Variability of soil hydraulic parameters and its impact on predictions of an agro hydrological model.

• Grant number: 17/21109-1
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: January 29, 2018
• End date: July 28, 2018
• Field of knowledge: Agronomical Sciences - Agronomy - Soil Science
• Principal Investigator: Quirijn de Jong van Lier
• Grantee: Thalita Campos Oliveira
• Supervisor: Jos C. Van Dam
• Host Institution: Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Institution abroad: Wageningen University, Netherlands
• Associated to the scholarship: 15/04430-5 - Variability of soil hydraulic parameters and its impact on agro-hydrological model predictions, BP.DR

Abstract

Soil hydraulic properties are known to be highly variable in space both in vertical and horizontal directions. The complexity of soil hydraulic properties relationships, i.e. the ¸ h K relation, imposes difficulties to the full understanding of the effect of soil variability by conventional statistics. In an agro hydrological modelling context, soil hydraulic properties are key input parameters and their variability may affect model predictions (water balance components and crop yield). The aim of this study is (i) to investigate the impact of within field variability of soil hydraulic properties on water balance components and crop yield predictions for different climate scenarios using the SWAP hydrological model, and (ii) to evaluate the sensitivity of the model to soil hydraulic parameters using a Bayesian Monte Carlo approach through a stochastic procedure. We will use a data set of soil hydraulic properties measured within a small agricultural field located at the University of São Paulo Campus in Piracicaba, São Paulo, Brazil. Soil hydraulic parameters were obtained from water retention data to which the Mualem-van Genuchten model was fitted; and from hydraulic conductivity data obtained by inverse modelling. As results we expect to generate information about the sensitivity of involved processes to the soil hydraulic properties and to understand the impact of the within-field variability for model predictions.