Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Applicability of a two-stage evaporation approximate in a semi-arid environment

Domain:

agriculture

Record type:

paper
Creator:
BouCheBraDuc
Editor:
UniHyd
Publisher:
CCSD
Host:avatar
[Departement_IRSTEA]MA [TR1_IRSTEA]RIE / TRANSFEAU For a regional assessment of water consumption in semi-arid agricultural zones, one needs robust and simple tools that provide space-time estimates of evaporation losses, esp. for flood irrigation. Most of the simplest and operational evaporation estimates rely on empirical relationships such as the "beta function" (e.g. the FAO56 method)that are not well suited for all climate, soil, irrigation methods and vegetation conditions. Some of them for instance overlook the first to second stage evaporation processes, or do not represent separately the bare soil evaporation component. Several authors have proposed physically-based simple expressions, such as the desorptive approach, which gives accurate integrated capillary flows under constant boundary conditions. We propose here a new analytical formulation that uses the desorptive approach and reduces as much as possible the number of empirical relationships. It is tested for a wide range of soil and vegetation conditions against 1) previous expressions available in the litterature, 2) a physically based complex SVAT scheme SiSPAT that uses the Richards equations, and eventually against 3) experimental data acquired over a winter wheat site during the 2003 SUDMED/IRRIMED field experiment in the semi-arid Haouz (mostly flood-irrigated) agricultural area around Marrakech, Morocco. This expression gives accurate predictions of first to second-stage evaporation time series for the bare soil and fully vegetated cover conditions, while computing as expected a sharper time of switching than SiSPAT. An evaluation of its performance for sparse vegetation is provided according to SiSPAT outputs.

Visit

hal.inrae.fr

Languages

Arabic, Moroccan Spoken

Tags

SISPATSVATFAO56HHLYHYDHHLYCEMAGREF[SDE]Environmental Sciences

Similar

Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environmentEvaporation from Water Surfaces in Arid and Semi-Arid Areas in AlgeriaEvaporation from Savanna and Agriculture in Semi-Arid West AfricaEstimation of Evaporation from a Reservoir in Semi arid Environments Using Artificial Neural Network and Climate Based ModelsSoil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluationA novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm

Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environment

Abstract Evaporation represents a fundamental hydrological cycle process that demands de

Evaporation from Water Surfaces in Arid and Semi-Arid Areas in Algeria

The evaporation from open water remains a difficult process to measure or estimate. The major source

Evaporation from Savanna and Agriculture in Semi-Arid West Africa

Abstract. Rain-fed farming is the primary livelihood of semi-arid West Africa. Changes in land cover

Estimation of Evaporation from a Reservoir in Semi arid Environments Using Artificial Neural Network and Climate Based Models

International audience Estimation of evaporation from reservoirs in arid and semi-ari

Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE
[Departement_IRSTEA]Territoires [TR1_IRSTE

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm

We introduce a novel two-stage parameter estimation framework designed to improve computational effi