Abstract:In the present study, using static land system parameters, such as geomorphology, land cover, and relief, we calculated the water yield potential (RP) of all the watersheds of the Jhelum basin (Kashmir Valley) using the analytical hierarchy process (AHP) based watershed evaluation model (AHP-WEM). The results revealed that among the 24 watersheds of the Jhelum basin, the Vishav watershed, with the highest RP, is the fastest water yielding catchment of the Jhelum basin followed by Bringi, Lidder, Kuthar, Sind, … Show more
“…It contributed to about 39.26% of worldwide natural disasters and caused USD 397.3 billion worth of damage between 2000 and 2014 [3,4]. Human activities such as urbanization, deforestation, and unplanned development all contribute towards the rise in the number of areas vulnerable to floods [3][4][5]. Moreover, the changing climate that has manifested the increase in the frequency of extreme precipitation events is also one of the primary forces behind the exacerbated flooding problem [6,7].…”
Section: Introductionmentioning
confidence: 99%
“…A comprehensive flood management strategy requires an understanding of its underlying causes by assessing the physio-climatic and hydrodynamic parameters as well as damage and exposure indices related to socioeconomic and ecological parameters [3][4][5]12]. The overall output of this exercise is finally the calculation of the flood risk that integrates hazard and vulnerability in assessing the latter's accumulation and propagation.…”
Section: Introductionmentioning
confidence: 99%
“…The overall output of this exercise is finally the calculation of the flood risk that integrates hazard and vulnerability in assessing the latter's accumulation and propagation. The flood risk analysis is exceedingly complex and challenging in developing countries, primarily because of the data limitations [5]. An assessment of the spatial exposure to floods is the primary requirement for devising an early warning system [13].…”
Urban floods are very destructive and have significant socioeconomic repercussions in regions with a common flooding prevalence. Various researchers have laid down numerous approaches for analyzing the evolution of floods and their consequences. One primary goal of such approaches is to identify the areas vulnerable to floods for risk reduction and management purposes. The present paper proposes an integrated remote sensing, geographic information system (GIS), and field survey-based approach for identifying and predicting urban flood-prone areas. The work is unique in theory since the methodology proposed finds application in urban areas wherein the cause of flooding, in addition to heavy rainfall, is also the inefficient urban drainage system. The work has been carried out in Delhi’s Yamuna River National Capital Territory (NCT) area, considered one of India’s most frequently flooded urban centers, to analyze the causes of its flooding and supplement the existing forecasting models. Research is based on an integrated strategy to evaluate and map the highest flood boundary and identify the area affected along the Yamuna River NCT of Delhi. In addition to understanding the causal factors behind frequent flooding in the area, using field-based information, we developed a GIS model to help authorities to manage the floods using catchment precipitation and gauge level relationship. The identification of areas susceptible to floods shall act as an early warning tool to safeguard life and property and help authorities plan in advance for the eventuality of such an event in the study area.
“…It contributed to about 39.26% of worldwide natural disasters and caused USD 397.3 billion worth of damage between 2000 and 2014 [3,4]. Human activities such as urbanization, deforestation, and unplanned development all contribute towards the rise in the number of areas vulnerable to floods [3][4][5]. Moreover, the changing climate that has manifested the increase in the frequency of extreme precipitation events is also one of the primary forces behind the exacerbated flooding problem [6,7].…”
Section: Introductionmentioning
confidence: 99%
“…A comprehensive flood management strategy requires an understanding of its underlying causes by assessing the physio-climatic and hydrodynamic parameters as well as damage and exposure indices related to socioeconomic and ecological parameters [3][4][5]12]. The overall output of this exercise is finally the calculation of the flood risk that integrates hazard and vulnerability in assessing the latter's accumulation and propagation.…”
Section: Introductionmentioning
confidence: 99%
“…The overall output of this exercise is finally the calculation of the flood risk that integrates hazard and vulnerability in assessing the latter's accumulation and propagation. The flood risk analysis is exceedingly complex and challenging in developing countries, primarily because of the data limitations [5]. An assessment of the spatial exposure to floods is the primary requirement for devising an early warning system [13].…”
Urban floods are very destructive and have significant socioeconomic repercussions in regions with a common flooding prevalence. Various researchers have laid down numerous approaches for analyzing the evolution of floods and their consequences. One primary goal of such approaches is to identify the areas vulnerable to floods for risk reduction and management purposes. The present paper proposes an integrated remote sensing, geographic information system (GIS), and field survey-based approach for identifying and predicting urban flood-prone areas. The work is unique in theory since the methodology proposed finds application in urban areas wherein the cause of flooding, in addition to heavy rainfall, is also the inefficient urban drainage system. The work has been carried out in Delhi’s Yamuna River National Capital Territory (NCT) area, considered one of India’s most frequently flooded urban centers, to analyze the causes of its flooding and supplement the existing forecasting models. Research is based on an integrated strategy to evaluate and map the highest flood boundary and identify the area affected along the Yamuna River NCT of Delhi. In addition to understanding the causal factors behind frequent flooding in the area, using field-based information, we developed a GIS model to help authorities to manage the floods using catchment precipitation and gauge level relationship. The identification of areas susceptible to floods shall act as an early warning tool to safeguard life and property and help authorities plan in advance for the eventuality of such an event in the study area.
“…Even after the devastating flood of September 2014, which left nearly 3.5% geographical area of the valley submerged and killing nearly 300 people, there has been no implementation of an operational flood forecasting system in the region. Although some studies for simulation and prediction of floods have been conducted using neural networks (Tabbussum and Dar 2021), statistical approaches (Parvaze et al 2021), hydrological models (Meraj et al 2018) and other soft computing techniques (Himayoun et al 2020), real time flood forecasting has yet not been implemented in the basin. The key component of the study is the use of a combination of hydrological and hydraulic model, on the river basin using open-source, geospatial, meteorological and hydrological information.…”
Flood forecasting using hydrological and hydraulic models is an efficient non-structural flood management option. For real-time flood forecasting in the data scarce Jhelum basin, the present study evaluated the capability of MIKE 11 Nedbor-Afstromings Model (NAM), hydrodynamic (HD) and flood forecasting (FF) models to forecast streamflow of four principal gauging stations of river Jhelum (Sangam, Ram Munshi Bagh, Asham and Baramulla). European Centre for Medium-Range Weather Forecast (ECMWF) model-forecasted precipitation and temperature data was used to increase the forecast lead time to 7 days. The forecast results were assessed by calculating coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and various error indices. Integration of the MIKE 11 NAM and HD modules improved the results of MIKE 11 NAM significantly. Values of R2 improved by 8–15% while that of NSE improved by 8–14% at various stations by using integrated model. MIKE 11 FF with ECMWF input was used to simulate the streamflow with lead times of 1to 10-days. The forecast results were efficient for lead times up to 7-days with R2 and NSE values 0.8 and 0.7, respectively. Peak error for 7-days forecast was 19.1% and the peak time error was 1.2 h. The developed model can be used for short to medium range flood forecasting for the data scarce, snow dominated Jhelum basin and can apply to similar basins around the globe as a component of the early flood warning system.
“…Geomorphic studies of some watersheds have been analyzed using various geospatial techniques which revealed that Kashmir Valley is highly susceptible to environmental hazards such as erosion, landslides, and floods due to its complex topography and perplexed geography (Gull et al 2017;Rather et al 2017;Meraj et al 2018). Lidder watershed, being one of the biggest watersheds of Kashmir Valley, needs continuous monitoring and proper assessment for effective management of its land and water resources.…”
The conjunction of heavy snowfall during winters and intensive rainfall during monsoons along with the mountainous topography expose the Lidder watershed to serious erosion and flood aggravation issues. Barely any attempts have been made for an in-depth examination of Lidder watershed for precise estimation of sub-basin level runoff and erosion. In this study Soil and Water Assessment Tool (SWAT) was calibrated using Sequential Uncertainty Fitting algorithm (SUFI-2) for modelling streamflow and sediment yield of the Lidder watershed. Daily runoff and sediment event data from 2003–2013 were used in this study; data from 2003–2008 was used for calibration and 2009–2013 for validation. Model performance was evaluated using various statistical tools which showed good results revealing excellent potential of SWAT model to simulate streamflow and sediment yield for both calibration and validation periods. The annual rate of average upland sediment drawn from the watershed was approximately 853.96 Mg/ha for an average surface runoff of 394.15 mm/year. This study identifies the vulnerable areas of the Lidder watershed which can be thoroughly examined by decision-makers for effective management and planning. Further, the calibrated model can be applied to other watersheds with similar characterization to influence strategies in the management of watershed processes.
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