Are Model Transferability And Complexity Antithetical? Insights From Validation of a Variable‐Complexity Empirical Snow Model in Space and Time

Abstract

The related challenges of predictions in ungauged basins and predictions in ungauged climates point to the need to develop environmental models that are transferable across both space and time. Hydrologic modeling has historically focused on modelling one or only a few basins using highly parameterized conceptual or physically based models. However, model parameters and structures have been shown to change significantly when calibrated to new basins or time periods, suggesting that model complexity and model transferability may be antithetical. Empirical space‐for‐time models provide a framework within which to assess model transferability and any tradeoff with model complexity. Using 497 SNOTEL sites in the western U.S., we develop space‐for‐time models of April 1 SWE and Snow Residence Time based on mean winter temperature and cumulative winter precipitation. The transferability of the models to new conditions (in both space and time) is assessed using non‐random cross‐validation tests with consideration of the influence of model complexity on transferability. As others have noted, the algorithmic empirical models transfer best when minimal extrapolation in input variables is required. Temporal split‐sample validations use pseudoreplicated samples, resulting in the selection of overly complex models, which has implications for the design of hydrologic model validation tests. Finally, we show that low to moderate complexity models transfer most successfully to new conditions in space and time, providing empirical confirmation of the parsimony principal.

Glacier Recession and the Response of Summer Streamflow in the Pacific Northwest United States, 1960–2099

“…Above 3 °C the ability of increased precipitation to buffer mass losses from increases in temperature diminishes. For these glaciers an increase of 3 °C results in the mean winter temperature being greater than ~ −2 °C, a threshold above which snowpack sensitivity to precipitation strongly decays as greater amounts of snow fall as rain (e.g., Lute & Luce, ).…”

“…The sensitivity of glacier mass to climate change in these generalized classes closely reflects theoretical temperature and precipitation based sensitivities of seasonal snowpack identified in previous studies. The historical mean winter temperature of classes 1 and 2 (Table ) are near the threshold where April 1 snowpack shows less sensitivity to changes in precipitation, ~ −2 °C (Lute & Luce, ), at which above this threshold less precipitation falls as snow. Below this threshold, changes in precipitation have a stronger impact on snowpack in class 4, with the coldest temperatures and moderate precipitation, translating to potential gains in glacier mass from increased precipitation (section 4.3 and Figure ).…”

Principal components of thermal regimes in mountain river networks

“…Because precipitation, air temperatures, snowpack, runoff volume, and runoff timing are all evolving in response to climate change in mountain environments across the study region (Mote et al, 2005;Luce et al, 2013) and globally (Stewart, 2009), better understanding of these connections is needed. In particular, more insight into the relationship of water temperatures with annual unit-area runoff and whether the underlying mechanisms relate to changes in snowpack accumulation (Luce et al, 2014a;Lute and Luce, 2017), snowmelt timing and rate (Musselman et al, 2017), the volume of water stored in groundwater (e.g., Tague et al, 2007), or the outcomes of extreme low flows (e.g., Kormos et al, 2016;Luce and Holden, 2009) could lead to better predictions about water temperatures and the evolution of thermal regimes in response to expected changes in air temperatures and precipitation.…”

How will snow alter exposure of organisms to cold stress under climate warming?

“…Several modelling approaches have been developed for prediction of soil temperature as a function of snow cover (e.g., Anderson, 2006; Avanzi et al, 2016; Bartlett, MacKay, & Verseghy, 2006; Cuntz & Haverd, 2018; Essery, Morin, Lejeune, & Ménard, 2013; Hamman, Nijssen, Bohn, Gergel, & Mao, 2018; Jordan, 1991; Liston & Sturm, 1998; Lute & Luce, 2017; Zhang, 2003). These approaches range from fully physical schemes that consider energy and mass balances explicitly to more phenomenological approaches that use descriptive formulations.…”