Modeling the Rainfall-Runoff Data in Snow-Affected Watershed

Vafakhah, Mehdi; Sedighi, Fatemeh; Javadi, Mohammad Reza

doi:10.7763/ijcee.2014.v6.790

Cited by 5 publications

(8 citation statements)

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“…The emergence of data-driven models has benefited from the growth of massive data and the rapid increase in computational power. These models, Such as ANN [26] and LSTM [27], simulate the changes in snowmelt runoff using machine learning algorithms to select appropriate parameters (e.g., daily rainfall, temperature, solar radiation, snow area, snow water equivalent) from different data sources. According to the spatial distribution characteristics of models, the snowmelt model can be divided into lumped, semi-distributed and distributed models (blue categories in Figure 2).…”

Section: Categories Of Snowmelt Modelsmentioning

confidence: 99%

“…There have been many studies on snowmelt runoff prediction using machine learning. Vafakhah et al [26] used artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to simulate the snowmelt runoff in the Taleghan, Alborz. Acar et al [78] utilized the Multilayer Perceptron (MLP) neural network to simulate the snowmelt runoff in Turkey's semi-arid climate.…”

Section: Data-driven Modelmentioning

confidence: 99%

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A Review on Snowmelt Models: Progress and Prospect

et al. 2021

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The frequency and intensity of flood events have been increasing recently under the warming climate, with snowmelt floods being a significant part. As an effective manner of simulating snowmelt flood, snowmelt models have attracted more and more attention. Through comprehensive analysis of the literature, this paper reviewed the characteristics and current status of different types of snowmelt models, as well as the different coupling methods of models for runoff generation and confluence. We then discussed key issues in snowmelt modelling, including blowing snow model, frozen ground model, and rain-on-snow model. Finally, we give some perspectives from four aspects: data, model structure, forecast and early warning, and forecast and estimation. At present, most of the snowmelt models do not have blowing snow or frozen ground modules. Explicit consideration of blowing snow and soil freezing/thawing processes can improve the accuracy of snowmelt runoff simulations. With climate warming, rain-on-snow events have increased, but the mechanism of enhanced rain and snow mixed flooding is still unclear, particularly for the mechanism of rain-snow-ice mixed runoff generation. The observation and simulation of rain and snow processes urgently need further study. A distributed physical snowmelt model based on energy balance is an advanced tool for snowmelt simulation, but the model structure and parameter schemes still need further improvements. Moreover, the integration of satellite-based snow products, isotopes, and terrestrial water storage change, monitored by gravity satellites, can help improve the calibration and validation of snowmelt models.

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Section: Categories Of Snowmelt Modelsmentioning

confidence: 99%

Section: Data-driven Modelmentioning

confidence: 99%

A Review on Snowmelt Models: Progress and Prospect

et al. 2021

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“…The importance of rainfall runoff modelling extends beyond hydrology and water resource management, as it aids in planning for watershed water resources, managing reservoirs, and preparing for drought and potential flood hazard events. Additionally, it provides insights into catchment yields and responses, water availability, and changes over time, making it a fundamental issue in watershed hydrology modelling and research [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the complexity associated with the transformation of rainfall into runoff, runoff analysis is crucial in predicting natural disasters such as floods and droughts. Furthermore, it plays a crucial role in the design and operation of water resource projects such as barrages, dams, and water supply schemes, and is necessary for water resources planning, development, and flood mitigation efforts [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Effect of Deforestation on Decadal Runoffs in Malaysia Using the Revised Curve Number Rainfall Runoff Approach

Khor

Lim

Ling

2023

Water

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This study presents a revised and calibrated Soil Conservation Service (SCS) curve number (CN) rainfall runoff model for predicting runoff in Malaysia using a new power correlation Ia = SL, where L represents the initial abstraction coefficient ratio. The traditional SCS-CN model with the proposed relation Ia = 0.2S is found to be unreliable, and the revised model exhibits improved accuracy. The study emphasizes the need to design flood control infrastructure based on the maximum estimated runoff amount to avoid underestimation of the runoff volume. If the flood control infrastructure is designed based on the optimum CN0.2 values, it could lead to an underestimation of the runoff volume of 50,100 m3 per 1 km2 catchment area in Malaysia. The forest areas reduced by 25% in Peninsular Malaysia from the 1970s to the 1990s and 9% in East Malaysia from the 1980s to the 2010s, which was accompanied by an increase in decadal runoff difference, with the most significant rises of 108% in Peninsular Malaysia from the 1970s to the 1990s and 32% in East Malaysia from the 1980s to the 2010s. This study recommends taking land use changes into account during flood prevention planning to effectively address flood issues. Overall, the findings of this study have significant implications for flood prevention and land use management in Malaysia. The revised model presents a viable alternative to the conventional SCS-CN model, with a focus on estimating the maximum runoff amount and accounting for land use alterations in flood prevention planning. This approach has the potential to enhance flood management in the region.

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“…A number of models have been developed by various researchers for runoff prediction using SCS method (Hawkins, 1993;Hamer et al, 2007 andQui et al, 2014) with Muskingum routing technique (Gelegenis and Serrano, 2000) and by using back propagation artificial neural network (Jain et al, 1999;Raghuwanshi et al, 2006;Vivekanandan, 2011;Phukoetphim et al, 2014 andVafakhah et al, 2014). Several models have been developed for reservoir operation (HEC, 1971;Tu et al, 2003;Ganji et al, 2007;Talukdar et al, 2011 andGuo et al, 2014), crop water demand calculation (Rowshon et al, 2009;George et al, 2011;Hlavinka et al, 2011 andSakaguchi et al, 2014), and for canal flow simulation: Merkely (1995) reported a hydraulic simulation model CANALMAN (Canal Management) for unsteady flow simulation in branching canal networks.…”

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confidence: 99%

Testing of catchment module of integrated reservoir-based canal irrigation model for kangsabati irrigation project

Dhage¹,

Bhadra²,

Raghuwanshi³

et al. 2014

Intern. Jour. of Agricul., Environ. and Biotech.

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Bhadra, (2007) developed Integrated reservoir based canal irrigation model (IRCIM). It consist of catchment, reservoir, crop water demand modules. In this study, IRCIM was applied on Kangsabati irrigation project, West Bengal, India for period of 1998 to 2003. Runoff was predicted using two techniques namely, Distributed SCS Curve Number (CN) with Muskingum routing and Artificial Neural Network (ANN) Backpropogation techniques available in catchment module. Distributed SCS CN method requires subbasin information, land cover characteristics, overland and channel information and daily rainfall on subbasin, whereas ANN method requires daily rainfall and runoff values. Catchment module was calibrated and validated using performance criteria modelling efficiency (ME) and coefficient of residual mass (CRM). ANN technique of runoff prediction involves extensive training of the network, where the unpredictable correlation of rainfall and runoff is also been taken into consideration which is not possible for conceptual model such as SCS CN method. Thus, results showed that for Kangsabati reservoir catchment, runoff values, predicted using ANN result in better match with observed runoff values compared to semi-distributed conceptual SCS CN method. HighlightsRunoff prediction by Empirical method ANN with Levenberg-Marquardt algorithm more accurate than Physical based distributed (GIS-based SCS CN method with muskingum routing)

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Modeling the Rainfall-Runoff Data in Snow-Affected Watershed

Cited by 5 publications

References 0 publications

A Review on Snowmelt Models: Progress and Prospect

A Review on Snowmelt Models: Progress and Prospect

Evaluating the Effect of Deforestation on Decadal Runoffs in Malaysia Using the Revised Curve Number Rainfall Runoff Approach

Testing of catchment module of integrated reservoir-based canal irrigation model for kangsabati irrigation project

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