Skip to main content

Advertisement

SpringerLink
Log in

PLS regression-based pan evaporation and minimum–maximum temperature projections for an arid lake basin in India

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures (T max and T min) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948–2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001–2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for T max and T min for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO2 concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abdi H (2003) Partial least squares regression (PLS-regression). In: Lewis-Beck M, Bryman A, Futing T (eds) Encyclopedia for research methods for the social sciences. Sage, Thousand Oaks, pp 792–795

    Google Scholar 

  • Amor IVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138:44–53

    Article  Google Scholar 

  • Anandhi A, Srinivas VV, Kumar DN, Nanjundiah RS (2009) Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine. Int J Climatol 29:583–603

    Article  Google Scholar 

  • Arnell NW, Hudson DA, Jones RG (2003) Climate change scenarios from a regional climate model: estimating change in runoff in southern Africa. J Geophys Res 108:4519

    Article  Google Scholar 

  • Benestad RE (2001) A comparison between two empirical downscaling strategies. Int J Climatol 21:1645–1668

    Article  Google Scholar 

  • Bergant K, Kajfež-Bogataj L (2005) N–PLS regression as empirical downscaling tool in climate change studies. Theor Appl Climatol 81:11–23

    Article  ADS  Google Scholar 

  • Cannon AJ, Lord ER (2000) Forecasting summertime surface-level ozone concentrations in the Lower Fraser Valley of British Columbia: an ensemble neural network approach. J Air Waste Manage Assoc 50:322–339

    CAS  Google Scholar 

  • Cannon AJ, Whitfield PH (2002) Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models. J Hydrol 259(1):136–151

    Article  Google Scholar 

  • Cavazos T, Hewitson BC (2005) Performance of NCEP variables in statistical downscaling of daily precipitation. Clim Res 28:95–107

    Article  Google Scholar 

  • Chong IG, Jun C-H (2005) Performance of some variable selection methods when multicollinearity is present. Chemometr Intell Lab Syst 78:103–112

    Article  CAS  Google Scholar 

  • Chu JT, Xia J, Xu CY, Singh VP (2010) Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China. Theor Appl Climatol 99:149–161

    Article  ADS  Google Scholar 

  • Crane RG, Hewitson BC (1998) Doubled CO2 precipitation changes for the Susquehanna basin: down-scaling from the genesis general circulation model. Int J Climatol 18:65–76

    Article  Google Scholar 

  • Dahm CN, Cleverly JR, Coonrod JA, Thibault JR, McDonennell DE, Gilroy DJ (2002) Evapotranspiration at the land/water interface in a semi-arid drainage basin. Freshw Biol 47(4):831

    Article  Google Scholar 

  • Eriksson L, Johansson E, Kettaneh-Wold N, Wold S (2001) Multi- and megavariate data analysis: principles and applications. Umetrics Academy, Umeå

    Google Scholar 

  • Gangopadhyay S, Clark M, Rajagopalan B (2005) Statistical downscaling using K-nearest neighbors. Water Resour Res 41:W02024

    Article  Google Scholar 

  • Gardner MW, Dorling SR (1998) Artificial neural networks (the multi layer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32:2627–2636

    Article  CAS  Google Scholar 

  • Ghosh S, Mujumdar PP (2008) Statistical downscaling of GCM simulations to streamflow using relevance vector machine. Adv Water Resour 31:132–146

    Article  ADS  Google Scholar 

  • Goyal MK, Ojha CSP (2009) “Downscaling of surface temperature for lake catchment in Arid Region in India using linear multiple regression and neural networks”, Interim Report, Hydraulic Engg. Section, CED, IIT Roorkee

  • Goyal MK, Ojha CSP (2010a) Downscaling of Surface Temperature for Lake Catchment in Arid Region in India using Linear Multiple Regression and Neural Networks. Int J Climatology, doi:10.1002/joc.2286

  • Goyal MK, Ojha CSP (2010b) Evaluation of Linear Regression Methods As Downscaling Tool in Temperature Projections Over Pichola lake Basin in India. Hydrological Processes, doi:10.1002/hyp.7911

  • Goyal MK and Ojha CSP (2010c) Evaluation of Various Linear Regression Methods for Downscaling of Mean Monthly Precipitation in Arid Pichola Watershed. Natural Resources, Scientific Research, 1(1),11–18 doi:10.4236/nr.2010.11002

  • Goyal MK, Ojha CSP (2010d) Robust Weighted Regression As A Downscaling Tool In Temperature Projections. International Journal of Global Warming, 2(3): 234–251

  • Goyal MK, Ojha CSP (2010e) Downscaling of Precipitation on a Lake Basin: Evaluation of Rule and Decision Tree Induction Algorithms. Hydrology Research (Accepted)

  • Hellström C, Chen D, Achberger C, Räisänen J (2001) Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation. Clim Res 19:45–55

    Article  Google Scholar 

  • Hertig E, Jacobeit J (2008) Downscaling future climate change: temperature scenarios for the mediterranean area. Glob Planet Change 63:127–131

    Article  ADS  Google Scholar 

  • Hewitson BC, Crane RG (eds) (1994) Neural nets applications in geography. Kluwer, Dordrecht

    Google Scholar 

  • Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Clim Res 7:85–95

    Article  Google Scholar 

  • Heyen H, Zorita E, von Storch H (1996) Statistical downscaling of monthly mean North Atlantic air-pressure to sea level anomalies in the Baltic Sea. Tellus 48A:312–323

    ADS  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC) (2007) Climate Change 2007—The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In: Solomon S et al. (ed) Cambridge University Press, Cambridge

  • Jessie CR, Antonio RM, Stahis SP (1996) Climate variability, climate change and social vulnerability in the semi-arid tropics. Cambridge University Press, Cambridge

    Google Scholar 

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471

    Article  Google Scholar 

  • Kendall MG (1975) Rank correlation methods. Charles Griffin, London, p 202

    MATH  Google Scholar 

  • Kettle H, Thompson R (2004) Statistical downscaling in European mountains: verification of reconstructed air temperature. Clim Res 26(2):97–112

    Article  Google Scholar 

  • Khobragade SD (2009) Studies on evaporation from open water surfaces in tropical climate. Ph.D. thesis, Indian Institute of Technology, Roorkee, India

  • Kilsby CG, Cowpertwait PSP, O’Connell PE, Jones PD (1998) Predicting rainfall statistics in England and Wales using atmospheric circulation variables. Int J Climatol 18:523–539

    Article  Google Scholar 

  • Lindgren F, Geladi P, Wold S (1993) The kernel algorithm for PLS I, Many observations and few variables. J Chemom 7:45–59

    Article  CAS  Google Scholar 

  • Linz H, Shiklomanov I, Mostefakara K (1990) Chapter 4 hydrology and water likely impact of climate change IPCC WGII report WMO/UNEP, Geneva

  • Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259

    Article  MathSciNet  MATH  Google Scholar 

  • Manne R (1987) Analysis of two partial least squares algorithms for multivariate calibration. Chemom Intell Lab Syst 1:187–197

    Article  Google Scholar 

  • Mishra AK, Ozger M, Vijay PS (2009) Trend and persistence of precipitation under climate change scenarios for Kansabati basin, India. Hydrol Process 23:2345–2357

    Article  ADS  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part I—a discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  • Ojha CSP, Goyal MK, Adeloye AJ (2010) Downscaling of Precipitation for Lake Catchment in Arid Region in India using Linear Multiple Regression and Neural Networks. The Open Journal of Hydrology 4:122–136

    Google Scholar 

  • Prudhomme C, Jakob D, Svensson C (2003) Uncertainty and climate change impact on the flood regime of small UK catchments. J Hydrol 277:1–23

    Article  Google Scholar 

  • Rannar S, Geladi P, Lindgren F, Wold S (1994) The kernel algorithm for PLS II, Few observations and many variables. J Chemom 8:111–125

    Article  Google Scholar 

  • Rosenberg NJ, McKenney MS, Martin P (1989) Evapotranspiration in a greenhouse-warmed world: a review and a simulation. Agric For Meteorol 47:303–320

    Article  Google Scholar 

  • Schoof JT, Pryor SC (2001) Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networks. Int J Climatol 21:773–790

    Article  Google Scholar 

  • Shannon DA, Hewitson BC (1996) Cross-scale relationships regarding local temperature inversions at Cape Town and global climate change implications. S Afr J Sci 92(4):213–216

    CAS  Google Scholar 

  • Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330(3–4):621–640

    Article  Google Scholar 

  • Vimont DJ, Battisti DS, Naylor RL (2009) Downscaling Indonesian precipitation using large-scale meteorological fields. Int J Climatol. doi:10.1002/joc.2010

    Google Scholar 

  • Weisse R, Oestreicher R (2001) Reconstruction of potential evaporation for water balance studies. Clim Res 16(2):123–131

    Article  Google Scholar 

  • Wilby RL (1998) Modelling low-frequency rainfall events using airflow indices, weather patterns and frontal frequencies. J Hydrol 213(1–4):380–392

    Article  Google Scholar 

  • Wilby RL, Dawson CW, Barrow EM (2002) SDSM—a decision support tool for the assessment of climate change impacts. Environ Modell Softw 17:147–159

    Google Scholar 

  • Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) The guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change (IPCC), prepared on behalf of Task Group on Data and Scenario Support for Impacts and Climate Analysis (TGICA)

  • Willmott CJ, Rowe CM, Philpot WD (1985) Small-scale climate map: a sensitivity analysis of some common assumptions associated with the grid-point interpolation and contouring. Am Cartogr 12:5–16

    Article  Google Scholar 

  • Wold S (1995) PLS for multivariate linear modelling. In: van de Waterbeemd H (ed) QSAR: chemometric methods in molecular design, vol 2. Wiley, Weinheim, pp 195–218

    Google Scholar 

  • Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab Syst 58:109–130

    Article  CAS  Google Scholar 

  • Xoplaki E, Luterbacher J, Burkard R, Patrikas I, Maheras P (2000) Connection between the large-scale 500 hPa geopotential height fields and precipitation over Greece during wintertime. Clim Res 14:129–146

    Article  Google Scholar 

  • Zhang XC, Nearing MA, Garbrecht JD, Steiner JL (2004) Downscaling monthly forecasts to simulate impacts of climate change on soil erosion and wheat production. Soil Sci Soc Am J 68(4):1376–1385

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology, Roorkee, India

    Manish Kumar Goyal & C. S. P. Ojha

  2. Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Canada

    Manish Kumar Goyal

Authors
  1. Manish Kumar Goyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. S. P. Ojha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manish Kumar Goyal.

Appendix

Appendix

Abbreviations used in text

CCCma: Canadian Center for Climate Modeling and Analysis

CGCM: Canadian Coupled Global Climate Model

CGCM3: Third-generation Canadian Global Climate Model

GCM: Global Climate Model

IPCC: Intergovernmental panel on climate change

NCAR: National Center for Atmospheric Research, USA

RMSE: Root mean square error

SRES: Special report of emission scenarios

Ta 925: Air temperature at 925 hPa

Ua 925: Zonal wind at 925 hPa

Va 925: Meridional wind at 925 hPa

Ta 950: Air temperature at 500 hPa

Va 500: Meridional wind at 500 hPa

Zg 500: geo-potential height at 500 hPa

Ta 200: Air temperature at 200 hPa

Ua 200: Zonal wind at 200 hPa

Va 200: Meridional wind at 200 hPa

Rights and permissions

Reprints and permissions

About this article

Cite this article

Goyal, M.K., Ojha, C.S.P. PLS regression-based pan evaporation and minimum–maximum temperature projections for an arid lake basin in India. Theor Appl Climatol 105, 403–415 (2011). https://doi.org/10.1007/s00704-011-0406-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-011-0406-z

Keywords

Search

Navigation