Effects of climate variability changes on runoff and erosion in the Western European Loess Belt region (NW, France).

Runoff and soil erosion are very pronounced in the Western European Loess Belt. In this study, the distributed physically-based model CLiDE is calibrated, validated, and applied to a catchment of this area (Dun, NW, France) to assess the hydro-sedimentary impacts of climate change scenarios. Despite considerable progress over the last decade in the study of runoff and soil erosion in the context of climate change, the effects of changes in the temporal variability of precipitation remain poorly understood, especially at the scale of a river basin. To examine these relationships more closely, we developed a stochastic weather generator to individually adjust the components that structure the temporal variability of rainfall. The climate scenarios considered represent projections to the year 2100 of the temporal variability of rainfall over NW Europe. The scenarios are based on historical daily rainfall records (1990-2012) and 4 exploratory assumptions: a 50 % decrease in the interannual rainfall regime (scenario 6yD), a 100 % increase in the interannual rainfall regime (scenario 6yI), a 50 % increase in the seasonal rainfall regime (scenario 1yI) and a 50 % increase in the synoptic rainfall regime (scenario 3dI). Simulated daily water and sediment discharges and erosion/deposition maps for each scenario are compared to those simulated for the situation without changes in rainfall. The time series were aggregated over different time intervals to allow for a multi-scale analysis of the differences. The results indicate that the model provides a satisfactory prediction of the catchment's water and sediment discharges, especially over the calibration period. Increased climate variability, whether on a synoptic (3dI), seasonal (1yI) or interannual (6yI) scale, leads to increased runoff and erosion. Increasing the synoptic rainfall variability (3dI) leads to the largest increase in mean annual runoff and erosion. Only the reduction of the interannual rainfall variability (6yD) provokes the decrease of these values.

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

Groundwater 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.

Author Correction: Global karst springs hydrograph dataset for research and management of the world's fastest-flowing groundwater.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Global karst springs hydrograph dataset for research and management of the world's fastest-flowing groundwater.

Karst aquifers provide drinking water for 10% of the world's population, support agriculture, groundwater-dependent activities, and ecosystems. These aquifers are characterised by complex groundwater-flow systems, hence, they are extremely vulnerable and protecting them requires an in-depth understanding of the systems. Poor data accessibility has limited advances in karst research and realistic representation of karst processes in large-scale hydrological studies. In this study, we present World Karst Spring hydrograph (WoKaS) database, a community-wide effort to improve data accessibility. WoKaS is the first global karst springs discharge database with over 400 spring observations collected from articles, hydrological databases and researchers. The dataset's coverage compares to the global distribution of carbonate rocks with some bias towards the latitudes of more developed countries. WoKaS database will ensure easy access to a large-sample of good quality datasets suitable for a wide range of applications: comparative studies, trend analysis and model evaluation. This database will largely contribute to research advancement in karst hydrology, supports karst groundwater management, and promotes international and interdisciplinary collaborations.

Polycyclic aromatic hydrocarbon transport processes on the scale of a flood event in the rural watershed of Le Bebec, France.

Transport processes of polycyclic aromatic hydrocarbons (PAHs) were investigated in a rural watershed in France, which led to new quantitative results on the scale of a flood event. For that purpose, principal components analysis (PCA) combined with signal decomposition of PAH Breakthrough curves were used. The results obtained by PCA enabled us to distinguish (i) the transport of low molecular weight PAHs in dissolved form and (ii) the transport of high molecular weight PAHs in particulate form. The intermediate molecular weight PAHs are distributed in these two clusters. The application of a signal decomposition method confirms the results of PCA and makes it possible to quantify the mass PAH resulting, respectively, from resuspension (13.3mg, i.e. 77% of the total mass) and from direct transfer (2.8mg=16% of the total mass). For the resuspension phase, desorption of PAH (38% of the total exported mass) could be also distinguished from sediment resuspension (39%).

Anthropogenic contaminants as tracers in an urbanizing karst aquifer.

Karst aquifers are uniquely vulnerable to contamination. In the Barton Springs segment of the karstic Edwards aquifer (Texas, U.S.A.), urban contaminants such as pesticides and volatile organic compounds frequently are detected in spring base flow. To determine whether contaminant concentrations change in response to storms, and if they therefore might act as tracers of focused recharge, samples were collected from Barton Springs at closely spaced intervals following three storms. Two herbicides (atrazine and simazine), two insecticides (carbaryl and diazinon), and a solvent (tetrachloroethene) described breakthrough curves over a 1-week period following one or more storms. The breakthrough curves were decomposed into two to five log-normal subcurves, which were interpreted as representing pulses of contaminants moving through the aquifer. Each subcurve could be used in the same way as an artificial tracer to determine travel time to and recovery at the spring. The contaminants have several advantages over artificial tracers: they represent the actual compounds of interest, they are injected essentially simultaneously at several points, and they are injected under those conditions when transport is of the most interest, i.e., following storms. The response of storm discharge, specific conductance, and contaminant loading at the spring depended on initial aquifer flow conditions, which varied from very low (spring discharge of 0.48 m3/s) to high (spring discharge of 2.7 m3/s): concentrations and recovery were the highest when initial aquifer flow conditions were low. This behavior provides information about aquifer structure and the influence of aquifer flow condition on transport properties.

Assessing the Nonlinearity of Karst Response Function under Variable Boundary Conditions.

The hydraulic and transfer response of karst aquifers is complex and often highly nonlinear: due to their high transmissivity and connection with the surface, such systems are very sensitive to modifications of their boundary conditions. The aim of this study was to assess the variation of the response depending on both upstream and downstream parameters, and propose a methodology to simulate the response of the karst system depending on those parameters. The impact of the variations of multiple environmental parameters on the response of a karstic system submitted to tidal variations (Normandy, France) was investigated after a campaign of artificial tracer tests acquired in very different hydrologic conditions (rainfall events, low tide, high tide, low/high piezometric level, and low/high waters). Principal components analysis and hierarchical clustering were applied on both environmental variables and karstic system response variables (parameters of the residence time distribution [RTD] curves). Equations between the RTD parameters and the most relevant variables were established using a symbolic regression algorithm. It appeared that the variations of the RTD parameters depend mainly on the cumulated rainfall preceding the injection, the piezometric level of the aquifer, and on the tide parameters. The hydraulic conditions downstream of the aquifer have a strong influence on the hydraulic and transfer response of the aquifer. The response of the aquifer in various and extreme conditions has been simulated using the equations resulting from the symbolic regression algorithm. Such relationships can be useful for management of water resources in karst media, and support decision making.