基于SMAR模型的森林小流域根区土壤湿度模拟

Estimation of Root Zone Soil Moisture in a Small Forested Watershed Based on the SMAR Mod

根区土壤水分(RZSM)对水文模拟和农业管理等具有重要作用,但直接测量RZSM难度很大。SMAR模型可模拟RZSM的变化,但目前在复杂地形地区的应用还较少。使用美国Shale Hills流域32个站点土壤水分剖面三年期间(2011~2014年)的日实测数据,探究基于物理机制的SMAR模型对RZSM模拟的准确性,评价该方法不同时空条件下的模拟性能。研究结果表明,SMAR模型能够准确利用表层土壤水分(SSM)数据估计RZSM;模拟结果与实测数据相比,平均RRMSE为0.034,说明能够很好地模拟出流域尺度根系层土壤湿度空间分布情况。从时间上看,模型在湿冷季节的准确性优于在干暖季节的准确性。研究结果增加了对复杂地形下利用SSM模拟RZSM的理解,为复杂地形条件下根系土壤层湿度的模拟提供了支撑。

Root zone soil moisture(RZSM)plays an important role in hydrological simulation and agricultural management.However,direct measurement of RZSM is very difficult.Estimating variations in RZSM is possible with Soil Moisture Analysis Relationships(SMAR).However,it is rarely assessed in regions with complex terrain.In this paper,soil moisture profiles at 32 sites in the Shale Hills,USA,were measured daily for a three-year period(2011 to 2014)to examine the accuracy of the physical mechanism-based SMAR model for RZSM estimations.Additionally,the method's efficiency was assessed in various spatial and temporal contexts.The results show that the SMAR model is able to accurately estimate RZSM using surface soil moisture(SSM)data.The mean RMSE of the simulation results was 0.034 compared with the measured data,indicating that the spatial distribution of rhizospheric soil moisture at the watershed scale could be well estimated.Temporally,the accuracy of the model is better in the wet and cold seasons than in the dry and warm seasons.This study increases the understanding of using SSM to simulate RZSM in complex terrain,which provides support for simulation of root zone soil moisture at condition of complex terrain.