Disasters in the Complex Himalayan Terrains

Kanga, Shruti; Meraj, Gowhar; Farooq, Majid; Singh, Suraj Kumar; Nathawat, Mahendra Singh

doi:10.1007/978-3-030-89308-8_1

Cited by 17 publications

(5 citation statements)

References 11 publications

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“…The continuous changing trends in the LULC will have adverse impacts on the land system processes; in particular, the increase in the built-up and the decrease in natural vegetation, wetlands, and water bodies [42][43][44]. India is facing recurrent heat waves and an increased number of annual urban heat island (UHI) events [45,46]. These adverse processes come as a result of rampant urbanization and the decrease in the natural landscape.…”

Section: Built-up Vegetationmentioning

confidence: 99%

Cellular Automata-Based Artificial Neural Network Model for Assessing Past, Present, and Future Land Use/Land Cover Dynamics

et al. 2022

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Land use and land cover change (LULCC) is among the most apparent natural landscape processes impacted by anthropogenic activities, particularly in fast-growing regions. In India, at present, due to the impacts of anthropogenic climate change, supplemented by the fast pace of developmental activities, the areas providing the highest agricultural yields are facing the threat of either extinction or change in land use. This study assesses the LULCC in the fastest-changing landscape region of the Indian state of Bihar, District Muzaffarpur. This district is known for its litchi cultivation, which, over the last few years, has been observed to be increasing in acreage at the behest of a decrease in natural vegetation. In this study, we aim to assess the past, present and future changes in LULC of the Muzaffarpur district using support vector classification and CA-ANN (cellular automata-artificial neural network) algorithms. For assessing the present and past LULC of the study area, we used Landsat Satellite data for 1990, 2000, 2010, and 2020. It was observed that between 1990 and 2020, the area under vegetation, wetlands, water body, and fallow land decreased by 44.28%, 34.82%, 25.56%, and 5.63%, respectively. At the same time, the area under built-up, litchi plantation, and cropland increased by 1451.30%, 181.91%, and 5.66%, respectively. Extensive ground truthing was carried out to assess the accuracy of the LULC for 2020, whereas historical google earth images were used for 1990, 2000, and 2010, through the use of overall accuracy and kappa coefficient indices. The kappa coefficients for the final LULC for the years 1990, 2000, 2010, and 2020 were 0.79, 0.75, 0.87, and 0.85, respectively. For forecasting the future LULC, first, the LULC of 1990 and 2010 were used to predict the landscape for 2020 using the CA-ANN model. After calibrating and validating the CA-ANN outputs, LULC for 2030 and 2050 were generated. The generated future LULC scenarios were validated using kappa index statistics by comparing the forecast outcomes with the original LULC data for 2020. It was observed that in both 2030 and 2050, built-up and vegetation would be the major transitioning LULC. In 2030 and 2050, built-up will increase by 13.15% and 108.69%, respectively, compared to its area in 2020; whereas vegetation is expected to decrease by 14.30% in 2030 and 32.84% in 2050 compared to its area in 2020. Overall, this study depicted a decline in the natural landscape and a sudden increase in the built-up and cash-crop area. If such trends continue, the future scenario of LULC will also demonstrate the same pattern. This study will help formulate better land use management policy in the study area, and the overall state of Bihar, which is considered to be the poorest state of India and the most vulnerable to natural calamities. It also demonstrates the ability of the CA-ANN model to forecast future events and comprehend spatiotemporal LULC dynamics.

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Section: Built-up Vegetationmentioning

confidence: 99%

Cellular Automata-Based Artificial Neural Network Model for Assessing Past, Present, and Future Land Use/Land Cover Dynamics

et al. 2022

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“…Large scale to short-term effects has been noticed due to the effect of climatic instability on natural components and also on human being. Globally, climate change is interconnected with the variations in essential climate and weather parameters and this has increased the rapid and slow onset of climate events globally (Sarkar et al, 2021;Kanga et al, 2022). IPCC (2014) have estimated that by 2100, earth's temperature could rise about 1.1-6.4ºC and sea level could rise by 28-79 cm, collectively it will make the weather pattern less predictable and some extreme climatic instability and phenomena like storms, floods, heat waves and droughts will emerge with high frequency and severity.…”

Section: Introductionmentioning

confidence: 99%

Trend and variability analysis of long-term meteorological parameters for detecting climate change in Kolkata District, India

Paira,

Roy,

et al. 2023

Preprint

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Assessing the temporal dynamics of weather parameters in the perspective of climate change, especially in an urban space characterized by tremendous demographic pressure, various anthropogenic activities, is imperative to examine climatological changes and recommend fruitful policies. Hence, trend and variability analysis wasapplied to examine the change of temperature and rainfall in Kolkata district applying the data of “climate Research Unit (CRU TS 4.04with 0.5° by 0.5° resolution)” and “NASA data access viewer” from 1902 to 2021. Data has been examined using “kurtosis”, “skewness”, and “coefficient of variation”, “anomaly index”, “precipitation concentration index. Furthermore, Spearman’s Rho test, Mann-Kendall test and Innovative trend analysis methods has been applied to detect the trend and variability. Both“Mann-Kendall and spearman’s Rho test”shown that the seasonal (summer and winter) and annual temperature is increasing significantly (P < 0.01), whereas the monthly data record revealed a significant (P < 0.01 and 0.05) rising trend of temperature in all months. The annual and monsoonal rainfall increased significantly. Innovative trend analysis slope recorded rising trend of temperature (+ 0.10) and rainfall (+ 1.67). Study does not found any uniform year (PCI = < 10) of precipitation, however, high (79years) and very high (25years) concentration of precipitation was observed. The variability of temperature implies high and positive outlier frequencies in January (+ 5.31), March (+ 33.5), April (+ 21.83), May (+ 46.00), September (+ 2.94), and seasonally in summer (+ 6.57) and winter (+ 7.89). Present study suggests the need tackle the rising temperature by making awareness about the global warming and its consequences on climate change.

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“…Climate change is a major concern that has increased the rapid and slow onset of climate events globally [1,2]. Rising ocean and air temperatures, increasing occurrence and intensity of tidal surges, violent stormy cyclones, severe flooding, and extreme precipitation events are some of the manifestations of climate change [3].…”

Section: Introductionmentioning

confidence: 99%

Vulnerability and Risk Assessment to Climate Change in Sagar Island, India

Bera

Meraj

Kanga

et al. 2022

Water

Self Cite

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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.

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Disasters in the Complex Himalayan Terrains

Cited by 17 publications

References 11 publications

Cellular Automata-Based Artificial Neural Network Model for Assessing Past, Present, and Future Land Use/Land Cover Dynamics

Cellular Automata-Based Artificial Neural Network Model for Assessing Past, Present, and Future Land Use/Land Cover Dynamics

Trend and variability analysis of long-term meteorological parameters for detecting climate change in Kolkata District, India

Vulnerability and Risk Assessment to Climate Change in Sagar Island, India

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