Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study
Human activities have a profound influence on river discharge, hydrological extremes, and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simul...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2018
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| Online Access: | https://eprints.nottingham.ac.uk/50917/ |
| _version_ | 1848798368024756224 |
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| author | Veldkamp, Ted Isis Elize Zhao, Fang Ward, Philip J. Moel, Hans de Aerts, Jeroen C.J.H. Müller Schmied, Hannes Portmann, Felix T. Masaki, Yoshimitsu Pokhrel, Yadu Liu, Xingcai Satoh, Yusuke Gerten, Dieter Gosling, Simon N. Zaherpour, Jamal Wada, Y. |
| author_facet | Veldkamp, Ted Isis Elize Zhao, Fang Ward, Philip J. Moel, Hans de Aerts, Jeroen C.J.H. Müller Schmied, Hannes Portmann, Felix T. Masaki, Yoshimitsu Pokhrel, Yadu Liu, Xingcai Satoh, Yusuke Gerten, Dieter Gosling, Simon N. Zaherpour, Jamal Wada, Y. |
| author_sort | Veldkamp, Ted Isis Elize |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Human activities have a profound influence on river discharge, hydrological extremes, and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of the mean, high, and low flows. The analysis is performed for 471 gauging stations across the globe and for the period 1971-2010. We find that the inclusion of HIP improves the performance of GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across GHMs, although the level of improvement and reasons for improvement vary greatly by GHM. The inclusion of HIP leads to a significant decrease in the bias of long-term mean monthly discharge in 36-73% of the studied catchments, and an improvement in modelled hydrological variability in 31-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in simulated high-flows, it can lead to either increases or decreases in low-flows. This is due to the relative importance of the timing of return flows and reservoir operations and their associated uncertainties. Even with the inclusion of HIP, we find that model performance still not optimal. This highlights the need for further research linking the human management and hydrological domains, especially in those areas with a dominant human impact. The large variation in performance between GHMs, regions, and performance indicators, calls for a careful selection of GHMs, model components, and evaluation metrics in future model applications. |
| first_indexed | 2025-11-14T20:18:39Z |
| format | Article |
| id | nottingham-50917 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:18:39Z |
| publishDate | 2018 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-509172018-06-15T09:30:20Z https://eprints.nottingham.ac.uk/50917/ Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study Veldkamp, Ted Isis Elize Zhao, Fang Ward, Philip J. Moel, Hans de Aerts, Jeroen C.J.H. Müller Schmied, Hannes Portmann, Felix T. Masaki, Yoshimitsu Pokhrel, Yadu Liu, Xingcai Satoh, Yusuke Gerten, Dieter Gosling, Simon N. Zaherpour, Jamal Wada, Y. Human activities have a profound influence on river discharge, hydrological extremes, and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of the mean, high, and low flows. The analysis is performed for 471 gauging stations across the globe and for the period 1971-2010. We find that the inclusion of HIP improves the performance of GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across GHMs, although the level of improvement and reasons for improvement vary greatly by GHM. The inclusion of HIP leads to a significant decrease in the bias of long-term mean monthly discharge in 36-73% of the studied catchments, and an improvement in modelled hydrological variability in 31-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in simulated high-flows, it can lead to either increases or decreases in low-flows. This is due to the relative importance of the timing of return flows and reservoir operations and their associated uncertainties. Even with the inclusion of HIP, we find that model performance still not optimal. This highlights the need for further research linking the human management and hydrological domains, especially in those areas with a dominant human impact. The large variation in performance between GHMs, regions, and performance indicators, calls for a careful selection of GHMs, model components, and evaluation metrics in future model applications. IOP Publishing 2018-03-26 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/50917/1/Veldkamp_etal_2018_AAM.pdf Veldkamp, Ted Isis Elize, Zhao, Fang, Ward, Philip J., Moel, Hans de, Aerts, Jeroen C.J.H., Müller Schmied, Hannes, Portmann, Felix T., Masaki, Yoshimitsu, Pokhrel, Yadu, Liu, Xingcai, Satoh, Yusuke, Gerten, Dieter, Gosling, Simon N., Zaherpour, Jamal and Wada, Y. (2018) Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study. Environmental Research Letters, 13 (5). 055008/1-055008/16. ISSN 1748-9326 https://doi.org/10.1088/1748-9326/aab96f doi:10.1088/1748-9326/aab96f doi:10.1088/1748-9326/aab96f |
| spellingShingle | Veldkamp, Ted Isis Elize Zhao, Fang Ward, Philip J. Moel, Hans de Aerts, Jeroen C.J.H. Müller Schmied, Hannes Portmann, Felix T. Masaki, Yoshimitsu Pokhrel, Yadu Liu, Xingcai Satoh, Yusuke Gerten, Dieter Gosling, Simon N. Zaherpour, Jamal Wada, Y. Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title | Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title_full | Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title_fullStr | Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title_full_unstemmed | Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title_short | Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title_sort | human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| url | https://eprints.nottingham.ac.uk/50917/ https://eprints.nottingham.ac.uk/50917/ https://eprints.nottingham.ac.uk/50917/ |