Valérie Borrell-Estupina

Assessing the long-term impact of climatic variability and human activities on the water resources of a meso-scale Mediterranean catchment

Abstract This article addresses the critical need for a better quantitative understanding of how water resources from the Hérault River catchment in France have been influenced by climate variability and the increasing pressure of human activity over the last 50 years. A method is proposed for assessing the relative impacts of climate and growing water demand on the decrease in discharge observed at various gauging stations in the periods 1961–1980 and 1981–2010. An annual water balance at the basin scale was calculated first, taking into account precipitation, actual evapotranspiration, water withdrawals and water discharge. Next, the evolution of the seasonal variability in hydroclimatic conditions and water withdrawals was studied. The catchment was then divided into zones according to the main geographical characteristics to investigate the heterogeneity of the climatic and human dynamics. This delimitation took into account the distribution of climate, topography, lithology, land cover and water uses, as well as the availability of discharge series. At the area scale, annual water balances were calculated to understand the internal changes that occurred in the catchment between both past periods. The decrease in runoff can be explained by the decrease in winter precipitation in the upstream areas and by the increase during summer in both water withdrawals and evapotranspiration in the downstream areas, mainly due to the increase in temperature. Thus, water stress increased in summer by 35%. This work is the first step of a larger research project to assess possible future changes in the capacity to satisfy water demand in the Hérault River catchment, using a model that combines hydrological processes and water demand. Editor Z.W. Kundzewicz Citation Collet, L., Ruelland, D., Borrell-Estupina, V., and Servat, E., 2014. Assessing the long-term impact of climatic variability and human activities on the water resources of a meso-scale Mediterranean catchment. Hydrological Sciences Journal, 59 (8), 1457–1469. http://dx.doi.org/10.1080/02626667.2013.842073

Feed-forward vs recurrent neural network models for non-stationarity modelling using data assimilation and adaptivity

Abstract Artificial neural networks (ANN) are nonlinear models widely investigated in hydrology due to their properties of universal approximation and parsimony. Their performance during the training phase is very good, and their ability to generalize can be improved by using regularization methods such as early stopping and cross-validation. In our research, two kinds of generic models are implemented: the feed-forward model and the recurrent model. At first glance, the feed-forward model would seem to be more effective than the recurrent one on non-stationary datasets, because measured information on the state of the system (measured discharge) is used as input, thereby implementing a kind of data assimilation. This study investigates the feasibility and effectiveness of data assimilation and adaptivity when implemented in both feed-forward and recurrent neural networks. Based on the IAHS Workshop held in Göteborg, Sweden (July 2013), the hydrological behaviour of two watersheds of different sizes and different kind of non-stationarity will be modelled: (a) the Fernow watershed (0.2 km2) in the USA, affected by significant modifications in land cover during the study period, and (b) the Durance watershed (2170 km2) in France, affected by an increase in temperature that is causing a decrease in the extent of glaciers. Two methods were applied to evaluate the ability of ANN to adapt on the test set: (i) adaptivity using observed data to adapt parameter values in real time; and (ii) data assimilation using observed data to modify inaccurate inputs in real time. The goal of the study is thus re-analysis and not forecasting. This study highlights how effective the feed-forward model is compared to the recurrent model for dealing with non-stationarity. It also shows that adaptivity and data assimilation improve the recurrent model considerably, whereas improvement is marginal for the feed-forward model in the same conditions. Finally, this study suggests that adaptivity is effective in the case of changing conditions of the watershed, whereas data assimilation is better in the case of climate change (inputs modification).

Influence of Karstic System on Surface Flooding in Mediterranean Climate

Many Mediterranean watersheds are composed by or linked with karstic systems. During a rainfall event, different parameters influence their recharge and drained runoff such as: the state and humidity of soil or the epikarst, the rainfall intensity, the cumulative precipitated water and the internal structure of the karstic system. As a consequence, karsts can be connected with rivers or the hill slopes during flood events. Karstic systems may attenuate surface floods or boost overland and river flows. This latter possibility may arise from the activation of ephemeral streams or an increase of the runoff coefficient. A precise characterization of the interactions between surface and subsurface flows is important for the understanding and anticipation of flash floods. This study proposes a generic method based on an analysis of the geological structure, rainfall, runoff, tracers tests and geochemical parameters. The method was applied to 3 watersheds in Southern France, which are connected to a complex karstic system. This paper presents first results obtained on this site.

Neural Networks for Karst Spring Management. Case of the Lez Spring (Southern France)

Karst hydrosystems constitute important water resource but their recharge and emptying process are poorly known and quantified. Water resource management is thus difficult. Nevertheless, it is a major issue when rainfall is not uniformly distributed during the year, as in Mediterranean climate. This study proposes a method based on neural networks permitting to simulate karst emptying as a function of the pumping volume during the dry period. Applied to the Lez karst system, the model provides excellent simulations of the water level at the main outlet of the system by using mean pumping discharge and zero rainfall hypothesis during dry period. An arbitrary extreme scenario is also provided by introducing a mean pumping volume.