Impacts of Natural Disasters on Households and Small Businesses in India

Patankar, Archana

doi:10.2139/ssrn.3590902

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…Recent studies by Bisht et al 2018;Dash et al 2009;Deshpande et al 2016;Dubey and Sharma 2018;Goswami et al 2006;Guhathakurta et al 2011;Jain et al 2017;Krishnamurthy et al 2009 reported a significant increase in extreme precipitation events on a national and regional scale. Flooding is becoming more common in India as extreme weather events become more common, accounting for half of all natural disasters (Patankar 2020). According to flood statistics from the Government of India, the flood-affected area in India has increased from 25 million hectares (Mha) in 1952 to 40 Mha in 1980 and 49.8 Mha in 2011 (Bhanduri 2019).…”

Section: Introductionmentioning

confidence: 99%

Real-time flood forecasting using an integrated hydrologic and hydraulic model for the Vamsadhara and Nagavali basins, Eastern India

Venkata Rao,

Nagireddy,

Keesara

et al. 2024

Nat Hazards

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Due to recent rainfall extremes and tropical cyclones that form over the Bay of Bengal during the pre- and post-monsoon seasons, the Nagavali and Vamsadhara basins in India experience frequent floods, causing significant loss of human life and damage to agricultural lands and infrastructure. This study provides an integrated hydrologic and hydraulic modeling system that is based on the Soil and Water Assessment Tool model and the 2-Dimensional Hydrological Engineering Centre-River Analysis System, which simulates floods using Global Forecasting System rainfall forecasts with a 48-h lead time. The integrated model was used to simulate the streamflow, flood area extent, and depth for the historical flood events (i.e., 1991–2018) with peak discharges of 1200 m3/s in the Nagavali basin and 1360 m3/s in the Vamsadhara basin. The integrated model predicted flood inundation depths that were in good agreement with observed inundation depths provided by the Central Water Commission. The inundation maps generated by the integrated modeling system with a 48-h lead time for tropical cyclone Titli demonstrated an accuracy of more than 75%. The insights gained from this study will help the public and government agencies make better decisions and deal with floods.

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Section: Introductionmentioning

confidence: 99%

Real-time flood forecasting using an integrated hydrologic and hydraulic model for the Vamsadhara and Nagavali basins, Eastern India

Venkata Rao,

Nagireddy,

Keesara

et al. 2024

Nat Hazards

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“…Extreme ood events in uence around 21 million people globally, which may rise to 54 million by 2030 due to climate change and socioeconomic growth (Luo et al, 2015). According to Patankar (2019), 278 ood events occurred between 1980 and 2017 in India, which caused a loss of 58.7 billion USD and affected 750 million people. Gangopadhyay et al (2018) reported that ood causes around USD 7500 million in economic losses annually in India.…”

Section: Introductionmentioning

confidence: 99%

Development of an Automated Method for Flood Inundation Monitoring, Flood Hazard and Soil Erosion Susceptibility Assessment Using Machine Learning and AHP-MCE Techniques

Prakash

Begum

Vilímek

et al. 2023

Preprint

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Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms such as Google Earth Engine (GEE) and robust methodology (Otsu’s algorithm). The current study employs the Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam state, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART) and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. The highest prediction accuracy was observed for the RF model (overall accuracy [OA]: > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Further, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. Over 26% of the study area indicated high flood hazard-prone areas, and about 60% showed a high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as they help to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of emergency response efforts. The periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, climate resilience studies, etc.

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Drivers, changes, and impacts of hydrological extremes in India: A review

Mishra,

Nanditha,

Dangar

et al. 2024

WIREs Water

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The frequency of hydrological extremes (droughts and floods) has increased in recent years in India. A dense population, intensive agriculture, and rapidly developing physical infrastructure put India under the risk of hydrological extremes. However, a comprehensive overview of the drivers, changes, and impacts associated with hydrological extremes in India has been lacking. We examine the key processes and the role of climate and human interventions on hydrological extremes in India. We discuss the observed and projected changes in hydrological extremes along with their impacts. While the Indian summer monsoon is the primary driver of the hydrological extremes in India, human interventions (irrigation, reservoir storage, and groundwater pumping) play a crucial role in the changes and variability of hydrological extremes. The relative role of climate change and direct human interventions on changing the intensity and impacts of hydrological extremes need to be considered for future adaptation planning under climate change. The observed increase in the frequency and intensity of hydrological extremes will continue in the future with compound and cascading impacts due to the warming climate. We need to strengthen the observational network, improve hydrological modeling, reduce climate uncertainty, and develop robust early warning systems to adapt and mitigate hydrological extremes in India.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Extremes Science of Water > Water and Environmental Change

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Impacts of Natural Disasters on Households and Small Businesses in India

Cited by 10 publications

References 14 publications

Real-time flood forecasting using an integrated hydrologic and hydraulic model for the Vamsadhara and Nagavali basins, Eastern India

Real-time flood forecasting using an integrated hydrologic and hydraulic model for the Vamsadhara and Nagavali basins, Eastern India

Development of an Automated Method for Flood Inundation Monitoring, Flood Hazard and Soil Erosion Susceptibility Assessment Using Machine Learning and AHP-MCE Techniques

Drivers, changes, and impacts of hydrological extremes in India: A review

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