Quantile-Based Hydrological Modelling
Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e., assumptions on the probability distribution of the hydrological model’s output are necess...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/23/3420 |
| _version_ | 1797507186146934784 |
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| author | Hristos Tyralis Georgia Papacharalampous |
| author_facet | Hristos Tyralis Georgia Papacharalampous |
| author_sort | Hristos Tyralis |
| collection | DOAJ |
| description | Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e., assumptions on the probability distribution of the hydrological model’s output are necessary). To alleviate possible limitations related to these specific attributes, in this work we propose the calibration of the hydrological model by using the quantile loss function. By following this methodological approach, one can directly simulate pre-specified quantiles of the predictive distribution of streamflow. As a proof of concept, we apply our method in the frameworks of three hydrological models to 511 river basins in the contiguous US. We illustrate the predictive quantiles and show how an honest assessment of the predictive performance of the hydrological models can be made by using proper scoring rules. We believe that our method can help towards advancing the field of hydrological uncertainty. |
| first_indexed | 2024-03-10T04:43:05Z |
| format | Article |
| id | doaj.art-feacc0d3c17741a0bdbf38f195cab12d |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T04:43:05Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-feacc0d3c17741a0bdbf38f195cab12d2023-11-23T03:15:19ZengMDPI AGWater2073-44412021-12-011323342010.3390/w13233420Quantile-Based Hydrological ModellingHristos Tyralis0Georgia Papacharalampous1Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, GreeceDepartment of Engineering, Roma Tre University, Via V. Volterra 62, 00146 Rome, ItalyPredictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e., assumptions on the probability distribution of the hydrological model’s output are necessary). To alleviate possible limitations related to these specific attributes, in this work we propose the calibration of the hydrological model by using the quantile loss function. By following this methodological approach, one can directly simulate pre-specified quantiles of the predictive distribution of streamflow. As a proof of concept, we apply our method in the frameworks of three hydrological models to 511 river basins in the contiguous US. We illustrate the predictive quantiles and show how an honest assessment of the predictive performance of the hydrological models can be made by using proper scoring rules. We believe that our method can help towards advancing the field of hydrological uncertainty.https://www.mdpi.com/2073-4441/13/23/3420big datacatchment modelshydrological modelshydrological uncertaintylarge-sample hydrologypoint predictions |
| spellingShingle | Hristos Tyralis Georgia Papacharalampous Quantile-Based Hydrological Modelling Water big data catchment models hydrological models hydrological uncertainty large-sample hydrology point predictions |
| title | Quantile-Based Hydrological Modelling |
| title_full | Quantile-Based Hydrological Modelling |
| title_fullStr | Quantile-Based Hydrological Modelling |
| title_full_unstemmed | Quantile-Based Hydrological Modelling |
| title_short | Quantile-Based Hydrological Modelling |
| title_sort | quantile based hydrological modelling |
| topic | big data catchment models hydrological models hydrological uncertainty large-sample hydrology point predictions |
| url | https://www.mdpi.com/2073-4441/13/23/3420 |
| work_keys_str_mv | AT hristostyralis quantilebasedhydrologicalmodelling AT georgiapapacharalampous quantilebasedhydrologicalmodelling |