Browsing by Author "Barnali Kundu"

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Comprehensive Drought Vulnerability Assessment in Northwestern Odisha: A Fuzzy Logic and Analytical Hierarchy Process Integration Approach
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Susanta Mahato; Gita Mandal; Barnali Kundu; Sonali Kundu; P.K. Joshi; Pankaj Kumar
Crafting a comprehensive strategy to mitigate the impact of droughts, a complex geo-hazard profoundly affecting socio-economic aspects, entails the creation of a drought vulnerability map as a primary step. This study harmonizes geospatial techniques and the Fuzzy Analytical Hierarchy Process (fuzzy AHP) to formulate such a map for northwestern Odisha, India. From six principal drought-induced vulnerability parameters, namely physical attributes, water demand and usage, agriculture, land use, groundwater and population/development, 22 sub-parameters were selected. Spatial layers were generated for each sub-parameter, followed by their fuzzification using a fuzzy membership approach. Subsequently, AHP was employed to establish parameter weights through pair-wise comparisons. By applying the weighted overlay method, drought vulnerability maps were generated, classifying regions into five vulnerability levels: very high, high, moderate, low, and very low. The outcomes indicate that roughly 33% of the area is classified as having high drought vulnerability. Validation of the approach using statistical metrics, including accuracy, root mean square error and mean absolute error, demonstrates its efficacy in gauging drought vulnerability, thereby aiding planners in devising effective drought mitigation strategies. © 2023 by the authors.
Early summer temperature anomalies and potential impacts on achieving Sustainable Development Goals (SDGs) in National Capital Region (NCR) of India
(Elsevier B.V., 2023) Susanta Mahato; Barnali Kundu; Nikunj Makwana; P.K. Joshi
This research aims to investigate the repercussions of an anomalous early summer Land Surface Temperature (LST) surge on food, energy, and human health within the National Capital Region (NCR) of India, with a specific focus on its potential influence on the Sustainable Development Goals (SDGs). To attain this objective, the study employed various methods to evaluate the magnitude of the deviation in LST. MODIS (Moderate Resolution Imaging Spectroradiometer) images were utilized to compute the monthly diurnal LST range, and the Standard Anomaly (StA) approach was employed to account for data dispersion. Additionally, Innovative Trend Analysis (ITA) and Detrended Fluctuation Analysis (DFA) were conducted to perform regionally specific trend analysis. Furthermore, the Ordinary Least Square (OLS) regression method was applied to investigate the relationship between StA and crop yields. The findings indicate a significant temperature increase in March, with a deviation of 3.5 °C above the average range. Additionally, the study reveals the standard anomaly (StA) of Land Surface Temperature (LST) during March fell within the range of −0.706 to 2.783 °C, while in April, it ranged from −0.781 to 3.263 °C, and in May, it ranged from −3.001 to 0.525 °C. The key significance of the study lies in the impacts of this early summer warming on the attainment of the Sustainable Development Goals. The reduction in crop yields as a result of this warming poses a substantial threat to achieving the SDG-2 target of Zero Hunger. Moreover, the adverse health effects stemming from the early summer warming impede the achievement of the SDG-3.4.1 target. Additionally, the high energy consumption induced by the warming directly affects SDG-6 on affordable and clean energy. The research underscores the critical importance of addressing the impacts of early summer warming to ensure the successful achievement of the Sustainable Development Goals in the National Capital Region of India. Policymakers and stakeholders should take into account the findings of this study to implement targeted strategies that mitigate the adverse effects of early summer warming on food, energy, and human health, and thereby contribute to the realization of the SDGs. © 2023 Elsevier B.V.
Enhancing drought resilience: machine learning–based vulnerability assessment in Uttar Pradesh, India
(Springer, 2024) Barnali Kundu; Narendra Kumar Rana; Sonali Kundu
Drought is a natural and complex climatic hazard. It has both natural and social connotations. The purpose of this study is to use machine learning methods (MLAs) for drought vulnerability (DVM) in Uttar Pradesh, India. There were 18 factors used to determine drought vulnerability, separated into two groups: physical drought and meteorological drought. The study found that the eastern part of Uttar Pradesh is high to very highly prone to drought, which is approximately 31.38% of the area of Uttar Pradesh. The receiver operating characteristic curve (ROC) was then used to evaluate the machine learning models (artificial neural networks). According to the findings, the ANN functioned with AUC values of 0.843. For policy actions to lessen drought sensitivity, DVMs may be valuable. Future exploration may involve refining machine learning algorithms, integrating real-time data sources, and assessing the socio-economic impacts to continually enhance the efficacy of drought resilience strategies in Uttar Pradesh. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India
(Springer, 2024) Barnali Kundu; Narendra Kumar Rana; Sonali Kundu; Devendra Soren
Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Wetland degradation and its impacts on livelihoods and sustainable development goals: An overview
(Elsevier B.V., 2024) Sonali Kundu; Barnali Kundu; Narendra Kumar Rana; Susanta Mahato
Wetlands, vital ecosystems that support 40 % of the world's species and serve as nature's water filters, are disappearing three times faster than forests. While global research extensively examines the increasing degradation of wetland health, there exists a significant research gap concerning its impact on livelihoods and the achievement of Sustainable Development Goals (SDGs). To address this gap, a systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, utilising data from 1270 database records and 350 research studies spanning 1990 to 2023. The study reveals alarming annual wetland health loss rates ranging from 0.02 % to 3.14 %, driven globally by built-up areas, agricultural expansion, climate change, and pollution. Notably, developing countries and those with lower development indices exhibit the highest rates of wetland health degradation, primarily attributed to agricultural and urban expansion, as well as pollution. The analysis establishes a negative correlation between wetland health degradation rates, driving factors, and key indicators such as the Sustainable Development Goal Index (SDGI) (r = −0.38), Environmental Performance Index (EPI) (r = −0.34), Income Classification (r = −0.42), and Human Development Index (HDI) (r = −0.38). The study emphasizes the imperative of improving economic and socio-ecological conditions to enhance conservation and restoration efforts in wetland areas, thereby contributing to the achievement of SDGs. The interconnectedness of wetland health with broader SDGs underscores the need for targeted interventions. Recommendations include prioritizing comprehensive strategies for environmental and societal well-being, urging policymakers and practitioners to consider the holistic implications of wetland health degradation in their decision-making processes. © 2024 Institution of Chemical Engineers