Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basin
Study Region: The Kwando (Cuando) River and the western headwaters of the Zambezi River, which are data-scarce basins of southern Africa. Study Focus: A comparative analysis of the performance of two fundamentally different hydrological modelling approaches (a conceptual model and a theory guided ma...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581823001696 |
| _version_ | 1797779193901088768 |
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| author | D.A. Hughes L. Read M. Jeuland E. Kapangaziwiri M. Elkurdy D. Lambl E. Hale J.J. Opperman |
| author_facet | D.A. Hughes L. Read M. Jeuland E. Kapangaziwiri M. Elkurdy D. Lambl E. Hale J.J. Opperman |
| author_sort | D.A. Hughes |
| collection | DOAJ |
| description | Study Region: The Kwando (Cuando) River and the western headwaters of the Zambezi River, which are data-scarce basins of southern Africa. Study Focus: A comparative analysis of the performance of two fundamentally different hydrological modelling approaches (a conceptual model and a theory guided machine learning model) in a data-sparse region. New Hydrological Insights for the Region: The machine learning model (HydroForecast) generally performs better – in terms of statistical fit between simulated and observed flows – than the conceptual model (Pitman). For the Kwando River, the conceptual model explicitly simulates the expected attenuation effects of a large floodplain, while the machine learning model represents this and other processes implicitly. The two models quantify the Kwando sub-basin flow contributions differently, with the conceptual model calibrated manually to align with the available qualitative information that suggests that the majority of the runoff is generated in the upstream sub-basin and then attenuated in the downstream floodplain. Generally, this work offers insight into how the two very different models can simulate historical flows in a large basin when streamflow observations and the forcing rainfall data are limited and of unknown quality, and suggests that a machine learning model better leverages information from multiple training parameters to reproduce the measured streamflows. |
| first_indexed | 2024-03-12T23:27:12Z |
| format | Article |
| id | doaj.art-6af071e242644b4fa22a1ef46e8459c4 |
| institution | Directory Open Access Journal |
| issn | 2214-5818 |
| language | English |
| last_indexed | 2024-03-12T23:27:12Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj.art-6af071e242644b4fa22a1ef46e8459c42023-07-16T04:18:49ZengElsevierJournal of Hydrology: Regional Studies2214-58182023-08-0148101482Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basinD.A. Hughes0L. Read1M. Jeuland2E. Kapangaziwiri3M. Elkurdy4D. Lambl5E. Hale6J.J. Opperman7Institute for Water Research, Rhodes University, South Africa; Corresponding author.Upstream Tech, Natel Energy Inc., CA, USASanford School of Public Policy and Duke Global Health Institute, Duke University, Durham, NC, USACouncil for Scientific and Industrial Research, South AfricaUpstream Tech, Natel Energy Inc., CA, USAUpstream Tech, Natel Energy Inc., CA, USAUpstream Tech, Natel Energy Inc., CA, USAGlobal Science Team, World Wildlife Fund, Washington, DC, USAStudy Region: The Kwando (Cuando) River and the western headwaters of the Zambezi River, which are data-scarce basins of southern Africa. Study Focus: A comparative analysis of the performance of two fundamentally different hydrological modelling approaches (a conceptual model and a theory guided machine learning model) in a data-sparse region. New Hydrological Insights for the Region: The machine learning model (HydroForecast) generally performs better – in terms of statistical fit between simulated and observed flows – than the conceptual model (Pitman). For the Kwando River, the conceptual model explicitly simulates the expected attenuation effects of a large floodplain, while the machine learning model represents this and other processes implicitly. The two models quantify the Kwando sub-basin flow contributions differently, with the conceptual model calibrated manually to align with the available qualitative information that suggests that the majority of the runoff is generated in the upstream sub-basin and then attenuated in the downstream floodplain. Generally, this work offers insight into how the two very different models can simulate historical flows in a large basin when streamflow observations and the forcing rainfall data are limited and of unknown quality, and suggests that a machine learning model better leverages information from multiple training parameters to reproduce the measured streamflows.http://www.sciencedirect.com/science/article/pii/S2214581823001696Hydrological modellingConceptual modelsMachine learningData scarce areas |
| spellingShingle | D.A. Hughes L. Read M. Jeuland E. Kapangaziwiri M. Elkurdy D. Lambl E. Hale J.J. Opperman Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basin Journal of Hydrology: Regional Studies Hydrological modelling Conceptual models Machine learning Data scarce areas |
| title | Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basin |
| title_full | Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basin |
| title_fullStr | Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basin |
| title_full_unstemmed | Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basin |
| title_short | Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basin |
| title_sort | insights from a comparison of two hydrological modelling approaches in the kwando cuando river and the western tributaries of the zambezi river basin |
| topic | Hydrological modelling Conceptual models Machine learning Data scarce areas |
| url | http://www.sciencedirect.com/science/article/pii/S2214581823001696 |
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