A Land Cover‐Based Snow Cover Representation for Distributed Hydrologic Models

Abstract

A snow cover depletion curve (SDC) summarizes the relationship between snow cover distribution and an average snow cover property, such as depth or water equivalent, for a given area. Snow cover depletion curves have been developed for, and applied in, hydrological models on a watershed or elevation zone basis. However, land cover-based SDCs are not prominent in the literature. For this study the areal distribution of snow cover for dominant land cover units was measured during the winters of 1991 and 1992 in the Laurel Creek watershed in southern Ontario, Canada. On the basis of these data a general model for land cover-based SDCs is developed for these land cover units, namely, short grass, ploughed fields, and deciduous forests. This model is derived from the three-parameter lognormal distribution, which is shown to characterize the areal depletion curves of the land cover units studied. The SDCs based on this new model provide a formal distributed snow cover representation that can be used in vegetation-based distributed hydrological models requiring accurate spatial representations of snow cover attributes. Recent advances in data acquisition can provide detailed hydrological information on a basin-wide scale, thereby increasing our fundamental understanding of the system as a whole.The response to this newly available information has been a change in modeling emphasis from lumped index models to physically based distributed models (e.g., Systame Hydrologique Europ6en (SHE) [Abbot et al., 1986] and WAT-FLOOD [Kouwen, 1988]). Despite the advances in modeling and remote measurement of watershed state and input parameters, simulation of snow cover runoff in low-relief regions using distributed runoff models has yet to be addressed. This is due to the inability to adequately measure and represent the spatial distribution of precipitation and snow cover [MacLaren Plansearch, 1984]. Rain gauges are usually inoperative during winter and early spring, and a distributed representation of the rainfall is difficult to obtain, especially if hourly data are required. The main source of snow cover information is snow course data, which are not generally representative of the watershed terrain and cover [Goodison et al., 1987]. As a result, snowmelt runoff models have not evolved to the point where spatially distributed representation of the important snow cover parameters can be established on a watershed scale. The spatial distribution of the snow cover is described by snow cover depletion curves (SDCs), which summarize the percent areal coverage of the snowpack as it increases in average depth. Watershed-wide SDC relationships are currently used in lumped hydrological models such as the National Weather Service River Forecast System [Anderson, 1973] to describe the snow cover distribution as the snow cover melts. These relationships have not been based on vegetation or terrain classifications and typically require calibration for each specific watershed, making them difficult to obtain. Despite the recognized limitation...

Using wind fields from a high‐resolution atmospheric model for simulating snow dynamics in mountainous terrain

“…A scheme of Liston (SSNOWD, 2004) is used to describe the subscale snow heterogeneity in regional scale models (the equations used here can be found in the Appendix II). The model is based on two basic assumptions that are supported by many studies over the last decades (Donald et al 1995;Marks et al 1999;Faria et al 2000;Liston 2004). First, the spatial distribution of the snow cover is persistent and can be seen as approximately similar over the years ( fig.…”

“…Liston (1999) found that the internal grid cell snow water equivalent distribution, the average According to Donald et al (1995), Pomeroy et al (1998) and Faria et al (2000), snow water equivalent depth distributions can be approximated by a two parameter log-normal Distribution parameter ξ: CV = coefficient of variation (Donald et al 1995) The average SWE depth of a grid cell D a under assumption of a certain melt depth D m is given by Donald et al (1995): Figures65a and 66 can be used to explain the operation of the presented routine. Figure 65a shows different log-normal functions which are representative for the sub-scale snow distribution of a grid element of e.g.…”

“…If the snow free and the snow covered fractions are known, the per grid cell energy balance can be weighted linearly proportionally to the percentages of snow free and snow covered fractions (eq 38): The subscale snow distribution is described with a set of formulas based on the works of Donald et al (1995), Pomeroy et al (1998), Faria et al (2000) and Liston (1999 and. Liston (1999) found that the internal grid cell snow water equivalent distribution, the average According to Donald et al (1995), Pomeroy et al (1998) and Faria et al (2000), snow water equivalent depth distributions can be approximated by a two parameter log-normal Distribution parameter ξ: CV = coefficient of variation (Donald et al 1995) The average SWE depth of a grid cell D a under assumption of a certain melt depth D m is given by Donald et al (1995): Figures65a and 66 can be used to explain the operation of the presented routine.…”

Characterizing snowmelt variability in a land‐surface‐hydrologic model

“…WATCLASS uses land-cover based snow depletion curves (SDC) where the y-axis corresponds to average snow depth for the land-class and the x-axis represents SCA. The approach is outlined in more detail in Donald et al (1995). The land-class dependence of the SDC assumes that each land-class has a characteristic depth at which bare ground begins to appear during snowmelt.…”

Modelling the spatial variability of snow water equivalent at the catchment scale

“…This routine is developed under the assumptions that precipitation as snow is lognormally distributed in space with a fixed coefficient of variation. Donald et al (1995) presented a snow accumulation model where, once the snow coverage was 100%, the spatial variability of SWE remained constant. These static ways of representing the spatial distribution of SWE are thus in conflict with the central limit theorem, reported observations Pomeroy et al, 2004) and observations presented in this paper.…”