Future changes in rainfall, temperature and reference evapotranspiration in the central India by least square support vector machine

Abstract Climate change affects the environment and natural resources immensely. Rainfall, temperature and evapotranspiration are major parameters of climate affecting changes in the environment. Evapotranspiration plays a key role in crop production and water balance of a region, one of the major parameters affected by climate change. The reference evapotranspiration or ET 0 is a calculated parameter used in this research. In the present study, changes in the future rainfall, minimum and maximum temperature, and ET 0 have been shown by downscaling the HadCM3 (Hadley Centre Coupled Model version 3) model data. The selected study area is located in a part of the Narmada river basin area in Madhya Pradesh in central India. The downscaled outputs of projected rainfall, ET 0 and temperatures have been shown for the 21st century with the HADCM3 data of A2 scenario by the Least Square Support Vector Machine (LS-SVM) model. The efficiency of the LS-SVM model was measured by different statistical methods. The selected predictors show considerable correlation with the rainfall and temperature and the application of this model has been done in a basin area which is an agriculture based region and is sensitive to the change of rainfall and temperature. Results showed an increase in the future rainfall, temperatures and ET 0 . The temperature increase is projected in the high rise of minimum temperature in winter time and the highest increase in maximum temperature is projected in the pre-monsoon season or from March to May. Highest increase is projected in the 2080s in 2081–2091 and 2091–2099 in maximum temperature and 2091–2099 in minimum temperature in all the stations. Winter maximum temperature has been observed to have increased in the future. High rainfall is also observed with higher ET 0 in some decades. Two peaks of the increase are observed in ET 0 in the April–May and in the October. Variation in these parameters due to climate change might have an impact on the future water resource of the study area, which is mainly an agricultural based region, and will help in proper planning and management.

[1]  Akihiko Ito,et al.  Simulated impacts of climate and land‐cover change on soil erosion and implication for the carbon cycle, 1901 to 2100 , 2007 .

[2]  T. Wigley,et al.  Obtaining sub-grid-scale information from coarse-resolution general circulation model output , 1990 .

[3]  Deepak Khare,et al.  Shifting shoreline of Sagar Island Delta, India , 2014 .

[4]  Dimitrios Melas,et al.  Statistical downscaling of daily precipitation over Greece , 2008 .

[5]  Ashish Pandey,et al.  Statistical downscaling of temperature using three techniques in the Tons River basin in Central India , 2015, Theoretical and Applied Climatology.

[6]  Subimal Ghosh,et al.  Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output , 2011 .

[7]  S. Harun,et al.  Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature , 2013, Theoretical and Applied Climatology.

[8]  M. Collins,et al.  The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments , 2001 .

[9]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[10]  Ravi S. Nanjundiah,et al.  Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine , 2009 .

[11]  J. Suykens.  Nonlinear modelling and support vector machines , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).

[12]  Jeffrey G. Arnold,et al.  Estimating hydrologic budgets for three Illinois watersheds , 1996 .

[13]  J. Houghton,et al.  Climate change 2001 : the scientific basis , 2001 .

[14]  Dingfang Li,et al.  A macro-scale and semi-distributed monthly water balance model to predict climate change impacts in China , 2002 .

[15]  Xi Chen,et al.  Application of a SWAT Model for Hydrological Modeling in the Xixian Watershed, China , 2013 .

[16]  P. Mujumdar,et al.  Regional impacts of climate change on irrigation water demands , 2012 .

[17]  P. Jones,et al.  New estimates of future changes in extreme rainfall across the UK using regional climate model integrations. 1. Assessment of control climate , 2005 .

[18]  D. Wilks.  Multisite generalization of a daily stochastic precipitation generation model , 1998 .

[19]  H. Grassl,et al.  Theoretical and Applied Climatology , 2011 .

[20]  M. Babel,et al.  Forecasting climate change impacts and evaluation of adaptation options for maize cropping in the hilly terrain of Himalayas: Sikkim, India , 2015, Theoretical and Applied Climatology.

[21]  M. Nearing,et al.  POTENTIAL EFFECTS OF CLIMATE CHANGE ON RAINFALL EROSIVITY IN THE YELLOW RIVER BASIN OF CHINA , 2005 .

[22]  Raneesh Ky,et al.  Bias Correction for RCM Predictions of Precipitation and Temperature in the Chaliyar River Basin , 2013 .

[23]  Max P. Bleiweiss,et al.  Alternative climate data sources for distributed hydrological modelling on a daily time step , 2011 .

[24]  R. Schnur,et al.  A case study of statistical downscaling in Australia using weather classification by recursive partitioning , 1998 .

[25]  Ravi S. Nanjundiah,et al.  Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine , 2008 .

[26]  D. Allen,et al.  Evaluating different GCMs for predicting spatial recharge in an irrigated arid region , 2009 .

[27]  W. Landman,et al.  Statistical downscaling of GCM simulations to Streamflow , 2001 .

[28]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[29]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[30]  J. W. Kidson,et al.  Patterns of convection in the tropical pacific and their influence on New Zealand weather , 2002 .

[31]  T. Carter,et al.  IPCC technical guidelines for assessing climate change impacts and adaptations : part of the IPCC special report to the first session of the conference of the parties to the UN framework convention on climate change , 1994 .

[32]  J. Mercer.  Functions of positive and negative type, and their connection with the theory of integral equations , 1909 .

[33]  Z. Samani,et al.  Estimating Potential Evapotranspiration , 1982 .

[34]  F. Kamga.  Impact of greenhouse gas induced climate change on the runoff of the Upper Benue River (Cameroon) , 2001 .

[35]  R. Young,et al.  AGNPS: A nonpoint-source pollution model for evaluating agricultural watersheds , 1989 .

[36]  Toshio Koike,et al.  Global potential soil erosion with reference to land use and climate changes , 2003 .

[37]  Gilbert T. Bernhardt,et al.  A comprehensive surface-groundwater flow model , 1993 .

[38]  William J. Elliot,et al.  WEPP: soil erodibility experiments for rangeland and cropland soils , 1991 .

[39]  P. Mujumdar,et al.  A comparison of three methods for downscaling daily precipitation in the Punjab region , 2011 .

[40]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[41]  P. E. O'connell,et al.  An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system , 1986 .

[42]  P. P. Mujumdar,et al.  Assessment of hydrologic impacts of climate change in Tunga–Bhadra river basin, India with HEC‐HMS and SDSM , 2013 .

[43]  K. Keuler,et al.  High Resolution Climate Change Simulation for Central Europe , 2003 .

[44]  K. K. Kumar,et al.  Spatial asymmetry of temperature trends over India and possible role of aerosols , 2012, Theoretical and Applied Climatology.

[45]  Chong-Yu Xu,et al.  Statistical precipitation downscaling in central Sweden with the analogue method , 2005 .

[46]  S. G. Thampi,et al.  Influence of Scale on SWAT Model Calibration for Streamflow in a River Basin in the Humid Tropics , 2010 .

[47]  Y. Loo,et al.  Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia , 2015 .

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

[49]  Subimal Ghosh,et al.  Statistical downscaling of GCM simulations to streamflow using relevance vector machine , 2008 .

[50]  R. Leconte,et al.  Stochastic multi-site generation of daily weather data , 2009 .

[51]  A. C. Pandey,et al.  Variations in diurnal temperature range over India: Under global warming scenario , 2012 .

[52]  V. V. Srinivas,et al.  Downscaling of precipitation for climate change scenarios: A support vector machine approach , 2006 .

[53]  T. A. Buishand,et al.  On the Choice of the Temporal Aggregation Level for Statistical Downscaling of Precipitation , 2004 .

[54]  Pao-Shan Yu,et al.  Statistical downscaling of daily precipitation using support vector machines and multivariate analysis , 2010 .

[55]  Sergio M. Vicente-Serrano,et al.  Reference evapotranspiration variability and trends in Spain, 1961–2011 , 2014 .

[56]  R. Leander,et al.  Resampling of regional climate model output for the simulation of extreme river flows , 2007 .

[57]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[58]  P. Jones,et al.  New estimates of future changes in extreme rainfall across the UK using regional climate model integrations. 2. Future estimates and use in impact studies , 2005 .

[59]  P. K. Meena,et al.  Impact of Climate Change on Future Soil Erosion in Different Slope, Land Use, and Soil-Type Conditions in a Part of the Narmada River Basin, India , 2015 .

[60]  Z. Samani,et al.  Reference crop evapotranspiration from ambient air temperature , 1985 .

[61]  G. B. Pant,et al.  High-resolution climate change scenarios for India for the 21st century , 2006 .

[62]  Patrick Willems,et al.  Parameter estimation in semi‐distributed hydrological catchment modelling using a multi‐criteria objective function , 2007 .