Irrigation water management driven by agro-hydrological modelling and satellite data in drought- and salinity-affected areas in Morocco

Abstract

Climate change and global population growth, with increased vulnerability of agricultural areas and enhanced food demand, are particularly affecting arid and densely populated regions. The decreasing availability of freshwater for agricultural use is increasing the appeal of unconventional water sources, like grey and desalinated water. Accurate knowledge of crop development and vulnerability to changed environmental conditions is critical to prepare for these future scenarios.The objective of this work is the evaluation of irrigation water management scenarios considering water availability and quality, and the impact on crop growth, by merging satellite data and a distributed, high-resolution agro-hydrological model for crop monitoring and management. Specifically, this activity focused on an irrigation district in Morocco, which has been exposed to a prolonged drought and has seen an increase in the use of (partially) saline water for irrigation. Because of the drought, all available freshwater was reserved for civil use, causing a surge in groundwater pumping to satisfy the irrigation demand. This, in turn, has progressively increased the salinity of the groundwater reserve.The monitoring of salinity-affected areas was performed at high spatial resolution (30m) by integrating into the crop-energy-water balance model FEST-EWB-SAFY the remote sensing data of leaf area index (LAI, from Sentinel-2) and land surface temperature (LST, from Landsat-8/9 and also from Sentinel-3, downscaled to 30m using Sentinel-2) to monitor crop development. The crop-energy-water balance FEST-EWB-SAFY model couples the distributed energy-water balance FEST-EWB model, which allows computing continuously in time and distributed in space all the components of the surface energy and water balances (without requiring LST as an input, but instead computing it internally), and the SAFY (Simple Algorithm For Yield estimates) model, for crop development. Both satellite LST and LAI data were used for the calibration and validation of the different branches of the modelling framework. The model was able to pick up information regarding soil salinity via its effect on crops visible from the satellite imagery.The application of the FEST-EWB-SAFY model, through the synergy with satellite observations of LST and LAI, constitutes a valuable tool to evaluate the impact on the crop of mutating environmental conditions and to formulate sustainable water and food policies in areas facing the harsh consequences of climate change.