Optimization of Soil Texture and Hydraulic Parameters Using the Soil Moisture Observation in Land Surface Model

Soil moisture is a key variable in the hydrologic cycle and affecting to weather and climate, thus accurate soil moisture prediction is necessary in the land surface modeling. In this study, we use UTOPIA (University of Torino land surface Process model for Interaction in the Atmosphere) that is a one-dimensional land surface model representing the interactions at the interface between atmospheric surface, vegetation and soil layers. Soil texture estimated by percentages of clay, silt, and sand is the dominant factor to predict soil moisture. However, it is hard to measure the accurate soil information due to insufficient and uncertain observation. Therefore, we have implemented the micro-genetic algorithm (micro-GA) within UTOPIA to optimize the percentages of clay, silt, and sand estimating the soil texture and hydraulic parameters by evaluating the soil moisture performance against in-situ observation. As a global optimization algorithm, the micro-GA evolves to the best potential solution based on the natural selection or survival of the fittest. Compared to the control experiments using a soil database or in situ observation, optimization results show that the optimal soil texture and hydraulic parameters lead to an improvement in soil moisture prediction.