StreamFlow 1.0: an extension to the spatially distributed snow model Alpine3D for hydrological modelling and deterministic stream temperature prediction

Abstract

Abstract. Climate change is expected to strongly impact the hydrological and thermal regimes of Alpine rivers within the coming decades. In this context, the development of hydrological models accounting for the specific dynamics of Alpine catchments appears as one of the promising approaches to reduce our uncertainty of future mountain hydrology. This paper describes the improvements brought to StreamFlow, an existing model for hydrological and stream temperature prediction built as an external extension to the physically based snow model Alpine3D. StreamFlow's source code has been entirely written anew, taking advantage of object-oriented programming to significantly improve its structure and ease the implementation of future developments. The source code is now publicly available online, along with a complete documentation. A special emphasis has been put on modularity during the re-implementation of StreamFlow, so that many model aspects can be represented using different alternatives. For example, several options are now available to model the advection of water within the stream. This allows for an easy and fast comparison between different approaches and helps in defining more reliable uncertainty estimates of the model forecasts. In particular, a case study in a Swiss Alpine catchment reveals that the stream temperature predictions are particularly sensitive to the approach used to model the temperature of subsurface flow, a fact which has been poorly reported in the literature to date. Based on the case study, StreamFlow is shown to reproduce hourly mean discharge with a Nash-Sutcliffe efficiency (NSE) of 0.82 and hourly mean temperature with a NSE of 0.78.

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

“…The streamflow from the Dischmabach is calculated using a spatially explicit and semi-distributed hydrologic response model that casts the soil moisture dynamics in a travel time distribution framework (Comola et al, 2015b;Gallice et al, 2016). Specifically, the model simulates hydrologic transport within subcatchment soil compartments and the stream network, identified through geomorphological analysis of the digital elevation model (Tarboton, 1997).…”

“…Here, the capabilities of Alpine3D to capture the soil moisture state is assessed. Furthermore, the Alpine3D model provides the surface scheme for a travel time distribution hydrologic response model to simulate catchment discharge (Comola et al, 2015b;Gallice et al, 2016) and here the role of soil moisture in the coupling of Alpine3D to the hydrologic response model, as well as the influence of the soil moisture state on streamflow generation in the catchment is investigated.…”

Evaluating a Coupled Phenology‐Surface Energy Balance Model to Understand Stream‐Subsurface Temperature Dynamics in a Mixed‐Use Farmland Catchment

“…Primarily, two types of stream temperature models have been developed to date: regression models that make use of statistical linkages between meterological and geophysical conditions to predict stream temperatures (Benyahya et al, ; Jackson et al, , ; Mohseni et al, ) and mechanistic models based on conservation of energy that directly simulate the underlying stream temperature dynamics (Cox & Bolte, ; Loinaz et al, ; St‐Hilaire et al, ; see review by Dugdale et al, ). Mechanistic models usually demonstrate more clear cause‐effect relationships because of the direct descriptions of the underlying controlling processes in their governing equations (Caissie et al, ; Gallice et al, ; MacDonald et al, ; Sun et al, ).…”

“…Studies have shown the effectiveness of using the streambed thermal regime to quantify vertical water fluxes between the surface and subsurface domains (Anderson, ; Anibas et al, , ; Gordon et al, ; Vandersteen et al, ). Some mechanistic stream temperature models have incorporated the thermal advection and streambed conduction fluxes (Gallice et al, ; Haag & Luce, ; MacDonald et al, ). They considered the heat flux through the streambed as being propotional to the difference between stream surface water and streambed temperatures at a given depth (Moore et al, ).…”

“…For example, Haag and Luce () introduced the concept of effective streambed temperature as a function of two lumped parameters to estimate the streambed conduction flux. Gallice et al () assumed that the streambed temperature is the same as simulated soil temperature. MacDonald et al () used an empirical function that depends on the air temperature to estimate the streambed temperature.…”