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Climate variability, particularly that of the annual air temperature and rainfall, has received a great deal of attention worldwide. The magnitude of the variability or fluctuations of the factors varies according to locations. Hence, examining the spatiotemporal dynamics of meteorological variables in the context of changing climate, particularly in countries where rainfed agriculture is predominant, is vital to assess climate‐induced changes and suggest feasible adaptation strategies. To that end, the present study examines long‐term changes and short‐term fluctuations in monsoonal rainfall and temperature over Kalahandi, Bolangir and Koraput (hereafter KBK) districts in the state of Odisha. Both rainfall and temperature data for period of 1980–2017 were analyzed in this study. Statistical trend analysis techniques namely Mann–Kendall test and Sen's slope estimator were used to examine and analyze the problems. The detailed analysis of the data for 37 years indicate that the annual maximum temperature and annual minimum temperature have shown an increasing trend, whereas the monsoon's maximum and minimum temperatures have shown a decreasing trend. Statistically significant trends are detected for rainfall and also the result is statistically significant at 99% confidence limit during the period of 1980–2017. Rainfall is showing a quite good increasing trend (Sen's slope = 4.034) for JJAS season. In the case of maximum temperature for the observed period, it showed a slight warming or increasing trend (Sen's slope = 0.29) while the minimum temperature trend showed a cooling trend (Sen's slope = −0.006) but result of maximum temperature trend analysis is statistically significant at 95% confidence limit, on the contrary, the trend analysis result of minimum temperature is not statistically significant. Location map of Kalahandi, Bolangir and Koraput (KBK) districts in Odisha.

Indian monsoon rainfall has a very strong connection with the Indian economy. Any variation in trend or pattern of Indian summer monsoon rainfall will have serious implications on agronomy, water resources and various associated sectors of the economy in India. In this study, an in-depth investigation of the monsoon rainfall trend is analyzed for 146 years period (1871–2016). Three different spatial scales using a multimethod approach consisting of the Linear Regression Model (LRM), Mann Kendall Test (MKT) and Innovative Trend Analysis (ITA) are analyzed in particular and synchronized way. Monotonic trend with one or the other tests are found in Meteorological Sub-division (MetSD) 3, 4, 14, 11, 10, 19, 20, 27, 8, 29, 28, 23 and 32 (Assam, Meghalaya, Manipur, Mizoram, Nagaland, Tripura, Punjab, Uttar Pradesh, Madhya Pradesh, Chhattisgarh, Jharkhand, Andhra Pradesh, Telangana , Konkan & Goa and Coastal Karnataka). Whereas, no significant monotonic trend was found for India as a unit. Two Homogenous Monsoon Regions (HMR) i.e. Central Northeast and Northeast have the monotonic rainfall trend. Moreover, the synchronized methodology made it possible to identify the most refined significant monotonic trend. It revealed a decreasing monotonic trend in MetSD 4, 20 and 27 (Manipur, Mizoram, Nagaland, Tripura, East Madhya Pradesh and Chhattisgarh) only. ITA based results revealed that MetSD 14, 8, 19 and 29 (Punjab, Jharkhand, West Madhya Pradesh and Telangana) are a new addition to the list of MetSDs with the significant monotonic trend. Changepoint in the trend is obtained for Northeast HMR in the year 1956 and MetSD 4, 20, 23 and 27 in the year 1969, 1961, 1930 and 1961 respectively. This study provides insight into the most refined trend on monsoon rainfall at different spatial scales in India using the updated methods of analysis.

Based on the Gini-coefficients, this study has presented an analysis of the impacts of anthropogenic forcing on the temporal inequality (i.e., increase in unevenness or disparity) of precipitation amounts (PRCPTOT), intensity (SDII), and extremes (R95p and RX5day) at national and regional scales (eight regions) in China. A positive anthropogenic influence on the temporal inequality is found for precipitation extremes over China, especially in southern regions during the period 1961–2005. Projections of future precipitation indices except R95p have a stepped upward trend in temporal precipitation variability with increasing anthropogenic forcing in most regions of China under SSP126, SSP370, and SSP585 scenarios. Except for Southern China (SC) and SWC2, R95p has a significant decrease in the future, and the largest decrease is up to 29.5% in Northwest China under SSP370. Results obtained from this study offer insights into temporal variability of precipitation extremes and help policy makers for managing water-related disasters.

Apple cultivation is one of the most important sources of livelihood in Indian side of the Himalayas. The present study focuses on the apple orchards of Himachal Pradesh, a state within the Himalayan Mountains, a major apple producers of India. In the study, it is found that the optimum apple growing conditions in the region have been consistently shifting and farmers are shifting their orchards to the higher altitudes. For example, orchards have shifted to 1500–2500 meters in the 2000s compared to the cultivated elevation of 1200–1500 meters during 1980s. As of 2014, apples are being cultivated at an elevation of more than 3500 meters, for example, the newly developed orchards of Leo village in upper Kinnaur and Keylong area of Lahul and Spiti districts. Chilling hours for different districts are calculated. The trend of temperature during the growth period, winter session and annual rainfall have been analysed using Mann-Kendall and Sen’s slope test. Data catalogued from different time periods indicates that the northward shift (towards higher altitude) is due to changes in chilling hours, total annual rainfall and mean surface temperature during the apple growing season. The mean surface temperature in all the districts has increased by almost 0.5°C during last 2000–2014. These changes are directly related to global warming. While the changing climate is reducing the apple production in low altitudinal regions of the state, it is creating new opportunities for apple cultivation in higher altitudes as conditions are getting more favourable for apple growth in those higher regions. The associated socio-economic changes are posing new societal issues for the local farmers.

In recent decades, the effects of climate change and climate variability have attracted significant global attention due to their growing impact on extreme weather and climate events [...]

Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop production parameters over large geographic areas. The present work aims to estimate the wheat (Triticum aestivum) acreage and yield of Maharajganj, Uttar Pradesh, India, using satellite-based data products and the Carnegie-Ames-Stanford Approach (CASA) model. Uttar Pradesh is the largest wheat-producing state in India, and this district is well known for its quality organic wheat. India is the leader in wheat grain export, and, hence, its monitoring of growth and yield is one of the top economic priorities of the country. For the calculation of wheat acreage, we performed supervised classification using the Random Forest (RF) and Support Vector Machine classifiers and compared their classification accuracy based on ground-truthing. We found that RF performed a significantly accurate acreage assessment (kappa coefficient 0.84) compared to SVM (0.68). The CASA model was then used to calculate the winter crop (Rabi, winter-sown, and summer harvested) wheat net primary productivity (NPP) in the study area for the 2020–2021 growth season using the RF-based acreage product. The model used for wheat NPP-yield conversion (CASA) showed 3100.27 to 5000.44 kg/ha over 148,866 ha of the total wheat area. The results showed that in the 2020–2021 growing season, all the districts of Uttar Pradesh had similar wheat growth trends. A total of 30 observational data points were used to verify the CASA model-based estimates of wheat yield. Field-based verification shows that the estimated yield correlates well with the observed yield (R2 = 0.554, RMSE = 3.36 Q/ha, MAE −0.56 t ha−1, and MRE = −4.61%). Such an accuracy for assessing regional wheat yield can prove to be one of the promising methods for calculating the whole region’s agricultural yield. The study concludes that RF classifier-based yield estimation has shown more accurate results and can meet the requirements of a regional-scale wheat grain yield estimation and, thus, can prove highly beneficial in policy and decision making.

Planning for a sustainable future involves understanding the past and present problems associated with urban centers. Rapid urbanization has caused significant adverse impacts on the environment and natural resources. In cities, one such impact is the unsettling urban growth, resulting in the urban heat island (UHI) effect, which causes considerable positive feedback in the climate system. It can be assessed by investigating the relationships between urban Land Use/Land Cover (LULC) changes and changes in land surface temperature. This study links the urban transformations in Bangalore, India, between 2001 and 2021, with the city’s changing average land surface temperatures. LULC classification was performed on Landsat satellite images for the years 2001, 2011, and 2021, using the support vector machine (SVM) classification algorithm. LULC change analysis revealed an increase in the built-up area coinciding with a decreasing trend of water bodies, vegetation, and the area under the others (wasteland/open land/barren land) category. The results show that built-up increased from 462.49 km2 to 867.73 km2, vegetation decreased from 799.4 km2 to 485.72 km2, and waterbody declined from 34.28 km2 to 24.69 km2 in 20 years. The impact of urbanization was evident in Bangalore’s land temperature changes between 2001 and 2021, showing the average temperature increased by 0.34 °C per year between the highest UHI events, contrary to 0.14 °C per year in non-urbanized areas. It is hoped that the results of this study can help the urban planners of Bangalore city identify critical areas where improvement in urban dwelling could be planned sustainably according to the global smart cities concept, an offshoot concept of the Sustainable Development Goal (SDG)-11.

The tropical cyclones are very destructive during landfall, generating high wind speeds, heavy intensive rainfall, and severe storm surges with huge coastal inundations that have massive socioeconomic and ecological catastrophic effects on human beings and the economic well-being. The sizable ecological effects of cyclonic storms cannot be ignored because of the uncertainty of impact, intensity induced by a warming ocean, and sea level rise. The Super Cyclonic Storm Amphan which falls under the category five classifications under the scheme of the India Meteorological Department (IMD), on the basis the maximum sustained wind speeds gusting up to 168 km/h affected parts of West Bengal and Odisha in India, and south-west Bangladesh between May 16 and 20, 2020. In this work, we have focused on the coastal districts of Kendrapada, Bhadrak, Balasore in Odisha, Purba Medinipur, and South Twenty-Four Parganas in West Bengal, India and, Khulna, Barisal division of Bangladesh that have been seriously affected by the Super Cyclonic Storm Amphan. The objective of the study is to analyze the eco-physical assessment of tropical cyclone Amphan using geospatial technology. Therefore, shoreline change detection and enhance vegetation index have been used in this research work to systematically analyze the eco-physical impact parameters of Cyclonic Storm Amphan using ortho-rectified Landsat 8/OLI imagery and MODIS dataset of USGS with high spatial resolutions of 30–500 m. The result highlights that about 60.33% of the total transects of the study area was eroded, but only 24.99% of the total transects experienced accretion, and 14.68% of the total transects depicted stability. The scientific study will benefit coastal managers and policymakers in formulating action plans for coastal zone management, natural resilience, and sustainable future development.

Inhabitants of low-lying islands face increased threats due to climate change as a result of their higher exposure and lesser adaptive capacity. Sagar Island, the largest inhabited estuarine island of Sundarbans, is experiencing severe coastal erosion, frequent cyclones, flooding, storm surges, and breaching of embankments, resulting in land, livelihood, and property loss, and the displacement of people at a huge scale. The present study assessed climate change-induced vulnerability and risk for Sagar Island, India, using an integrated geostatistical and geoinformatics-based approach. Based on the IPCC AR5 framework, the proportion of variance of 26 exposure, hazard, sensitivity, and adaptive capacity parameters was measured and analyzed. The results showed that 19.5% of mouzas (administrative units of the island), with 15.33% of the population at the southern part of the island, i.e., Sibpur–Dhablat, Bankimnagar–Sumatinagar, and Beguakhali–Mahismari, are at high risk (0.70–0.80). It has been concluded that the island has undergone tremendous land system transformations and changes in climatic patterns. Therefore, there is a need to formulate comprehensive adaptation strategies at the policy- and decision-making levels to help the communities of this island deal with the adverse impacts of climate change. The findings of this study will help adaptation strategies based on site-specific information and sustainable management for the marginalized populations living in similar islands worldwide.

Forest fires have significant impacts on economies, cultures, and ecologies worldwide. Developing predictive models for forest fire probability is crucial for preventing and managing these fires. Such models contribute to reducing losses and the frequency of forest fires by informing prevention efforts effectively. The objective of this study was to assess and map the forest fire susceptibility (FFS) in the Indian Western Himalayas (IWH) region by employing a GIS-based fuzzy analytic hierarchy process (Fuzzy-AHP) technique, and to evaluate the FFS based on forest type and at district level in the states of Jammu and Kashmir, Himachal Pradesh, and Uttarakhand. Seventeen potential indicators were chosen for the vulnerability assessment of the IWH region to forest fires. These indicators encompassed physiographic factors, meteorological factors, and anthropogenic factors that significantly affect the susceptibility to fire in the region. The significant factors in FFS mapping included FCR, temperature, and distance to settlement. An FFS zone map of the IWH region was generated and classified into five categories of very low, low, medium, high, and very high FFS. The analysis of FFS based on the forest type revealed that tropical moist deciduous forests have a significant vulnerability to forest fire, with 86.85% of its total area having very high FFS. At the district level, FFS was found to be high in sixteen districts and very high in seventeen districts, constituting 25.7% and 22.6% of the area of the IWH region. Particularly, Lahul and Spiti had 63.9% of their total area designated as having very low FSS, making it the district least vulnerable to forest fires, while Udham Singh Nagar had a high vulnerability with approximately 86% of its area classified as having very high FFS. ROC-AUC analysis, which provided an appreciable accuracy of 79.9%, was used to assess the validity of the FFS map produced in the present study. Incorporating the FFS map into sustainable development planning will assist in devising a holistic strategy that harmonizes environmental conservation, community safety, and economic advancement. This approach can empower decision makers and relevant stakeholders to take more proactive and informed actions, promoting resilience and enhancing long-term well-being.