Statistical and Simulation Modeling Approach
5.3 Materials and Methods
5.3.2 Projected impact of climate change on rice productivity
5.3.2.4 Design of experiment for evaluating climate change impact on rice productivity
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5.3.2.4 Design of experiment for evaluating climate change impact on rice
transplanting dates starting from 20th June to 9th August at an interval of 10 days (TP1: 20th June, TP2: 30th June, TP3: 10th July, TP4: 20th July, TP5: 30th July and TP6: 9th August) were considered for realistic results. This option was expected to help in identifying the easiest adaptation strategy (Krishnan et al. 2007) as the ‘adjustment of transplanting dates’
for the study area which may help to offset detrimental effects of climate change, if any on rice productivity. The relative yield changes under different climate change scenarios referred to yields predicted over the baseline climate were used in this analysis rather than absolute yields.
5.3.2.4.2 Sensitivity scenarios
The sensitivity of the CERES-Rice model to temperature, CO2 levels, rainfall and solar radiation were analyzed by arbitrarily creating anomalies in the baseline climate data created. The potential yield of rice variety Ranjit was simulated under different combinations of CO2 and temperature including with the ‘fixed increment’ changes in CO2
(390, 400, 450, 500, 550, 600, 650 and 750 ppm) and temperature (ambient, +1, +2, +3, +4, +5, +6 and +7) individually, and with all combinations of these levels of CO2 and temperature. For simplicity, anomalies ranging from ±10% to ±20% of growing season rainfall over the observed baseline climate data were used for rainfall sensitivity experiment. To study the physiological effects of solar radiation, levels ranging from –1 MJ/m2/day to 3 MJ/m2/day with an increment of 1 MJ/m2/day were used. The inbuilt weather generator of the DSSAT software generated the modified weather data accordingly over the baseline, and these data were used as the daily weather inputs for CERES-Rice model.
5.3.2.4.3 Building up of climate change scenarios
To create climate change scenarios for the study area, output of global circulation models (GCMs) available in literature were not considered in this study because of their already recognized relatively poor micro-level resolution, inadequate coupling of atmospheric and ocean processes, apparently poor simulation of cloud processes and inadequate representation of the biosphere and its feedbacks (Krishnan et al. 2007). The poor resolution is likely to be significant in the northeastern parts of India where the relief is varied and local climate may be quite different from the average across the area used by
GCMs. An alternative to global modeling is regional climate modeling. Regional climate models (RCM) can better take into account of regional geography and topography (e.g.
mountains and oceans), and are therefore better at representing local variations in climate.
Regional models that give fine resolution climate information over limited areas are being coupled into GCMs to produce climate information for impact assessments.
To provide an estimate of the overall effect of climate change on rice productivity, different possible scenarios were considered in simulation of CERES-Rice model based on the Hadley Centre’s high-resolution RCM – PRECIS (Providing REgional Climate for Impact Studies) output which had been run at the Indian Institute of Tropical Meteorology (IITM), Pune, at 50 km x 50 km horizontal resolution for A1B scenario over South Asian domain (MoEF 2010) as well as taking current trends of temperature, solar radiation and rainfall in the baseline climate data of the study area. The output of Q14 (Quantifying Uncertainty in Model Prediction) simulation of PRECIS indicated an increase of monsoon rainfall by 8% and post-monsoon rainfall by 4% over 1961−1990 in the 2030s (2020–
2050) in northeastern part of India. The temperature scenario represented an increase in temperature in the order of 1.6°C during monsoon season and 2.3°C during post-monsoon season over 1961−1990 baseline (MoEF 2010). The limitations of these projections are based on a small number of simulations from one regional climate model.
As the baseline scenario of the study area already contained weather data up to 2010, temperature increments were kept within 1.5°C. Although post-monsoon temperature is predicted to rise at a higher magnitude than monsoon season temperature, temperature increments were added uniformly for simplicity. Similarly, rainfall anomalies were also added in the baseline scenario uniformly over the entire rice growing season.
Temperature scenarios were built by adding 0.5°C, 1.0°C and 1.5°C in the baseline temperature data (Table 5.3). In order to assess the impact of any future increase or decrease in the observed rainfall in the study area, a 10% decrease and 10% increase in rainfall was considered in model simulations. Recent observations as well as climate model simulations indicate that future temperature change linked to global warming might be characterized by a marked asymmetry between daytime and maxima nighttime minima (Karl et al. 1991). The increase in temperature would be more pronounced in the nighttime
temperature, thus leading to a decline in diurnal temperature range. This asymmetry between daytime and nighttime temperature was also considered in the model simulation (Scenario G and H). Due to non-availability of future solar radiation scenario in literature, observed solar radiation trend in baseline data (1971–2010) were considered, while building the solar radiation scenario for coming 30 years. The observed linear trend of solar radiation during rice growing season of 1971−2010 was −0.038MJ/m2/day/year.
Assuming this trend will continue more or less consistently in the coming 30 years, solar radiation during rice growing season is likely to decline by 1.14 MJ/m2/day in 2040.
The CO2 concentration considered for the model simulations is based on the Business-as- usual scenario of IPCC (IPCC 1994) with a 1.3% per year compounded increase of CO2, yielding an average equivalent CO2 concentration of about 660 ppm for the 2040–2049 decade. The equivalent concentration represents CO2 and other greenhouse gases. As such, the actual plant usable CO2 concentration is only about 460 ppm (Sinha 1993). Hence, a value of 460 ppm of CO2 concentration has been used in all simulations (Table 5.3).
Altogether, eight scenarios were built with different combinations of temperature and rainfall along with an atmospheric CO2 concentration of 460 ppm and solar radiation (−1MJ/m2/day). In addition to these, scenario I was built considering the observed linear trend of maximum temperature, minimum temperature and growing season rainfall data during the baseline (1971–2010) of the study area for 2040 (Table 5.3).
Table 5.4 Climate change scenarios considered in climate change impact studies on rice Scenarios CO2
concentration (ppm)
Changes over baseline climate Solar radiation
(MJ/m2/day)
Tmax (°C)
Tmin (°C)
Rainfall (%)
A 460 −1 0.5 0.5 −10
B 460 −1 0.5 0.5 +10
C 460 −1 1.0 1.0 −10
D 460 −1 1.0 1.0 +10
E 460 −1 1.5 1.5 −10
F 460 −1 1.5 1.5 +10
G 460 −1 0.5 1.5 0
H 460 −1 1.0 2.0 0
I 460 −1 0.2 0.5 −10