Projected changes in temperature and rainfall during 21st century simulated by CSIRO-Mk-3-6-0 model under RCP based scenarios in Punjab (original) (raw)
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CLIMATE CHANGE PROJECTIONS FOR PUNJAB DURING 21ST CENTURY
The summary of the projected changes in climatic parameters in the Punjab state of India as simulated by by five GCMs (CSIRO-Mk3-6-0, FIO-ESM, IPSL-CM5A-MR and Ensemble model) under four RCP scenarios in the mid (2020-2049) and end (2066-2095) of 21 st century are described below. Maximum temperature is projected to vary in the state from the baseline period.
Climate Predictions for Ludhiana District of Indian Punjab under RCP 4.5 and RCP 8.5
International Journal of Environment and Climate Change
Climate change poses significant threats to global food security and water resources. In a present study, a Global Climate Model HAD GEM2-ES under RCPs 4.5 and 8.5 was used for climate prediction study. The study spanned 46 years of baseline (1970-2015) as well as two future periods’ mid-century (MC) (2020-2050) and end century EC (2060-2090). The results showed that the temperature would increase by 1.56°C and rainfall would decrease by 98 mm in MC (2020-2050); and 3.11°C and 90 mm in EC (2060-2090), respectively under RCP 4.5. In RCP 8.5 the increase in temperature and rainfall was 2.75°C and 153 mm, respectively in MC and the corresponding values in EC was 5.46°C and 251 mm, respectively.
MAUSAM
The projected temperature, rainfall and solar radiation derived from four General Circulation Models namely CSIRO-Mk3-6-0, FIO-ESM, IPSL-CM5A-MR and Ensemble model under four RCP (Representative Concentration Pathways) scenarios were analyzed on annual basis for four agro-climatic zones of Punjab. The inherent bias in the simulated data was corrected by the difference method at monthly scale for maximum temperature, rainfall and at daily scale for solar radiation. In general, the temperatures are expected to increase linearly in future while the solar radiation and rainfall would decrease under the four RCP scenarios. Amongst the four models, the maximum temperature is predicted to increase from the baseline during mid/end century by 0.5 to 1.2/1.6 to 2.6 °C and minimum temperature is predicted to increase from the baseline during mid/end century by 1.1 to 2.6/2.7 to 4.1 °C by CSIRO-Mk3-6-0, respectively followed by IPSL-CM5A-MR model in decreasing order. The solar radiation predict...
MAUSAM, 2021
In this study,the future simulated climatic data (temperature and rainfall) for the 21st century were downscaled using the regional climate model, viz., PRECIS model (Providing Regional Climates for Impact Studies) for different agro-climatic zones, i.e., Zone II (Ballowal Saunkhri), Zone III (Ludhiana, Amritsar, Patiala and Jalandhar) and Zone V (Bathinda) of Punjab. The corrected simulated data were then analyzed on the annual and seasonal basis to quantify the changes in maximum and minimum temperature and rainfall. The study showed that the maximum and minimum temperature and rainfall by the end of 21st century are likely to increase by 2.0 to 2.2 °C, 3.3 to 5.4 °C and 33 to 66% respectively in agro-climatic zone II; by 0.4 to 5.8 °C, 2.5 to 7.4 °C and 3 to 62% respectively in agro-climatic zone III and by 0.5 to 4.0 °C, 4.7 to 7.7 °C and 58 to 69% respectively in agro-climatic zone V at different locations of Punjab state under various scenarios of climate change. The trend an...
Current Science, 2021
Trend analysis of annual rainfall over the coastal districts of Odisha, India showed statistically nonsignificant increasing trend in all districts, except Ganjam. Whereas the maximum and minimum temperature showed significant increasing trend. Warming in these districts is mainly due to increasing minimum temperature during summer and rainy season, and maximum temperature during winter. Future climate projection results revealed, the annual mean rainfall is expected to change by 0.1-2.2%,-0.3-0.7% and 1.5-3.2% (RCP 4.5), and 3.6-7.9%, 3.7-6.6% and 8.5-14% (RCP 8.5) during the near (2011-39), mid (2040-69) and late (2070-99) centuries respectively. Anticipate climate change will have a marginal impact on total rainfall, and a major impact on its distribution. The annual mean minimum temperature is expected to increase by 0.60-0.73°C, 0.71-0.88°C, 1.20-1.42°C (RCP 4.5), and 1.77-2.14°C, 1.56-1.68°C, 3.06-3.73°C (RCP 8.5) during near, mid and late centuries respectively. Similarly, the annual mean maximum temperature is expected to increase by 0.61-0.66°C, 0.68-0.72°C and 1.35-1.55°C (RCP 4.5), and 1.79-1.97°C, 1.73-2.01°C and 3.08-3.44°C (RCP 8.5) during near, mid and late centuries respectively. Season-wise projection revealed that the change in rainfall and temperature is expected to be more in winter and summer under both the RCP scenarios. The projected future climate change will have both positive and negative impacts on agriculture. The negative impacts are expected to be more pronounced during kharif in comparison to rabi.
2020
A simulation study was conducted to analyse the effect of projected changes in climatic parameters on yield of cereal (rice, maize and wheat) crops in different agro-climatic zones of Punjab state. The summary of projected changes in temperature and rainfall (Table I) along with baseline (2010-2021) values during the growing season of respective crops in the state as simulated by Ensemble model under two Representative Concentration Pathways (RCP 4.5 and RCP 6.0) and three time periods (EC : 2030-50, MC : 2051-70 and LC : 2071-90) are given below: Projected changes in maximum temperature • Rice season-An increase in maximum temperature from the baseline (35.4 o C) is predicted under RCPs 4.5 and 6.0 respectively by 1.3 and 1.2 o C during EC, by 1.8 and 1.7 o C during MC and by 2.2 and 2.0 o C during LC. • Maize season-An increase in maximum temperature from the baseline (35.2 o C) is predicted under RCPs 4.5 and 6.0 respectively by 1.0 and 0.7 o C during EC, by 1.5 and 1.2 o C during MC and by 1.8 and 1.7 o C during LC. • Wheat season-A variation in maximum temperature from the baseline (24.9 o C) is predicted under RCPs 4.5 and 6.0 respectively by-0.1 and-0.6 o C during EC, by 0.6 and 0.1 o C during MC and by 1.1 and 0.9 o C. Projected changes in minimum temperature • Rice season-An increase in minimum temperature from the baseline (24.0 o C) is predicted under RCPs 4.5 and 6.0 respectively by 4.1 and 3.8 o C during EC, by 4.7 and 4.4 o C during MC and by 4.9 and 5.0 o C during LC. • Maize season-An increase in minimum temperature from the baseline (25.7 o C) is predicted under RCPs 4.5 and 6.0 respectively by 2.6 and 2.4 o C during EC, by 3.2 and 3.0 o C during MC and by 3.5 and 3.6 o C during LC. • Wheat season-A variation in minimum temperature from the baseline (10.6 o C) is predicted under RCPs 4.5 and 6.0 respectively by-1.1 and-1.5 o C during EC, by-0.5 and-0.8 o C during MC and by-0.2 and 0.1 o C during LC. Projected changes in rainfall • Rice season-A decrease in rainfall from the baseline (556 mm) is predicted under RCPs 4.5 and 6.0 respectively by 137 and 148 mm during EC, by 94 and 107 mm during MC and by 88 and 48 mm during LC. 1 2 • Maize season-A decrease in rainfall from the baseline (524 mm) was observed under RCPs 4.5 and 6.0 respectively by 157 and 166 mm during EC, by 111 and 123 mm during MC and by 103 and 68 mm during LC. • Wheat season-A decrease in rainfall from the baseline (125 mm) was observed under RCPs 4.5 and 6.0 respectively by 67 and 67 mm during EC, by 67 and 68 mm during MC and by 72 and 66 mm during LC. Optimization of crop management practices for cereal crops in Punjab The yield of rice, maize and wheat were simulated using models (CERES-Rice, CERES-Maize and CERES-Wheat) with temperature and rainfall data predicted by the Ensemble model during the 60 years (2030-2090) time period. Later the crop models were used as a tool to identify/ fine tune agronomic practices for the sustaining high productivity of crops under two scenarios (RCP 4.5 and RCP 6.0) of climate change during three time periods (EC: 2030-50, MC: 2051-70 and LC: 2071-90) in the state. The salient findings of the study (Fig I, II and III) are given below: Optimized crop management practices for rice crop • The suitable transplanting window will be from 26 June to 16 July in Punjab. • The suitable rice cultivar for the state under future conditions would be PR126. • The increased nitrogen application @155 kg/ha during suitable transplanting window. • The agroclimatic zone V (Abohar) was found as not suitable for rice cultivation under future climatic scenarios. Optimized crop management practices for maize crop • The suitable sowing window will be from 14 to 16 June in agro-climatic zone II and III and 5-20 May in agro-climatic zone V (Faridkot) of Punjab. • The suitable maize cultivar for the state under future conditions would be PMH1. • The increased nitrogen application @145-185 kg/ha during suitable sowing window. • The agroclimatic zone IV (Bathinda) and V (Abohar) were found as not suitable for maize cultivation under future climatic scenarios. Optimized crop management practices for wheat crop • The suitable sowing window was observed from 24 to 29 November in agro-climatic zone II, III and V of Punjab under futuristic climatic scenarios. • The suitable wheat cultivars for the state under future conditions would be HD2967 and PBW725. 2 3 • The increased nitrogen application @ 150-230 kg/ha during suitable sowing window. • In agro-climatic zone IV (Bathinda) none of the sowing dates were found suitable for sustainable wheat cultivation under future climatic scenarios. Table I-Baseline and projected temperature and rainfall during the crop growth season in Punjab Crop Baseline period (2010-21) Early century (2030-50) Mid century (2051-70) Late century (2071-90) RCP 4.5 RCP 6.0 RCP 4.5 RCP 6.0 RCP 4.5 RCP 6.0 Maximum temperature (o C)
The changing climate pattern in terms of maximum and minimum temperature, rainfall and relative humidity were analyzed based on relevant time series data/information for fairly long period of about four decades in Ludhiana district of Punjab using statistical tools such as coefficient of variation, graphical representation and samples mean (t-test) for periodical shifts. The study has brought out that during the last four decades, region experienced significant increase in average temperature (both maximum as well as minimum temperature) for the months of February, March, April, August and November. Similarly, months of May, October and December experienced significant increment of average minimum temperature. On the whole, consistent rise in average monthly temperature leading to enhanced level of warming has been observed. Regarding rainfall, various months observed with significant changes in rainfall were March, September, October, November and December. Among these, average rainfall in the months of March, November and December showed a significant decrease while September and October months showed a significant increase over the last about four decades. Monthly relative humidity increased in almost all months except April. In case such climate trend continues over the next few decades, may have detrimental effects on the agricultural output if suitable climate adaptive strategies are not put in place with top priorities in terms of suitable research and development.
Atmospheric Research, 2018
Climate change is a global issue that's affecting food security. An increase and decrease in temperature due to climate change is expected across many regions of the world. Analysis of 39 weather stations (Pakistan) trend for maximum and minimum temperatures was done on monthly, seasonal and annual observations. Two statistical tests (Sen's slope and Mann-Kendall) were applied to find out the slopes and magnitude of climate change trend. This statistical analysis was carried out to study the possible variations for maximum and minimum temperature trend. A statistical downscaling climate projection model (SimCLIM) was used to predict magnitude of maximum and minimum temperature for 2030 and 2060. Ensemble of 40 General Circulation Models (GCMs) was used with median Representative Concentration Pathway (RCP-6.0) for future projections in SimCLIM. This study showed more number of positive trends for maximum temperature over all the weather stations. Significantly positive temperature trend was observed in February and March for maximum temperature for all sites ranges from 0.06 to 0.51 o C. Mostly, statistically significant negative trend (-0.06 to-0.30 o C) was found in Balochistan province and northern areas of Pakistan. In future, minimum temperature projected by model showed negative trends for 60% of weather sites for December where, the negative trend also increased for monthly and seasonal analysis. Minimum temperature trend reveal that December has large number of sites with negative trends with high magnitude, which further decreased for annual followed by seasonal analysis. Minimum temperature projections showed similar trends with past December results but negative trends decreased for seasonal and annual resolution. Future projections also reveal that annual maximum and minimum temperature will be increased for 2060 as compared to 2030. These results may have significant effect on agriculture of northern and high mountain areas of Pakistan, which could be managed by sustainable agricultural activities.
Climate change projections - A district wise analysis for rainfed regions in India
Rainfed agriculture is practised in arid, semi-arid and dry-sub-humid-regions of the country. The climate projections are reported generally at the all India level or at the resolution of grids of different dimensions. This paper attempted to derive and report the climate projections given by the PRECIS for A1B scenario in the form agriculturally relevant variables for 220 districts where rainfed agriculture is predominant. Districts with an average rainfall of less than 1500 mm and are included either in DPAP or DDP and those with less than 30 per cent of net sown area under irrigation are included in the study. The climate projections provided at a grid size of 50 x 50 km are converted into district level estimates. Annual rainfall is projected to increase by more than five per cent in 173 districts and decrease by more than five per cent in 42 districts during the mid-century compared to the baseline. The later part of the century is likely to be much wetter with 205 districts showing an increase in rainfall by more than five percent and only five districts projected to receive relatively less rainfall. The number of rainy days is projected to not change much in a majority of districts during the both periods. The end-century is likely to witness much variability in the onset of monsoon, which is projected to arrive late by more than five days in 42 districts. Incidence of drought is observed to increase in 62 districts during midcentury and in 134 districts during the end-century. The average maximum temperature is projected to increase by 1.5 to 2 0 C in a majority of districts during mid-century and by more than 2 0 C in a few districts. Temperature is likely to be much warmer during the end-century with projections of 3.5 to 4 0 C in most districts. These projections have implications to planning and targeting technology development and transfer as well as planning for development interventions.
Assessment of Climatic Parameters for Future Climate Change in a Major Agricultural State in India
Climate, 2021
The change in future climate will have a prominent impact on crop production and water requirement. Crop production is directly related to climatic variables. Temperature, solar radiation, wind, precipitation, CO2 concentration and other climatic variables dictate crop yield. This study, based on long-term historical data, investigates the patterns and changes in climatic variables (precipitation, temperature, and solar radiation) that would most significantly affect the future crop production in many parts of the world, and especially in India, where most farmers depend on rainfall for rice production. Statistical analyses—box and whisker plot, mean absolute error, Taylor diagram, double mass curve, Mann–Kendall trend test, and projected climate change—were used to assess the significance of the climatic factors for the purpose of agricultural modeling. Large variability in precipitation may cause the flash floods and affect the farming, and at the same time, increase in temperatur...