Climate Change Assessment Using Precis-RCM Data for Hardwar District of Uttarakhand.

In the past few decades, continuous intervention with the environmental landscape in the form of land use practices (water diversions, deforestation, local agriculture practices, industrialization etc.) in the Haridwar district of Uttarakhand India has impacted the region on various accounts. This is likely to be further aggravated in view of the increasing variability in the weather condition. Thus there is urgent need felt to quantitatively asses the future climatic scenario for initiating and effectively undertaking the adaptation strategies for safe and sustained agricultural growth. For undertaking this study, observed daily weather data (rainfall, maximum temperature and minimum temperature) was obtained from the Department of Water Resources Development and Management, Indian Institute of Technology Roorkee (29⁰52’N and 77⁰ 53’E) for the period 1979-2013. The PRECIS RCM data was also downloaded for Roorkee grid station for the same period. Downloaded data was bias corrected using linear correlation method. Observed and bias corrected daily data were tested for their significance at 95% probability of occurrence employing various statistical tools viz. correlation coefficient (rainfall, 0.64; maximum temperature, 0.84; minimum temperature, 0.93), ratio score (rainfall, 0.7), MBE (rainfall, -0.9; maximum temperature,-0.2; minimum temperature, 0.1) and NMSE (rainfall, 0.005; maximum temperature, 0.08; minimum temperature, 0.06), Z (1.96 at 0.05 ls) (rainfall,0.05; maximum temperature,0.35; minimum temperature, -0.26) & F(1.26 at 0.05 ls)  (rainfall, 0.85; maximum temperature,0.98; minimum temperature,0.96) tests. These tests revealed that the observed and bias corrected PRECIS RCM data were statistically insignificantly different.

In view of this, the daily PRECIS RCM weather data (rainfall, maximum and minimum temperature) was also downloaded for grid station Roorkee (29⁰52’N and 77⁰ 53’E) for the period 2014-2090. This was bias corrected using linear correlation method. Trend analysis was done using Mann-Kendall test and Theil Sen’s Slope. Analysis of the bias corrected data revealed that the Roorkee station will in general record increasing trends in rainfall (@ 5.83 mm/year), maximum temperature (@ 0.05⁰C/year) and minimum temperature (@ 0.04⁰C/year). The PRECIS- RCM bias corrected trend line indicates that the present normal rainfall of 948 mm/year will rise @ 5.79 mm/ year and become 1446 mm/year by 2090. In case of maximum temperature of the present normal temperature of 28 ⁰C will rise @ 0.047 ^oC/ year and become 32.0⁰C whereas the minimum temperature of 16.1⁰C will rise @ 0.038 ^oC and become 19.4⁰C by 2090. Out of 77 year (2014-2090) it is observed that 14 years will be observed as drought years (<25 % of Normal) whereas 13 years will be the years of excessive rainfall (>25 % of Normal). The remaining 50 years will be observed as normal years with respect to rainfall occurrence.

After the completion of bias correction of PRECIS RCM data monthly trend analysis was made by using Mann Kendall Trend Test and slope was detected using Theil Sen’s method. Results indicated that rainfall during July (@ 4.4 mm/yr) and August (@ 4.68 mm/yr) will increase.

Bias corrected weather data was used to run DSSAT-Rice -CERES model to calculate yield under the best management practices with the currently available Rice varieties (12 numbers). It is observed that almost all the varieties showed a decreasing trend in their productivity after 2020.  DSSAT was also run under changing CO2 levels (350, 450, 550, 650 and 750 ppm) and was observed that all the varieties responded positively.

 

Keywords: PRECIS, Climate change, Bias correction, Mann Kendall Trend test, Sen’s Slope.