Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India

Flood is the most devastating and prevalent disaster among all-natural disasters. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. The main objective of this study is to delineate floodprone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. Eight flood parameters including slope, elevation, land use, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), drainage density, and rainfall were used to map the flood-prone areas in the study region. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. The results of the model were found reliable with area under curve values for success and prediction rate of 82.04% and 84.74%, respectively. The findings of this study can play a key role in flood hazard management in the target region; they can be used by the local disaster management authority, researchers, planners, local government, and line agencies dealing with flood risk management.

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“…• LULC directly or indirectly affects penetration, evapotranspiration, and surface runoff generation [1,54].…”

Section: Land Use/ Land Covermentioning

confidence: 99%

“…• Literature indicates that rainfall has a direct relationship with river discharge and a large amount of rainfall in a short time can generate flash floods in semi-arid regions [12,43,53,54]. • The monthly rainfall data from 1980 to 2016 were collected from the Regional Meteorological Center (RMC) Peshawar.…”

Section: Rainfallmentioning

confidence: 99%

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

2020

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“…Flood vulnerable maps can be easily verified using flood event inventory data. For this study, the location of the flood event was collected from old unpublished reports, newspapers and physical field visits where interviews with residents were conducted [13]. In this study, for the purpose of validation using Area Under Curve (AUC), where the efficiency and performance of the SMCE model is assessed.…”

Section: Validation and Accuracy Of Physical To Flood Vulnerability Mapmentioning

confidence: 99%

Flood vulnerability mapping using the spatial multi-criteria evaluation (SMCE) method in the Minraleng Watershed, Maros Regency, South Sulawesi

et al. 2020

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This study focuses on the assessment of flood-vulnerable areas in the Minraleng watershed, Maros Regency, where the area experiences floods every year. Spatial analysis in the Geographic Information System (GIS) environment has been applied to estimate flood-vulnerable zones using six relevant physical factors, such as rainfall intensity, slope, Elevation, distance from the rivers, land use and soil type. The relative importance of physical factors has been compared in paired matrices to obtain weight values using the Spatial Multi-Criteria Evaluation (SMCE) method. The result showed that the areas located in Camba sub-district had the high vulnerability. The region with a high and very high vulnerability to flood were spread with an area of 436 ha (0,84 %) and 6.168 ha (11.8%).

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“…A positive plan curvature value indicates the sideward convexity while a positive profile curvature indicates upward concavity of the surface. Recent days, with the advancement of GIS, calculation of plan and profile curvature is very easy and these indices are considered as significant landscape variable due to strong correlation between sediment yield (Zhang et al 2015;Das 2019a).…”

Section: Plan and Profile Curvaturesmentioning

confidence: 99%

“…Topographic wetness index of a catchment represents two types of measurements, are hydrographic positions and the flat lands (Papaioannou et al 2015;Das 2019a). This factor is a physical representation of areas having higher potential.…”

Section: Topographic Wetness Indexmentioning

confidence: 99%

Landscape variables in the Indian (Peninsular) catchments: insights into hydro-geomorphic evolution

Das¹

2019

Preprint

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The river systems in peninsular India are remained unexplored in terms of hydro-geomorphic evolution, though a few works are carried out in order to understand the tectonic and structural evolution with paleoclimate. Morphometric analysis at catchment scale delivers insights into the dynamics, erosion capacity, probability of flood occurrence, lithological and structural control, and genetic response to the tectonics. The present study aimed to characterize the hydro-geomorphic evolution of 12 major catchments in Peninsular India via morphometric analysis. A total 25 morphometric parameters were computed and several statistical analyses performed in establishing the inter-correlation and making classification of Indian rivers. The results indicated that most of the rivers in peninsular India are 7th to 9th order catchments. A high variability of the rainfall was observed in these rivers where the northern catchments experience higher amount of precipitation contrasting to the southern basins. Almost all the basins showed a moderate relief ratio, hypsometric integral, ruggedness etc. Cauvery, Baitarni, and Brahmani showed exceptionally steeper gradient, high relief ratio, LS factor, and ruggedness index which may indicate higher erosion potential. A positive association between erosion rate and hypsometric integral was observed. All these rivers were classified in three categories based on cluster analysis where the medium and large sized catchments formed different groups. Despite of the extensive data and statistical analysis, the outcomes of this study are highly general due to large scale variation in lithology and climate. In foreseeable works dealing with such morphometric variables, a higher resolution sub-catchment scale analysis would be required for a better description of the hydro-geomorphic response of these large catchments in peninsular India.

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Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India

Cited by 139 publications

References 100 publications

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

Flood vulnerability mapping using the spatial multi-criteria evaluation (SMCE) method in the Minraleng Watershed, Maros Regency, South Sulawesi

Landscape variables in the Indian (Peninsular) catchments: insights into hydro-geomorphic evolution

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