Spatio-Temporal Rainfall Variability and Concentration over Sri Lanka

Pawar, Uttam; Karunathilaka, Pasindu; Rathnayake, Upaka

doi:10.1155/2022/6456761

Cited by 10 publications

(4 citation statements)

References 52 publications

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“…A brief methodology was mentioned as follows. A detailed methodology can be found in Pawar and Upaka [ 65 ] and Pawar [ 66 ]. GoF tests for example Anderson-Darling (AD) test, Kolmogorov-Smirnov (KS), and Chi-Square test (

) were used to choose the best-fit probability distribution at every site on the Mekong River.…”

Section: Methodsmentioning

confidence: 99%

Frequency and trend analyses of annual peak discharges in the Lower Mekong Basin

Pawar,

Try,

Muttil

et al. 2023

Heliyon

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) were used to choose the best-fit probability distribution at every site on the Mekong River.…”

Section: Methodsmentioning

confidence: 99%

Frequency and trend analyses of annual peak discharges in the Lower Mekong Basin

Pawar,

Try,

Muttil

et al. 2023

Heliyon

Self Cite

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“…The long-term rainfall data for the UKB were collected from various sources (refer to Table 1). Several scholars have used the Inverse Distance Weighting (IDW) method for spatial and temporal analysis of rainfall [68,69]. Accordingly, the rainfall distribution map of the UKB based on annual average rainfall (AAR) was prepared (refer to Figure 4g).…”

Section: Frequency Ratio (Fr) Modelmentioning

confidence: 99%

A GIS-Based Comparative Analysis of Frequency Ratio and Statistical Index Models for Flood Susceptibility Mapping in the Upper Krishna Basin, India

Pawar¹,

Suppawimut

Muttil

et al. 2022

Water

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The Upper Krishna Basin in Maharashtra (India) is highly vulnerable to floods. This study aimed to generate a flood susceptibility map for the basin using Frequency Ratio and Statistical Index models of flood analysis. The flood hazard inventory map was created by 370 flood locations in the Upper Krishna Basin and plotted using ArcGIS 10.1 software. The 259 flood locations (70%) were selected randomly as training samples for analysis of the flood models, and for validation purposes, the remaining 111 flood locations (30%) were used. Flood susceptibility analyses were performed based on 12 flood conditioning factors. These were elevation, slope, aspect, curvature, Topographic Wetness Index, Stream Power Index, rainfall, distance from the river, stream density, soil types, land use, and distance from the road. The Statistical Index model revealed that 38% of the area of the Upper Krishna Basin is in the high- to very-high-flood-susceptibility class. The precision of the flood susceptibility map was confirmed using the receiver operating characteristic and the area under the curve value method. The area under the curve showed a 66.89% success rate and a 68% prediction rate for the Frequency Ratio model. However, the Statistical Index model provided an 82.85% success rate and 83.23% prediction rate. The comparative analysis of the Frequency Ratio and Statistical Index models revealed that the Statistical Index model was the most suitable for flood susceptibility analysis and mapping flood-prone areas in the Upper Krishna Basin. The results obtained from this research can be helpful in flood disaster mitigation and hazard preparedness in the Upper Krishna Basin.

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“…The variability and concentration of rainfall precipitation considering climate change have caused extreme droughts and flood conditions in several regions of the world (Pawar et al, 2022). In recent decades, extreme rainfall events have increased around the world.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the daily rainfall concentration index in Brazil

Back,

Souza,

Galatto

2024

CLIUM

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The concentration of rain in short periods can trigger erosion processes and natural disasters such as floods and landslides. In order to investigate the spatial and temporal distribution of daily precipitation throughout the Brazilian territory, the Concentration Index (CI) of daily precipitation was used. 1,402 daily rainfall series were used based on regular records from 1991 to 2020. The results of the annual CI allowed the identification of five climatic zones: 0.7% of the Brazilian territory with CI <0.54 located in the state of Mato Grasso do Sul; 29.4% with CI values between 0.54 and 0.56 located in the central-west region, and in small portions of the south, southeast and north regions; 48.1% with CI values between 0.56 and 0.58 distributed in the south, southeast, north and northeast regions of the Brazilian territory; 14.7% with a CI between 0.58 and 0.60 in the northeast region, and in interior portions of the south and southeast regions; 7.1% with CI values between 0.60 and 0.70 characterized by coastal regions from the north of the state of Santa Catarina to the state of Rio Grande do Norte

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Spatio-Temporal Rainfall Variability and Concentration over Sri Lanka

Cited by 10 publications

References 52 publications

Frequency and trend analyses of annual peak discharges in the Lower Mekong Basin

Frequency and trend analyses of annual peak discharges in the Lower Mekong Basin

A GIS-Based Comparative Analysis of Frequency Ratio and Statistical Index Models for Flood Susceptibility Mapping in the Upper Krishna Basin, India

Evaluation of the daily rainfall concentration index in Brazil

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