Observation of the Spatio-Temporal Variability of Snowmelt and Runoff Generation During Rain-on-Snow in a Catchment With Complex Terrain

Abstract:

Rain-on-snow (ROS) is a typical flood type in montane catchments with temperate climates in many parts of the world and their frequency will most likely increase under changing climate conditions. These flood events have been notoriously hard to predict due to their complex nature and the lack of high resolution spatial and temporal data that could be used for model evaluation and detailed investigations of how a ROS event actually develops in a catchment.

The presented study will focus on the spatio-temporal variability of the snow cover distribution, snowmelt energy dynamics, and runoff generation during two ROS events in December 2012 by using hourly measurements of 30 standalone snow monitoring stations set-up in a mesoscale montane catchment in the Black Forest region of southwestern Germany. A multiple linear regression analysis using elevation, aspect, and land cover as predictors for the snow water equivalent distribution within the catchment was applied on an hourly basis. The generated snowmelt water, liquid precipitation, as well as the total retention storage of the snow cover were considered in order to estimate the amount of water potentially available for runoff.

The study shows in a very high spatial and temporal resolution how the observed ROS floods developed in the catchment. It became evident that the distributed retention capacity of the snow cover is a crucial mechanism during ROS. Due to higher amounts of snow and increased rainfall in the higher parts of the catchment, elevation was the most important terrain feature for runoff generation. South-facing terrain contributed more water to runoff than north-facing slopes, and only slightly more runoff was generated at open compared to forested areas. The results highlight the importance of the combination of snowmelt together with liquid precipitation for the generation of flood runoff during ROS events and the large temporal and spatial variability of the relevant processes.