Abstract
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.
Highlights
Surface soil moisture plays a key role in the hydrological cycle as it controls the water fluxes between soil, vegetation, and atmosphere [1,2,3]
The experimental and representative catchments have been monitored by the Network of Hydrology of the Semi-Arid Region (REHISA), which currently consists of eight federal universities in Brazil [47]
The results found at this step demonstrate the good efficiency of hydrodynamic parameters determined in situ with the Beerkan Estimation of Soil Transfer Parameters (BEST) model, adjusted by the Retension Curve (RETC), to of R2
Summary
Surface soil moisture plays a key role in the hydrological cycle as it controls the water fluxes between soil, vegetation, and atmosphere [1,2,3]. Monitoring and understanding of soil moisture variability and its exchange relationships with the surface and atmosphere are essential to improve weather forecasting, flooding and drought predictions, and climate projections [7,8]. Water 2019, 11, 1007 due to the combined influence of many factors in a nonlinear fashion such as the hydraulic properties of the soil, topographic characteristics, interaction with surface water systems, precipitation features, and additional meteorological conditions [3,9,10]. Because of its high variability in natural conditions, achieving accuracy in soil moisture estimation to obtain areal information at different spatial and temporal scales is still a challenging task, especially at high spatiotemporal scales [6,11]. In the last 40 years, the scientific community has clearly recognized the importance of soil moisture as input for earth science applications, developing new approaches and techniques for monitoring, modelling, and use of soil moisture data [12]
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