Affiliation:
1. College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Abstract
Abstract
Drought stress under a changing climate can significantly affect agricultural production. Simulation of soil water dynamics in field conditions becomes necessary to understand changes of soil water conditions to develop irrigation guidelines. In this study, three models including Auto-Regressive Integrated Moving Average (ARIMA), Back-Propagation Artificial Neural Network (BP-ANN), and Least Squares Support Vector Machine (LS-SVM) were used to simulate the soil water content in the 0–14 cm and 14–33 cm soil layers across the Taihu Lake region of China. Rainfall, evaporation, temperature, humidity and wind speed that affect soil water content changes were considered in the BP-ANN and LS-SVM, but not in ARIMA. The results showed that the variability of soil water content in the 0–14 cm soil layer was greater than that in 14–33 cm. Correlation coefficients (r) of soil water content between simulations and observations were highest (0.9827) using LS-SVM in the 14–33 cm soil layer, while they were the lowest (0.7019) using ARIMA in the 0–14 cm soil layer; but no significant difference in r values was observed between the two soil layers with the BP-ANN model. Compared with the other two models, the LS-SVM model seems to be more accurate for forecasting the dynamics of soil moisture. The results suggested that agro-climatic data can be used to predict the severity of soil drought stress and provide guidance for irrigation to increase crop production in the Taihu Lake region of China.
Funder
the Natural Science Foundation of China
Regional Innovation and Development Joint Fund of National Natural Science Foundation of China
the Natural Science Foundation of Jiangsu Province, China
State Key Laboratory of Soil and Sustainable Agriculture
Foundation of Chinese postdoctoral
Subject
Water Science and Technology
Cited by
3 articles.
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