface processes become more influential in later events, providing valuable insights that may be applicable to similar semi‐arid regions. Overall, we emphasize the importance of hypothesis testing in runoff process identification, which can compensate for the absence of hydrochemical data for hydrograph separation. Additionally, our results highlight the value of a landscape‐based modeling approach for distinguishing alternative runoff generation processes.https://doi.org/10.1029/2024WR039394runoff generation mechanismshydrological‐hydrodynamic modelinghypothesis testingflexible model structurelandscape‐based model development |
| spellingShingle | Pasquale Perrini Vito Iacobellis Andrea Gioia Luis Cea Hubert H. G. Savenije Fabrizio Fenicia Can Dominant Runoff Generation Mechanisms Be Disentangled Through Hypothesis Testing? Insights From Integrated Hydrological‐Hydrodynamic Modeling Water Resources Research runoff generation mechanisms hydrological‐hydrodynamic modeling hypothesis testing flexible model structure landscape‐based model development |
| title | Can Dominant Runoff Generation Mechanisms Be Disentangled Through Hypothesis Testing? Insights From Integrated Hydrological‐Hydrodynamic Modeling |
| title_full | Can Dominant Runoff Generation Mechanisms Be Disentangled Through Hypothesis Testing? Insights From Integrated Hydrological‐Hydrodynamic Modeling |
| title_fullStr | Can Dominant Runoff Generation Mechanisms Be Disentangled Through Hypothesis Testing? Insights From Integrated Hydrological‐Hydrodynamic Modeling |
| title_full_unstemmed | Can Dominant Runoff Generation Mechanisms Be Disentangled Through Hypothesis Testing? Insights From Integrated Hydrological‐Hydrodynamic Modeling |
| title_short | Can Dominant Runoff Generation Mechanisms Be Disentangled Through Hypothesis Testing? Insights From Integrated Hydrological‐Hydrodynamic Modeling |
| title_sort | can dominant runoff generation mechanisms be disentangled through hypothesis testing insights from integrated hydrological hydrodynamic modeling |
| topic | runoff generation mechanisms hydrological‐hydrodynamic modeling hypothesis testing flexible model structure landscape‐based model development |
| url | https://doi.org/10.1029/2024WR039394 |
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