A study has been conducted on Extreme Weather Events (EWEs) induced mortalities in Jammu and Kashmir, India during 2010-2022. In the present study, we used the frequency of heavy rain, heavy snow, lightning/thunderstorm, Hailstorm and squall during the period 2010 to 2022 of 10 stations of J&K from India Meteorological Department. The mortalities occurred due to these extreme weather events for each district were collected from the Meteorological Centre Srinagar. The mean monthly precipitation and number of rainy days for each month was calculated for each station based on 40 years data (1982 to 2022). During the past 12 years, (2010-2022) a total of 2863 EWEs occurred over J&K in which 552 deaths occurred till 31st December 2022. Among the various EWEs, lightning (1942) and heavy rainfall (409) events were more frequent. When we compare the mortality per event, the heavy snow was more destructive compared to any other EWEs. The mortality per event due to heavy snow was highest (4.33) as compared to other extreme events, although the number of events of heavy snow is less (42) as compared to heavy rain (409), flash floods (168) and lightning (1942). District wise results of EWEs results revealed the highest deaths due to heavy snow were observed over Kupwara, Bandipora, Baramulla and Ganderbal. Similarly for flash floods, the highest deaths were observed over Kishtwar, Anantnag, Ganderbaland Doda. The Pearson correlation results revealed highest correlation of total deaths for heavy rain (0.77) and heavy snow (0.69) (significant at p value p<0.01) followed by flash floods (0.492) (significant at p value p<0.05). Negative correlation result was observed between heavy snow and windstorm (0.584) (significant at p value p<0.05). The present study has shown that, for the union territory as a whole, the heavy rain and heavy snow have been two major disasters causing mortality, though flashfloods, thunderstorms and windstorms are gaining importance. The trend analysis results also revealed that there is a significant increase in mortality over the years particularly due to flash floods (R2 value 0.434) and windstorm (R2 value 0.371).

Remote sensing technology has demonstrated its significant utility in the monitoring and mapping of agricultural drought on a global scale. This study focused on the assessment of agricultural drought in the Saurashtra region of Gujarat, India, utilizing a comprehensive dataset spanning 33 years from Landsat and Sentinel satellites. It employed various vegetation indices, including NDVI (Normalized Difference Vegetation Index), Anomaly Index (NAI), Vegetation Condition Index (VCI) and NDWI Anomaly index (NDWIA), to gauge drought conditions. The performance of these indices was evaluated through the generation of drought severity maps and their correlation analysis with major Kharif crops in the region, specifically cotton and groundnut. The analysis pinpointed major agricultural drought years, such as 1986, 1987, 1991, 2000, 2002 and 2012, which corresponded to substantial crop yield losses ranging from 37% to 76% for cotton and 66% to 95% for groundnut, varying by district. Despite VCI demonstrating equivalent or superior correlations with crop yields (ranging from 0.32 to 0.73 for cotton and 0.33 to 0.75 for groundnut) compared to NAI in various districts, it tended to underestimate drought severities, designating only 2 to 9 drought years for different districts. Consequently, this study recommends revised VCI drought severity thresholds, which enhance the categorization of agricultural drought in terms of severity levels and corresponding yield losses for cotton and groundnut in the Saurashtra region of Gujarat. Furthermore, it underscores the need to establish region-specific drought severity thresholds by identifying the most suitable vegetation index for effective quantification of agricultural drought, thereby facilitating informed drought mitigation measures.

Maharashtra experienced a series of calamitous flood events during July and September months of monsoon season of 2019 affecting millions of people. Mumbai, Palghar, Thane, Raigad, Satara, Sangli, Pune and Kolhapaur were most affected districts of Maharashtra. Near real time satellite observations from space have been used in this study to monitor these events. Availability of accurate precipitation information at very fine resolution of 5 km (half hourly) from a rainfall model that integrates observations from multi-spectral satellite sensors offers an excellent opportunity to monitor flood events effectively. Utility of this model was tested by investigating flood events of Kedarnath in 2013, Jammu and Kashmir in 2014 and Tamil Nadu in 2015. This model was also used to explore recent flood events of Kerala and Assam in 2019. Mumbai, Palghar, Thane, Raigad, Satara, Sangli, Pune and Kolhapaur districts received very heavy rainfall from multiple rain episodes during first, third and last week of July, and second and last week of September that resulted in heavy flooding over these districts. Results reveal that few of these districts received cumulative rainfall in excess of 2000 mm from multiple heavy rainy events during July to September. Mumbai, Palghar, Thane and Raigad received a cumulative rainfall in excess of 1700 mm during July and September. Sangali district received an excess of about 200% rainfall than average monthly rain during July 2019. Heavy cumulative rainfall from multiple rain spells resulted in heavy flooding over various districts of Maharashtra. Results reported in this study highlight the importance of mitigation and adaptation strategies against flood disasters.

A field experiment was conducted at Assam Agricultural University, Jorhat, Assam during rabi 2018-19 for assessing the PAR interception and radiation use efficiency in potato variety Kufri Jyoti under different microclimates, which was planted in split plot design with 4 dates of plantings and three mulching treatments with water hyacinth, black polythene and without mulching. The incident, reflected and transmitted PAR were measured periodically over the crop with line quantum sensor and daily incident radiation were calculated from incident PAR and bright sunshine hours. The interception of PAR (iPAR) varied considerably among different treatments, while highest iPAR was recorded under first date of planting and mulching treatment with water hyacinth. The leaf area index (LAI) and biomass production was highest in crop planted in first date planting and grown under water hyacinth mulch. The RUE for tuber yield was highest under water hyacinth (2.35 g MJ-1) followed by black polythene (2.03 g MJ-1) and non-mulched (1.67 g MJ-1) condition, while among planting dates it was highest in case of first date of planting. The LAI, biomass production and yield of potato were found to be significantly correlated with iPAR and RUE. The predictive models were developed by using stepwise regression method to predict tuber yield from iPAR and REU, which have R2 value of 0.96 and 0.99, respectively. 

Regional climate models (RCMs) give more reliable results for a regional impact study of climate change, but they still have a significant bias that has to be corrected before they can be utilised in climate change research. In this study, two methods for local bias correction of Tmax, Tmin and precipitation data at monthly scales, namely the modified difference approach (MDA) and the linear scaling (LS) method, were applied and validated to minimize the bias between the modelled (HAD GEM2-ES-GCM) and observed climate data in Central Kashmir Valley. Tmax, Tmin and precipitation correction functions generated using the LS method on a monthly time scale were shown to be excellent than MDA for bias correction of weather data to make it close to observed data in both scenarios (RCP 4.5 & 8.5). Comparison between two scenarios was done to determine the climate change extent in Central Kashmir Valley using LS method. The past 30 years observed average temperature and precipitation was 14.17 °C and 734.06 mm, respectively considered as a baseline for comparison purpose. Annual Taverage (°C) showed increase in all the three time slices and maximum increase by 3.09 and 5.72 °C during far future (FF) (2071-2095) under RCP 4.5 & 8.5, respectively. Whereas, the results of average annual precipitation also showed increase in future scenario and maximum increase by 29.25 mm (3.98%) during mid future (2046-2070) and 215.98 mm (29.42%) during end future (2071-2095), under RCP 4.5 & 8.5 respectively. It was concluded that under RCP 8.5 scenario climate change was quite significant than RCP 4.5.

The core purpose of this study is to investigate the spatial variation in monthly, seasonally, and yearly rainfall patterns in the Kolkata district of West Bengal, India, between 1901 and 2019. (Around 119 years). The trend's reliability and intensity were assessed non-parametrically by applying monthly rainfall data series and the Mann–Kendall and Sen's slope estimators. The data showed a considerable increase in pre-monsoon, monsoon, post-monsoon, and also annual rainfall while decreasing in winter rainfall across the district of Kolkata. The positive trend is identified in the data series of pre-monsoon, monsoon, post-monsoon, and annual rainfall, however, winter rainfall exhibited negative trends. The highest increase in rainfall was observed during the post-monsoon season (0.365091 mm year-1), with the smallest increase (0.232591 mm year-1) occurring during the pre-monsoon season. In the winter season, there was a notable rain that has declined substantially(-0.01356 mm year-1). The coefficient CV, %, was used to determine the pattern of rainfall variability. The winter rainfall exhibited the highest CV rating (72.89%), but annual rainfall showed a minimum CV value (17.68%). Generally speaking, a high variance in CV was discovered, indicating that the whole area is very sensitive to droughts and floods. For future forecasts, there is a considerable difference in monthly rainfall data between linear regression and SMOreg, while the annual rainfall is little difference between linear regression, SMOreg, and CA-ANN analysis.

The continuous change in climatic conditions has created a very difficult situation for the people living all over the world. The cities with higher population and poor air quality have been hard hit by the rising temperature and humidity, bad air quality of an urban environment plays a significant role in affecting climatic variables. As Delhi, the capital of India, tops the list of air pollution hotspots among all top polluted cities around the world is selected for this study. Through this study a relationship was assessed, among criteria air pollutants and meteorological parameters. It was hypothesized that criteria air pollutants will positively predict the change in temperature and relative humidity (pillars of climate change) during daily dataset(January 01, 2015 – December 31, 2021) and average annual dataset (2000 to 2021) in Delhi. To test this hypothesis, elastic net-applied regularization has been used in model exploration and coefficient estimation using EVIEWS 12. It was found that during the selected study period, most of the criteria air pollutants were playing a significant role in increasing the changes in climatic conditions of Delhi. This research further explains about the interlinkage between air pollution and climate change with the help of available literature.

This paper attempts to compare the weather indices based regression approach and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) approach for rice yield prediction at district level of West Bengal. The weather indices for weather variables, viz., minimum temperature, maximum temperature, rainfall, and relative humidity are used as input variables along with time variable t and yield of rice as output variable. The study reveals that the ANN approach works better than the standard regression approach in crop yield prediction. The prediction error percentages are found to be consistently less than 5% in MLP ANN approach except for one district.