Understanding groundwater droughts using detrended historical meteorological data and long-term groundwater modelling
Groundwater is a vital resource for various water users in the Netherlands. However, due to a changing climate, increasing water demand and changes in the water system, the country is increasingly exposed to groundwater droughts. Water managers use various indicators and statistics to identify groun...
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Cambridge University Press
2024-01-01
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| Series: | Netherlands Journal of Geosciences |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S0016774624000222/type/journal_article |
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| author | Wout A. Schutten Michiel Pezij Rick J. Hogeboom U. Nicole Jungermann Denie C.M. Augustijn |
| author_facet | Wout A. Schutten Michiel Pezij Rick J. Hogeboom U. Nicole Jungermann Denie C.M. Augustijn |
| author_sort | Wout A. Schutten |
| collection | DOAJ |
| description | Groundwater is a vital resource for various water users in the Netherlands. However, due to a changing climate, increasing water demand and changes in the water system, the country is increasingly exposed to groundwater droughts. Water managers use various indicators and statistics to identify groundwater droughts. These indicators characterise the drought for example in terms of intensity, duration and probability of occurrence. Often, these indicators require information on long-term average groundwater conditions and extreme situations that can occur over long periods. However, the availability of long-term groundwater observations of more than ten years in length is limited. Particularly, extreme groundwater drought events are ill-described and subject to large uncertainty in their characterisation. This study explores a novel method for obtaining long-term phreatic groundwater levels by combining a data-driven time series model using transfer function-noise modelling with detrended historical meteorological time series representing the current climate. The method is applied to an area in the Netherlands to generate groundwater levels for the period 1910–2022. Our results reveal differences in the characterisation of groundwater droughts when the extended groundwater time series are compared with observations of a limited duration (eight years). Using the 2018 summer drought event as an example, we find that the probability of this groundwater drought occurring is approximately once every twelve years, based on the eight-year observation period. However, this probability reduces to a once every 24-year event when using historically generated groundwater time series to characterise the groundwater drought. We conclude that characterising droughts with the extended groundwater time series based on historical meteorological data can provide a more comprehensive insight into the intensity and frequency of groundwater droughts, as well as the probability of occurrence of current groundwater levels. Hence, the proposed method supports water managers in establishing return period-based criteria for measures, such as deciding when to implement irrigation bans. |
| format | Article |
| id | doaj-art-f7c93fffca854f67ab0d30dafadb85b3 |
| institution | Kabale University |
| issn | 0016-7746 1573-9708 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Netherlands Journal of Geosciences |
| spelling | doaj-art-f7c93fffca854f67ab0d30dafadb85b32024-12-05T08:09:45ZengCambridge University PressNetherlands Journal of Geosciences0016-77461573-97082024-01-0110310.1017/njg.2024.22Understanding groundwater droughts using detrended historical meteorological data and long-term groundwater modellingWout A. Schutten0https://orcid.org/0009-0002-7058-4606Michiel Pezij1Rick J. Hogeboom2U. Nicole Jungermann3Denie C.M. Augustijn4https://orcid.org/0000-0001-5841-5506University of Twente, Enschede, the Netherlands Royal Haskoning DHV, Amersfoort, the NetherlandsHKV Lijn in Water, Lelystad, the NetherlandsUniversity of Twente, Enschede, the Netherlands Water Footprint Network, Enschede, the NetherlandsHKV Lijn in Water, Lelystad, the NetherlandsUniversity of Twente, Enschede, the NetherlandsGroundwater is a vital resource for various water users in the Netherlands. However, due to a changing climate, increasing water demand and changes in the water system, the country is increasingly exposed to groundwater droughts. Water managers use various indicators and statistics to identify groundwater droughts. These indicators characterise the drought for example in terms of intensity, duration and probability of occurrence. Often, these indicators require information on long-term average groundwater conditions and extreme situations that can occur over long periods. However, the availability of long-term groundwater observations of more than ten years in length is limited. Particularly, extreme groundwater drought events are ill-described and subject to large uncertainty in their characterisation. This study explores a novel method for obtaining long-term phreatic groundwater levels by combining a data-driven time series model using transfer function-noise modelling with detrended historical meteorological time series representing the current climate. The method is applied to an area in the Netherlands to generate groundwater levels for the period 1910–2022. Our results reveal differences in the characterisation of groundwater droughts when the extended groundwater time series are compared with observations of a limited duration (eight years). Using the 2018 summer drought event as an example, we find that the probability of this groundwater drought occurring is approximately once every twelve years, based on the eight-year observation period. However, this probability reduces to a once every 24-year event when using historically generated groundwater time series to characterise the groundwater drought. We conclude that characterising droughts with the extended groundwater time series based on historical meteorological data can provide a more comprehensive insight into the intensity and frequency of groundwater droughts, as well as the probability of occurrence of current groundwater levels. Hence, the proposed method supports water managers in establishing return period-based criteria for measures, such as deciding when to implement irrigation bans.https://www.cambridge.org/core/product/identifier/S0016774624000222/type/journal_articleData-driven modellinggroundwater statisticsPastastransfer function-noise modellingwater resource management |
| spellingShingle | Wout A. Schutten Michiel Pezij Rick J. Hogeboom U. Nicole Jungermann Denie C.M. Augustijn Understanding groundwater droughts using detrended historical meteorological data and long-term groundwater modelling Netherlands Journal of Geosciences Data-driven modelling groundwater statistics Pastas transfer function-noise modelling water resource management |
| title | Understanding groundwater droughts using detrended historical meteorological data and long-term groundwater modelling |
| title_full | Understanding groundwater droughts using detrended historical meteorological data and long-term groundwater modelling |
| title_fullStr | Understanding groundwater droughts using detrended historical meteorological data and long-term groundwater modelling |
| title_full_unstemmed | Understanding groundwater droughts using detrended historical meteorological data and long-term groundwater modelling |
| title_short | Understanding groundwater droughts using detrended historical meteorological data and long-term groundwater modelling |
| title_sort | understanding groundwater droughts using detrended historical meteorological data and long term groundwater modelling |
| topic | Data-driven modelling groundwater statistics Pastas transfer function-noise modelling water resource management |
| url | https://www.cambridge.org/core/product/identifier/S0016774624000222/type/journal_article |
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