Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang River Basin,north-west China

To improve the accuracy of runoff forecasting,an uncertain multiple linear regression(UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variable to the dependent variable, as this affects prediction accuracy. On this basis, an inexact two-stage stochastic programming(ITSP) model is used for crop planting structure optimization(CPSO) with the inputs that are interval flow values under different probabilities obtained from the UMLR model. The developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop planting structure optimization are integrated, is applied to a real case study. The aim of the developed system is to optimize crops planting area with limited available water resources base on the downstream runoff forecasting in order to obtain the maximum system benefit in the future. The solution obtained can demonstrate the feasibility and suitability of the developed system, and help decision makers to identify reasonable crop planting structure under multiple uncertainties.

Responses of River Runoff to Climate Change Based on Nonlinear Mixed Regression Model in Chaohe River Basin of Hebei Province, China

Taking the nonlinear nature of runoff system into account,and combining auto-regression method and multi-regression method,a Nonlinear Mixed Regression Model (NMR) was established to analyze the impact of temperature and precipitation changes on annual river runoff process. The model was calibrated and verified by using BP neural network with observed meteorological and runoff data from Daiying Hydrological Station in the Chaohe River of Hebei Province in 1956–2000. Compared with auto-regression model,linear multi-regression model and linear mixed regression model,NMR can improve forecasting precision remarkably. Therefore,the simulation of climate change scenarios was carried out by NMR. The results show that the nonlinear mixed regression model can simulate annual river runoff well.

Uncertainty analysis of hydrological processes based on ARMA-GARCH model

Uncertainty analysis and risk analysis are two important areas of modern water resource management,in which accurate variance estimation is required.The traditional runoff model is established under the assumption that the variance is a constant or it changes with the seasons.However,hydrological processes in the real world are often heteroscedastic,which can be tested by McLeod-Li test and Engle Lagrange multiplier test.In such cases,the GARCH model of hydrological processes is established in this article.First,the seasonal factors in the sequence are removed.Second,the traditional ARMA model is established.Then,the GARCH model is used to correct the residual.At last,the daily runoff data in 1949-2001 of Yichang Hydrological Station is taken to be an example.The result shows that compared to the traditional ARMA model,the GARCH model has the ability to predict more accurate confidence intervals under the same confidence level.