A hybrid data-driven approach to analyze the drivers of lake level dynamics Article Swipe
Márk Somogyvári
,
Dieter Scherer
,
Frederik Bart
,
Ute Fehrenbach
,
Akpona Okujeni
,
Tobias Krueger
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2023-2111
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2023-2111
Lakes are directly exposed to climate variations, as their recharge processes are driven by precipitation and evapotranspiration, and indirectly via groundwater trends, changing ecosystems and changing water use. In this study, we present a downward model development approach that uses models of increasing complexity to identify and quantify the dependence of lake level variations on climatic and other factors. The presented methodology uses high-resolution gridded weather data inputs that were obtained from dynamically downscaled ERA5 reanalysis data. Previously missing fluxes and previously unknown turning points in the system behavior are identified via a water balance model. The detailed lake level response to weather events is analyzed by calibrating data-driven models over different segments of the data timeseries. Changes in lake level dynamics are then inferred from the parameters and simulations of these models. The methodology is developed and presented on the example of the Groß Glienicker Lake, a groundwater-fed lake in eastern Germany, that has been experiencing increasing water loss in the last half century. We show that lake dynamics were mainly controlled by climatic variations in this period, and we identified two different phases with systematic differences in behavior. The increasing water loss during the last decade, however, cannot be accounted for by climate change. Our analysis suggests that this alteration is caused by the combination of regional groundwater decline and vegetation growth in the catchment area, with some additional impact from changes in the local rainwater infrastructure.
Related Topics
Concepts
Evapotranspiration
Groundwater recharge
Environmental science
Water balance
Precipitation
Climate change
Rainwater harvesting
Vegetation (pathology)
Groundwater
Hydrology (agriculture)
Water level
Climatology
Drainage basin
Physical geography
Meteorology
Geography
Ecology
Geology
Medicine
Biology
Cartography
Pathology
Aquifer
Geotechnical engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-2023-2111
- OA Status
- gold
- Cited By
- 1
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389088237
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389088237Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/egusphere-2023-2111Digital Object Identifier
- Title
-
A hybrid data-driven approach to analyze the drivers of lake level dynamicsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-11-28Full publication date if available
- Authors
-
Márk Somogyvári, Dieter Scherer, Frederik Bart, Ute Fehrenbach, Akpona Okujeni, Tobias KruegerList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-2023-2111Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/egusphere-2023-2111Direct OA link when available
- Concepts
-
Evapotranspiration, Groundwater recharge, Environmental science, Water balance, Precipitation, Climate change, Rainwater harvesting, Vegetation (pathology), Groundwater, Hydrology (agriculture), Water level, Climatology, Drainage basin, Physical geography, Meteorology, Geography, Ecology, Geology, Medicine, Biology, Cartography, Pathology, Aquifer, Geotechnical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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