Search

Under global warming and climate change (CC), the vulnerable piece of land is India's Eastern-Himalayan-Region (EHR), where millions' livelihood activities are at risk. Quantifying the degree of CC-induced vulnerability over the highly climate-sensitive mountain zone is challenging due to data inadequacy. This paper attempted to assess the CC perception of kiwifruit growers under EHR and the impact of CC on its cultivation. Perceived and scientific data were used for accurate decision-support information. The climatic variables of two locations were analyzed for trend using Mann–Kendall's test. Results suggested that the rainfall trend was found non-significant for both locations, but there was a negative trend during winter for Pasighat and during all seasons for Itanagar. Maximum temperature exhibited an increasing trend for annual, and minimum temperature showed an increasing trend across seasons for the Pasighat region. For the Itanagar region, the maximum temperature was found to have a significantly increasing trend in all seasons, while the minimum temperature showed an increasing trend during winter and annual seasons. Morning relative humidity showed an increasing trend for annual data for Pasighat, while annual evening relative humidity was significantly increased for Itanagar. The study shows that the indigenous farming communities were following shifting cultivation and diversifying their traditional agriculture to kiwifruit cultivation as an adaptation strategy. The community had a clear perception of CC trends and an inadequate perception of the impact on agriculture, therefore, engaging indigenous communities and other stakeholders to postulate a comprehensive adaptation and mitigation strategy for CC. [ABSTRACT FROM AUTHOR]

It is important to estimate a high-resolution spatiotemporal distribution of temperature rise under climate change for effective adaptation and mitigation planning. This paper studies the threshold crossing time (TCT) for eight Indian warming targets (IWTs) between 1.5 and 5 °C based on regional climate models (RCMs) participating in The Coordinated Regional Downscaling Experiment (CORDEX)—South Asia project. To evaluate the performance of CORDEX experiments in simulating the near-surface mean temperature, the model performance index (MPI) is used which represents that the CanESM experiment performs best for simulating Tmean over India. All India average Tmean projected by the multi-model ensemble (MME) represents that 1.5 °C TCT is about 2033 ± 3 and 2028 ± 3 in RCP4.5 and RCP8.5 scenarios, respectively. During the twenty-first century, Indian subcontinent can experience five IWTs (1.5–3.5 °C) under RCP4.5 and all eight under scenario RCP8.5. The fastest warming region in India is the western Himalayan region (North India) and north-west region (Indian Thar Desert). [ABSTRACT FROM AUTHOR]

Given the general notion that dynamical downscaling leads to added accuracy in both historical simulations as well as climate change projections, this paper investigates its validity over India using historical data (1975-2005) from the CORDEX models and their driving global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5), and comparing them against observed temperature and rainfall. We find that downscaling invariably leads to an improvement in the spatial pattern of surface air temperature, but compared to the driving GCMs, the errors in magnitude after downscaling are even worse in some cases. In regard to JJAS rainfall simulations, the CMIP5 driving GCMs are found to be superior to their dynamically downscaled counterparts both in terms of spatial patterns as well as magnitude of errors. Both CMIP5 driving GCMs as well as the CORDEX models underestimate rainfall during JJAS; however, negative bias in CORDEX models is worse. Unlike the driving CMIP5 GCMs, their dynamically downscaled counterparts simulate an early onset followed by a slow and late withdrawal of the Indian summer monsoon rainfall. The frequency of occurrence of rainfall intensities is simulated well by both sets of models in the lower intensity regime (0-20 mm/day); however, for higher intensities, the driving CMIP5 GCMs underestimate whereas the CORDEX models overestimate. [ABSTRACT FROM AUTHOR]

In this paper, we present regional climate change projections for the Tamil Nadu state of India, simulated by the Met Office Hadley Centre regional climate model. The model is run at 25 km horizontal resolution driven by lateral boundary conditions generated by a perturbed physical ensemble of 17 simulations produced by a version of Hadley Centre coupled climate model, known as HadCM3Q under A1B scenario. The large scale features of these 17 simulations were evaluated for the target region to choose lateral boundary conditions from six members that represent a range of climate variations over the study region. The regional climate, known as PRECIS, was then run 130 years from 1970. The analyses primarily focus on maximum and minimum temperatures and rainfall over the region. For the Tamil Nadu as a whole, the projections of maximum temperature show an increase of 1.0, 2.2 and 3.1 °C for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095), respectively, with respect to baseline period (1970-2000). Similarly, the projections of minimum temperature show an increase of 1.1, 2.4 and 3.5 °C, respectively. This increasing trend is statistically significant (Mann-Kendall trend test). The annual rainfall projections for the same periods indicate a general decrease in rainfall of about 2-7, 1-4 and 4-9 %, respectively. However, significant exceptions are noticed over some pockets of western hilly areas and high rainfall areas where increases in rainfall are seen. There are also indications of increasing heavy rainfall events during the northeast monsoon season and a slight decrease during the southwest monsoon season. Such an approach of using climate models may maximize the utility of high-resolution climate change information for impact-adaptation-vulnerability assessments. [ABSTRACT FROM AUTHOR]

Climate variability is directly or indirectly affecting crop production and threatens the food security of livelihood. The Cauvery delta region (CDR) is the agricultural protective zone of Tamil Nadu state, the crop production in this region is vulnerable to climate change impacts. In this study, the future climate change impact of black gram yield has been assessed using the DSSAT-CROPGRO model in the CDR. The model was calibrated during the 2018 sowing period and validated during 2019 cultivation practices for determining the genetic coefficient of the black gram cultivar VBN6. The RMSE values of observed and simulated yield were 910 and 890 kg ha−1 respectively. RegCM4 model is used for the simulation of Vigna mungo yield under RCP 4.5 and 8.5 scenarios during the 1985–2015 (baseline), 2020–2039 (near-century), 2040–2069 (mid-century), and 2070–2099 (end-century). The results reveal that simulation of black gram yield change could be around 34%, 52%, and 25% during the near-century, mid-century, and end-century respectively for RCP 4.5 based on the baseline period. Adjusting the sowing date is evaluated as an adaptation option in future climate change scenarios, and results revealed that early sowing in the winter season could be the best suitable growing period for better yield in this region. [ABSTRACT FROM AUTHOR]

Projections of future streamflow extremes are subjected to various sources of uncertainties. Uncertainties arising from different stages in the modelling process of extremes contribute to the overall uncertainty in the final estimates. In this study, some important sources of uncertainties that occur in the modelling chain for projection of future extremes are analysed. The contribution of climate models, hydrological models, representative concentration pathways (RCPs) and the parameters of the extreme value distribution to the overall uncertainty in the streamflow return levels of the Chaliyar river basin in Kerala, India, was quantified using analysis of variance. 5, 10, 25, and 50-year return levels for the near (2021–2050) and far (2070–2099) future periods were computed. Results of the study indicate that the median values of return levels for the near (0.6–13.5%) and far (21.5–37.3%) future periods are higher than the corresponding values for the observed data. Also the uncertainty ranges of the return levels in the near (2–2.4 times) and far (3–3.7 times) future for different return periods are higher compared to that of the observed. The contribution to uncertainties in the return levels from climate models, parameters of the extreme value distribution employed and hydrological models is higher than that from the RCPs. The contribution of parameter uncertainty increases with the return period and that of the climate models reduces with the return period for the two future periods considered in this study, indicating that parameter uncertainty is an important source of uncertainty and has to be considered while predicting the future extremes. The contribution to uncertainty by the hydrological models and by the RCPs does not exhibit much variation for different return periods in both the future time periods considered. Understanding the uncertainties in future extremes would help to devise proper adaptation strategies to mitigate the probable impacts of climate change. [ABSTRACT FROM AUTHOR]

The incidence of droughts and their intensity in recent times are affected by climatic variability and change, consequently affecting the agro-based economy of red and laterite zone (RLZ) India. In the present study, changing characteristics of meteorological droughts have been investigated over the sub-humid RLZ of West Bengal, India, using the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). SPI and SPEI were computed over 1-, 3-, and 12-month time scales from monthly meteorological data from 1930 to 2019 to explore variations in drought frequency, intensity, duration, and spatial extent in the RLZ. It was observed that since the 1990s, the RLZ has been frequently affected by short-term extreme to severe drought, even in the wet-monsoon months. The frequency and intensity of droughts were observed to have increased in the recent period with a decrease in the duration. The Mann–Kendall test on the drought trend analysis of the region indicated a rising trend in monsoon months. The SPEI-monsoon was found to be more significantly correlated (r2 = 0.65) with the rainfed Kharif (monsoon)-rice production anomaly than the SPI. SPEI appeared to have a more pronounced impact on drought incidences in the region over the recent decades. Field surveys were conducted to validate the two recent drought occurrences and associated crop failure. A total of 95% of the farmers in the survey reported crop failure during the short-term meteorological droughts in monsoon months. It is therefore suggested to monitor changing patterns and extent of droughts, particularly in water-scarce RLZs, to design appropriate drought preparedness planning. [ABSTRACT FROM AUTHOR]

In the present study, we investigated the recent climate change in India. Our examination of changes for the recent past three decades (1990–2019) in the relevant meteorological parameters in a space and time frame revealed rise in droughts in the recent years, mainly due to substantial decrease in rainfall and increase in surface temperature over Central Northeast Indian and Northeast Indian regions (which includes fertile region). There is a significant rise in rainfall over the North western regions that includes desert regions. The scenario represents a dipole type pattern with drying over the eastern fertile region and moistening over western desert regions. The reduction in rainfall and rise in surface temperatures is due to the rise in static stability. Static stability (SS) exhibited a significant positive trend over most of Indian region, which is a robust feature of climate change. This increment in static stability contributed to the weakening of rising limb of Hadley cell, provoking sinking motion, which in turn led to decrease of monsoon rainfall over parts of Northeast India (NEI), Central Northeast India (spanning Indo Gangetic Plain) (CNEI), and adjoining northern Indian regions. SS over (35.75–36°N, 65–98°E) (SS*) exhibited a significant positive trend and is very significantly correlated with surface temperature changes over northern parts of India, (especially over CNEI). SS* resulted in a sinking response at lower levels over CNEI and adjoining regions. The rise in sinking motion due to the increment in static stability resulted in increase of temperatures near the surface over CNEI (spanning Indo Gangetic Plain) and adjoining regions. In the recent years, the observed decrease of rainfall associated with increase of surface temperature (especially over IGP) is calamitous, as this led to a significant evaporation and drying of soil, leading to severe droughts over these regions. [ABSTRACT FROM AUTHOR]

Analysis of climatic variables is important for the detection and attribution of climate change trends and has received considerable attention from researchers across the globe including in India. The mountains surrounding the Kashmir Valley in northwestern India are often glacierized. Hence, the area will react strongly to even small changes in temperature and precipitation. To this end, the current study analyzes the changing patterns in precipitation and temperature for various elevation zones of the Kashmir Valley and also investigates farmers' perceptions of climate change. The results revealed that during the last 40 years (1980–2019), the annual minimum and maximum temperatures have increased by 0.02 and 0.017 °C/year. With some numerical variations, the warming trends were observed in all seasons of different elevation zones. The rate of increasing temperature in plains and mountains was more than Karewas and foothill regions. Conversely, a downward trend of annual precipitation at − 5.01 mm/year has been recorded due to declining precipitation during spring, winter, autumn, and summer seasons at − 4.95, − 0.30, − 0.28, and − 0.06 mm/year, respectively. Higher rates of declining precipitation around the mountainous area may be detrimental to the crops of Kashmir Valley by disturbing the water supply and groundwater recharge. Focused on the farmers' perception of climate change, the majority of farmers (> 65%) perceived the changes in temperature and precipitation in line with the above results of historical meteorological data analysis, conforming with an upward and downward trend of temperature and precipitation respectively over time. Farmers' knowledge coupled with the actual data analysis may concertedly give a clearer understanding of climate change–related instability and patterns in weather variables, which is critically important for planning and implementing appropriate adaptation measures in their farming against climate change in the Kashmir Valley. [ABSTRACT FROM AUTHOR]

Climate change is adversely affecting the development, management, and planning of surface and groundwater resources. The meteorological drought becomes a severe natural problem, and it can occur in any climatic region of the world. Thus, monitoring and minimizing drought is crucial for analyzing and predicting drought impacts. The meteorological drought is considered basin or region-specific since atmospheric conditions are highly variable from region to region. However, agricultural drought links the various characteristics of hydrological or meteorological drought to agricultural impacts focusing on groundwater deficit, soil moisture deficit, etc. A single drought index cannot assess each aspect of the meteorological drought. In this study, we considered seven drought indices such as the Standardized Precipitation Index (SPI), China Z Index (CZI), Modified China Z Index (MCZI), Percent Normal Drought Index (PNI), Deciles Index (DI), Rainfall Anomaly Index (RAI), and Z-score Index (ZSI). The drought was analyzed for 3, 6, 9, and 12 months' time step, and drought classification and threshold values were estimated. SPI showed maximum correlation values (0.389, 0.412, 0.560, and 0.996) for 3, 6, 9, and 12 month time steps compared to the other drought indices. The value of correlation is increased with the increase in time step for all drought indices; therefore, the accuracy of drought assessment also increases with an increase in time step. The Mann–Kendall's trend test was analyzed at a 5% significance level for drought assessments. The drought magnitude and the severity of the Betwa River basin were estimated based on the meteorological data (Rainfall) from 1970 to 2014. [ABSTRACT FROM AUTHOR]

The extreme temperature events are a concern in recent years due to climate variability particularly in India as there is an increase in the temperature intensity, frequency, and duration. This study represents stationary temperature-duration-frequency (TDF) analysis over two mega cities in India Delhi (north) and Bengaluru (south) using the daily maximum temperatures at meteorological stations for the period 1969–2016 observed by India Meteorological Department (IMD).The interannual variability of maximum temperature and the maximum daily recorded value indicates the increasing trend in both the cities. The study investigates the extreme analysis of the maximum temperature using two distributions, i.e., Gumbel's Extreme Value Type 1 (GEVT) and Log Pearson Type III (LPT), for return periods 2, 5, 10, 25, 50, and 100 years at both the locations and the positive temporal trend is observed. The TDF curves were build using annual maximum temperature values for total 8 durations (different days) of 48 years analyzed and results show the increasing trend of maximum temperature at lower duration and high return period values. The TDF is also used for prediction of the maximum temperature for the 2 hottest years in India, i.e., 2012 and 2015, and it is comparable with the observed maximum temperature. Similarly, the predictions for 11 years, i.e. 2006 to 2016, over both the cities are simulated using both the GEVT-I and LPT-III and the models have better potential skill in predicting the extreme maximum temperature. These results can be useful for the sectors like health, energy, agriculture, urban management, and ecology management and can help the policy decision makers and disaster managers in the mitigation and adoption steps to face the extreme temperature disaster at city scale. [ABSTRACT FROM AUTHOR]

This study presents the reversal nature in rainfall over heavy rainfall zone (HRZ; more than 80% of the long period average (LPA) of the Indian summer monsoon rainfall (ISMR)) and low rainfall zone (LRZ; less than 40% of ISMR-LPA) in India. The India Meteorological Department (IMD) high-resolution (0.25° × 0.25°) dataset is used from 1901 to 2016. The single and multiple change-point detection techniques are used to find the change in rainfall patterns over both regions. Further, the study period is divided into two halves P1 (1901–1958) and P2 (1959–2016) to examine the change in rainfall patterns in the recent and past periods. In P2, the rainfall pattern gets reversed, and ISMR has shown a significant increasing (decreasing) trend over the LRZ (HRZ). The increasing/decreasing number of moderate- and high-intensity rainfall events is one of the causes for this reversal pattern. Additionally, the number of dry days is increased over the HRZ and decreased over the LRZ. This study further confirms that the "dry becomes drier and wet becomes wetter" paradigm is not solely acceptable for India. The present study provides information about changes in dry days and ISMR variability in the context of climate change, which will be useful to agricultural risk management, water resources, drought monitoring, model developers, and policy planner on the adaptation strategies for climate change. [ABSTRACT FROM AUTHOR]

High-resolution regional climate model (RCM) simulations are found to be very useful in deriving realistic climate change projection information. This study uses high-resolution dynamical downscaling framework (CCSM4-WRF) for India. To delineate the advantage of high resolution, we compared the results of 9-km resolution CCSM4-WRF simulations against the 50-km resolution RCM simulations under Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) program. Quantitative estimations show that majority of CORDEX-SA models exhibit large dry bias (< − 4 mm/day) and low pattern correlation coefficient (PCC) over the Western Ghats (WG). Mean climatology of Indian summer monsoon (ISM) rainfall simulated by high-resolution CCSM4-WRF outperforms the CORDEX-SA RCMs with low negative biases (~ 1 mm/day) and high PCC (≥ 0.755). This skill of CCSM4-WRF provides better confidence in its future projection at local scale. CCSM4-WRF projects future intensification of monsoon rainfall over most parts of India and reduction over southern WG, which is consistent with recent observed trends, but none of the CORDEX-SA RCMs could simulate this rainfall reduction. For all-India rainfall, ensemble mean of CORDEX-SA models projects an increase by 1.3 ± 0.9 mm/day and CCSM4-WRF projects 0.67 mm/day. Projected changes in socioeconomic variables such as population and gross domestic product (GDP) exhibit future enhancement over most parts of India but with spatial heterogeneity. Shared socioeconomic pathways scenarios show pronounced future population growth over Indian coastal areas and large enhancement in productivity over urban areas. Therefore, climate change projection information of ISM rainfall, together with enhanced future population and GDP, is useful for taking necessary steps for adaptation and mitigation in a sustainable manner. [ABSTRACT FROM AUTHOR]

The study of rainfall trends and spatio-temporal evolution (patterns) helps in understanding and planning the utilization of water resources for diverse sectors of the economy, such as agriculture and industries, and also provides indications of climate change. The present study investigates trends and patterns of seasonal, half yearly, and annual rainfall over Visakhapatnam-Chennai Industrial Corridor (VCIC) using non-parametric Mann Kendall and Getis-Ord-Gi* statistics at a district level from 1901 to 2015. The annual rainfall trend for the entire VCIC region shows a significant positive trend at 90%, which is driven by a significant positive trend in monsoons. The intensity of spatial pattern differs across the seasons. However, the annual pattern shows an intensifying hot spot (23% of VCIC), oscillating hot spot (23%), and no pattern (18%). The positive trend in rainfall during monsoon seasons in north-eastern districts and post-monsoon seasons in the southern districts of VCIC has influenced the intensifying hot spot patterns. These trends and patterns are required for carrying out a climate risk assessment for various sectors like urban and industrial areas to plan and propose suitable adaptation measures to cope with extreme rainfall events. [ABSTRACT FROM AUTHOR]

Temperature-depth profiles of boreholes of a region can be analysed to estimate the past variations in climate. Perturbations over the surface ground temperature are imparted deep into the Earth's subsurface. Western Ghats regions of India are considered to be one of the most ecologically rich regions and also play a crucial role in deciding the climatological characteristics of the country. This study quantifies the possible climate warming or cooling in the past century in the Western Ghats using temperature-depth profiles of five boreholes in a depth range of 140–198 m located in Koyna region (17.38° N, 73.74° E) of Maharashtra state, India. The analysis shows that there has been a significant warming of 0.8 ± 0.2 °C over the past 100 years in the region. The inferred warming is comparable with the trends of meteorological records of the area which yields an increasing trend of 0.56 °C in the surface air temperature (SAT). For further validation of results, a combined study of borehole temperature profiles and surface air temperature (SAT) profiles was done which showed that the period was warmer than the earlier long-term temperature records of the region by 0.8 °C. This pre-observational mean (POM) value seems to be consistent with previously studied POMs over India. This study provides a 'true estimate' of climate change and can be used as a robust technique to reconstruct the past climate warming. [ABSTRACT FROM AUTHOR]

It is well recognized that the global climate has been warming and it can have adverse regional impacts in the future. Besides producing changes in mean climate, global warming can also substantially alters the weather and climate extremes. Considering the impact of weather extremes on agriculture as well as water resource management in an agrarian country like India, present study aims to characterize future changes in precipitation extremes in northeast monsoon season (October–November–December) over south peninsular India based on statistically downscaled high-resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) datasets. This NEX-GDDP dataset simulates the mean rainfall reasonably well. There may be an increase in precipitation in near (5% under RCP 4.5 and 11% under RCP 8.5) and far future (21% and 38% under RCP4.5 and RCP 8.5 respectively). Future climate projections indicate that both the intensity and frequency of precipitation extremes in most parts of the south peninsular India may increase under both the warming scenarios during northeast monsoon season. [ABSTRACT FROM AUTHOR]

The present study assesses the response of farm income to climatic variations and projects the impact of climate change on agriculture in the Trans- and Upper Gangetic Plains Region of India. Panel data estimation of crop response function has been carried out by pooling long time series (1980–2009) and cross-section data of 84 districts. Climate change impact projections have been made with Representative Concentration Pathway (RCP) 4.5 climate scenario for the time slice 2010–2039 and two sub periods, viz. 2020–2029 and 2030–2039. The negative effect of rise in temperature on gross margin per hectare ranges from 10.5% in monsoon season (June to September) to 6.5% in winter season (October to February). The marginal effect of precipitation change is positive albeit of small magnitude, 0.04% and 0.03% , in the monsoon and winter season, respectively. Loss in annual gross margin per hectare in Trans- and Upper Gangetic Plains is projected to be 14.47% for the time slice 2010–2039. The losses are likely to escalate in 2030–2039 (17.06%) over 2020–2029 (15.86%). The northern part of the region will be affected adversely during the monsoon season, with the exception of a few districts which are likely to gain marginally due to winter warming. [ABSTRACT FROM AUTHOR]

The present study analyzed precipitation and temperature trends of 45 years (1971–2015) over two protected areas (Kanha Tiger Reserve representing Sal forest and Satpura Tiger Reserve representing Teak forest) in Central India. The specific trends (decadal, annual, and seasonal) are calculated from various available data sources (IMD, REMO, and Station) which are at different horizontal resolutions. The non-parametric Mann–Kendall test statistic and Sen's estimate methods are used to detect the presence and quantum of a trend. The results indicate significant decrease in post-monsoon and winter precipitation and an increasing significant temperature trend (Tmean, Tmax, Tmin) at seasonal, annual, and decadal scale, with a maximum increase during post-monsoon. Also, the last decade (2001–2010) was warmer than the previous three decades. The presence of break points and structural changes in climatic variables are analyzed using OLS-CUSUM and MOSUM tests available in "strucchange" package in R. A structural change in precipitation was observed only in REMO dataset over Satpura Tiger Reserve, whereas significant structural change in temperature was observed in REMO dataset over both the regions. These findings are useful for assessing precipitation and temperature trends in protected areas. In particular, it can aid in understanding the impact of climate change on forest/species. On comparative analysis among different data sources at varying resolutions, the study recommends the use of finer resolution datasets for regional/micro-scale studies for formulating and planning effective adaptation strategies. [ABSTRACT FROM AUTHOR]

The present study assessed climate change vulnerability in agricultural sector of low-lying Sagar Island of Bay of Bengal. Vulnerability indices were estimated using spatially aggregated biophysical and socio-economic parameters by applying principal component analysis and equal weight method. The similarities and differences of outputs of these two methods were analysed across the island. From the integration of outputs and based on the severity of vulnerability, explicit vulnerable zones were demarcated spatially. Results revealed that life subsistence agriculture in 11.8% geographical area (2829 ha) of the island along the western coast falls under very high vulnerable zone (VHVZ VI of 84-99%) to climate change. Comparatively higher values of exposure (0.53 ± 0.26) and sensitivity (0.78 ± 0.14) subindices affirmed that the VHV zone is highly exposed to climate stressor with very low adaptive capacity (ADI= 0.24 ± 0.16) to combat vulnerability to climate change. Hence, food security for a population of >22 thousands comprising >3.7 thousand agrarian households are highly exposed to climate change. Another 17% area comprising 17.5% population covering 20% villages in north-western and eastern parts of the island also falls under high vulnerable (VI= 61%--77%) zone. Findings revealed large spatial heterogeneity in the degree of vulnerability across the island and thus, demands devising area specific planning (adaptation and mitigation strategies) to address the climate change impact implications both at macro and micro levels. [ABSTRACT FROM AUTHOR]

Production of the principal paddy crop in West Bengal state of India is vulnerable to climate change due to limited water resources and strong dependence on surface irrigation. Therefore, assessment of impact of temperature scenarios on crop evapotranspiration (ET) is essential for irrigation management in Kangsabati command (West Bengal). In the present study, impact of the projected temperatures on ET was studied under climate change scenarios. Further, the performance of the bias correction and spatial downscaling (BCSD) technique was compared with the two well-known downscaling techniques, namely, multiple linear regression (MLR) and Kernel regression (KR), for the projections of daily maximum and minimum air temperatures for four stations, namely, Purulia, Bankura, Jhargram, and Kharagpur. In National Centers for Environmental Prediction (NCEP) and General Circulation Model (GCM), 14 predictors were used in MLR and KR techniques, whereas maximum and minimum surface air temperature predictor of CanESM2 GCM was used in BCSD technique. The comparison results indicated that the performance of the BCSD technique was better than the MLR and KR techniques. Therefore, the BCSD technique was used to project the future temperatures of study locations with three Representative Concentration Pathway (RCP) scenarios for the period of 2006-2100. The warming tendencies of maximum and minimum temperatures over the Kangsabati command area were projected as 0.013 and 0.014 °C/year under RCP 2.6, 0.015 and 0.023 °C/year under RCP 4.5, and 0.056 and 0.061 °C/year under RCP 8.5 for 2011-2100 period, respectively. As a result, kharif (monsoon) crop evapotranspiration demand of Kangsabati reservoir command (project area) will increase by approximately 10, 8, and 18 % over historical demand under RCP 2.6, 4.5, and 8.5 scenarios, respectively. [ABSTRACT FROM AUTHOR]

This study has been undertaken to examine the occurrence of climate change in Tamil Nadu, the southernmost state of India and its impact on rainfall pattern which is a primary constraint for agricultural production. Among the five sample stations examined across the state, the minimum temperature has increased significantly in Coimbatore while the same has decreased significantly in Vellore whereas both minimum and maximum temperatures have increased significantly in Madurai since 1969 with climate change occurring between late 1980s and early 1990s. As a result, the south-west monsoon has been disturbed with August rainfall increasing with more dispersion while September rainfall decreasing with less dispersion. Thus, September, the peak rainfall month of south-west monsoon before climate change, has become the monsoon receding month after climate change. Though there has been no change in the trend of the north-east monsoon, the quantity of October and November rainfall has considerably increased with increased dispersion after climate change. On the whole, south-west monsoon has decreased with decreased dispersion while north-east monsoon has increased with increased dispersion. Consequently, the season window for south-west monsoon crops has shortened while the north-east monsoon crops are left to fend against flood risk during their initial stages. Further, the incoherence in warming, climate change and rainfall impact seen across the state necessitates devising different indigenous and institutional adaptation strategies for different regions to overcome the adverse impacts of climate change on agriculture. [ABSTRACT FROM AUTHOR]

The global model analysis and forecast have a significant impact on the regional model predictions, as global model provides the initial and lateral boundary condition to regional model. This study addresses an important question whether the regional model can improve the short-range weather forecast as compared to the global model. The National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) and the Weather Research and Forecasting (WRF) model are used in this study to evaluate the performance of global and regional models over the Indian region. A 24-h temperature and specific humidity forecast from the NCEP GFS model show less error compared to WRF model forecast. Rainfall prediction is improved over the Indian landmass when WRF model is used for rainfall forecast. Moreover, the results showed that high-resolution global model analysis (GFS4) improved the regional model forecast as compared to low-resolution global model analysis (GFS3). [ABSTRACT FROM AUTHOR]

Impacts of the changing climatic regime on the trends of Indian summer monsoon rainfall (ISMR) are explored in this study for the Indian Himalayan region (IHR). The analysis is carried out for the period of 1951-2007 using a daily high resolution gridded data from APHRODITE project. At first, the percent departures of decadal rainfall are estimated from the long-term June to September rainfall values for the western, central, and eastern Himalayan (WH, CH, and EH) regions. Next, changes in the frequency of strong and weak phases of monsoon intra-seasonal oscillation are investigated. A non-parametric statistical method (Sen's slope estimator) is applied to the seasonal (i) mean rainfall, (ii) maximum rainfall, and (iii) frequency of extreme rainfall events of WH, CH, and EH regions to identify changes in their decadal, multiple year normals (NY1; 1951-1980 and NY2; 1981-2007) and long-term (NY3; 1951-2007) trends. The inter annual to inter decadal variabilities of the frequency of extreme rainfall events are explored by analyzing statistically significant intrinsic mode functions of the empirical mode decomposition (EMD) method. Results of our analyses have revealed existence of an alternative decadal oscillation of scanty and excessive summer monsoon rainfall trends for the WH, whereas excessive rainfall is observed in the last three decades (1980-2007) over the CH region. It is also observed that the frequencies of both monsoon strong and weak phases are decreasing for the entire Himalayan region. No significant trend is observed for the WH and CH regions for the normal periods NY1, NY2, and NY3 when seasonal average rainfall is considered. However, a significant ( p value < 0.05) negative trend of −0.04 mm/day rain is observed for the EH region during NY1 period. Similarly, the seasonal maximum rainfall trends for all the normal periods are found to be negative of which trends of −0.12 and −0.43 mm/day during NY3 and NY1 are observed for WH and EH regions, respectively ( p value < 0.05). No significant enhancement in the extreme rainfall event frequencies is observed for the entire IHR during 1951-2007. However, a statistically insignificant positive trend in the extreme event frequencies is observed for the EH region. A dominant cycle of ∼ 2.7 years of high frequency of extreme rainfall events is observed for all the regions whereas, a 12.2-, 15.3-, and 5.8-year cycles are observed for the WH, CH, and EH regions, respectively. [ABSTRACT FROM AUTHOR]