Strategies for smarter catchment hydrology models: incorporating scaling and better process representation
Abstract Hydrological models have proliferated in the past several decades prompting debates on the virtues and shortcomings of various modelling approaches. Rather than critiquing individual models or modelling approaches, the objective here is to address the critical issues of scaling and hydrolog...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-06-01
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| Series: | Geoscience Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40562-021-00193-9 |
| _version_ | 1818639888545742848 |
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| author | Roy C. Sidle |
| author_facet | Roy C. Sidle |
| author_sort | Roy C. Sidle |
| collection | DOAJ |
| description | Abstract Hydrological models have proliferated in the past several decades prompting debates on the virtues and shortcomings of various modelling approaches. Rather than critiquing individual models or modelling approaches, the objective here is to address the critical issues of scaling and hydrological process representation in various types of models with suggestions for improving these attributes in a parsimonious manner that captures and explains their functionality as simply as possible. This discussion focuses mostly on conceptual and physical/process-based models where understanding the internal catchment processes and hydrologic pathways is important. Such hydrological models can be improved by using data from advanced remote sensing (both spatial and temporal) and derivatives, applications of machine learning, flexible structures, and informing models through nested catchment studies in which internal catchment processes are elucidated. Incorporating concepts of hydrological connectivity into flexible model structures is a promising approach for improving flow path representation. Also important is consideration of the scale dependency of hydrological parameters to avoid scale mismatch between measured and modelled parameters. Examples are presented from remote high-elevation regions where water sources and pathways differ from temperate and tropical environments where more attention has been focused. The challenge of incorporating spatially and temporally variable water inputs, hydrologically pathways, climate, and land use into hydrological models requires modellers to collaborate with catchment hydrologists to include important processes at relevant scales—i.e. develop smarter hydrological models. |
| first_indexed | 2024-12-16T23:02:32Z |
| format | Article |
| id | doaj.art-ebeaea0ad5824842aa87bac12c47283a |
| institution | Directory Open Access Journal |
| issn | 2196-4092 |
| language | English |
| last_indexed | 2024-12-16T23:02:32Z |
| publishDate | 2021-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Geoscience Letters |
| spelling | doaj.art-ebeaea0ad5824842aa87bac12c47283a2022-12-21T22:12:40ZengSpringerOpenGeoscience Letters2196-40922021-06-018111410.1186/s40562-021-00193-9Strategies for smarter catchment hydrology models: incorporating scaling and better process representationRoy C. Sidle0Mountain Societies Research Institute, University of Central AsiaAbstract Hydrological models have proliferated in the past several decades prompting debates on the virtues and shortcomings of various modelling approaches. Rather than critiquing individual models or modelling approaches, the objective here is to address the critical issues of scaling and hydrological process representation in various types of models with suggestions for improving these attributes in a parsimonious manner that captures and explains their functionality as simply as possible. This discussion focuses mostly on conceptual and physical/process-based models where understanding the internal catchment processes and hydrologic pathways is important. Such hydrological models can be improved by using data from advanced remote sensing (both spatial and temporal) and derivatives, applications of machine learning, flexible structures, and informing models through nested catchment studies in which internal catchment processes are elucidated. Incorporating concepts of hydrological connectivity into flexible model structures is a promising approach for improving flow path representation. Also important is consideration of the scale dependency of hydrological parameters to avoid scale mismatch between measured and modelled parameters. Examples are presented from remote high-elevation regions where water sources and pathways differ from temperate and tropical environments where more attention has been focused. The challenge of incorporating spatially and temporally variable water inputs, hydrologically pathways, climate, and land use into hydrological models requires modellers to collaborate with catchment hydrologists to include important processes at relevant scales—i.e. develop smarter hydrological models.https://doi.org/10.1186/s40562-021-00193-9Hydrological processesRemote sensingCatchment modelsHydrological connectivitySpatial–temporal scalingFlexible model structure |
| spellingShingle | Roy C. Sidle Strategies for smarter catchment hydrology models: incorporating scaling and better process representation Geoscience Letters Hydrological processes Remote sensing Catchment models Hydrological connectivity Spatial–temporal scaling Flexible model structure |
| title | Strategies for smarter catchment hydrology models: incorporating scaling and better process representation |
| title_full | Strategies for smarter catchment hydrology models: incorporating scaling and better process representation |
| title_fullStr | Strategies for smarter catchment hydrology models: incorporating scaling and better process representation |
| title_full_unstemmed | Strategies for smarter catchment hydrology models: incorporating scaling and better process representation |
| title_short | Strategies for smarter catchment hydrology models: incorporating scaling and better process representation |
| title_sort | strategies for smarter catchment hydrology models incorporating scaling and better process representation |
| topic | Hydrological processes Remote sensing Catchment models Hydrological connectivity Spatial–temporal scaling Flexible model structure |
| url | https://doi.org/10.1186/s40562-021-00193-9 |
| work_keys_str_mv | AT roycsidle strategiesforsmartercatchmenthydrologymodelsincorporatingscalingandbetterprocessrepresentation |