title: Mid- and long-term runoff predictions by an improved phase-space reconstruction model.
abstract: In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall-runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ''wet years and dry years predictability barrier,'' to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff.
authors.affiliation: Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and oceanography, PLA University of Science and Technology, Nanjing, China.
authors.forename: Mei
authors.initials: M
authors.lastname: Hong
authors.affiliation: Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210093, China. Electronic address: wangdong@nju.edu.cn.
authors.forename: Dong
authors.initials: D
authors.lastname: Wang
authors.affiliation: Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210093, China.
authors.forename: Yuankun
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210093, China.
authors.forename: Xiankui
authors.initials: X
authors.lastname: Zeng
authors.affiliation: Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and oceanography, PLA University of Science and Technology, Nanjing, China.
authors.forename: Shanshan
authors.initials: S
authors.lastname: Ge
authors.affiliation: Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and oceanography, PLA University of Science and Technology, Nanjing, China.
authors.forename: Hengqian
authors.initials: H
authors.lastname: Yan
authors.affiliation: Department of Biological and Agricultural Engineering Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843, USA.
authors.forename: Vijay P
authors.initials: VP
authors.lastname: Singh
mesh_terms: D002681:China; D005544:Forecasting; D062070:Hydrology; D008962:Models, Theoretical; D011891:Rain; D045483:Rivers; D013696:Temperature; D014872:Water Movements
keywords: Largest Lyapunov exponent; Mid- and long-term runoff; Runoff forecast; The Yellow River; The improved phase-space reconstruction model