- Technische Universität Berlin, Institute of Landscape Architecture and Environmental Planning, Artificial Intelligence and Land Use Change, Germany (marie-christin.eckert@outlook.de)
One major challenge in reliable groundwater level forecasting is to correctly account for the amount and rate of precipitation percolating through the unsaturated zone prior to reaching the aquifer. Especially under a changing climate already impacting weather and climate extremes globally, increased frequency of heatwaves, heavy precipitation, and drought periods will have significant impact on recharge patterns through soil hydraulic properties and unsaturated zone dynamics. However, as soon as groundwater predictions concern long-term environmental changes, extrapolations beyond the short-term often lack to fully account for increased frequency of extreme events under climate change. Consequently, estimates and forecasts overlook the actual impacts of weather extremes, particularly imprinting themselves in changes in the hydraulic connection between groundwater and soil surface.
We used weekly groundwater level data (1990 – 2024) from over a hundred measuring wells, well distributed over the federal state of Brandenburg, Germany, to train a deep neural network, that is able to predict groundwater level development under the impacts of climate change. To account for the soil hydraulic properties, we included soil moisture from different depths as a proxy for the amount and timing of water percolating through the vadose zone.
We show that purely climatic inputs, such as air temperature and precipitation are not sufficient to explain regional groundwater level development, as suggested by previous studies. Instead, including soil moisture turns out be the factor with the highest impact (feature importance) on the entire regional model, increasing the explained variance for most sites, while being able to reduce the model error constantly (RSME). Our findings demonstrate that future predictions of groundwater level can be enhanced by integrating the effects of climate on soil moisture into predictive models.
How to cite: Eckert, M.-C. and Rudolph, A.: The Impact of Soil Moisture on Groundwater Level Forecasting Using Deep Neural Networks: Evidence from Brandenburg, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2148, https://doi.org/10.5194/egusphere-egu25-2148, 2025.
