Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

In this study, we used deep learning models with recurrent structure neural networks to simulate large-scale groundwater level (GWL) fluctuations in northern France. We developed a multi-station collective training for GWL simulations, using both “dynamic” variables (i.e. climatic) and static aquifer characteristics. This large-scale approach offers the possibility of incorporating dynamic and static features to cover more reservoir heterogeneities in the study area. Further, we investigated the performance of relevant feature extraction techniques such as clustering and wavelet transform decomposition, intending to simplify network learning using regionalised information. Several modelling performance tests were conducted. Models specifically trained on different types of GWL, clustered based on the spectral properties of the data, performed significantly better than models trained on the whole dataset. Clustering-based modelling reduces complexity in the training data and targets relevant information more efficiently. Applying multi-station models without prior clustering can lead the models to learn the dominant station behavior preferentially, ignoring unique local variations. In this respect, wavelet pre-processing was found to partially compensate clustering, bringing out common temporal and spectral characteristics shared by all available time series even when these characteristics are “hidden” because of too small amplitude. When employed along with prior clustering, thanks to its capability of capturing essential features across all time scales (high and low), wavelet decomposition used as a pre-processing technique provided significant improvement in model performance, particularly for GWLs dominated by low-frequency variations. This study advances our understanding of GWL simulation using deep learning, highlighting the importance of different model training approaches, the potential of wavelet preprocessing, and the value of incorporating static attributes

Interpretation of the piezometric fluctuations and precursors associated with the November 29, 2007, magnitude 7.4 earthquake in Martinique (Lesser Antilles) Interprétation des fluctuations piézométriques et des précurseurs associés au séisme de magnitude 7,4 du 29 novembre 2007 sur l'île de la Martinique (Petites Antilles)

International audiencen November 29, 2007, a 7.4 earthquake occurred near the volcanic island of Martinique (French West Indies). It was widely felt in the Caribbean. Piezometric level changes correlated with the earthquake were recorded by 12 of the 24 piezometers in the groundwater monitoring network. A methodology has been developed for the interpretation of long-duration piezometric anomalies. It enables us to demonstrate that the hydraulic conductivity increased at the scale of the whole aquifer, by an order of magnitude of 5 to 10%, as a consequence of the earthquake. With this methodology, it is possible to compute either the aquifer hydraulic conductivity increase during the earthquake or its hydrodynamic parameters: diffusivity and relative location of the piezometer along a flow line. It shows, for instance, that the amplitude of the piezometric change due to the earthquake is not directly related to its intensity, but rather to the structure and hydrodynamic properties of the aquifer and also to the location of the piezometer. It also proves that a piezometric increase due to an earthquake cannot be straightforwardly related to a decrease in the hydraulic conductivity of the aquifer. Consequently, in such an active geodynamical context, tectonic processes appear to be among the factors responsible of the magnitude of the hydraulic conductivity of shallow aquifers. Piezometric precursors of the earthquake were definitely observed, but the operational use of such signals is, as yet, far from obvious

Experimental study of time series forecasting methods for groundwater level prediction

International audienceGroundwater level prediction is an applied time series forecasting task with important social impacts to optimize water management as well as preventing some natural disasters: for instance, floods or severe droughts. Machine learning methods have been reported in the literature to achieve this task, but they are only focused on the forecast of the groundwater level at a single location. A global forecasting method aims at exploiting the groundwater level time series from a wide range of locations to produce predictions at a single place or at several places at a time. Given the recent success of global forecasting methods in prestigious competitions, it is meaningful to assess them on groundwater level prediction and see how they are compared to local methods. In this work, we created a dataset of 1026 groundwater level time series. Each time series is made of daily measurements of groundwater levels and two exogenous variables, rainfall and evapotranspiration. This dataset is made available to the communities for reproducibility and further evaluation. To identify the best configuration to effectively predict groundwater level for the complete set of time series, we compared different predictors including local and global time series forecasting methods. We assessed the impact of exogenous variables. Our result analysis shows that the best predictions are obtained by training a global method on past groundwater levels and rainfall data

Experimental study of time series forecasting methods for groundwater level prediction

International audienceGroundwater level prediction is an applied time series forecasting task with important social impacts to optimize water management as well as preventing some natural disasters: for instance, floods or severe droughts. Machine learning methods have been reported in the literature to achieve this task, but they are only focused on the forecast of the groundwater level at a single location. A global forecasting method aims at exploiting the groundwater level time series from a wide range of locations to produce predictions at a single place or at several places at a time. Given the recent success of global forecasting methods in prestigious competitions, it is meaningful to assess them on groundwater level prediction and see how they are compared to local methods. In this work, we created a dataset of 1026 groundwater level time series. Each time series is made of daily measurements of groundwater levels and two exogenous variables, rainfall and evapotranspiration. This dataset is made available to the communities for reproducibility and further evaluation. To identify the best configuration to effectively predict groundwater level for the complete set of time series, we compared different predictors including local and global time series forecasting methods. We assessed the impact of exogenous variables. Our result analysis shows that the best predictions are obtained by training a global method on past groundwater levels and rainfall data

Experimental study of time series forecasting methods for groundwater level prediction

International audienceGroundwater level prediction is an applied time series forecasting task with important social impacts to optimize water management as well as preventing some natural disasters: for instance, floods or severe droughts. Machine learning methods have been reported in the literature to achieve this task, but they are only focused on the forecast of the groundwater level at a single location. A global forecasting method aims at exploiting the groundwater level time series from a wide range of locations to produce predictions at a single place or at several places at a time. Given the recent success of global forecasting methods in prestigious competitions, it is meaningful to assess them on groundwater level prediction and see how they are compared to local methods. In this work, we created a dataset of 1026 groundwater level time series. Each time series is made of daily measurements of groundwater levels and two exogenous variables, rainfall and evapotranspiration. This dataset is made available to the communities for reproducibility and further evaluation. To identify the best configuration to effectively predict groundwater level for the complete set of time series, we compared different predictors including local and global time series forecasting methods. We assessed the impact of exogenous variables. Our result analysis shows that the best predictions are obtained by training a global method on past groundwater levels and rainfall data

Groundwater level reconstruction using long-term climate reanalysis data and deep neural networks

Study region: Northern Metropolitan France. Study focus: Assessing long-term changes in groundwater is crucial for understanding the impacts of climate change on aquifers and for managing water resources.However, long-term groundwater level (GWL) records are often scarce, limiting the understanding of historical trends and variability. In this paper, we present a deep learning approach to reconstruct GWLs up to several decades back in time using recurrent-based neural networks with wavelet pre-processing and climate reanalysis data as inputs. GWLs are reconstructed using two different reanalysis datasets with distinct spatial resolutions (ERA5: 0.25° x 0.25° & ERA20C: 1° x 1°) and monthly time resolution, and the performance of the simulations were evaluated. New insights: Long term GWL timeseries are now available for northern France, corresponding to extended versions of observational timeseries back to early 20th century. All three types of piezometric behaviours could be reconstructed reliably and consistently capture the multi-decadal variability even at coarser resolutions, which is crucial for understanding long-term hydroclimatic trends and cycles. GWLs'multidecadal variability was consistent with the Atlantic multidecadal oscillation. From a synthetic experiment involving a modified long-term observational time series, we highlighted the need for longer training datasets for some low-frequency signals. Nevertheless, our study demonstrated the potential of using DL models together with reanalysis data to extend GWL observations and improve our understanding of groundwater variability and climate interactions

A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability

International audienceGroundwater level (GWL) simulations allow the generation of reconstructions for exploring the past temporal variability of groundwater resources or provide the means for generating projections under climate change on decadal scales. In this context, analyzing GWLs affected by low-frequency variations is crucial. In this study, we assess the capabilities of three deep learning (DL) models (long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM)) in simulating three types of GWLs affected by varying low-frequency behavior: inertial (dominated by low-frequency), annual (dominated by annual cyclicity) and mixed (in which both annual and low-frequency variations have high amplitude). We also tested if maximal overlap discrete wavelet transform pre-processing (MODWT) of input variables helps to better identify the frequency content most relevant for the models (MODWT-DL models). Only external variables (i.e., precipitation, air temperature as raw data, and effective precipitation (EP)) were used as input. Results indicate that for inertial-type GWLs, MODWT-DL models with raw data were notably more accurate than standalone models. However, DL models performed well for annual-type GWLs, while using EP as input, with MODWT-DL models exhibiting only minor improvements. Using raw data as input improved MODWT-DL models compared to standalone models; nevertheless, all models using EP performed better for annual-type GWLs. For mixed-type GWLs, while using EP as input, MODWT-DL models performed well, with substantial improvements over standalone models. Using raw data as input, improvement of MODWT-DL models is marginal compared to that of standalone models; nevertheless, they perform better than standalone models with EP. The Shapley Additive exPlanations (SHAP) approach used to interpret models highlighted that they preferentially learned from low-frequency in precipitation data to achieve the best simulations for inertial and mixed GWLs. This study showed that MODWT-based input pre-processing is highly suitable to better simulate low-frequency varying GWLs

Lag Time as an Indicator of the Link between Agricultural Pressure and Drinking Water Quality State

Diffuse nitrogen (N) pollution from agriculture in groundwater and surface water is a major challenge in terms of meeting drinking water targets in many parts of Europe. A bottom-up approach involving local stakeholders may be more effective than national- or European-level approaches for addressing local drinking water issues. Common understanding of the causal relationship between agricultural pressure and water quality state, e.g., nitrate pollution among the stakeholders, is necessary to define realistic goals of drinking water protection plans and to motivate the stakeholders; however, it is often challenging to obtain. Therefore, to link agricultural pressure and water quality state, we analyzed lag times between soil surface N surplus and groundwater chemistry using a cross correlation analysis method of three case study sites with groundwater-based drinking water abstraction: Tunø and Aalborg-Drastrup in Denmark and La Voulzie in France. At these sites, various mitigation measures have been implemented since the 1980s at local to national scales, resulting in a decrease of soil surface N surplus, with long-term monitoring data also being available to reveal the water quality responses. The lag times continuously increased with an increasing distance from the N source in Tunø (from 0 to 20 years between 1.2 and 24 m below the land surface; mbls) and La Voulzie (from 8 to 24 years along downstream), while in Aalborg-Drastrup, the lag times showed a greater variability with depth—for instance, 23-year lag time at 9–17 mbls and 4-year lag time at 21–23 mbls. These spatial patterns were interpreted, finding that in Tunø and La Voulzie, matrix flow is the dominant pathway of nitrate, whereas in Aalborg-Drastrup, both matrix and fracture flows are important pathways. The lag times estimated in this study were comparable to groundwater ages measured by chlorofluorocarbons (CFCs); however, they may provide different information to the stakeholders. The lag time may indicate a wait time for detecting the effects of an implemented protection plan while groundwater age, which is the mean residence time of a water body that is a mixture of significantly different ages, may be useful for planning the time scale of water protection programs. We conclude that the lag time may be a useful indicator to reveal the hydrogeological links between the agricultural pressure and water quality state, which is fundamental for a successful implementation of drinking water protection plans

A fifty-year chronicle of tritium data for characterising the functioning of the Evian and Thonon (France) glacial aquifers

International audienceUsing lumped models and a transfer function model, this paper deals with the interpretation of exceptionally long (up to 50 years (y)) and precise tritium chronicles characterising the rainfall, recharge (efficient rainfall) and outflow from various types of glacial aquifers from the French Alps (Evian-Thonon area). The efficient rainfall tritium chronicle was computed from tritium measurements performed for 11 years (1969-1979) in a lysimeter. The evapotranspiration induces a mean 15% drop of the annual tritium signal. The three superficial glacial aquifers (two fluvio-glacial kame terraces and a lateral till) provide similar results: a best fit with an exponential flow model (EM) (playing the major role) combined in parallel with a piston flow model (PFM), and a rather short mean transit time (T 5-7 y). The deepest mineral aquifer (Evian) can only be fitted with the in a series combination of a highly dispersive model (DM; T 68 y; DP = 0.5) and a piston flow model (T 2.5 y) or, better, by the in a series combination of an EM (T 8 y) modelling the subsurface aquifer and a DM (T 60 y; DP = 0.75) and the same piston flow model (T 2.5 y) modelling the deep mineral aquifer, this latest combination of models providing the following parameters: T 70 y and median transit time 45.5 y. It is also to be noted that a very small part of the recharge; about 1.3%, avoids both the EM and the DM, and directly enters the PFM (at the Northern limit of the Gavot Plateau). These models are very sensitive regarding the T (±1 y, 0.25 y for the PFM), less so with DP. These results will prompt hydrologists to (re)work historical data to determine if important hydrologic information is available. The interest and limits of such a modelling, also for other constituents than tritium, along with the future for tritium as a tracer are discussed and it also provides new insights on the structure and functioning of alpine paleo glacial hydrosystems

In vitro exposure to triazoles used as fungicides impairs human granulosa cells steroidogenesis

Triazoles are the main components of fungicides used in conventional agriculture. Some data suggests that they may be endocrine disruptors. Here, we found five triazoles, prothioconazole, metconazole, difenoconazole, tetraconazole, and cyproconazole, in soil or water from the Centre-Val de Loire region of France. We then studied their effects from 0.001 µM to 1000 µM for 48 h on the steroidogenesis and cytotoxicity of ovarian cells from patients in this region and the human granulosa line KGN. In addition, the expression of the aryl hydrocarbon receptor (AHR) nuclear receptor in KGN cells was studied. Overall, all triazoles reduced the secretion of progesterone, estradiol, or both at doses that were non-cytotoxic but higher than those found in the environment. This was mainly associated, depending on the triazole, with a decrease in the expression of CYP51, STAR, CYP11A1, CYP19A1, or HSD3B proteins, or a combination thereof, in hGCs and KGN cells and an increase in AHR in KGN cells