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We propose a novel analytical description of the streamflow probability distribution functions (pdfs) in Alpine catchments characterized by pronounced snow dominated winter low flows. Knowledge about such hydrological regimes is crucial for water resources management in mountain environments and the related wide range of socio economic environmental and ecological services. We use a stochastic framework generalizing that employed by Botter et al. [2007b] to link precipitation (rain and snow) and streamflow dynamics. The effect of snow dynamics on the flow regime is specifically included by incorporating the temporary disconnection of high elevation areas that experience freezing conditions over the entire winter season and the delay produced on streamflow formation by the temporary accumulation (and later melting) of snow at lower elevations. The novel analytical model employs four parameters that can be directly estimated from observed discharge precipitation and air temperatures and one calibration parameter (the elevation threshold z delimiting catchment areas with a permanent seasonal snow cover that is non responsive during winter owing to snow accumulation without melt). We test the developed model for 14 catchments with contrasting hydroclimatic conditions located in the Swiss and the Italian Alps. Overall the proposed analytic model reproduces the observed streamflow pdfs remarkably well. Exceptions exist though and the possible origin of deviations between observed and modeled pdfs are discussed. We suggest that our approach marks a progress towards the general statistical characterization of catchment streamflow variability.

In this Commentary we argue that it is possible to improve the physical realism of hydrologic models by making better use of existing hydrologic theory. We address the following questions: (1) what are some key elements of current hydrologic theory; (2) how can those elements best be incorporated where they may be missing in current models; and (3) how can we evaluate competing hydrologic theories across scales and locations? We propose that hydrologic science would benefit from a model-based community synthesis effort to reframe, integrate and evaluate different explanations of hydrologic behavior, and provide a controlled avenue to find where understanding falls short. This article is protected by copyright. All rights reserved.