Soil Water Simulation and Predication in Typical Red Soil Region
|School||Nanjing Agricultural College|
|Keywords||Soil water models Nonlinear simulation Artificial intelligence De-noising methods Wavelet analysis|
Soil water plays a significant part in the hydrosphere, atmosphere and biosphere systems. In terms of the global hydrological cycle, the quantity of soil water is smaller than0.001%. However, it is an important factor affecting evaporation, land surface energy fluxes, plant growth and biogeochemical cycling. The red soils which are the most important soil resource in southern China occupy approximately account for22.7%of the total Chinese land. Presently, Soil water can be measured in-situ by Neutron Probes or Time Domain Refectometry (TDR). However, long-term direct measurements of soil water are expensive and impractical. Therefore, simulations and predictions based on limited measured data must be used to develop understanding of soil water dynamics in this region. Results were as follows:(1) The wavelet transformation was applied to analysis the variation of meteorological factors within51years. It showed that periodic variations (27-29years) of meteorological factors are localized in the time domain, and exhibited "high-low-high" variation regularity except the minimum temperature. The results suggested that meteorological factors will decrease except the low temperature, and the decreasing rate of precipitation will be more than the evaporation.(2) The sensitivity analysis basing on the BP-ANN was applied to study nonlinear relationship between the soil water and meteorological factors. It was showed that the sensitivity between the soil water and precipitation were most than others. The result suggested that the soil water will decrease with12-15years in this study region.(3)In this study, the various nonlinear Stochastic Model of soil water simulation systems and chaotic time series analysis methods of prediction systems had been set up. In the nonlinear Stochastic Model of soil water simulation systems, the daily soil water content simulated by Least squares support vector machine (LS-SVM) with the meteorological factors had more stabilities and advantages in soil water simulation performance over the Back Propagation Artificial Neural Network (BP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The soil water dynamic diurnal variation was simulated by LS-SVM model and extracted its trend by bior3.3making five layers wavelet decomposing. The trend implied that the soil water dynamic change can be divided into four stages:filling period, deficit period, recovery period and drought period. The results can provide scientific data for the water utilization and the soil water prediction in the study region.(4) In chaotic time series analysis method of prediction systems, the various signal preprocessing methods including the appropriate de-noising methods and wavelet decomposition methods were applied to preprocess the original chaotic soil water signal. The results of the prediction systems showed that the appropriate de-noising methods and the tendency of wavelet transformation had less effect on the delay time (r) and embedding dimension (m). The de-noising methods may ignore the detail information of the signal; however the appropriate wavelet transformation to get smaller Maximum Lyapunov Exponent (λ1) of the chaotic soil water signal detail and tendency information can improve the predicting capacity.