Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment

Abstract

Abstract. The assessment of flood risks in alpine, snowcovered catchments requires an understanding of the linkage between the snow cover, soil and discharge in the stream network. Here, we apply the comprehensive, distributed model Alpine3D to investigate the role of soil moisture in the predisposition of the Dischma catchment in Switzerland to high flows from rainfall and snowmelt. The recently updated soil module of the physics-based multilayer snow cover model SNOWPACK, which solves the surface energy and mass balance in Alpine3D, is verified against soil moisture measurements at seven sites and various depths inside and in close proximity to the Dischma catchment. Measurements and simulations in such terrain are difficult and consequently, soil moisture was simulated with varying degrees of success. Differences between simulated and measured soil moisture mainly arise from an overestimation of soil freezing and an absence of a groundwater description in the Alpine3D model. Both were found to have an influence in the soil moisture measurements. Using the Alpine3D simulation as the surface scheme for a spatially explicit hydrologic response model using a travel time distribution approach for interflow and baseflow, streamflow simulations were performed for the discharge from the catchment. The streamflow simulations provided a closer agreement with observed streamflow when driving the hydrologic response model with soil water fluxes at 30 cm depth in the Alpine3D model. Performance decreased when using the 2 cm soil water flux, thereby mostly ignoring soil processes. This illustrates that the role of soil moisture is important to take into account when understanding the relationship between both snowpack runoff and rainfall and catchment discharge in high alpine terrain. However, using the soil water flux at 60 cm depth to drive the hydrologic response model also decreased its performance, indicating that an optimal soil depth to include in surface simulations exists and that the runoff dynamics are controlled by only a shallow soil layer. Runoff coefficients (i.e. ratio of rainfall over discharge) based on measurements for high rainfall and snowmelt events were found to be dependent on the simulated initial soil moisture state at the onset of an event, further illustrating the important role of soil moisture for the hydrological processes in the catchment. The runoff coefficients using simulated discharge were found to reproduce this dependency, which shows that the Alpine3D model framework can be successfully applied to assess the predisposition of the catchment to flood risks from both snowmelt and rainfall events.

Hydrologic connectivity at the hillslope scale through intra‐snowpack flow paths during snowmelt

“…As data retrieval methods improve and higher resolution products become available (Bühler, Adams, Bosch, & Stoffel, 2016;Nolan, Larsen, & Sturm, 2015;Painter et al, 2016), modelling efforts will similarly increase in resolution (Brauchli, Trujillo, Huwald, & Lehning, 2017;Hedrick et al, 2018). Hydrologic models often assume snowmelt infiltrates vertically into the soil through one-dimensional intra-snowpack percolation occurring uniformly across a grid cell or lumped hydrologic response unit (e.g., Kormos et al, 2014;Wever, Comola, Bavay, & Lehning, 2017). In order to obtain physically realistic results from these models, it is necessary to understand the nature of intra-snowpack flow paths and the resulting meltwater outflow variability (Webb, Williams, et al, 2018).…”

Spatio‐temporal aspects of snowpack runoff formation during rain on snow

“…However, because of the sparse availability of spatially distributed measurements of LWC, snowmelt runoff and SWE — especially over large areas such as the catchments investigated in this study — such a direct validation of the model results seems unfortunately impossible. Yet the SNOWPACK model has been validated extensively in the past, including point‐scale simulations (Schmucki et al, ; Wever, Jonas, et al, ), focussing on the water transport scheme within the model (Wever et al, ; Wever, Fierz, et al, ; Würzer et al, ), and by distributed simulations using MeteoIO (Lehning et al, ; Schlögl, Marty, Bavay, & Lehning, ; Wever et al, ). Schlögl et al () conducted a sensitivity analysis to systematically investigate the impact of different model setups on the robustness of modelled SWE and found that the input uncertainties of distributed simulations are in the same range as uncertainties of SWE measurements.…”

“…The results presented in this research strongly depend on the proper representation of snow cover processes by the model. This (Schmucki et al, 2014;Wever, Jonas, et al, 2014), focussing on the water transport scheme within the model (Wever et al, 2015;Würzer et al, 2017), and by distributed simulations using MeteoIO (Lehning et al, 2006;Schlögl, Marty, Bavay, & Lehning, 2016;Wever et al, 2017). Schlögl et al (2016) conducted a sensitivity analysis to systematically investigate the impact of different model setups on the robustness of modelled SWE and found that the input uncertainties of distributed simulations are in the same range as uncertainties of SWE measurements.…”

“…Although on the point scale complex snow cover processes and uncertainties in meteorological forecast make it difficult to accurately predict snowpack runoff, assessing ROS on the catchment scale additionally comprises to deal with the spatial heterogeneity of snow cover properties and spatially variable meteorological inputs that influence both snowmelt and hydrological processes (Westrick & Mass, ). For example, high antecedent soil moisture is often observed during spring snowmelt conditions (Allan & Roulet, ; Fang & Pomeroy, ; Kampf, Markus, Heath, & Moore, ; Webb, Fassnacht, & Gooseff, ; Wever, Comola, Bavay, & Lehning, ) and can augment catchment runoff significantly. Preferential flow of liquid water through snow can have a distinct impact on timing and amount of snowpack runoff and has been examined using dye tracers (Schneebeli, ; Williams, Erickson, & Petrzelka, ; Würzer, Wever, Juras, Lehning, & Jonas, ), radar measurements (Albert, Koh, & Perron, ), temperature investigations (Conway & Benedict, ), and by measuring the spatial variability of snowpack runoff (Kattelmann, ; Marsh, ; Marsh & Pomeroy, ; Marsh & Woo, ).…”

Examining controls on peak annual streamflow and floods in the Fraser River Basin of British Columbia

“…Predictors were selected based on physical intuition, inspection of the relevant literature and initial exploratory data analysis. Prior studies that were particularly helpful in this regard were Gurrapu et al (2016), who examined the influence of the PDO on streamflow in western Canada, Jenicek et al (2016), who examined the influence of snow accumulation and other variables on summer low flows, Coles et al (2017), who studied snowmeltrunoff generation on Canadian prairie hillslopes, and Wever et al (2017), who conducted model simulations of the joint effect of snowmelt and soil moisture on streamflow in a Swiss Alpine catchment. The predictor variables chosen are listed in Table 2.…”