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

• Published in: Theoretical and applied climatology Vol. 105; no. 3-4; pp. 403 - 415
• Main Authors: Goyal, Manish Kumar, Ojha, C. S. P.
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.10.2011; Springer; Springer Nature B.V
• Subjects: Analysis; Aquatic Pollution; Arid zones; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Carbon dioxide; Climate change; Climate models; Climatology; Correlation coefficient; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Evaporation; Exact sciences and technology; External geophysics; Freshwater; General circulation models; Global climate; Global temperature changes; Lake basins; Lakes; Meteorology; Neural networks; Original Paper; Pan evaporation; Regression analysis; Statistical models; Waste Water Technology; Water Management; Water Pollution Control; Asia; India; Statistical Downscaling Model; Partial Little Square Regression; Partial Little Square Regression Model; Partial Little Square; Root Mean Square Error; Coupled model; efficiency; trend-surface analysis; General circulation models; Scale reduction; Carbon dioxide; neural networks; Model output; Impact study; Climate models; digital simulation; monthly average; greenhouse gas; Dynamical climatology; Arid region; Statistical model; Reanalysis; Minimum temperature; Maximum temperature; lakes; correlation coefficient; evaporation; Comparative study; climate change
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-011-0406-z

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 CO 2 concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method.