Search

Estimating evapotranspiration (ET) is a primary challenge in modern hydrology. Hydrogravimetry is an integrative approach providing highly precise continuous measurement of gravity acceleration. However, large‐scale effects (e.g., tides, polar motion, atmospheric loading) limit the fine time‐scale interpretation of the gravity data and processing leads to residual signal noise. To circumvent this limitation, we exploited the difference between two superconducting gravimeters (SGs) vertically spaced by 512 m. The gravity difference allows to remove common large‐scale effects. Daily variation of the gravity difference is significantly correlated with daily evapotranspiration as estimated using the water balance model SimpKcET (p‐value = 4.10−10). However, this approach is effective only during rain‐free periods. In the future, comparison with direct ET measurements (e.g., eddy‐covariance, scintillometer) may confirm and strengthen our interpretation. Improved hydrogravimetric data processing could extend the proposed approach to other experimental sites equipped with a single SG. Plain Language Summary: Land evaporation and vegetation transpiration are crucial parameters in ecohydrology because evapotranspiration constitutes more than two‐thirds of precipitated water at the continental scale. However, this invisible flux is difficult to characterize, especially at kilometric scale, and its quantification is challenging for the hydrologist community. Continuous gravity monitoring using a superconducting gravimeter is a direct estimation of the mass change of lands with high precision. At a mountain site in southern France, we highlight a significant association between evapotranspiration calculated by a numerical model and the mass loss of the mountain. This approach provides a novel way to monitor evapotranspiration that will reinforce traditionally used methods. Key Points: For the first time, two vertically spaced gravimeters allow to interpret small gravity hydrologically induced signal (<5 nm/s²)Superconducting gravimetric signal are correlated with evapotranspiration at daily time stepGravimetry enables an integrative estimate of evapotranspiration particularly relevant for hydrology [ABSTRACT FROM AUTHOR]

This article presents the MISTIGRI project of a microsatellite developed by the French space agency Centre National d'Etudes Spatiales (CNES) in cooperation with Spain (Image Processing Laboratory of the University of Valencia and Centro para el Desarrollo Tecnológico Industrial (CDTI)). MISTIGRI is a mission that has the originality of combining a high spatial resolution (∼50 m) with a daily revisit in the thermal infrared (TIR). MISTIGRI is an experimental mission devoted to demonstrate the potential of such TIR data for future operational missions. The scientific goals and expected applications of the mission are described: they encompass the monitoring of (i) agricultural areas and related hydrological processes, (ii) urban areas, and (iii) coastal areas and continental waters. Then, the specifications on spatial resolution, revisit frequency, overpass time, and spectral configuration are justified. The strategy of the mission is based on the combination with a network of long-term experimental sites. It will also make possible observing some areas facing rapid climatic change. The choice of the orbit is presented. Finally, we give rapid overviews of both the instrumental concept and the proposed mission architecture. [ABSTRACT FROM PUBLISHER]

Physically-based, distributed modelling approaches are usually preferred for assessing recharge of porous aquifers. In most cases, such approaches are inapplicable to karst systems because of insufficient knowledge of karst heterogeneity. Lumped models allow recharge assessment at catchment scale, but they are by nature unable to provide insights on its spatial variability. Semi-distributed models are a priori suitable for assessing the spatial variability of recharge, but lack of physical significance of their parameters may confront to equifinality issues and thus hinder their relevance to cope with spatial variability.This work aims to strengthen the link between the parameters of semi-distributed models and environmental variables. Recharge may vary greatly depending on numerous environmental variables mainly related to the properties of climate, soil and geology of the aquifer. We assume that these three factors are the main parameters for spatial distribution of recharge and thus for discharge at the outlet of the karst. We thus hypothesize that the implementation of these variables in a rainfall-recharge-discharge model should improve both the assessment of the spatial variability of recharge (which can not be evaluated by direct measurements) and the simulation of discharge at the outlet of karst system (which is a measured variable).Taking into account that knowledge of aquifer properties decreases with depth, we propose a semi-distributed model structure with a parameterization adapted to the contrasted levels of knowledge in the different karst compartments: i) the spatial variability of the atmosphere-vegetation-soil system is derived from environmental variables ; consideration of this upper compartment makes it possible to model the daily distribution of the recharge, ii) the less constrained underground flows are assumed to have a coarser distribution at the catchment scale. The proposed semi-distributed rainfall-recharge-discharge model is applied to the karst aquifer of Fontaine de Vaucluse (South-East of France). Combined direct and indirect use of heterogeneity sources in the model parameterization effectively improves the simulation of the aquifer discharge, which confirms the adequacy of the proposed approach for the assessment of recharge distribution. Our simulation results show high contrasts in annual recharge and groundwater storage throughout the catchment. Half of the system could suffer from storage deficit, while the other half is oversupplied. Such information is crucial for sustainable resource management. Spatial distribution of precipitation also strongly influences discharge at the outlet, suggesting that semi-distributed or distributed models may be needed to accurately assess the impact of climate change on groundwater resources. [ABSTRACT FROM AUTHOR]

This paper aimed at estimating albedo and Leaf Area Index (LAI) from FORMOSAT-2 satellite that offers a unique source of high spatial resolution (eight meters) images with a high revisit frequency (one to three days). It mainly consisted of assessing the FORMOSAT-2 spectral and directional configurations that are unusual, with a single off nadir viewing angle over four visible–near infra red wavebands. Images were collected over an agricultural region located in South Eastern France, with a three day frequency from the growing season to post-harvest. Simultaneously, numerous ground based measurements were performed over various crops such as wheat, meadow, rice and maize. Albedo and LAI were estimated using empirical approaches that have been widely used for usual directional and spectral configurations (i.e. multidirectional or single nadir viewing angle over visible–near infrared wavebands). Two methods devoted to albedo estimation were assessed, based on stepwise multiple regression and neural network (NNT). Although both methods gave satisfactory results, the NNT performed better (relative RMSE=3.5% versus 7.3%), especially for low vegetation covers over dark or wet soils that corresponded to albedo values lower than 0.20. Four approaches for LAI estimation were assessed. The first approach based on a stepwise multiple regression over reflectances had the worst performance (relative RMSE=65%), when compared to the equally performing NDVI based heuristic relationship and reflectance based NNT approach (relative RMSE≈34%). The NDVI based neural network approach had the best performance (relative RMSE=27.5%), due to the combination of NDVI efficient normalization properties and NNT flexibility. The high FORMOSAT-2 revisit frequency allowed next replicating the dynamics of albedo and LAI, and detecting to some extents cultural practices like vegetation cuts. It also allowed investigating possible relationships between albedo and LAI. The latter depicted specific trends according to vegetation types, and were very similar when derived from ground based data, remotely sensed observations or radiative transfer simulations. These relationships also depicted large albedo variabilities for low LAI values, which confirmed that estimating one variable from the other would yield poor performances for low vegetation cover with varying soil backgrounds. Finally, this empirical study demonstrated, in the context of exhaustively describing the spatiotemporal variability of surface properties, the potential synergy between 1) ground based web-sensors that continuously monitor specific biophysical variables over few locations, and 2) high spatial resolution satellite with high revisit frequencies. [Copyright &y& Elsevier]