Performance evaluation of evapotranspiration estimations in a model of soil water balance

M Gassmann, J Gardiol, L Serio - Meteorological Applications, 2011 - Wiley Online Library
M Gassmann, J Gardiol, L Serio
Meteorological Applications, 2011Wiley Online Library
Soil water content models have huge applications from an agronomic point of view and they
are usually used as a sub‐model for weather and climate modelling. They are also useful
tools for efficient water management irrigation practices. The aim of this investigation is to
evaluate the performance of two different parameterizations of evapotranspiration when
applied to a soil water balance model. Experimental data of a maize crop is used to evaluate
model accuracy. The first methodology proposes a parallel resistance arrangement to …
Abstract
Soil water content models have huge applications from an agronomic point of view and they are usually used as a sub‐model for weather and climate modelling. They are also useful tools for efficient water management irrigation practices. The aim of this investigation is to evaluate the performance of two different parameterizations of evapotranspiration when applied to a soil water balance model. Experimental data of a maize crop is used to evaluate model accuracy. The first methodology proposes a parallel resistance arrangement to represent the latent heat fluxes of the soil surface and the leaves in the canopy layer considering the leaf area index (LAI). The second methodology uses the parameterization proposed by the United Nations Food and Agriculture Organization (FAO), based on the crop coefficient (Kc) and the potential evapotranspiration obtained from the Penman–Monteith equation. The crop was divided into five plots with different irrigation systems according to their phenological stages. The model suitably predicts daily soil water content in five different irrigation systems. Predictions of soil water content using the LAI or Kc methodology tend to overestimate observations. In addition, the model has better predictions using the LAI methodology than the Kc methodology. The root mean square error and the determination coefficient were 0.059 and 0.92, respectively, with the LAI methodology and 0.063 and 0.87, respectively, using the Kc methodology. Copyright © 2010 Royal Meteorological Society
Wiley Online Library
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