Including Parameter Uncertainty in an Intercomparison of Physically-Based Snow Models

Daniel Günther,Richard Essery,Ulrich Strasser,Florian Hanzer,Michael Warscher

doi:10.3389/feart.2020.542599

Daniel Günther, Richard Essery + Show 3 more

Open Access

https://doi.org/10.3389/feart.2020.542599

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Abstract

Snow models that solve coupled energy and mass balances require model parameters to be set, just like their conceptual counterparts. Despite the physical basis of these models, appropriate choices of the parameter values entail a rather high degree of uncertainty as some of them are not directly measurable, observations are lacking, or values are not adaptable from literature. In this study, we test whether it is possible to reach the same performance with energy balance snow models of varying complexity by means of parameter optimization. We utilize a multi-physics snow model which enables the exploration of a multitude of model structures and model complexities with respect to their performance against point-scale observations of snow water equivalent and snowpack runoff observations, and catchment-scale observations of snow cover fraction and spring water balance. We find that parameter uncertainty can compensate structural model deficiencies to a large degree, so that model structures cannot be reliably differentiated within a calibration period. Even with deliberately biased forcing data, comparable calibration performances can be achieved. Our results also show that parameter values need to be chosen very carefully, as no model structure guarantees acceptable simulation results with random (but still physically meaningful) parameters.

Highlights

Information about seasonal snow cover such as the magnitude and timing of melt rates is crucial in many regions of the world for a whole suite of interests (Viviroli et al, 2011), including water resources management for hydropower generation (Beniston, 2012), drinking water supply and irrigation (Barnett et al, 2005), or climate change, flood, drought and avalanche risk assessments (Hamlet and Lettenmaier, 2007)
We evaluate various process formulations commonly found in energy-balance snow models against point-scale and spatially integrated observations using the Factorial Snow Model (FSM) (Essery, 2015)
All 32 model structures are calibrated against point-scale observations of snow water equivalent and snowpack runoff for the two simulation periods 2004, 2006 and 2009, 2014

Summary

Introduction

Information about seasonal snow cover such as the magnitude and timing of melt rates is crucial in many regions of the world for a whole suite of interests (Viviroli et al, 2011), including water resources management for hydropower generation (Beniston, 2012), drinking water supply and irrigation (Barnett et al, 2005), or climate change, flood, drought and avalanche risk assessments (Hamlet and Lettenmaier, 2007). To better understand and predict the respective snow processes, many different physically-based snow models have been developed. This type of snow model follows physical principles of energy and mass conservation in the snowpack. Even though the general objective of accounting for physical processes is the same for all of these models, their numerical representations differ in detail and complexity. We focus on snow models of medium complexity that account for the transport of mass and heat across multiple snow layers. Unlike more detailed snow physics models such as SNOWPACK (Bartelt and Lehning, 2002) that simulate detailed, real world snowpack layering based on common

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