Application of ANNs model with the SDSM for the hydrological trend prediction in the sub-catchment of Kurau River, Malaysia

The paper describes the application of SDSM (statistical downscaling model) and ANNs (artificial neural networks) models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibrated and generated for the possible future scenarios of meteorological variables, which are temperature and rainfall by using GCMs (global climate models). The GCM used is SRES A2. The downscaled meteorological variables corresponding to SDSM were then used as input to the ANNs model calibrated with observed station data to simulate the corresponding future streamflow changes in the sub-catchment of Kurau River. This study has discovered the hydrological trend over the catchment. The projected monthly streamflow has shown a decreasing trend due to the increase in the mean of temperature for overall months, except the month of August and November.

[1]  P. Coulibaly,et al.  Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models , 2005 .

[2]  A. Shamseldin,et al.  Statistical downscaling of precipitation: state-of-the-art and application of bayesian multi-model approach for uncertainty assessment , 2009 .

[3]  Vijay P. Singh,et al.  Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China , 2009 .

[4]  Christian W. Dawson,et al.  SDSM - a decision support tool for the assessment of regional climate change impacts , 2002, Environ. Model. Softw..

[5]  Caterina Valeo,et al.  Stormwater quantity and quality response to climate change using artificial neural networks , 2011 .

[6]  R. Abrahart,et al.  Detection of conceptual model rainfall—runoff processes inside an artificial neural network , 2003 .

[7]  Peggy A. Johnson,et al.  Stream hydrological and ecological responses to climate change assessed with an artificial neural network , 1996 .

[8]  Chong-Yu Xu,et al.  Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method , 2011 .

[9]  T. Rientjes,et al.  Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation , 2005 .

[10]  Said Hassan Matan.  Statistical precipitation variability changes under climate change scenarios simulations using a statistical downscaling model (SDSM) , 2007 .

[11]  D. Dokken,et al.  Climate change 2001 , 2001 .

[12]  J. Palutikof,et al.  Climate change 2007 : impacts, adaptation and vulnerability , 2001 .

[13]  T. Wigley,et al.  Downscaling general circulation model output: a review of methods and limitations , 1997 .

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

[15]  Hayley J. Fowler,et al.  Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling , 2007 .

[16]  King Kuok Kuok.  Parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling , 2010 .

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

[18]  P. Gachon,et al.  On the Linkage of Large-Scale Climate Variability with Local Characteristics of Daily Precipitation and Temperature Extremes: An Evaluation of Statistical Downscaling Methods , 2006 .

[19]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[20]  Christian W. Dawson,et al.  An artificial neural network approach to rainfall-runoff modelling , 1998 .

[21]  Bo Liu,et al.  Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network , 2010 .