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Groundwater levels (GWLs) very often fluctuate over a wide range of timescales (intra-annual, annual, multi-annual, and decadal). In many instances, aquifers act as low-pass filters, dampening the high-frequency variability and amplifying low-frequency variations (from multi-annual to decadal timescales) which basically originate from large-scale climate variability. Within the aim of better understanding and ultimately anticipating groundwater droughts and floods, it appears crucial to evaluate whether (and how much) the very high or very low GWLs are resulting from such low-frequency variability (LFV), which was the main objective of the study presented here. As an example, we focused on exceedance and non-exceedance of the 80 % and 20 % GWL percentiles respectively, in the Paris Basin aquifers over the 1976–2019 period. GWL time series were extracted from a database consisting of relatively undisturbed GWL time series regarding anthropogenic influence (water abstraction by either continuous or periodic pumping) over metropolitan France. Based on this dataset, our approach consisted in exploring the effect of GWL low-frequency components on threshold exceedance and non-exceedance by successively filtering out low-frequency components of GWL signals using maximum overlap discrete wavelet transform (MODWT). Multi-annual (∼7-year) and decadal (∼17-year) variabilities were found to be the predominant LFVs in GWL signals, in accordance with previous studies in the northern France area. By filtering out these components (either independently or jointly), it is possible to (i) examine the proportion of high-level (HL) and low-level (LL) occurrences generated by these variabilities and (ii) estimate the contribution of each of these variabilities in explaining the occurrence of major historical events associated with well-recognized societal impacts. A typology of GWL variations in Paris Basin aquifers was first determined by quantifying the variance distribution across timescales. Four GWL variation types could be found according to the predominance of annual, multi-annual, or/and decadal variabilities in these signals: decadal-dominant (type iD), multi-annual- and decadal-dominant (type iMD), annual-dominant (type cA), and annual- and multi-annual-dominant (type cAM). We observed a clear dependence of high and low GWL on LFV for aquifers exhibiting these four GWL variation types. In addition, the respective contribution of multi-annual and decadal variabilities in the threshold exceedance varied according to the event. In numerous aquifers, it also appeared that the sensitivity to LFV was higher for LLs than HLs. A similar analysis was conducted on the only available long-term GWL time series which covered a hundred years. This allowed us to highlight the potential influence of multidecadal variability on HLs and LLs too. This study underlined the key role of LFV in the occurrence of HLs and LLs. Since LFV originates from large-scale stochastic climate variability as demonstrated in many previous studies in the Paris Basin or nearby regions, our results point out that (i) poor representation of LFV in general circulation model (GCM) outputs used afterwards for developing hydrological projections can result in strong uncertainty in the assessment of future groundwater extremes (GWEs), and (ii) potential changes in the amplitude of LFV, be they natural or induced by global climate change, may lead to substantial changes in the occurrence and severity of GWEs for the next decades. Finally, this study also stresses the fact that due to the stochastic nature of LFV, no deterministic prediction of future GWEs for the mid- or long-term horizons can be achieved, even though LFV may look periodic.

Understanding how water resources vary in response to climate at different temporal and spatial scales is crucial to inform long-term management. Climate change impacts and induced trends may indeed be substantially modulated by low-frequency (multi-year) variations, whose strength varies in time and space, with large consequences for risk forecasting systems. In this study, we present a spatial classification of precipitation, temperature, and discharge variability in France, based on a fuzzy clustering and wavelet spectra of 152 near-natural watersheds between 1958 and 2008. We also explore phase–phase and phase–amplitude causal interactions between timescales of each homogeneous region. A total of three significant timescales of variability are found in precipitation, temperature, and discharge, i.e., 1, 2–4, and 5–8 years. The magnitude of these timescales of variability is, however, not constant over the different regions. For instance, southern regions are markedly different from other regions, with much lower (5–8 years) variability and much larger (2–4 years) variability. Several temporal changes in precipitation, temperature, and discharge variability are identified during the 1980s and 1990s. Notably, in the southern regions of France, we note a decrease in annual temperature variability in the mid 1990s. Investigating cross-scale interactions, our study reveals causal and bi-directional relationships between higher- and lower-frequency variability, which may feature interactions within the coupled land–ocean–atmosphere systems. Interestingly, however, even though time frequency patterns (occurrence and timing of timescales of variability) were similar between regions, cross-scale interactions are far much complex, differ between regions, and are not systematically transferred from climate (precipitation and temperature) to hydrological variability (discharge). Phase–amplitude interactions are indeed absent in discharge variability, although significant phase–amplitude interactions are found in precipitation and temperature. This suggests that watershed characteristics cancel the negative feedback systems found in precipitation and temperature. This study allows for a multi-timescale representation of hydroclimate variability in France and provides unique insight into the complex nonlinear dynamics of this variability and its predictability.

This research examines the nonstationary dynamics of extreme surges along the English Channel coasts and seeks to make their connection to the climate patterns at different timescales by the use of a detailed spectral analysis in order to gain insights into the physical mechanisms relating the global atmospheric circulation to the local-scale variability of the monthly extreme surges. This variability highlights different oscillatory components from the interannual (∼1.5, ∼2–4, ∼5–8 years) to the interdecadal (∼12–16 years) scales with mean explained variances of ∼25 %–32 % and ∼2 %–4 % of the total variability, respectively. Using the two hypotheses that the physical mechanisms of the atmospheric circulation change according to the timescales and their connection with the local variability improves the prediction of the extremes, we have demonstrated statistically significant relationships of ∼1.5, ∼2–4, ∼5–8 and 12–16 years with the different climate oscillations of sea level pressure, zonal wind, North Atlantic Oscillation and Atlantic Multidecadal Oscillation, respectively. Such physical links have been used to implement the parameters of the time-dependent generalized extreme value (GEV) distribution models. The introduced climate information in the GEV parameters has considerably improved the prediction of the different timescales of surges with an explained variance higher than 60 %. This improvement exhibits their non-linear relationship with the large-scale atmospheric circulation.

In a context of climate, environmental, ecological and socio-economical changes, understanding and predicting the response of hydrological systems on regional to global spatial scales, and on infra-seasonal to multidecadal time-scales, are major topics that must be considered to tackle the challenge of water resource management sustainability. In this context, a number of strongly-linked key issues need to be addressed by the scientific community, including: (i) identifying climate drivers of hydrological variations, (ii) understanding the multi-frequency characteristics of hydroclimate variability, including evolution of extremes (meteorological/hydrological event scale to long-term natural/internal climate- or anthropogenic-driven variations and trends), (iii) assessing the influence of local- to regional-scale basin properties on hydrological system response to climate variability and change, (iv) identifying the evolving contribution of anthropogenic water use in observed hydrological variations. Based on pan-European collaborations, activities of the EURO-FRIEND “Large-scale variations in hydrological characteristics” group aim at generating new findings to improve our understanding of hydrological systems behavior sensu lato (i.e. surface and sub-surface) on large spatial and temporal scales (i.e continental – multidecadal). Through selected examples, this contribution emphasizes recent research developments in characterizing and modeling of climate-hydrology linkages at different temporal and spatial scales, as well as recent insights on climate-hydrology scaling characteristics (i.e. long-term persistence, dependance of processes, of hydrological behaviors, of large-scale climate/hydrology linkages on time-/spatial scales), long-term hydrometeorological reconstructions, and large-scale hydrological model refinement taking into account spatial heterogeneity of watershed physical characteristics.

Reanalysis data show an increasing trend in Arctic precipitation over the 20th century, but changes are not homogenous across seasons or space. The observed hydroclimate changes are expected to continue and possibly accelerate in the coming century, not only affecting pan-Arctic natural ecosystems and human activities, but also lower latitudes through the atmospheric and ocean circulations. However, a lack of spatiotemporal observational data makes reliable quantification of Arctic hydroclimate change difficult, especially in a long-term context. To understand Arctic hydroclimate and its variability prior to the instrumental record, climate proxy records are needed. The purpose of this review is to summarise the current understanding of Arctic hydroclimate during the past 2000 years. First, the paper reviews the main natural archives and proxies used to infer past hydroclimate variations in this remote region and outlines the difficulty of disentangling the moisture from the temperature signal in these records. Second, a comparison of two sets of hydroclimate records covering the Common Era from two data-rich regions, North America and Fennoscandia, reveals inter- and intra-regional differences. Third, building on earlier work, this paper shows the potential for providing a high-resolution hydroclimate reconstruction for the Arctic and a comparison with last-millennium simulations from fully coupled climate models. In general, hydroclimate proxies and simulations indicate that the Medieval Climate Anomaly tends to have been wetter than the Little Ice Age (LIA), but there are large regional differences. However, the regional coverage of the proxy data is inadequate, with distinct data gaps in most of Eurasia and parts of North America, making robust assessments for the whole Arctic impossible at present. To fully assess pan-Arctic hydroclimate variability for the last 2 millennia, additional proxy records are required.

To put recent climate change in perspective, it is necessary to extend the instrumental climate records with proxy data from paleoclimate archives. Arctic climate variability for the last 2 millennia has been investigated using statistical and signal analyses from three regionally averaged records from the North Atlantic, Siberia and Alaska based on many types of proxy data archived in the Arctic 2k database v1.1.1. In the North Atlantic and Alaska, the major climatic trend is characterized by long-term cooling interrupted by recent warming that started at the beginning of the 19th century. This cooling is visible in the Siberian region at two sites, warming at the others. The cooling of the Little Ice Age (LIA) was identified from the individual series, but it is characterized by wide-range spatial and temporal expression of climate variability, in contrary to the Medieval Climate Anomaly. The LIA started at the earliest by around AD 1200 and ended at the latest in the middle of the 20th century. The widespread temporal coverage of the LIA did not show regional consistency or particular spatial distribution and did not show a relationship with archive or proxy type either. A focus on the last 2 centuries shows a recent warming characterized by a well-marked warming trend parallel with increasing greenhouse gas emissions. It also shows a multidecadal variability likely due to natural processes acting on the internal climate system on a regional scale. A ∼ 16–30-year cycle is found in Alaska and seems to be linked to the Pacific Decadal Oscillation, whereas ∼ 20–30- and ∼ 50–90-year periodicities characterize the North Atlantic climate variability, likely in relation with the Atlantic Multidecadal Oscillation. These regional features are probably linked to the sea ice cover fluctuations through ice–temperature positive feedback.

Karst aquifers and watersheds represent a major source of drinking water around the world. They are also known as complex and often highly vulnerable hydrosystems due to strong surface–groundwater interactions. Improving the understanding of karst functioning is thus a major issue for the efficient management of karst groundwater resources. A comprehensive understanding of the various processes can be achieved only by studying karst systems across a wide range of spatiotemporal scales under different geological, geomorphological, climatic, and soil cover settings. The objective of the French Karst National Observatory Service (SNO KARST) is to supply the international scientific community with appropriate data and tools, with the ambition of (i) facilitating the collection of long-term observations of hydrogeochemical variables in karst, and (ii) promoting knowledge sharing and developing cross-disciplinary research on karst. This paper provides an overview of the monitoring sites and collective achievements, such as the KarstMod modular modeling platform and the PaPRIKa toolbox, of SNO KARST. It also presents the research questions addressed within the framework of this network, along with major research results regarding (i) the hydrological response of karst to climate and anthropogenic changes, (ii) the influence of karst on geochemical balance of watersheds in the critical zone, and (iii) the relationships between the structure and hydrological functioning of karst aquifers and watersheds.

The French critical zone initiative, called OZCAR (Observatoires de la Zone Critique–Application et Recherche or Critical Zone Observatories–Application and Research) is a National Research Infrastructure (RI). OZCAR-RI is a network of instrumented sites, bringing together 21 pre-existing research observatories monitoring different compartments of the zone situated between “the rock and the sky,” the Earth’s skin or critical zone (CZ), over the long term. These observatories are regionally based and have specific initial scientific questions, monitoring strategies, databases, and modeling activities. The diversity of OZCAR-RI observatories and sites is well representative of the heterogeneity of the CZ and of the scientific communities studying it. Despite this diversity, all OZCAR-RI sites share a main overarching mandate, which is to monitor, understand, and predict (“earthcast”) the fluxes of water and matter of the Earth’s near surface and how they will change in response to the “new climatic regime.” The vision for OZCAR strategic development aims at designing an open infrastructure, building a national CZ community able to share a systemic representation of the CZ , and educating a new generation of scientists more apt to tackle the wicked problem of the Anthropocene. OZCAR articulates around: (i) a set of common scientific questions and cross-cutting scientific activities using the wealth of OZCAR-RI observatories, (ii) an ambitious instrumental development program, and (iii) a better interaction between data and models to integrate the different time and spatial scales. Internationally, OZCAR-RI aims at strengthening the CZ community by providing a model of organization for pre-existing observatories and by offering CZ instrumented sites. OZCAR is one of two French mirrors of the European Strategy Forum on Research Infrastructure (eLTER-ESFRI) project.

Since the first suggestion of 1500-year cycles in the advance and retreat of glaciers (Denton and Karlen, 1973), many studies have uncovered evidence of repeated climate oscillations of 2500, 1500, and 1000 years. During last glacial period, natural climate cycles of 1500 years appear to be persistent (Bond and Lotti, 1995) and remarkably regular (Mayewski et al., 1997; Rahmstorf, 2003), yet the origin of this pacing during the Holocene remains a mystery (Rahmstorf, 2003), making it one of the outstanding puzzles of climate variability. Solar variability is often considered likely to be responsible for such cyclicities, but the evidence for solar forcing is difficult to evaluate within available data series due to the shortcomings of conventional time-series analyses. However, the wavelets analysis method is appropriate when considering non-stationary variability. Here we show by the use of wavelets analysis that it is possible to distinguish solar forcing of 1000- and 2500- year oscillations from oceanic forcing of 1500-year cycles. Using this method, the relative contribution of solar-related and ocean-related climate influences can be distinguished throughout the 10 000 yr Holocene intervals since the last ice age. These results reveal that the 1500-year climate cycles are linked with the oceanic circulation and not with variations in solar output as previously argued (Bond et al., 2001). In this light, previously studied marine sediment (Bianchi and McCave, 1999; Chapman and Shackleton, 2000; Giraudeau et al., 2000), ice core (O'Brien et al., 1995; Vonmoos et al., 2006) and dust records (Jackson et al., 2005) can be seen to contain the evidence of combined forcing mechanisms, whose relative influences varied during the course of the Holocene. Circum-Atlantic climate records cannot be explained exclusively by solar forcing, but require changes in ocean circulation, as suggested previously (Broecker et al., 2001; McManus et al., 1999).

Abstract  In a karst system, the characterization of transport properties is based on the comparison of natural tracers observed at the inlet (a swallow hole on the karst plateau) and the outlets of the system (a spring and a well). At Norville, northwest France, electrical conductivity (EC) and turbidity (T) were used as natural tracers for characterizing dissolved elements (surface water geochemistry) and particulate matter transport, respectively. Two methods were used for this study: (1) a comparison of the relations between EC, T and spring discharge (Q) by means of normalized EC–T–Q curves, and (2) a principal component analysis (PCA) including water geochemistry data in addition to EC, T and Q. Three different characteristic flood events have been chosen for the analyses. EC–T–Q curves highlighted the direct transfer, resuspension and deposition of particles during their transport in the karst network. Transport from the swallow hole to both the spring and the well appeared to be dominated by karst-conduit flow. On the other hand, PCA results showed a diffuse source of contamination of groundwaters by nitrate and a point-source contamination of groundwaters by streaming/runoff surface waters with high turbidity and phosphate concentration infiltrated at the swallow hole. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Accurate prediction of river discharge is critical for a wide range of sectors, from human activities to environmental hazard management, especially in the face of increasing demand for water resources and climate change. To address this need, a multivariate model that incorporates both local and global data sources, including river and piezometer gauges, sea level, and climate parameters. By employing phase shift analysis, the model optimizes correlations between the target discharge and 12 parameters related to hydrologic and climatic systems, all sampled daily. In addition, a stacked LSTM - a more complex neural network architecture - is used to improve information extraction ability. Exploring river dynamics in the Loire-Bretagne basin and its surroundings, the investigation delves into predictions in daily time steps for one, three, and six months ahead. The resulting forecast features high accuracy and efficiency in predicting river discharge fluctuations, showcasing superior performance in forecasting drought periods over flood peaks. A detailed examination on data used highlights the significance of both local and global datasets in predicting river discharge, where the former dictates short-term predictions, while the latter drives long-range forecasts. Seasonally extended forecasting confirms a strong connection between the forecast leading time and the shift in data correlation, with lower correlation at a lag of 3 months due to seasonal changes affecting forecast quality, compensated by a higher correlation at a longer lag of 6 months. Such mutual effect in this multi-time-step forecasting improves the predictive quality of a six-month horizon, thus encourages progress in long-term prediction to a seasonal scale. The research establishes a practical foundation for effectively utilizing big data to leverage long-term forecasting of environmental dynamics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The accurate prediction of water dynamics is critical for operational water resource management. In this study, we propose a novel approach to perform long-term forecasts of daily water dynamics, including river levels, river discharges, and groundwater levels, with a lead time of 7-30 days. The approach is based on the state-of-the-art neural network, bidirectional long short-term memory (BiLSTM), to enhance the accuracy and consistency of dynamic predictions. The operation of this forecasting system relies on an in-situ database observed for over 50 years with records gauging in 19 rivers, the karst aquifer, the English Channel, and the meteorological network in Normandy, France. To address the problem of missing measurements and gauge installations over time, we developed an adaptive scheme in which the neural network is regularly adjusted and re-trained in response to changing inputs during a long operation. Advances in BiLSTM with extensive learning past-to-future and future-to-past further help to avoid time-lag calibration that simplifies data processing. The proposed approach provides high accuracy and consistent prediction for the three water dynamics within a similar accuracy range as an on-site observation, with approximately 3 % error in the measurement range for the 7 day-ahead predictions and 6 % error for the 30 d-ahead predictions. The system also effectively fills the gap in actual measurements and detects anomalies at gauges that can last for years. Working with multiple dynamics not only proves that the data-driven model is a unified approach but also reveals the impact of the physical background of the dynamics on the performance of their predictions. Groundwater undergoes a slow filtration process following a low-frequency fluctuation, favoring long-term prediction, which differs from other higher-frequency river dynamics. The physical nature drives the predictive performance even when using a data-driven model., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of competing interest Sivarama Krishna Reddy Chidepudi reports financial support was provided by Normandy Region., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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

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.

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., (© 2015, National Ground Water Association.)

The impact of climate change and of other anthropogenic pressures on the structure and composition of phytoplankton communities of large European rivers remains poorly documented. Here we report the findings of a study of the changes in the phytoplankton community of the middle segment of the river Loire over the past 24 years. An attempt is made to distinguish between the impact of changes acting at the local scale and that of those acting more globally. A dramatic reduction in phytoplankton abundance was observed, particularly in the mid -1990s; this was concomitant with an increase in the relative proportion of cyanobacteria. At the same time, the phytoplankton community displayed increasing richness and diversity, and little change in its size structure. All these changes seem to be related to local changes, in particular to the reduction in phosphorus concentrations, as well as to changes in climate, throughout modifications in the river discharge and water temperature. Interestingly, herbicide contamination also appeared to be of particular importance in explaining the unexpected increase in the proportion of cyanobacteria in the phytoplankton community after the 1990s. These findings suggest that combinations of numerous anthropogenic pressures acting at different spatial and temporal scales have led to a mix of predictable and unpredictable changes occurring in the phytoplankton community of the river Loire, with probable consequences for the trophic networks in this river., (© 2013 Blackwell Publishing Ltd.)

Environmental data sets are often multidimensional and consequently display complex structure. This article shows the limitations of principal component analysis (PCA) for the study of such three-dimensional (3D) data sets. These limitations can be resolved by the use of the statistical tool STATIS. The inlet (a swallow hole) and the outlet (a spring) of a karst system of the Western Paris basin were sampled during three rain events of various intensities. These 3D geochemical data sets (variables x sites x dates) for a karst system were analyzed by STATIS method to identify hydrological processes. STATIS proceeds in three steps (interstructure, compromise, and intrastructure), which allows us to focus the analysis of hydrologic systems at different temporal and spatial scales. Compromise plane shows that suspended matter and flood are not simultaneous and highlights a rapid flow, characterized by turbidity and phosphate, which represents a point source contamination, and a ground water flow contaminated by nitrate. Intrastructure plane allows us to compare hydrochemical variations between the swallow hole and the spring lead. By this way, hydrological processes such as direct transfer and resuspension of intrakarstic sediments before and after the flood were identified what cannot be realized by comparison of inlet and outlet breakthrough curves. Finally, results obtained from the same data set by STATIS and a coupled study using PCA and normalized hysteresis curves were compared. This comparison shows the efficiency of STATIS at the identification of transport processes and vulnerability of karst system and its potential for hydrological applications.

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%).

Karst aquifers display a range of geologic and geomorphic characteristics in a wide range of climatic and land-use settings; identification of transport dynamics representative of karst aquifers in general could help advance our understanding of these complex systems. To this end, nutrient, turbidity, and major ion dynamics in response to storms were compared at multiple sites in two karst aquifers with contrasting characteristics and settings: the Chalk aquifer (Eure Department, Normandy, France) and the Barton Springs segment of the Edwards Aquifer (Texas, U.S.A.). The Chalk aquifer is typified by high matrix porosity, thick surficial deposits (up to 30 m thick), and agricultural land use; the Barton Springs segment is typified by low matrix porosity, outcropping limestone, and urban land use. Following one to three storms, from 5 to 16 samples from springs and wells were analyzed for major ions, and specific conductance and turbidity were monitored continuously. Comparison of the chemographs indicated some generalized responses, including an increase in turbidity and potassium concentrations and a decrease in major ion and nitrate concentrations with infiltrating storm runoff. Factor analysis of major ions and turbidity revealed strikingly similar behavior of the chemical variables for the two aquifers: The first two factors, explaining more than 75% of the variability, illustrate that dynamics of most major ions (including nitrate) are opposed to those of turbidity and of potassium. The results demonstrate that potassium and nitrate are effective tracers of infiltrating storm runoff and resident ground water, respectively, and the similar results for these two highly contrasting aquifers suggest that the dynamics identified might be applicable to karst systems in general.

Use of the coefficient of variation (CV) of specific conductance has been a simple and popular approach to classifying karst aquifers; however, problems with this approach arise because specific conductance frequency distributions (CFDs) are usually multimodal and the use of the CV sometimes erroneously classifies aquifers in terms of their dominant flow type or recharge type. Here, we demonstrate a more rigorous analysis of the CFD, which gives insight into the water types contributing to spring flow. The CFD for a water year is separated into an additive series of normal distributions, each related to a hydrogeochemical population. For each water type, its mean, variance, and contribution to the overall CFD can be quantified and compared between water types and water years. We applied this method to 4 years of data collected at Barton Springs, Austin, Texas. Although the overall shape of the CFD changed from year to year, it could consistently be separated into the same set of normally distributed populations. We suggest that each population represents a water type resulting from a particular mode of aquifer functioning. Changes in the parameters describing the curves reflect aquifer response to climatic variations. The results suggest that no single parameter of specific conductance can be used to describe the degree of karst behavior of an aquifer but that the degree of karst behavior itself varies from year to year depending on hydrologic conditions.

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.