This study presents a strategy to improve the evapotranspiration estimates in semi arid areas using data assimilation in a
SVAT (Soil Vegetation Atmosphere Transfer) modeling, the ISBA scheme (Interaction Soil Biosphere Atmosphere). In
the perspective to use remote sensing products, the overall objective of this work is to identify the best combination of
data (surface soil moisture / surface temperature / evapotranspiration), the temporal repetitiveness of acquisition (daily /
tri-daily / weekly / bi-monthly / monthly) and the kind of data assimilation technique (two dimensional variational
method / Extended Kalman filter) to constraint evapotranspiration predictions. Within this preliminary study, synthetic
data referring to a wheat crops experimental site located in the Haouz Plain, part of the Tensift basin near Marrakesh in
Morocco have been used (from January to May 2003). The results show that in order to improve the evapotranspiration
through the analysis of the root zone soil moisture, the surface soil moisture is the most informative observation to use in
the assimilation process (roughly 40% improvement in evapotranspiration RMSE). Combinations of observations
improve the results but not significantly (few % improvement in evapotranspiration RMSE). Assimilation is very
efficient for short assimilation windows. It is also shown that the propagation of the background error matrix done
through the Extended Kalman filter doesn’t represent a significant added value with regards to the constant matrix used
with two dimensional variational method.
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