Your search found 3 records
1 Duchemin, B.; Hadria, R.; Erraki, S.; Boulet, G.; Maisongrande, P.; Chehbouni, A.; Escadafal, R.; Ezzahar, J.; Hoedjes, J. C. B.; Kharrou, M. H.; Khabba, S.; Mougenot, B.; Olioso, A.; Rodriguez, J. C.; Simonneaux, V. 2006. Monitoring wheat phenology and irrigation in Central Morocco: On the use of Relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management, 79(1):1-27.
Wheat ; Evapotranspiration ; Irrigated farming ; Crop production ; Monitoring ; Remote sensing / Morocco
(Location: IWMI-HQ Call no: PER Record No: H038283)
2 Chaponniere, Anne; Boulet, G.; Chehbouni, A.; Aresmouk, A. 2008. Understanding hydrological processes with scarce data in a mountain environment. Hydrological Processes, 22(12):1908-1921. [doi: https://doi.org/10.1002/hyp.6775]
Hydrology ; Simulation models ; Calibration ; Stream flow ; Measurement ; Time series analysis ; Remote sensing ; Precipitation ; Water balance ; Highlands ; Mountains ; Watersheds ; Reservoirs ; Snow cover ; Geology ; Topography ; Land use ; Soil types ; Groundwater ; Evapotranspiration ; Runoff ; Infiltration ; Percolation / Morocco / Rheraya Watershed / Atlas Mountains
(Location: IWMI HQ Call no: e-copy only Record No: H042330)
(2.48 MB)
Performance of process-based hydrological models is usually assessed through comparison between simulated and measured streamflow. Although necessary, this analysis is not sufficient to estimate the quality and realism of the modelling since streamflow integrates all processes of the water cycle, including intermediate production or redistribution processes such as snowmelt or groundwater flow. Assessing the performance of hydrological models in simulating accurately intermediate processes is often difficult and requires heavy experimental investments. In this study, conceptual hydrological modelling (using SWAT) of a semi-arid mountainous watershed in the High Atlas in Morocco is attempted. Our objective is to analyse whether good intermediate processes simulation is reached when global-satisfying streamflow simulation is possible. First, parameters presenting intercorrelation issues are identified: from the soil, the groundwater and, to a lesser extent, from the snow. Second, methodologies are developed to retrieve information from accessible intermediate hydrological processes. A geochemical method is used to quantify the contribution of a superficial and a deep reservoir to streamflow. It is shown that, for this specific process, the model formalism is not adapted to our study area and thus leads to poor simulation results. A remote-sensing methodology is proposed to retrieve the snow surfaces. Comparison with the simulation shows that this process can be satisfyingly simulated by the model. The multidisciplinary approach adopted in this study, although supported by the hydrological community, is still uncommon.
3 Elfarkh, J.; Simonneaux, V.; Jarlan, L.; Ezzahar, J.; Boulet, G.; Chakir, A.; Er-Raki, S. 2022. Evapotranspiration estimates in a traditional irrigated area in semi-arid Mediterranean. Comparison of four remote sensing-based models. Agricultural Water Management, 270:107728. [doi: https://doi.org/10.1016/j.agwat.2022.107728]
Evapotranspiration ; Estimation ; Irrigated farming ; Semiarid zones ; Remote sensing ; Models ; Calibration ; Energy balance ; Surface temperature ; Soil moisture ; Vegetation / Mediterranean Region / Morocco / Marrakech
(Location: IWMI HQ Call no: e-copy only Record No: H051292)
(8.69 MB)
Quantification of actual crop evapotranspiration (ETa) over large areas is a critical issue to manage water resources, particularly in semi-arid regions. In this study, four models driven by high resolution remote sensing data were intercompared and evaluated over an heterogeneous and complex traditional irrigated area located in the piedmont of the High Atlas mountain, Morocco, during the 2017 and 2018 seasons: (1) SAtellite Monitoring of IRrigation (SAMIR) which is a software-based on the FAO-56 dual crop coefficient water balance model fed with Sentinel-2 high-resolution Normalized Difference Vegetation Index (NDVI) to derive the basal crop coefficient (); (2) Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) which is a surface energy balance model fed with land surface temperature (LST) derived from thermal data provided from Landsat 7 and 8; (3) a modified version of the Shuttleworth–Wallace (SW) model which uses the LST to compute surface resistances and (4) METRIC-GEE which is a version of METRIC model (“Mapping Evapotranspiration at high Resolution with Internalized Calibration”) that operates on the Google Earth Engine platform, also driven by LST. Actual evapotranspiration (ETa) measurements from two Eddy-Covariance (EC) systems and a Large Aperture Scintillometer (LAS) were used to evaluate the four models. One EC was used to calibrate SAMIR and SPARSE (EC1) which were validated using the second one (EC2), providing a Root Mean Square Error (RMSE) and a determination coefficient (R) of 0.53 mm/day (R=0.82) and 0.66 mm/day (R=0.74), respectively. SW and METRIC-GEE simulations were obtained respectively from a previous study and Google Earth Engine (GEE), therefore no calibration was performed in this study. The four models predict well the seasonal course of ETa during two successive growing seasons (2017 and 2018). However, their performances were contrasted and varied depending on the seasons, the water stress conditions and the vegetation development. By comparing the statistical results between the simulation and the measurements of ETa it has been shown that SAMIR and METRIC-GEE are the less scattered and the better in agreement with the LAS measurements (RMSE equal to 0.73 and 0.68 mm/day and R equal to 0.74 and 0.82, respectively). On the other hand, SPARSE is less scattered (RMSE = 0.90 mm/day, R = 0.54) than SW which is slightly better correlated (RMSE = 0.98 mm/day, R = 0.60) with the observations. This study contributes to explore the complementarities between these approaches in order to improve the evapotranspiration mapping monitored with high-resolution remote sensing data.
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