Snowmelt modeling in urban areas: sensitivity analysis of the energy and mass balance method
2012 (English)Conference paper, Presentation (Refereed)
Flooding is one of the main concerns in seeking safe and sustainable urban areas. In many cases the design criteria are based on intense rainfall. It is, therefore, assumed that the peak flow in cities’ drainage systems is due to heavy and fast rainfalls. However snowmelt pattern could be more important for places with cold climate; therefore the need of a better snowmelt and runoff simulation becomes more important particularly when the effects of climate change needs to be considered. Two main methods are basically used for urban snowmelt simulation i.e. temperature index and energy budged methods. Studies done previously show that the energy balance method gives a better estimation for volume and time compare to the temperature index. For urban areas though, it is argued that the data demanding of the energy balance method can be a disadvantage and it could affect the model precision. However, the advances in geographical information systems (GIS) and the requirement for better time resolution than daily have increased the tendency of applying it for urban snow melt. There are couples of studies during recent years e.g. (Ho& Valeo 2005) applying energy budget method in urban areas, even though the efforts basically focused on developing routines and comparing it with the degree day method. There is still a gap in parameter sensitivity analysis especially with two main features of urban snowmelt modeling; firstly, the importance of input data along with difficulties in providing them; and secondly the classification of snow in urban areas based on snow properties. These two concerns were the motives to go one step ahead and to conduct a sensitivity analysis. The aim of the study is therefore to investigate the dependency of the simulation results to the different model parameters as built-in parameters and input data. Such analysis eventually can be used for snow classification which along with GIS technology can provide a reliable platform to simulate snowmelt over an urban catchment more precisely than what the current models are capable of today. Here in this study, a model namely Utah Energy Balance Snow Model (UEB) is used. The model uses a complete energy and mass balance routine to simulate snow accumulation and melt at a point scale. Except using the measured climatic values to run the model, the routines in this model has the capability of producing (simulating) solar radiation and albedo if the measured values are not available. The model has simulated the snow accumulation and melts in rural area with reasonable accuracy in previous studies i.e. (Tarboton et al. 1995). For this research, three snow deposits from 1991 and 1992 are taken to calibrate the model with. The pilot snow packs are identical to municipal snow deposit with density more than natural snow, around 700 Kg/m3. The snowmelt runoff has been measure between March and Jun 1991 and 1992. The necessary input values are collected from Meteorological and Hydrological Institute (SMHI) for the same periods. All input parameters are available on hourly and 3 hourly periods. The method is to run the model with real values collected from SMHI and calibrate it versus the measured data. The model is run using modified parameters to investigate the possible change in the simulation result. Eventually an analysis is done on each parameter and the dependency of the model. An analysis also is done by running the model with different time resolution, i.e. hourly, 3-hourly, and 6-hourly and to investigate the effect of time span in modeling snowmelt and simulation precision.
Place, publisher, year, edition, pages
Research subject Urban Water Engineering
IdentifiersURN: urn:nbn:se:ltu:diva-38255Local ID: c97f5a90-456e-4c5a-b8f9-690c248260cfOAI: oai:DiVA.org:ltu-38255DiVA: diva2:1011754
Nordic Hydrological Conference : Catchment Restoration and Water Protection 13/08/2012 - 15/08/2012
Godkänd; 2012; 20121019 (shamog)2016-10-032016-10-03Bibliographically approved