Implementing a new texture-based soil evaporation reduction coefficient in the FAO dual crop coefficient method


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Output type: Journal article

UM6P affiliated Publication?: Yes

Author list: Amazirh A., Merlin O., Er-Raki S., Bouras E., Chehbouni A.

Publisher: Elsevier Masson

Publication year: 2021

Journal: Agricultural Water Management (0378-3774)

Volume number: 250

ISSN: 0378-3774

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101595518&doi=10.1016%2fj.agwat.2021.106827&partnerID=40&md5=dd1faa708a56607fe3676a2ebdcd34f8

Languages: English (EN-GB)


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Abstract

Crop evapotranspiration (ET) is a fundamental component of the hydrological cycle, especially in arid/semi-arid regions. The FAO-56 offers an operational method for deriving ET from the reduction (dual crop coefficient Kc) of the atmospheric evaporative demand (ET0). The dual coefficient approach (FAO-2Kc) is intended to improve the daily estimation of ET by separating the contribution of bare soil evaporation (E) and crop transpiration components. The FAO-2Kc has been a well-known reference for the operational monitoring of crop water needs. However, its performance for estimating the water use efficiency is limited by uncertainties in the modeled evaporation/transpiration partitioning. This paper aims at improving the soil module of the FAO-2Kc by modifying the E reduction coefficient (Kr) according to soil texture information and state-of-the-art formulations, hence, to amend the mismatch between FAO-2Kc and field-measured data beyond standard conditions. In practice this work evaluates the performance of two evaporation models, using the classical Kr (Kr,FAO) and a new texture-based Kr (Kr,text) over 33 bare soil sites under different evaporative demand and soil conditions. An offline validation is investigated by forcing both models with observed soil moisture (θs) data as input. The Kr,text methodology provides more accurate E estimations compared to the Kr,FAO method and systematically reduces biases. Using Kr,text allows reaching the lowest root means square error (RMSE) of 0.16 mm/day compared to the Kr,FAO where the lowest RMSE reached is 0.88 mm/day. As a step further in the assessment of the proposed methodology, ET was estimated in three wheat fields across the entire agricultural season. Both approaches were thus inter-compared in terms of ET estimates forced by SM estimated as a residual of the water balance model (online validation). Compared to ET measurements, the new formulation provided more accurate results. The RMSE was 0.66 mm/day (0.71 mm/day) and the R2 was 0.83 (0.78) for the texture-based (classical) Kr. © 2021 Elsevier B.V.


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Last updated on 2021-25-11 at 23:19