A conceptual, distributed snow redistribution model

Abstract

Abstract. When applying conceptual hydrological models using a temperature index approach for snowmelt to high alpine areas often accumulation of snow during several years can be observed. Some of the reasons why these "snow towers" do not exist in nature are vertical and lateral transport processes. While snow transport models have been developed using grid cell sizes of tens to hundreds of square metres and have been applied in several catchments, no model exists using coarser cell sizes of 1 km 2 , which is a common resolution for meso-and large-scale hydrologic modelling (hundreds to thousands of square kilometres). In this paper we present an approach that uses only gravity and snow density as a proxy for the age of the snow cover and land-use information to redistribute snow in alpine basins. The results are based on the hydrological modelling of the Austrian Inn Basin in Tyrol, Austria, more specifically the Ötztaler Ache catchment, but the findings hold for other tributaries of the river Inn. This transport model is implemented in the distributed rainfall-runoff model COSERO (Continuous Semidistributed Runoff). The results of both model concepts with and without consideration of lateral snow redistribution are compared against observed discharge and snow-covered areas derived from MODIS satellite images. By means of the snow redistribution concept, snow accumulation over several years can be prevented and the snow depletion curve compared with MODIS (Moderate Resolution Imaging Spectroradiometer) data could be improved, too. In a 7-year period the standard model would lead to snow accumulation of approximately 2900 mm SWE (snow water equivalent) in high elevated regions whereas the updated version of the model does not show accumulation and does also predict discharge with more accuracy leading to a Kling-Gupta efficiency of 0.93 instead of 0.9. A further improvement can be shown in the comparison of MODIS snow cover data and the calculated depletion curve, where the redistribution model increased the efficiency (R 2 ) from 0.70 to 0.78 (calibration) and from 0.66 to 0.74 (validation).

Impacts of microtopographic snow redistribution and lateral subsurface processes on hydrologic and thermal states in an Arctic polygonal ground ecosystem: a case study using ELM-3D v1.0

“…Two main drivers of SR include topography and surface wind (Warscher et al, 2013); previous SR models include mechanistically based (Bartelt and Lehning, 2002;Liston and Elder, 2006) and empirically based (Frey and Holzmann, 2015;Helfricht et al, 2012) approaches. To mimic the effects of wind, we used a conceptual model to simulate SR over the fine-resolution topography of our site by instantaneously redistributing the incoming snow flux such that lower elevation areas (polygon center) receive snow before higher elevation areas (polygon rims).…”

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

“…Water from snowmelt is crucial for water resources management including fresh water supply, hydropower generation, irrigation, or navigation (Immerzeel et al, 2009;Mankin et al, 2015;Sturm, 2015;Wesemann et al, 2018). In situ snow measurements are fundamental for the evaluation and validation of various remote sensing products (e.g., Parajka & Blöschl, 2006;Takala et al, 2011) and snowpack models that consider, for example, snow accumulation and melting processes as well as lateral snow transport (e.g., Bernhardt et al, 2012;Frey & Holzman, 2015;Warscher et al, 2013;Weber et al, 2016;Vionnet et al, 2012). Moreover, they help improving model parameterizations for SWE estimation or reconstructions (e.g., Raleigh & Lundquist, 2012).…”

A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation

“…SWE is simulated more realistically in that the seasonal snow is melted out every year and no trend in SWE is observed, which is consistent with the absence of trends in precipitation and temperature. The current operational routine for snow distribution (SD_LN), however, displays a tendency to produce ever increasing "snow towers" (Frey and Holzmann, 2015), which in turn gives the erroneous impression of an increasing trend in SWE and unrealistic annual durations of snow cover, which for most catchments approach a full year. Such behaviour can be remedied by adjusting the optimized parameter value for the spatial snow distribution, θ CV , but at the expense of the precision of simulated runoff.…”

“…Moving averages are shown using the same colour code. a log-normal distribution for SWE with a fixed, calibrated CV has recently been addressed in the literature (Frey and Holzmann, 2015). In Norway, using such a snow distribution model with the well-known Swedish rainfall-runoff model, HBV (Hydrologiska Byråns Vattenbalansmodell; Bergström, 1992), has led to the operational procedure of deleting the remaining snow reservoir at the end of summer.…”

“…It has been used operationally in Norwegian hydrology for many years, although it has the feature of being a calibrated model and hence not suitable for climate change studies and for predictions in ungauged basins. In addition, a fixed CV, and hence an assumption of perfect spatial correlation, is not supported by observations (Alfnes et al, 2004), and in a recent paper Frey and Holzmann (2015) show that a log-normal spatial distribution of SWE with a fixed CV of introduces so-called "snow towers". For high-elevation areas, and for the highest quantiles of the distribution, snow survived the summer and accumulated to give an overall positive trend in SWE, which was not observed.…”