Hydrological Modeling of Snow Accumulation and Melting on River Basin Scale

Abstract

Snowmelt is of importance for many aspects of hydrology, including water supply, erosion and flood control. In this study, snow accumulation and melt are modeled using a distributed hydrological model with two different snowmelt simulation modules. The model is applied for simulating river discharge in the Latyan dam watershed, in the southern part of central Alborz mountain range, Iran. The data consists of 3 years of observed daily precipitation, air temperature, potential evaporation, windspeed and discharge. The discharge data is used for model calibration. When using the temperature index method for snowmelt three parameters need to be calibrated, while for the energy balance approach all parameters are preset and not optimized. The model performance is satisfactory for both methods with efficiencies of more than 80%. In order to show the performance of the model, two interesting snow accumulation and melt periods are discussed in detail. This study shows that the model has great potentiality to simulate the impact of snow accumulation and melt on the hydrological behavior of a river basin.

Global evaluation of runoff from 10 state-of-the-art hydrological models

“…The GHMs use relatively simple conceptual temperature-index snow routines driven by air temperature, which can be estimated with relative ease, whereas the LSMs use more complex physically based energy balance snow routines driven by estimates of energy balance components, which are subject to considerable uncertainty, particularly in regions with complex topography (Ferguson, 1999). Although several previous studies have found that the two types of snow routines yield comparable performance (e.g., WMO, 1986;Franz et al, 2008;Zeinivand and De Smedt, 2009;Debele et al, 2010), these studies used a very small number of relatively well-instrumented catchments (six, two, one, and three, respectively), which may have led to lessgeneralizable conclusions. Overall, it appears that the energy balance estimates and snow routines used by the LSMs require re-evaluation (cf.…”

Assessment of optimum snowmelt model complexity, Cariboo Highlands, British Columbia, Canada.

“…This was primarily due to the over-estimation of melt resulting in an early snowfree date and thus a period of under-prediction of melt at the end of the season when simulated melt was zero yet observed melt was positive. Over-prediction of melt was also observed in several other energy balance studies (Whitaker et al 2003;Pellicciotti et al 2008;Zeinivand and De Smedt 2009). On the other hand, melt was often under-predicted in general for the north aspect open site and the forest sites.…”

“…Although melt simulations were not as good, periods of over-and under-prediction of melt cancelled each other out resulting in fairly good overall SWE prediction for this study. Other studies using similar simple energy balance models also reported satisfactory simulation of ablation and SWE (Ichii et al 2008, Zeinivand andDe Smedt 2009). …”

“…Knowledge of the date on which the snowpack became ripe for each site was therefore necessary to run both of these model types. There are various ways to track snowpack temperature or ripeness that could be utilized in these models either through inclusion of a two-layer snowpack structure or some variation of heat content accounting (Coughlan and Running 1997;Kane et al 1997;Whitaker et ah 2003;Zappa et al 2003;Garen and Marks 2005;Watson et al 2006;Franz et ah 2008;Zeinivand and De Smedt 2009). It is also possible to determine snowpack ripeness through remote sensing methods (Wang et ah 2008;Takala et ah 2009).…”

Spatially Explicit Modeling of Land Use Specific Phosphorus Transport Pathways to Improve TMDL Load Estimates and Implementation Planning

“…The latter is a function of air temperature and snow density, aspect, and slope (Gray and Prowse 1993;Male and Gray 1981). Process-based distributed snow melt models have been found to improve stream flow predictions at the catchment level when compared to temperature-index models (Zeinivand and De Smedt 2009). …”