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Excessive sediment discharge in karstic regions can be highly disruptive to water treatment plants. It is essential for catchment stakeholders and drinking water suppliers to limit the impact of high sediment loads on potable water supply, but their strategic choices must be based on simulations integrating surface and groundwater transfers and taking into account possible changes in land use. Karstic environments are particularly challenging as they face a lack of accurate physical descriptions for the modelling process, and they can be particularly complex to predict due to the non-linearity of the processes generating sediment discharge. The aim of the study was to assess the sediment discharge variability at a water treatment plant according to multiple realistic land use scenarios. To reach that goal, we developed a new cascade modelling approach with an erosion-runoff geographic information system (GIS) model (WaterSed) and a deep neural network. The model was used in the Radicatel hydrogeological catchment (106 km2 in Normandy, France), where karstic spring water is extracted to a water treatment plant. The sediment discharge was simulated for five design storms under current land use and compared to four land use scenarios (baseline, ploughing up of grassland, eco-engineering, best farming practices, and coupling of eco-engineering/best farming practices). Daily rainfall time series and WaterSed modelling outputs extracted at connected sinkholes (positive dye tracing) were used as input data for the deep neural network model. The model structure was found by a classical trial-and-error procedure, and the model was trained on 2 significant hydrologic years. Evaluation on a test set showed a good performance of the model (NSE = 0.82), and the application of a monthly backward-chaining nested cross-validation revealed that the model is able to generalize on new datasets. Simulations made for the four land use scenarios suggested that ploughing up 33 % of grasslands would increase sediment discharge at the water treatment plant by 5 % on average. By contrast, eco-engineering and best farming practices will significantly reduce sediment discharge at the water treatment plant (respectively in the ranges of 10 %–44 % and 24 %–61 %). The coupling of these two strategies is the most efficient since it affects the hydro-sedimentary production and transfer processes (decreasing sediment discharge from 40 % to 80 %). The cascade modelling approach developed in this study offers interesting opportunities for sediment discharge prediction at karstic springs or water treatment plants under multiple land use scenarios. It also provides robust decision-making tools for land use planning and drinking water suppliers.

The objective of this study is to analyze the links between climate indices and the variability of precipitation and flow series. In order to better understand the non-stationarity of the different stations, the flow and rainfall data used concern the Tortiya station (1960-1996) and the Lafigué station (1977-1996). The climate indices coupled to these series are the NAO (North Atlantic Oscillation) and ENSO (El Niño - Southern Oscillation) over the same study period. The methodology of this work consisted in applying wavelet analysis and wavelet coherence on the different time series. These methods highlighted the different modes of variability occurring in the time series, namely : the sub-annual mode (< 1 year), the annual mode (1 year) and the interannual mode (1-2 years ; 2-4 years; 4-8 years). Firstly, the results of the present analysis show that the variability of the signal is explained at high frequencies (6 months to 1 year) in the different time series. At this frequency, it is the annual mode (1 year) that records all the signal variability between 30% and 70%. On the other hand, this work presents signals on other frequencies and periods but fairly, especially 2-year frequency, in the 1990s at the Lafigué station (8.2%). Wavelet analysis revealed that the dominant signal is very broadly significant at the annual cycle level. In addition, the use of wavelet coherence between climate indices (ENSO, NAO) and precipitation, flow indicates a strong influence of NAO on rainfall and flow series.Key words : Wavelet, coherence, periodicity, variability, upstream Tortiya watershed

Les departements de Seine-Maritime et de l’Eure sont tres sensibles aux ruissellements. Des investissements ont ete realises ces dernieres annees pour reduire les risques associes a ces processus. ...

Summary: The decline in rainfall experienced by the West African band in the decades 70 and 80 strongly affected the flow in the various sub-catchments. In order to better identify the different modes of variability, this work is dedicated to studying the relationship between the temperature of the Atlantic Ocean and changes in flow in the N'zi watershed. Continuous wavelet analysis was used for signal search signal in the Fêtêkro (1960-1997) and N'zianouan (1960-2010) hydrometric series. As for the consistency in wavelet, it made it possible to verify the link between the flow and Atlantic Ocean temperature indices (North Atlantic Temperature: TNA and South Atlantic Temperature: TSA). Continuous wavelet analysis shows a fairly marked variability overall in high frequencies (6 months to 1 year) and interannual (> 1 year). Thus, at the Fêtêkro station north of the basin, the annual scale (1 year) records half of the variability ready with an estimated signal at 46.09%. For the N'zianouan station, 37.18% explains the variability of the signal. At this stage, the Fêtêkro station has a rather pronounced variability to the detriment of that of N'zianouan. At the level of low frequency variability, the N'zianouan station has a fairly pronounced variability from 1 to 7 years. Periodicity (1 – 2; 2 – 4 years) marks the highest signal (19.78%). The station of Fêtêkro shows a signal in the decade 60 estimated at 9.46% at 2-year frequency. As for wavelet consistency, it indicates a strong influence of the TSA. index. In Fêtêkro, a consistency in phase is perceptible from 2 years in the decades 60 and 70. At the frequency (4-8 years), this logic is observed over the entire time series. At the station of N'zianouan, we observe this reality in the decades 60 and 80 at periodicity (2-6 years), and (7-9 years) from 1990. Therefore, the results of the coherence show that the TSA index strongly impacts the flow in the N'zi watershed.Keywords: TSA, TNA, frequency, variability, N'zi watershed

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.

While soil erosion and runoff physical processes and socio-environmental impacts are widely addressed in the literature, few studies have focused on the economical dimension. However, it is essential to consider this dimension to conduct appropriate land use management policies. Erosion and runoff are known to result into on-site and off-site impacts. A fully exhaustive analysis of erosion and runoff economic costs may be difficult and ambitious due to the low availability of the data and considering that some issues are still unevaluated in the literature (cost of public policies to prevent erosion and runoff impacts, prohibition of drinking water due to turbidity, etc.). In this study, we chose to analyze the main off-site economic costs induced by these processes in two specific areas located in the northwestern European loess belt (Normandy, France). We quantified avoidance and social damages over the last 25 years through a global and retrospective analysis of financial databases provided by regional or local authorities (water agencies, departmental councils, reinsurance, drinking water companies, transport infrastructures managers) and literature review (scientific articles and technical reports). Our analysis suggested that from 1995 to 2017, the total damages cost ranged from 611 to 721 M€. Off-site avoidance damage costs accounted for almost 2/3 of the total expenditure. In the Seine-Maritime area, the mean cost was evaluated to 4 319 € yr−1 km−2 and to 868 € yr−1 km−2 in the Eure area. Even if we tried to be as exhaustive as possible some off-site economic costs remained unknown. It appeared that more research is necessary for the scientific community to get a full picture of off-site economic costs induced by erosion and runoff.

(IF 5.42; Q1); International audience; Coastal systems are extremely dynamic environments exposed to many hazards, making accurate and regular monitoring a major challenge, particularly in the context of global change and sea level rise. In this frame of reference, high-frequency, high-resolution coastal Video Monitoring Systems (VMS) have been installed on three megatidal (tidal amplitude > 9 m) sites of Normandy (France) including a sandy beach at Villers-sur-Mer, a pebble beach at Etretat and a composite beach at Hautot-sur-Mer. This article proposes the use of Mask R–CNN to process images acquired at these sites and perform the automatic segmentation of the visible bodies of water in order to extract the waterline. The extracted waterlines are associated with a measured water level, which makes it possible to reconstruct the topography of the beaches at the scale of the tidal cycle. After training the neural network on manually labeled data, the segmentation by Mask R–CNN is very efficient by achieving a satisfactory segmentation on 69.87% of the images of Villers-sur-Mer, on 67.11% at Hautot-sur-Mer, and on 97.33% at Etretat. Once the waterlines have been extracted and georeferenced, the reproduction of the beaches’ morphology is satisfactory (averaged vertical RMSE = 28 cm). These results confirm that segmentation by Mask R–CNN is a particularly powerful tool that allows efficient and low-cost monitoring of the evolution of beach morphology, particularly in response to marine conditions. Its capabilities to detect and segment bodies of water while not being affected by the various sources of noise make it a notably effective tool for coastal science applications.

There is an increasing need for measuring the topography of intertidal environments in a regular manner. Spaceborne monitoring of intertidal environments is essentially performed using the waterline method. In this study we propose a new automatic approach for using the waterline method to derive intertidal Digital Elevation Models (DEMs). The changes include a faster, more efficient, and quasi-automatic detection and post-processing of waterlines. Using Sentinel-1 and Sentinel-2 images, DEMs were generated by this new waterline method approach for the Arcachon Bay and the Bay of Veys located both on the French Coast. The comparison of the generated DEMs with lidar observations showed an error of about 19–29 cm.

The aim of this study is to investigate the performance of SWOT in generating intertidal Digital Elevation Models (DEMs) using the waterline method. To this end, tests were made by generating intertidal DEMs from SWOT-type observables for two intertidal bays situated on the French coast: the Arcachon Bay and the Bay of Veys. SWOT-type observables were simulated using the SWOT Hydrology Toolbox developed by the French space agency (Centre National d'Etudes Spatiales (CNES)). By comparing SWOT-derived DEMs to the validation DEMs, SWOT showed a great potential for monitoring intertidal topographies. Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) reached respectively 5.2 cm and 8.4 cm for the Arcachon Bay and 10.2 cm and 17.3 cm for the Bay of Veys. However, the accuracy of SWOT-derived DEMs is dependent on SWOT's orbit.

(IF 1.42 [2018]; Q2); International audience; The present study aims to estimate the annual soil loss in the Soummam watershed in the northeast of Algeria, using the Revised Universal Soil Loss Equation (RUSLE), geographic information system (GIS), and remote sensing (RS). RUSLE model has been used for modelling the main factors involved in erosive phenomena. The Soummam watershed covers a surface area of 9108.45 km2 of irregular shape, northeast —southwest towards southeast. It is characterized by an altitude varying between 2 m in the northeast and 2308 m in the northwest. Results showed that the average erosivity factor (R) is 70.64 (MJ·mm)/(ha·h·year) and the maximum value reaches 140 (MJ·mm)/(ha·h·year), the average soil erodibility factor (K) is 0.016 (t·h·ha)/(MJ·ha·mm) and maximum values reach 0.0204 (t·h·ha)/(MJ·ha·mm) in the southeast regions of the watershed, the average slope length and steepness factor (LS) is 9.79 and the mean C factor is estimated to be 0.62. Thematic maps integration of different factors of RUSLE in GIS with their database, allowed with a rapid and efficient manner to highlight complexity and factors interdependence in the erosion risk analyses. The resulting map for soils losses, with an average erosion rate of 6.81 t/(ha·year) shows a low erosion (

(IF N/A [2018]; Q3); International audience; In this study, the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-El khroub valley has been processed simultaneously with Multivariate analysis, geostatistical modeling, WQI, and geochemical modeling. Cluster analysis identified three main water types based on the major ion contents, where mineralization increased from group 1 to group 3. These groups were confirmed by FA/PCA, which demonstrated that groundwater quality is influenced by geochemical processes (water–rock interaction) and human practice (irrigation). The exponential semivariogram model fitted best for all hydrochemical parameters values and WQI. Groundwater chemistry has a strong spatial structure for Mg, Na, Cl, and NO3, and a moderate spatial structure for EC, Ca, K, HCO3, and SO4. Water quality maps generated using ordinary Kriging are consistent with the HCA and PCA results. All water groups are supersaturated with respect to carbonate minerals, and dissolution of kaolinite and Ca-smectite is one of the processes responsible for hydrochemical evolution in the area.

Understanding how water resources vary at different temporal and spatial scales in response to climate 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 on 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 time scales of each homogeneous region. Three significant time scales of variability are found in precipitation, temperature and discharge: 1 year, 2–4 years and 5–8 years. The magnitude of those time scales 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, we note a sudden decrease in annual temperature variability in the mid 1990s in the Southern half of France. 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 time scales of variability) were similar between regions, cross-scale interactions are far much complex, differ between regions, and are not systematically transferred from climate variability (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-time scale representation of hydro-climate variability in France, and provides unique insight into the complex non-linear dynamics of this variability, and its predictability.

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.

Excessive sediment discharge at karstic springs and thus, water treatment plants, can be highly disruptive. It is essential for catchment stakeholders and drinking water supplier to reduce the impact of sediment on potable water supply, but their strategic choices must be based on simulations, integrating surface and groundwater transfers, and taking into account possible changes in land use. Karstic environments are particularly challenging as they face a lack of accurate physical description for the modelling process, and they can be seen as a black-box due to the non-linearity of the processes generating sediment discharge. The aim of the study was to assess the sediment discharge variability at a water treatment plant according to multiple realistic land use scenarios. To reach that goal, we developed a new coupled modelling approach with an erosion-runoff GIS model (WaterSed) and a deep neural network. The model was used in the Radicatel catchment (106 km2 in Normandy, France) where karstic spring water is extracted to a water treatment plant. The sediment discharge was simulated for five designed storm projects under current land use and compared to three land use scenarios (baseline, ploughing up of grassland, eco-engineering, best farming practices). Daily rainfall time series and WaterSed modelling outputs extracted at connected sinkholes were used as input data for the deep neural network model. The model structure was found by a classical trial and error procedure, and the model was trained on two significant hydrologic years. Evaluation on a test set showed a good performance of the model (NSE = 0.82), and the application of a monthly-backward chaining nested cross validation revealed that the model is able to generalize on new datasets. Simulations made for the three land use scenarios suggested that ploughing up 33 % of grasslands would not increase significantly sediment discharge at the water treatment plant (5 % in average). In the opposite, eco-engineering and best farming practices will significantly reduce sediment discharge at the water treatment plant (respectively in the range of 10–44 and 24–61 %). The coupling of these two strategies is the most efficient since it affects the hydro-sedimentary production and transfer processes (decreasing sediment discharge from 40 to 80 %). The coupled modelling approach developed in this study offers interesting opportunities for sediment discharge prediction at karstic springs or water treatment plant under multiple land use scenarios. It also provides robust decision-making tools for land use planning and drinking water suppliers.

Sediment Discharge (SD) at karstic springs refers to a black-box due to the non-linearity of the processes generating SD, and the lack of accurate physical description of karstic environments. Recent research in hydrology emphasized the use of data-driven techniques for black-box models, such as Deep Learning (DL), considering their good predictive power rather than their explanatory abilities. Indeed, their integration into traditional hydrology-related workflows can be particularly promising. In this study, a deep neural network was built and coupled to an erosion-runoff GIS model (WATERSED, Landemaine et al., 2015) to predict SD at a karstic spring. The study site is located in the Radicatel catchment (88 km² in Normandy, France) where spring water is extracted to a Water Treatment Plant (WTP). SD was predicted for several Designed Storm Project (DSP0.5-2-10-50-100) under different land-use scenarios by 2050 (baseline, ploughing up 33% of grassland, eco-engineering (181 fascines + 13ha of grass strips), best farming practices (+20% infiltration)). Rainfall time series retrieved from French SAFRAN database and WATERSED modelling outputs extracted at connected sinkholes were used as input data for the DL model. The model structure was found by a classical trial and error procedure, and the model was trained on two significant hydrologic years (nevents = 731). Evaluation on a test set suggested good performance of the model (NSE = 0.82). Additional evaluation was performed comparing the ‘Generalized Extreme Value’ (GEV) distribution for the five DSP under the baseline scenario. The SD predicted by the DL model was in perfect agreement with the GEV distribution (R² = 0.99). Application of the model on the other scenarios suggests that ploughing up 33% of grasslands will increase SD at the WTP to an average 5%. Eco-engineering and best farming practices will reduce SD in the range of 10-44% and 63-80% respectively. This novel approach offers good opportunities for SD prediction at karstic springs or WTP under multiple land use scenarios. It also provide robust decision making tools for land-use planning and drinking water suppliers.

Intertidal flats are very dynamic environments pressured by anthropogenic activities and climate change. There is an increasing need for updated intertidal topography maps for scientific and coastal management purposes. The future wide-swath altimetry mission SWOT (Surface Water and Ocean Topography) will provide valuable data for intertidal topography monitoring. SWOT will use interferometry to measure heights over water surfaces only, which will prevent the use of this method over intertidal areas. Since SWOT is expected to provide highly accurate 2D sea level measurements in coastal areas, it will be very beneficial for the waterline method. The waterline method is one of the most commonly used spaceborne methods for mapping intertidal topography. This method uses a combination between a series of satellite images acquired at different tidal stages (radar or optical) and sea level information (tide gauge records, models output, or satellite measurements). The aim of this study is to investigate the performance of SWOT in generating intertidal Digital Elevation Models (DEMs) using the waterline method (with a brief investigation regarding the interferometric capabilities of SWOT over intertidal areas). To this end, tests were made by generating intertidal DEMs from SWOT-type observables for two intertidal bays situated on the French coast: the Arcachon Bay and the Bay of Veys. SWOT-type observables were simulated using the SWOT large scale simulator developed by the French space agency (CNES - Centre National d’Etudes Spatiales). By comparing SWOT-derived DEMs to the validation DEMs (generated by the waterline method using Sentinel-1 and -2 images), SWOT showed a great potential for monitoring intertidal topographies. Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) reached respectively 5.2 cm and 8.4 cm for the Arcachon Bay and 10.2 cm and 17.3 cm for the Bay of Veys. However, the accuracy of SWOT is dependent on SWOT's orbit. An unfavorable sampling due to tidal aliasing decreases the accuracy of the DEMs. Thus, the period during which the series of satellite images are acquired (the acquisition period) shall be chosen carefully in order to generate accurate SWOT DEMs. SWOT will be able to generate relatively accurate DEMs with period of acquisitions of 3 months. With acquisition periods that provide adequate sampling of the tidal range, SWOT will be able to detect topographic changes between consecutive DEMs. The present work was supported by the CNES in the framework of the future mission SWOT.

Water erosion is the major process responsible for soil loss in Central Tunisia. It represents a main risk of sedimentation which threatens the lifetime of water and soil conservation works in the region, such as hillside reservoirs. This paper focuses on the study of spatial and inter-annual variability of specific sediment yield (SSY) estimated from sediment yield (SY) of 23 small watersheds of hill reservoirs located in the Tunisian Dorsal and the Cap Bon regions. The calculated values of mean inter-annual specific sediment yield varied from 180 to 3298 t/km2/year, over a period of 12 years (from 1994 to 2006). The selected catchments were classified into three groups according to their SSY data. The SSY map, elaborated with a geographical information system (GIS), showed an important spatial variability between watersheds of the same group. Moreover, analyses of the coefficient of variation (CV) showed that 70% of the considered sites demonstrate a tendency to moderate and high inter-annual variability of SSY. In order to identify the principal factors governing variability of erosion and sedimentation, a multiple regression analysis was carried out using nine parameters characterizing morphology, topography, land cover, climate and protection works of the 23 considered sites. The generated model revealed that 85% of SSY variability is correlated to seven principal factors, which are drainage area (A), rainfall intensity recorded in 30 min (I 30), percentage of water and soil conservation works, percentage of clay and carbonate in soils, vegetation land cover, overall slope index (I G) and rainfall amount (R a). The obtained SSY results were compared to others cited in a large bibliographical synthesis of studies carried out at the planet scale. We deduced that the SSY values obtained in this study are among the relatively high series in the world. The high variability of specific erosion rates in Central Tunisia is believed to be related to the semi-arid climate, characterized by the irregularity and the severity of storms and the spatial and temporal variability of other multiple factors affecting erosion processes.

The Indian and French Space Agencies, ISRO and CNES, have conceptualized a space-borne Thermal Infrared Reflectance (TIR) mission, TRISHNA (Thermal infRared Imaging Satellite for High-resolution Natural Resource Assessment). The primary design drivers of TRISHNA are the monitoring of (i) terrestrial water stress and use, and of (ii) coastal and continental water. A suit of four TIR bands and six optical bands is planned. The TIR bands will be centred at 8.6 μm, 9.1 μm, 10.3 μm and 11.5 μm to provide noon-night global observations at 57m nadir resolution over land and coastal regions. The field of view (FOV) is ±34° and the orbit of 761 km altitude was designed to allow 3 sub-cycle acquisitions during the 8-day cycle. The optical bands correspond to blue, green, red, and NIR plus two SWIR bands at 1.38 μm and 1.61 μm. The green, red, NIR and the 1.61 μm SWIR bands will have better radiometry quality than those of AWiFS. ISRO and CNES will develop optical and TIR payloads, respectively. Assessing evapotranspiration and furthermore Gross and Net Primary Productivity (GPP and NPP) will in turn assist in quantifying water use in rainfed and irrigated agriculture, water stress and water use efficiency, with expected applications to agricultural drought and early warning, crop yield prediction, water allocation, implementation of water rights, crop insurance business and agro-advisories to farmers. The other scientific objectives of TRISHNA are also briefly described. TRISHNA instrument will fly aboard a ISRO spacecraft scheduled to be launched from 2024 for a minimum period of 5 years’ mission lifetime.

(IF 3.53 [2018]; Q1); International audience; The goal of the Surface Water and Ocean Topography (SWOT) mission, a future wide-swath radar altimeter, is to measure the elevations of ocean and continental water surfaces with unprecedented precision. A simulator of SWOT high-rate (HR) interferometric SAR images has been developed to provide SWOT-like data to scientists and as a tool to develop and test HR ground processing algorithms. In this letter, we evaluate the errors of the extracted water levels in different hydrodynamic and topographic contexts in the Seine estuary based on simulated SWOT HR data. The results confirm height errors generally below 10 cm after km-scale averaging for the majority of the observations. Higher errors mostly occur in areas where the surrounding topography causes layover. This letter also reveals the interactions between tide and streamflow and their impact on the spatial variability of the water levels in the estuary.

(IF 4.64 [2018]; Q1); International audience; Hyperspectral core logging is a rapid and non-destructive technique in palaeolimnology.The technique is able to track lacustrine sediment sources.Environmental changes in the catchment-to-lake sedimentation and spatial sources are identified.This new method enables the fingerprinting of sedimentary sources with and/or without field samples.Data calibration allows reconstructing total organic carbon concentrations on several cores, at an unprecedented resolution.

International audience; The monitoring of the water cycle at the Earth surface which tightly interacts with the climate change processes as well as a number of practical applications (agriculture, soil and water quality assessment, irrigation and water resource management, etc...) requires surface temperature measurements at local scale. Such is the goal of the Indian-French high spatio-temporal TRISHNA mission (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment). The scientific objectives of the mission and research work conducted to consolidate the mission specifications are presented. Progress in modelling of surface fluxes is then discussed. The main specifications of the mission such as the revisit, the spatial resolution, the overpass time, the spectral bands and the orbit are analyzed and justified. The resulting baseline of the mission is given.

Five-year monitoring of physicochemical parameters was performed with two campaigns in low and high water periods of the Lower Soummam catchment. Data from 18 wells were processed by multivariate statistical tools in order to identify the principal factors influencing groundwater chemistry. Two matrices of 14 and 8 physicochemical parameters with 18 groundwater samples collected in wells were obtained. The correlation matrix showed strong associations between nine variables: K+, Ca2+, Na+, SO42−, Cl−, Mg2+, NO2−, Zn2+ and Sr2+. Principal component analysis and factor analysis showed that the cumulated variance of high and low water periods was of 83.19% and 78.55%, respectively. The variables assigned to the mineralization effect or to pollution indicators were presented by the factor analysis. The bivariate plots confirmed a mineralization model, ascribed to dissolution of geological materials, and to high levels of saline contamination attributed to leakages from sanitary systems. They also show...

It is interesting to try and classify the spatial data to infer the spatial scales associated with certain characteristics within a group of time series. It is indeed very important for operational practices such as flood management and/or climate change adaptation to not only know the severity of a potential impact but also predict the spatial extent of the impact, since similar stations should suffer similar consequences. Classifications on streamflow have already been studied (Hannah etal., 2000; Renard et al., 2006; Wilson et al., 2013) and climate to rainfall or streamflow classification has also been realized by Boé and Terray (2008), Martinez et al. (2008), Martinez and Garavaglia et al. (2010) Garavaglia. Most of those studies establish classification based on annual hydrological regimes. This is in deep contrast with the study that focuses on low frequency time series. There are two axes that the present study aims at improving on. First most of the previous works were carried on raw signals while it is known that different scales of variability (TSV) characterize variabilities of geophysical signals. For instance, atmospheric pressure, precipitation, annual cycle, aquifer contribution or even tidal cycle variability may all be contributing to streamflow variabilities (Labat et al., 2000; Massei and Fournier, 2012). Several studies have highlighted the correlation between some time scales of variability in streamflow or rainfall and the corresponding scales in climatic fields (Massei et al., 2007; Massei et al., 2010; Feliks and Ghil, 2010; Fritier et al., 2012; Pinault, 2012). More specifically on streamflow, Boé and Habets (2013) have shown that multi-decadal variability in streamflow in France was impacted by large multi-decadal climate oscillation especially the Atlantic Multi-decadal Variability. Dieppois et al. (2013) Dieppois highlighted the importance of considering the different TSV in assessing the link between climate fields and local temperature and rainfall variability. Relation of hydroclimate time scales to spatial scales has also been recently also in vestigated by Laepple and Huybers (2014). Those studies highlight the importance of considering each time scale separately. Thus prior to doing a clustering analysis, the input signals must be decomposed into nearly independent components (as Palus (2014) and Jajcay et al. (2016) Jajcay pointed out, pure independence is rarely seen). The second axis is the classification method itself. Classifying fields is always difficult because the extension in two dimensions brings the notion of "general similarity" which is different from "local similarity" i.e. two fields may have differences pointwise but an otherwise similar global shape. To deal with it, fields are either represented as index (Boé and Habets, 2013; Lopez and Frances, 2013) which necessarily lose a lot of spatial information or a mean of taking into account the global shape has to be found like, for example, the Teweles-Wobus score. However the performance of such score is very variable dependent (it works well on pressure fields but less on other field variables) (Teweles and Wobus, 1954). This study presents a method called Geostatistical Euclidean Distance Clustering (GeoEDC) whose performance, by considering the field as an image, is less variable-dependent.

Satellite radar altimetry was initially designed to measure the marine geoid. Thanks to the improvement in the orbit determination from the meter to the centimeter level, this technique has been providing accurate measurements of the sea surface topography over the open ocean since the launch of Topex/Poseidon in 1992. In spite of a decrease in the performance over land and coastal areas, it is now commonly used over these surfaces. This study analyzes the ability of satellite radar altimetry to retrieve for the very first time the topography of the intertidal zone in coastal lagoons.

The article which was recently published contained errors in Table 1. The values of coefficient of skewness (Cs) and coefficient of kurtosis (Ck) are not in their proper locations. Given in this article is the corrected table. The original article has been corrected.

(IF 1.14; Q2); International audience; This study examines spatial and temporal variability of rainfall in Bizerte-Ichkeul Watershed. The basin, located in the extreme north of Tunisia, covers an area of 3084 km2. Thirteen rainfall stations, with continuous monthly precipitation records over the period (1970–2011), were considered in the analysis. Two methods were used. In the first, the dimensionless standardized precipitation ratio is applied to examine precipitation temporal variation. The second method is represented by continuous wavelet analysis for the precipitation spatial analysis and the identification of the origin of its variability. The study of temporal variability of annual rainfall showed severe persistent and recurrent drought episodes over the period (1977–2001). Wavelet analysis resulted in detecting the modes and origins of precipitation variability. Three energy bands were clearly identified: (1, 2–4, and 4–8 years) for the entire watershed. The visualization of the power distribution showed that the observed modes of variability are different in their power distributions from one station to another. The approach adopted allowed the identification of two groups with the same precipitation frequency and temporal variation. These groups were defined according to the difference in occurrence of the frequency band for each station.

International audience; Lacustrine sediments contain a wide range of proxies that enable paleoenvironmental reconstructions. For instance, charcoal can be used to document past fire regime changes. In order to analyse high-temporal- and spatial-resolution records, however, it is necessary to develop fast, low-cost and high-stratigraphic-resolution methods. We developed a new paleo-fire proxy by studying a lacustrine core from the Esterel Massif, SE France, an area affected by two recent fire events, in AD 1987 and 2003. For this purpose, we searched for charcoal deposited and preserved in the lake sediments by combining a number of complementary methods, including: classic macrocharcoal tallying, scanning spectrophotometry, scanning hyperspectral imaging and high pressure liquid chromatography analyses. Macrocharcoal quantification is efficient, but time-consuming, and only provides intermediate-resolution data (cm scale). Spectrophotometry, used classically to quantify colour, is very fast, provides high-resolution data (4 mm) and is non-destructive (core preservation). Hyperspectral data have the same advantages as spectrophotometry, but offer higher spatial resolution (64-µm pixel size) and higher spectral resolution (6 nm) for core logging applications. The main result of this research is based on hyperspectral analysis at very high stratigraphic resolution using the I-band index. This index usually measures reflectance values at [660, 670 nm] corresponding to the trough in red reflectance produced by Chlorophyll a and its diagenetic products. This [660, 670 nm] reflectance trough, however, is also affected by the presence of altered organic matter and decreases with altered organic matter such as charcoal particles. Charcoal effect on the reflectance of Chlorophyll a and its diagenetic products is identified on first derivative spectra by a characteristic pattern around 675 nm, which is also in agreement with the Chlorophyll a concentrations measured by high-pressure liquid chromatography and charcoal particles. The I-band index is hence suitable for detecting burned organic matter, by quantifying the dilution of the chlorophyll signal by the charcoal signal. Thus, this adaptation of the I-band index can be applied in fire reconstruction studies

(IF 4.12; Q1); International audience; Radar altimetry was initially designed to measure the marine geoid. Thanks to the improvement in the orbit determination from the meter to the centimeter level, this technique has been providing accurate measurements of the sea surface topography over the open ocean since the launch of Topex/Poseidon in 1992. In spite of a decrease in the performance over land and coastal areas, it is now commonly used over these surfaces. This study presents a semi-automatic method that allows us to discriminate between acquisitions performed at high tides and low tides. The performances of four radar altimetry missions (ERS-2, ENVISAT, SARAL, and CryoSat-2) were analyzed for the retrieval of sea surface height and, for the very first time, of the intertidal zone topography in a coastal lagoon. The study area is the Arcachon Bay located in the Bay of Biscay. The sea level variability of the Arcachon Bay is characterized by a standard deviation of 1.05 m for the records used in this study (2001-2017). Sea surface heights are very well retrieved for SARAL (R~0.99 and RMSE < 0.23 m) and CryoSat-2 (R > 0.93 and RMSE < 0.42 m) missions but also for ENVISAT (R > 0.82 but with a higher RMSE >0.92 m). For the topography of the intertidal zone, very good estimates were also obtained using SARAL (R~0.71) and CryoSat-2 (R~0.79) with RMSE lower than 0.44 m for both missions.