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The Mediterranean region is a hot spot of climate change; it is also characterized by a scarcity of water resources that places intense pressure on agricultural productivity. We propose to analyze the potential of multi-remote sensing sensors for the quantification and predictability of drought for the period 2007-2018 at the monthly time step in northwest Africa. Three satellite products are considered: the Normalized Difference Vegetation Index (NDVI), Soil Moisture Index (SWI), and Land Surface Temperature (LST) on which anomalies are computed. An analysis of the inter-variable correlation and the time lagged correlation are performed. We then propose a method to classify drought intensity and apply it on the administrative areas. Finally, we propose the use of the analog statistical approach to identify similar evolutions of the three variables in the past. Although this technique is not a forecast, it provides a strong indication of the plausible future trajectory of a given hydrological season. [ABSTRACT FROM AUTHOR]

Water resources in Mediterranean regions are under increasing pressure due to climate change, economic development, and population growth. Many Mediterranean rivers have their headwaters in mountainous regions where hydrological processes are driven by snowpack dynamics and the specific variability of the Mediterranean climate. A good knowledge of the snow processes in the Mediterranean mountains is therefore a key element of water management strategies in such regions. The objective of this paper is to review the literature on snow hydrology in Mediterranean mountains to identify the existing knowledge, key research questions, and promising technologies. We collected 620 peer-reviewed papers, published between 1913 and 2016, that deal with the Mediterranean-like mountain regions in the western United States, the central Chilean Andes, and the Mediterranean basin. A large amount of studies in the western United States form a strong scientific basis for other Mediterranean mountain regions. We found that: (1) the persistence of snow cover is highly variable in space and time but mainly controlled by elevation and precipitation; (2) the snowmelt is driven by radiative fluxes, but the contribution of heat fluxes is stronger at the end of the snow season and during heat waves and rain-on-snow events; (3) the snow densification rates are higher in these regions when compared to other climate regions; and (4) the snow sublimation is an important component of snow ablation, especially in high-elevation regions. Among the pressing issues is the lack of continuous ground observation in high-elevation regions. However, a few years of snow depth (HS) and snow water equivalent (SWE) data can provide realistic information on snowpack variability. A better spatial characterization of snow cover can be achieved by combining ground observations with remotely sensed snow data. SWE reconstruction using satellite snow cover area and a melt model provides reasonable information that is suitable for hydrological applications. Further advances in our understanding of the snow processes in Mediterranean snow-dominated basins will be achieved by finer and more accurate representation of the climate forcing. While the theory on the snowpack energy and mass balance is now well established, the connections between the snow cover and the water resources involve complex interactions with the sub-surface processes, which demand future investigation. [ABSTRACT FROM AUTHOR]

Population growth and the associated increase in demand for food have led to an urgent need for efficient agricultural production monitoring systems. Furthermore, southern Mediterranean, water shortage is likely to be one of the main pressing problems, resulting from combined effects of alterations in the hydrological cycle, anticipated under climate change, and of the increase in water demands, especially for agriculture. The objective of this study is to develop a simple and spatial approach, based on remote sensing data, to optimize water irrigation and cereal production (dry matter DM and grain yield GY) in the semi-arid areas. The proposed method is based on the three efficiencies model of Monteith (1972). It consists of converting of solar radiation to the DM by the climate (Cc), interception (Ci) and conversion (Cconv) efficiencies. The proposed method combines the maximum of both Ci and Cconv (noted Cimax and Cconvmax) into a single parameter denoted Cmax, calculated as a function of cumulated growing degree day (CGDD). Also, the stress coefficient Ks, which affects the conversion of the absorbed solar radiation to the biomass, was derived from the surface temperature or the FAO-56 water balance at the root zone. In addition, the expression of Ks has been improved to optimize water irrigation amount and cereal production. It has been shown that the value 0.7 of Ks is considered as a suitable threshold for triggering irrigation in semi-arid areas. Otherwise, the developed method proposes a variable Harvest Index coefficient (HI) for partitioning the dry matter developed, between straw and grain. Since the ear apparition, the evolution of HI is derived from CGDD whereas, the final harvest Index (HI0) is estimated from the maximal value of Normalized Difference Vegetation Index (NDVI).).The developed model has been calibrated and validated on both semi-arid regions (Haouz in Morocco and Kairouan in Tunisia). The obtained results showed a good agreement between observed and estimated DM and GY values. Average values of R2 and RMSE are about 0.98 and 0.35 t/ha for DM and 0.98 and 0.19 t/ha for GY, respectively. [ABSTRACT FROM AUTHOR]

Water scarcity and the inter annual variability of water resources in semi arid areas are limiting factors for agricultural production. Characterization of plant water use is needed to better manage water resources. Plant water use is generally determined by estimating evapotranspiration. Furthermore, water stress derived from remote sensing data in the thermal infrared domain is particularily interesting for monitoring agrosystem health and adjusting irrigation requirements in irrigation deficit agrosystems. Evapotranspiration and water stress must be monitored at subdaily to daily scales. Both are simulated by a two source energy balance model from climatic observation time series (Air temperature, relative air humidity, global radiation and wind speed) and satellite information (NDVI, LAI, albedo and surface temperature obtained from the TERRA and AQUA sensors of the MODIS satellite). However, the gauge network is sparse and the observation periods are short. We seek to extend the observation series in space and in time to simulate evapotranspiration while accounting for its spatial and temporal variability. Our analyses are carried on in the Kairouan area in central Tunisia which is subject to semi arid climate. Climatic series are collected at different stations at half hourly time step. One is located upstream in a mountainous area. Three of them, in the footplain, are very close to each other so we clustered them into only one gauge: virtual station. The elaboration of this station is based on regression models specific for each variable. These models will be simulated to ensure imputation of the observed series. The last one is further away from the footplain. To extend the series in time, we rely on ERA interim re analysis. Its spatial resolution is low (about 75 km). Therefore, we use bias correction methods to account for the difference in spatial resolution and to produce series which mimic observations at the station scale. As several climatic variables are needed for the energy balance model, we assess the added value of a multivariate bias correction approach that corrects simultaneously all the variables in order to preserve the inter variable dependence. We compare the extended series produced by the multivariate bias correction approach with an extended series produced by univariate bias correction approach (for which each climatic variable is corrected independently). This comparison is twofold. First, we compare the ability of both bias correction approaches to reproduce the climatic series in terms of intensity and inter variable dependence. Second, we evaluate how the energy balance is simulated when the model is forced by either projected series. [ABSTRACT FROM AUTHOR]