Abstract:

Forest hydrological research plays a critical role in alternative landuse practices （eg， restoring farmlands to forests） and  management Mathematical simulation models have often been applied to hydrological processes in forest soils Based on longterm field experimental data， in this study， numerical simulation （NS） method was adopted to construct soil water movement model in typical woodland in land consolidation district of Jiangnan plain， and to predict soil water storage A total of 100 days of observed soil moisture data at four depths （400 observations） were adopted to calibrate the Richards equation by using the inverse option in HYDRUS1D The calibration was for the assessment of the Richards equation to simulate soil moisture content in the experimental site The result of NS model showed that the coefficient of determination （R2） between simulated and observed value of soil moisture in 10， 20， 40 and 60 cm depth was 0863，0870，0865 and 0665， respectively， indicating that soil moisture dynamics in woodland can be effectively predicted by this model It is noteworthy that the 60cm depth was found to have the lower accuracy than the other three depths This may be attributed to the “free drainage” bottom boundary condition at this depth， which often leads to the overestimation of the simulated drainage during the relatively wet periods Therefore， the simulated soil moisture contents at the lower depth （ie， 60 cm） tended to be systematically underestimated [JP2]In addition to NS， the artificial neural network （ANN） [JP]was also applied in this study Four independent variables including precipitation， daily maximum temperature， daily minimum temperature， and antecedent soil water storage were used to construct ANN model to predict the soil water storage as well When predicting the soil water storage of 0-60 cm layer， Root Mean Square Error （RMSE） for training period and validation period in NS model was 1109 and 829 mm， respectively， while that in ANN model was 417 and 408 mm， respectively， indicating that the prediction result of ANN model was better than NS model However， ANN model is not capable of estimating soil moisture variations for a long time Therefore， using a ensemble method， which has the ability to take both physical and statistical modeling and obtain better prediction results， can be an interesting avenue for future research At last， result of sensitivity analysis showed that the predicting precision of ANN model was most sensitive to antecedent soil water storage， followed by precipitation， daily maximum temperature， and daily minimum temperature