A regional data driven model for simulating phreatic ground water levels in Flanders

Abstract

<p>The summers of 2017 to 2020 were characterized by exceptional dry spells throughout Europe. Climate models show that such periods of drought could occur more frequently and become even more extreme in the future. The recent periods of intense droughts lead to significant ecological, economic and even societal damages in Flanders (Belgium). During these summers, receding groundwater levels were observed throughout Flanders that reached historical low levels. To monitor low ground water levels and to support a proactive drought management, the Flemish government developed an operational ground water indicator. This indicator gives an overview of the current phreatic ground water levels combined with a prediction for the next month for a selected number of phreatic wells. To increase the spatial resolution of the indicator, we developed a novel data driven regional ground water model for phreatic aquifers.</p><p>The ML model combines a gradient boosting decision tree model (CatBoost) with a Long Short Term Memory (LSTM) network. CatBoost is used to model the average ground water depth at each location. This value is passed to the LSTM network that predicts the temporal evolution of the groundwater at each location around its average. The training dataset for the CatBoost model contains the average groundwater depth of 5.673 wells spread across Flanders and a large set of explanatory variables related to soil texture, distance to a drainage, geology, topography, meteorology and land use. The model performance is evaluated using cross-validation which showed the model generalizes well with a mean absolute error of 69cm. The most important explanatory variables for the model are the thickness of the phreatic aquifer, the vertical distance to closest drain, the topographic index and the precipitation surplus.</p><p>The training dataset for the LSTM model contains 408 wells that have sufficiently long and reliable observations for training. The input data to the LSTM consists of rainfall and evapotranspiration up to 10 years prior to each observation, combined with the same explanatory variables as the CatBoost model. A single regional LSTM model is trained on all 408 wells simultaneously. The resulting model is accurate with a median RMSE of 20cm for the validation data, outperforming the currently used SWAP models [1]. The ML model is however less performant in simulating the higher ground water depths during summer and shows a consistent bias towards lower ground water depths during long dry spells.</p><p>[1] Kroes, J.G., J.C. van Dam, R.P. Bartholomeus, P. Groenendijk, M. Heinen, R.F.A. Hendriks, H.M. Mulder, I. Supit, P.E.V. van Walsum, 2017. SWAP version 4; Theory description and user manual. Wageningen, Wageningen Environmental Research, Report 2780</p>