Modelling Soil Water Dynamics from Soil Hydraulic Parameters Estimated by an Alternative Method in a Tropical Experimental Basin

Water

卷:11

Artur Paiva Coutinho¹  Bruno Silva Ursulino¹  Severino Martins dos Santos Neto¹  Suzana Maria Gico Lima Montenegro¹  Ana Cláudia Villar Gusmão¹  Diego Cezar dos Santos Araújo²  Victor Hugo Rabelo Coelho³  Laurent Lassabatere⁴  Rafael Angulo-Jaramillo⁴ 

[1] Centro de Tecnologia e Geociências, Universidade Federal de Pernambuco, Recife 50670-901, Brazil;

[2] Departamento de Engenharia Agrícola, Universidade Federal Rural de Pernambuco, Recife 52171-900, Brazil;

[3] Departamento de engenharia Civil e Ambiental, Universidade Federal da Paraíba, João Pessoa 58051-900, Brazil;

[4] Laboratoire d’Ecologie des Hydrosystèmes Naturels et Anthropisés, Université de Lyon, site ENTPE, 69120 Vaulx-en-Velin, France;

关键词: soil moisture content;    vadose zone;    soil properties;    BEST model;    Hydrus-1D;   

DOI  :  10.3390/w11051007

来源: DOAJ

【 摘 要 】

Knowledge about soil moisture dynamics and their relation with rainfall, evapotranspiration, and soil physical properties is fundamental for understanding the hydrological processes in a region. Given the difficulties of measurement and the scarcity of surface soil moisture data in some places such as Northeast Brazil, modelling has become a robust tool to overcome such limitations. This study investigated the dynamics of soil water content in two plots in the Gameleira Experimental River Basin, Northeast Brazil. For this, Time Domain Reflectometry (TDR) probes and Hydrus-1D for modelling one-dimensional flow were used in two stages: with hydraulic parameters estimated with the Beerkan Estimation of Soil Transfer Parameters (BEST) method and optimized by inverse modelling. The results showed that the soil water content in the plots is strongly influenced by rainfall, with the greatest variability in the dry–wet–dry transition periods. The modelling results were considered satisfactory with the data estimated by the BEST method (Root Mean Square Errors, RMSE = 0.023 and 0.022 and coefficients of determination, R² = 0.72 and 0.81) and after the optimization (RMSE = 0.012 and 0.020 and R² = 0.83 and 0.72). The performance analysis of the simulations provided strong indications of the efficiency of parameters estimated by BEST to predict the soil moisture variability in the studied river basin without the need for calibration or complex numerical approaches.

【 授权许可】

Unknown