Study on snowmelt runoff prediction using weekly weather forecast

Hatta, Shigemi; Nishimura, Tetsuji; Saga, Hiroshi; Fujita, Mutsuhiro

doi:10.1016/0160-4120(95)00049-q

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…During the melting process, substantial amounts of snow-melt runoff enter rivers, potentially leading to flooding. Shigemi Hatta proposed the use of weekly weather forecast data in snowmelt runoff prediction, which was calculated based on sunshine percentage at that time when solar activity was not well understood [4]. Subsequently, researchers have explored more variables such as topography, geology, air temperature, precipitation, and watershed area to improve the accuracy of flood damage avoidance predictions [5].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it incorporates the Indian Ocean Warm Pool Area Index from the previous five months and finally concludes with the Northern Hemisphere Vortex Intensity Index from the previous six months. (4) The method optimizes eight pre-climate indexes suitable for the coastal area of Jiangsu Province. In the future, these optimized pre-significant factors can be directly utilized for regional runoff prediction, providing a valuable reference for formulating flood control and relief strategies, farmland irrigation management, hydropower station operations, and other disaster relief scheduling plans in the region.…”

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confidence: 99%

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A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Yan,

Gao,

Wen

et al. 2023

Water

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Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Yan,

Gao,

Wen

et al. 2023

Water

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“…Runoff due to snowmelt from mountain regions is important for a sustained water supply (Nestler et al, 2014). Snow is thus an important water resource, because unlike rainfall, it is released gradually in the form of melt water over a long period (Hatta et al, 1995) and provides sustained flow during dry seasons to fulfill the water requirements. The presence of snow in a basin strongly affects the moisture that is stored at the surface and is available for future runoff (Tahir et al, 2011).…”

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confidence: 99%

Estimation of Discharge From Upper Kabul River Basin, Afghanistan Using the Snowmelt Runoff Model

Rasouli

Kayastha

Bhattarai

et al. 2016

J. Hydrol. and Meteorol.

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In this study, we estimated discharge from Upper Kabul River basin in the Hindu Kush Mountain (Paghman range) in Afghanistan. The Upper Kabul River basin covers an area of 1633.8km2 with a maximum elevation of 4522 m and minimum elevation of 1877 m. The Kabul River is one of the main rivers in Afghanistan and sustains a significant flow of water in summer months due to the melting of snow. In this study, daily discharge from Upper Kabul River basin, west of Kabul basin, for 2009 and 2011 is estimated by using Snowmelt Runoff Model (SRM) (Version 1.12, 2009), originally developed my J. Martinec in 1975. Daily precipitation, air temperature, discharge and snow cover data are used in the model as input variables. We calibrated the model for 2009 and validated in 2011. The observed and calculated annual average discharges in 2009 are 5.7m3/s and 5.6m3/s, respectively; and in 2011 are 1.33m3/s and 1.31m3/s, respectively. The model results are in good agreement with the measured daily discharges. With an increase of 1°C in temperature and 10% precipitation, the increase in discharge in winter, summer and annually relative to 2009 discharge are 39%, 18.5% and 17.9%, respectively. Similarly, with an increase of 2°C in temperature and 20% in precipitation, modeled discharge increases by 51.2%, 40.8% and 47.3%, respectively. The results obtained suggest that the SRM can be used efficiently for estimating discharge in the snow fed sub-catchment of the Upper Kabul River basin and other mountain basins in Afghanistan.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.85-94

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Evaluating different machine learning algorithms for snow water equivalent prediction

et al. 2022

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Study on snowmelt runoff prediction using weekly weather forecast

Cited by 5 publications

References 3 publications

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Estimation of Discharge From Upper Kabul River Basin, Afghanistan Using the Snowmelt Runoff Model

Evaluating different machine learning algorithms for snow water equivalent prediction

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