Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models
Climate change has a large influence on the occurrence of extreme hydrological events. However, reliable estimates of future extreme event probabilities, especially when needed locally, require very long time series with hydrological models, which is often not possible due to computational constrain...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Water |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frwa.2023.1108108/full |
| _version_ | 1797861873612226560 |
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| author | Sandra M. Hauswirth Karin van der Wiel Marc F. P. Bierkens Marc F. P. Bierkens Vincent Beijk Niko Wanders |
| author_facet | Sandra M. Hauswirth Karin van der Wiel Marc F. P. Bierkens Marc F. P. Bierkens Vincent Beijk Niko Wanders |
| author_sort | Sandra M. Hauswirth |
| collection | DOAJ |
| description | Climate change has a large influence on the occurrence of extreme hydrological events. However, reliable estimates of future extreme event probabilities, especially when needed locally, require very long time series with hydrological models, which is often not possible due to computational constraints. In this study we take advantage of two recent developments that allow for more detailed and local estimates of future hydrological extremes. New large climate ensembles (LE) now provide more insight on the occurrence of hydrological extremes as they offer order of magnitude more realizations of future weather. At the same time recent developments in Machine Learning (ML) in hydrology create great opportunities to study current and upcoming problems in a new way, including and combining large amounts of data. In this study, we combined LE together with a local, observation based ML model framework with the goal to see if and how these aspects can be combined and to simulate, assess and produce estimates of hydrological extremes under different warming levels for local scales. For this, first a new post-processing approach was developed that allowed us to use LE simulation data for local applications. The simulation results of discharge extreme events under different warming levels were assessed in terms of frequency, duration and intensity and number of events at national, regional and local scales. Clear seasonal cycles with increased low flow frequency were observed for summer and autumn months as well as increased high flow periods for early spring. For both extreme events, the 3C warmer climate scenario showed the highest percentages. Regional differences were seen in terms of shifts and range. These trends were further refined into location specific results. The shifts and trends observed between the different scenarios were due to a change in climate variability. In this study we show that by combining the wealth of information from LE and the speed and local relevance of ML models we can advance the state-of-the-art when it comes to modeling hydrological extremes under different climate change scenarios for national, regional and local scale assessments providing relevant information for water management in terms of long term planning. |
| first_indexed | 2024-04-09T22:10:21Z |
| format | Article |
| id | doaj.art-2c758c78272c44dd9a7a95a0e65a6568 |
| institution | Directory Open Access Journal |
| issn | 2624-9375 |
| language | English |
| last_indexed | 2024-04-09T22:10:21Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Water |
| spelling | doaj.art-2c758c78272c44dd9a7a95a0e65a65682023-03-23T11:00:42ZengFrontiers Media S.A.Frontiers in Water2624-93752023-03-01510.3389/frwa.2023.11081081108108Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning modelsSandra M. Hauswirth0Karin van der Wiel1Marc F. P. Bierkens2Marc F. P. Bierkens3Vincent Beijk4Niko Wanders5Department of Physical Geography, Utrecht University, Utrecht, NetherlandsRoyal Netherlands Meteorological Institute (KNMI), De Bilt, NetherlandsDepartment of Physical Geography, Utrecht University, Utrecht, NetherlandsUnit Subsurface and Groundwater Systems, Deltares, Utrecht, NetherlandsWater, Verkeer en Leefomgeving, Rijkswaterstaat, Utrecht, NetherlandsDepartment of Physical Geography, Utrecht University, Utrecht, NetherlandsClimate change has a large influence on the occurrence of extreme hydrological events. However, reliable estimates of future extreme event probabilities, especially when needed locally, require very long time series with hydrological models, which is often not possible due to computational constraints. In this study we take advantage of two recent developments that allow for more detailed and local estimates of future hydrological extremes. New large climate ensembles (LE) now provide more insight on the occurrence of hydrological extremes as they offer order of magnitude more realizations of future weather. At the same time recent developments in Machine Learning (ML) in hydrology create great opportunities to study current and upcoming problems in a new way, including and combining large amounts of data. In this study, we combined LE together with a local, observation based ML model framework with the goal to see if and how these aspects can be combined and to simulate, assess and produce estimates of hydrological extremes under different warming levels for local scales. For this, first a new post-processing approach was developed that allowed us to use LE simulation data for local applications. The simulation results of discharge extreme events under different warming levels were assessed in terms of frequency, duration and intensity and number of events at national, regional and local scales. Clear seasonal cycles with increased low flow frequency were observed for summer and autumn months as well as increased high flow periods for early spring. For both extreme events, the 3C warmer climate scenario showed the highest percentages. Regional differences were seen in terms of shifts and range. These trends were further refined into location specific results. The shifts and trends observed between the different scenarios were due to a change in climate variability. In this study we show that by combining the wealth of information from LE and the speed and local relevance of ML models we can advance the state-of-the-art when it comes to modeling hydrological extremes under different climate change scenarios for national, regional and local scale assessments providing relevant information for water management in terms of long term planning.https://www.frontiersin.org/articles/10.3389/frwa.2023.1108108/fullmachine learninglarge climate ensemblesextreme eventsclimate changedroughtsfloods |
| spellingShingle | Sandra M. Hauswirth Karin van der Wiel Marc F. P. Bierkens Marc F. P. Bierkens Vincent Beijk Niko Wanders Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models Frontiers in Water machine learning large climate ensembles extreme events climate change droughts floods |
| title | Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models |
| title_full | Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models |
| title_fullStr | Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models |
| title_full_unstemmed | Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models |
| title_short | Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models |
| title_sort | simulating hydrological extremes for different warming levels combining large scale climate ensembles with local observation based machine learning models |
| topic | machine learning large climate ensembles extreme events climate change droughts floods |
| url | https://www.frontiersin.org/articles/10.3389/frwa.2023.1108108/full |
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