Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network

Climate projections for the Huaihe River Basin, China, for the years 2001–2100 are derived from the ECHAM5/MPI-OM model based on observed precipitation and temperature data covering 1964–2007. Streamflow for the Huaihe River under three emission scenarios (SRES-A2, A1B, B1) from 2010 to 2100 is then projected by applying artificial neural networks (ANN). The results show that annual streamflow will change significantly under the three scenarios from 2010 to 2100. The interannual fluctuations cover a significant increasing streamflow trend under the SRES-A2 scenario (2051–2085). The streamflow trend declines gradually under the SRES-A1B scenario (2024–2037), and shows no obvious trend under the SRES-B1 scenario. From 2010 to 2100, the correlation coefficient between the observed and modeled streamflow in SRES-A2 scenario is the best of the three scenarios. Combining SRES-A2 scenario of the ECHAM5 model and ANN might therefore be the best approach for assessing and projecting future water resources in the Huaihe basin and other catchments. Compared to the observed period of streamflows, the projected periodicity of streamflows shows significant changes under different emission scenarios. Under A2 scenario and A1B scenario, the period would delay to about 32–33a and 27–28a, respectively, but under B1 scenario, the period would not change, as it is about 5–6a and the observed period is about 7–8a. All this might affect drought/flood management, water supply and irrigation projects in the Huaihe River basin.

[1]  Zbigniew W. Kundzewicz,et al.  Detectability of changes in hydrological records / Possibilité de détecter les changements dans les chroniques hydrologiques , 2004 .

[2]  M. Sarofim,et al.  Uncertainty Analysis of Climate Change and Policy Response , 2003 .

[3]  J. Morlet,et al.  Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media , 1982 .

[4]  E. Berthier,et al.  Neural Networks Applied to Estimating Subglacial Topography and Glacier Volume , 2009 .

[5]  Buda Su,et al.  Trends in frequency of precipitation extremes in the Yangtze River basin, China: 1960–2003 / Tendances d'évolution de la fréquence des précipitations extrêmes entre 1960 et 2003 dans le bassin versant du Fleuve Yangtze (Chine) , 2005 .

[6]  Wensheng Wang,et al.  Wavelet Network Model and Its Application to the Prediction of Hydrology , 2003 .

[7]  R. Blender,et al.  Long‐term memory of the hydrological cycle and river runoffs in China in a high‐resolution climate model , 2006 .

[8]  M. Lorrai,et al.  Neural nets for modelling rainfall-runoff transformations , 1995 .

[9]  Friedrich-Wilhelm Gerstengarbe,et al.  Estimation of the beginning and end of recurrent events within a climate regime , 1999 .

[10]  M. Kendall.  Rank Correlation Methods , 1949 .

[11]  H. B. Mann.  Nonparametric Tests Against Trend , 1945 .

[12]  Yongqiang Zhang,et al.  Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index , 2009 .

[13]  Sobri Harun,et al.  Rainfall-Runoff Modeling Using Artificial Neural Network , 2001 .

[14]  S. Schneider,et al.  Climate Change 2007 Synthesis report , 2008 .

[15]  A. Serrano,et al.  Trend analysis of monthly precipitation over the iberian peninsula for the period 1921–1995 , 1999 .

[16]  David Labat,et al.  Recent advances in wavelet analyses: Part 1. A review of concepts , 2005 .

[17]  Jason Smith,et al.  Neural-Network Models of Rainfall-Runoff Process , 1995 .

[18]  Asia Maccawi Ahmed,et al.  UNDP, 2006, beyond Scarcity: Power, Poverty and Global Water Circes Human Development Report , 2006 .

[19]  Jose C. Principe,et al.  Neural and adaptive systems : fundamentals through simulations , 2000 .

[20]  M. Kendall,et al.  Rank Correlation Methods , 1949 .

[21]  Robert J. Abrahart,et al.  Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural network rainfall-runoff model , 1999 .

[22]  Qingcun Zeng,et al.  Impact of the Madden–Julian Oscillation on Summer Rainfall in Southeast China , 2009 .

[23]  A. Jakeman,et al.  How much complexity is warranted in a rainfall‐runoff model? , 1993 .

[24]  Eric Gaume,et al.  Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations , 2008 .

[25]  C. Gaucherel.  Use of wavelet transform for temporal characterisation of remote watersheds , 2002 .

[26]  E. Foufoula‐Georgiou,et al.  Wavelet analysis for geophysical applications , 1997 .

[27]  Ping Zhao,et al.  Variability of Tibetan Spring Snow and Its Associations with the Hemispheric Extratropical Circulation and East Asian Summer Monsoon Rainfall: An Observational Investigation , 2007 .

[28]  Ning Li,et al.  Risk evaluation of heavy snow disasters using BP artificial neural network: the case of Xilingol in Inner Mongolia , 2008 .

[29]  T. Zhou,et al.  Seasonal Variation of the Diurnal Cycle of Rainfall in Southern Contiguous China , 2008 .

[30]  S. Kao,et al.  Simulating typhoon-induced storm hydrographs in subtropical mountainous watershed: an integrated 3-layer TOPMODEL , 2008 .

[31]  Zongxue Xu,et al.  Trend detection in hydrological time series by segment regression with application to Shiyang River Basin , 2010 .

[32]  A. Tokar,et al.  Rainfall-Runoff Modeling Using Artificial Neural Networks , 1999 .

[33]  T. Semmler,et al.  The impact of the climate change on discharge of Suir River Catchment (Ireland) under different climate scenarios , 2006 .

[34]  Yen-Chang Chen,et al.  A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction , 2001 .

[35]  Hans von Storch,et al.  Misuses of Statistical Analysis in Climate Research , 1995 .

[36]  Ozgur Kisi,et al.  Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data , 2009 .

[37]  E. McBean,et al.  Assessment of impact of climate change on water resources: a long term analysis of the Great Lakes of North America , 2008 .

[38]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[39]  Sheng Yue,et al.  The influence of autocorrelation on the ability to detect trend in hydrological series , 2002 .

[40]  Inmaculada Pulido-Calvo,et al.  Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds , 2007 .

[41]  Luca Bonaventura,et al.  The atmospheric general circulation model ECHAM 5. PART I: Model description , 2003 .

[42]  Sheng Yue,et al.  A comparison of the power of the t test, Mann-Kendall and bootstrap tests for trend detection / Une comparaison de la puissance des tests t de Student, de Mann-Kendall et du bootstrap pour la détection de tendance , 2004 .

[43]  Mojib Latif,et al.  The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates , 2003 .

[44]  Guobin Fu,et al.  Decadal Climatic Variability, Trends, and Future Scenarios for the North China Plain , 2009 .

[45]  Reza Modarres,et al.  Multi-criteria validation of artificial neural network rainfall-runoff modeling , 2008 .

[46]  Christian W. Dawson,et al.  Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze , China 619 , 2002 .

[47]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[48]  Zhang De,et al.  National Assessment Report of Climate Change (I): Climate change in China and its future trend , 2006 .