Evaluating the downscaling uncertainty of hydrometeorological data in snowmelt runoff simulation

Abstract. This study linked the Weather Research and Forecasting (WRF) modelling system and the Distributed Hydrology Soil Vegetation Model (DHSVM) to forecast snowmelt runoff. The study area was the 800 km2 Juntanghu watershed of the northern slopes of Tianshan Mountain Range. This paper investigated snowmelt runoff forecasting models suitable for meso-microscale application. In this study, a limited-region 24-h Numeric Weather Forecasting System was formulated using the new generation atmospheric model system WRF with the initial fields and lateral boundaries forced by Chinese T213L31 model. Using the WRF forecasts, the DHSVM hydrological model was used to predict 24 h snowmelt runoff at the outlet of the Juntanghu watershed. Forecasted results showed a good similarity to the observed data, and the average relative error of maximum runoff simulation was less than 15%. The results demonstrate the potential of using a meso-microscale snowmelt runoff forecasting model for forecasting floods. The model provides a longer forecast period compared with traditional models such as those based on rain gauges or statistical forecasting.

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Accuracy of Snowmelt Runoff Simulation

Hydrology Research ◽

10.2166/nh.1981.0021 ◽

1981 ◽

Vol 12 (4-5) ◽

pp. 265-274 ◽

Cited By ~ 24

Author(s):

A. Rango ◽

J. Martinec

Keyword(s):

Snow Cover ◽

Input Data ◽

Snowmelt Runoff ◽

Runoff Simulation ◽

Simulation Accuracy ◽

Temperature And Precipitation ◽

Recession Coefficient ◽

Snow Cover Extent ◽

Mountainous Basin

Results of runoff simulations from various basins using a snowmelt runoff model were analyzed in order to predict the accuracy of simulations in future applications of the model. It was found that the model can be applied to nearly any mountainous basin where snowmelt runoff is an important factor if input data on temperature, precipitation, and snow cover are available. The simulation accuracy will depend on the quality of the input data as well as on the density of observations, size of the basin, care in determination of the recession coefficient, and amount of precipitation during snowmelt. Most accurate simulations will result when: 1) temperature and precipitation are recorded at the basin mean elevation; 2) snow cover observations are available once per week; 3) several climatic stations are available for large basins; and 4) a few years of runoff records exist for determination of the recession coefficient. Decreases in simulation accuracy will be expected as these optimum conditions are compromised, however, acceptable simulations will result with the following minimum conditions: 1) temperature and precipitation data are available in the general vicinity of the basin; and 2) snow cover observations are available 2-3 times during the snowmelt season. The availability of satellite observations of snow cover extent has permitted successful application of the model to large basins.

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Use of satellite-derived data for characterization of snow cover and simulation of snowmelt runoff through a distributed physically based model of runoff generation

Hydrology and Earth System Sciences ◽

10.5194/hess-14-339-2010 ◽

2010 ◽

Vol 14 (2) ◽

pp. 339-350 ◽

Cited By ~ 24

Author(s):

L. S. Kuchment ◽

P. Romanov ◽

A. N. Gelfan ◽

V. N. Demidov

Keyword(s):

Snow Cover ◽

Initial Conditions ◽

Tree Cover ◽

Special Focus ◽

Snowmelt Runoff ◽

Runoff Generation ◽

Runoff Simulation ◽

Physically Based Model ◽

Physically Based ◽

Derived Data

Abstract. A technique of using satellite-derived data for constructing continuous snow characteristics fields for distributed snowmelt runoff simulation is presented. The satellite-derived data and the available ground-based meteorological measurements are incorporated in a physically based snowpack model. The snowpack model describes temporal changes of the snow depth, density and water equivalent (SWE), accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with a special focus on forest cover effects. The remote sensing data used in the model consist of products include the daily maps of snow covered area (SCA) and SWE derived from observations of MODIS and AMSR-E instruments onboard Terra and Aqua satellites as well as available maps of land surface temperature, surface albedo, land cover classes and tree cover fraction. The model was first calibrated against available ground-based snow measurements and then applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based meteorological data. The satellite-derived SWE data were used for assigning initial conditions and the SCA data were used for control of snow cover simulation. The simulated spatial distributions of snow characteristics were incorporated in a distributed physically based model of runoff generation to calculate snowmelt runoff hydrographs. The presented technique was applied to a study area of approximately 200 000 km2 including the Vyatka River basin with catchment area of 124 000 km2. The correspondence of simulated and observed hydrographs in the Vyatka River are considered as an indicator of the accuracy of constructed fields of snow characteristics and as a measure of effectiveness of utilizing satellite-derived SWE data for runoff simulation.

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Comparison of SRM and WetSpa models efficiency for snowmelt runoff simulation

Environmental Earth Sciences ◽

10.1007/s12665-016-5490-z ◽

2016 ◽

Vol 75 (8) ◽

Cited By ~ 13

Author(s):

Hamid Karimi ◽

Hossein Zeinivand ◽

Naser Tahmasebipour ◽

Ali Haghizadeh ◽

Mirhassan Miryaghoubzadeh

Keyword(s):

Snowmelt Runoff ◽

Runoff Simulation

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The snowmelt runoff forecasting model of coupling WRF and DHSVM

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-3335-2009 ◽

2009 ◽

Vol 6 (2) ◽

pp. 3335-3357 ◽

Cited By ~ 3

Author(s):

Q. Zhao ◽

Z. Liu ◽

M. Li ◽

Z. Wei ◽

S. Fang

Keyword(s):

Weather Forecasting ◽

Atmospheric Model ◽

Forecasting Model ◽

Snowmelt Runoff ◽

Runoff Simulation ◽

Limited Region ◽

Model Driven ◽

Absolute Relative Error ◽

Runoff Forecasting ◽

Average Absolute Relative Error

Abstract. This study used the Weather Research and Forecasting (WRF) modeling system and the Distributed Hydrology-Soil-Vegetation Model (DHSVM) to forecast the snowmelt runoff in the 800 km2 Juntanghu watershed of the northern slope of Tianshan Mountains from 29 February–6 March 2008. This paper made an exploration for snowmelt runoff forecasting model combing closely practical application in meso-microscale. It included: (1) A limited-region 24-h Numeric Weather Forecasting System was established by using the new generation atmospheric model system WRF with the initial fields and lateral boundaries forced by Chinese T213L31 model. (2) The DHSVM hydrological model driven by WRF forecasts was used to predicate 24 h snowmelt runoff at the outlet of Juntanghu watershed. The forecasted result shows a good agreement with the observed data, and the average absolute relative error of maximum runoff simulation result is less than 15%. The result demonstrates the potential of using meso-microscale snowmelt runoff forecasting model for flood forecast. The model can provide a longer forecast period compared to traditional models such as those based on rain gauges, statistical forecast.

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