Le Page Michel, Fakir Y., Jarlan Lionel, Boone A., Berjamy B., Khabba S., Zribi M. (2021). Projection of irrigation water demand based on the simulation of synthetic crop coefficients and climate change. Hydrology and Earth System Sciences, 25 (2), p. 637-651. ISSN 1027-5606.
Titre du document
Projection of irrigation water demand based on the simulation of synthetic crop coefficients and climate change
Le Page Michel, Fakir Y., Jarlan Lionel, Boone A., Berjamy B., Khabba S., Zribi M.
Source
Hydrology and Earth System Sciences, 2021,
25 (2), p. 637-651 ISSN 1027-5606
In the context of major changes (climate, demography, economy, etc.), the southern Mediterranean area faces serious challenges with intrinsically low, irregular, and continuously decreasing water resources. In some regions, the proper growth both in terms of cropping density and surface area of irrigated areas is so significant that it needs to be included in future scenarios. A method for estimating the future evolution of irrigation water requirements is proposed and tested in the Tensift watershed, Morocco. Monthly synthetic crop coefficients (K-c) of the different irrigated areas were obtained from a time series of remote sensing observations. An empirical model using the synthetic K-c and rainfall was developed and fitted to the actual data for each of the different irrigated areas within the study area. The model consists of a system of equations that takes into account the monthly trend of K-c the impact of yearly rainfall, and the saturation of K-c due to the presence of tree crops. The impact of precipitation change is included in the K u estimate and the water budget. The anthropogenic impact is included in the equations for K-c. The impact of temperature change is only included in the reference evapotranspiration, with no impact on the K-c cycle. The model appears to be reliable with an average r(2) of 0.69 for the observation period (2000-2016). However, different subsampling tests of the number of calibration years showed that the performance is degraded when the size of the training dataset is reduced. When subsampling the training dataset to one-third of the 16 available years, r(2) was reduced to 0.45. This score has been interpreted as the level of reliability that could be expected for two time periods after the full training years (thus near to 2050). The model has been used to reinterpret a local water management plan and to incorporate two downscaled climate change scenarios (RCP4.5 and RCP8.5). The examination of irrigation water requirements until 2050 revealed that the difference between the two climate scenarios was very small (< 2 %), while the two agricultural scenarios were strongly contrasted both spatially and in terms of their impact on water resources. The approach is generic and can be refined by incorporating irrigation efficiencies.
Plan de classement
Sciences du milieu [021]
;
Sciences du monde végétal [076]
