Simulation of Rainfall -Runoff of Kankai River Basin Using SWAT Model: A Case Study of Nepal

Karki, Mohan

doi:10.22214/ijraset.2020.30867

Cited by 5 publications

(5 citation statements)

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“…The main products of these models are basically runoff and flow depth [51]. The hydrometeorological inputs and topographic properties of the catchment are incorporated into the rainfall-runoff model to produce river flow characteristics and river depth at a given time during the process [52]. The RR models operating on a sub-basin level (distributed models) produce better results for large catchments, although they may not give the best results for small basins like the lumped models [53].…”

Section: Deterministic Modelsmentioning

confidence: 99%

“…The system has a high capability to predict several hydrological factors, like streamflow and river levels, which enhances the output of the most probable outcome [141]. When meteorological and hydrological modelling capabilities are integrated with advancements in data collection, satellite observations, land-surface hydrology models, and increasing computational capacity, the capacity to develop an ensemble that forecasts on a worldwide level is realised [52]. Currently, forecasting systems in operation like the Global Flood Monitoring System (GFMS) developed by National Aeronautics and Space Administration (NASA) and the University of Maryland, Disaster AWARE Pro by NASA, Met Office Global and Regional Ensemble Prediction System (MOGREPS) by the United Kingdom (UK) government, Global Flood Awareness System (GloFAS) developed by European Centre for Medium-Range Weather Forecasts (ECMWF), and the pre-operational stages like The THORPEX Interactive Grand Global Ensemble (TIGGE) commonly use ensemble weather predictions as inputs [142].…”

Section: Ensemble Predictionsmentioning

confidence: 99%

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Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Byaruhanga,

Kibirige,

Gokool

et al. 2024

Water

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Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems.

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Section: Deterministic Modelsmentioning

confidence: 99%

Section: Ensemble Predictionsmentioning

confidence: 99%

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Byaruhanga,

Kibirige,

Gokool

et al. 2024

Water

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“…The Kankai River (also known as Kankai Mai) is a perennial river (second category) that originated from the Mahabharat range [15,10]. Apart from supporting aquatic animals, including fi sh, this river is also famous for its pilgrimage value.…”

Section: Fish Fauna Of Kankai River Of Jhapa District Eastern Nepalmentioning

confidence: 99%

“…The study area was the Kankai River of Jhapa District, which fl ows through the Siwalik region and reaches the plain area, Terai. The total drainage area of this river is about 1,165 km 2 [10], and the elevation range varies from 100 m (Terai) to 3,500 m (origin point) [10]. Jogmai, Puwamai and Deumai Rivers are three major tributaries of this river [10], and it eventually merges with the Ganges River system of India.…”

Section: Study Areamentioning

confidence: 99%

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Fish Fauna of Kankai River of Jhapa District, Eastern Nepal

et al. 2021

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This study explored ﬁsh diversity in the Kankai River of the Jhapa district. Fishes were collected by cast net (mesh 0.5cm) from three sampling sites: Domukha, Kotihom and Bengdada in rainy and winter seasons. Water temperature, pH and water velocity were recorded at each site. A total of 20 ﬁsh species belonging to 4 orders and eight families were recorded. Cypriniformes has recorded as the dominant order with 16 species, followed by Siluriformes with two species and Perciformes, and Synbranchiformes with a single species each. The value of Shannon’s diversity Index (H’) was higher in winter (3.06; Domukha) than a rainy season (1.06; Bengdada), indicating higher diversity in winter than rainy season. Only a slight variation in pH was recorded during the study period, i.e. 7.9 (highest) and 7.8 (lowest). The highest and the lowest temperatures recorded in the rainy season were 280C and 270C, and that of winter were 170C and 150C. The highest and the lowest water current recorded in the rainy season were 0.75 m/s and 0.68 m/s, and that of winter were 0.55 m/s and 0.52 m/s. It should be noted that to have comprehensive ﬁsh diversity status, future investigation covering more seasons and more sampling sites is essential.

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Flood susceptibility assessment using machine learning approach in the Mohana-Khutiya River of Nepal

Maharjan,

Timilsina,

Ayer

et al. 2024

Natural Hazards Research

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Simulation of Rainfall -Runoff of Kankai River Basin Using SWAT Model: A Case Study of Nepal

Cited by 5 publications

References 0 publications

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Fish Fauna of Kankai River of Jhapa District, Eastern Nepal

Flood susceptibility assessment using machine learning approach in the Mohana-Khutiya River of Nepal

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