A Review of the Hydrologic Response Mechanisms During Mountain Rain-on-Snow

Brandt, W. Tyler; Haleakala, Kayden; Hatchett, Benjamin J.; Pan, Ming

doi:10.3389/feart.2022.791760

MINI REVIEW article

Front. Earth Sci., 26 April 2022

Sec. Cryospheric Sciences

Volume 10 - 2022 | https://doi.org/10.3389/feart.2022.791760

This article is part of the Research Topic Climate Impacts on Snowpack Dynamics View all 6 articles

A Review of the Hydrologic Response Mechanisms During Mountain Rain-on-Snow

W. Tyler Brandt¹*

Kayden Haleakala²

Benjamin J. Hatchett³

Ming Pan¹

¹Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, San Diego, CA, United States
²Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, CA, United States
³Western Regional Climate Center, Desert Research Institute, Reno, NV, United States

Mountain rain-on-snow (ROS) generates large flooding events worldwide. Climate warming will enhance the frequency, magnitude, and widespread nature of these events. Past studies indicate rainfall, not snowmelt, typically drives much of the runoff response during ROS. However, there is substantial event-to-event variability—resulting from shifting atmospheric drivers and nuanced physical mechanisms governing water flow through a snowpack. Historically, turbulent fluxes were assumed to dominate the energy balance for snowmelt during ROS. Recent research nonetheless suggests that other components of the energy balance might be larger drivers depending on: 1) the time of year; 2) the elevation; and 3) the aspect of the slope. This mini review summarizes the literature on the physical processes governing ROS and proposes that moving forward we utilize the terms “active” and “passive” to describe a snowpack’s contribution (via snowmelt) to terrestrial water input (TWI) during ROS. Active snowpacks readily contribute meltwater to TWI via the energy balance, bolstering rainfall-runoff totals. Passive snowpacks do not melt, but simply convey rainwater through the snow matrix. In both snowpack cases, preferential flow paths enhance transmissivity. This proposed classification scheme will help researchers and water managers better communicate and interpret past findings, and aid in forecasting discussions of future events.

Introduction

Snow-fed mountain watersheds supply freshwater to ∼2 billion people worldwide (Viviroli et al., 2007; Sturm et al., 2017; Immerzeel et al., 2020). However, climate change is endangering this natural service. Declines in winter snowfall and snow albedo are reducing snow’s natural reservoir function by producing both smaller and earlier snowmelt (Dudley et al., 2017; Huss et al., 2017; Mote et al., 2018; Skiles et al., 2018; Lynn et al., 2020; Kraaijenbrink et al., 2021; Siirila-Woodburn et al., 2021). Winter rain-on-snow (ROS), particularly on high elevation alpine snow (Freudiger et al., 2014; Beniston and Stoffel, 2016; Musselman et al., 2018; Hock et al., 2019), further diminishes the snowpack’s ability to accumulate, and simultaneously escalates the risk of extreme hazards including ROS floods, ROS initiated avalanches, and debris flows (Harr, 1981; DeGraff, 1994; Stimberis and Rubin, 2011; Surfleet and Tullos, 2013; Hatchett et al., 2020).

What is ROS? The simplest definition is: measurable rainfall on an existing snow cover (Pomeroy et al., 2016). However, observing and/or measuring the process is easier said than done (Harpold et al., 2017), which gives rise to numerous “definitions”. For example, McCabe et al. (2007) defined ROS as any day in which precipitation occurs and snow depth declines—a good definition given available instrumentation, but the metric fails to quantify flood risk. Alternatively, Musselman et al. (2018) and Huang et al. (2022) defined ROS (with flood potential) as heavy rainfall $\geq 10 m m d^{- 1},$ or $\geq 25 m m d^{- 1}$ , respectively, that falls on a snow water equivalent (SWE) $\geq 10 m m$ and where the snowmelt contribution to Terrestrial Water Input (TWI; integrated rainfall and snowmelt reaching the land surface over the event duration) exceeds 20%, or 25%, respectively. However, as Wayand et al. (2015) noted, ROS flooding can still occur with minimal snowmelt contributions, and therefore depending on the ROS definition, could lead to an undercount of rainfall-driven, but nonetheless still technically ROS-type floods.

Definitions aside, rain falling on a snowpack tends to occur between a few to ∼10 times a season (Moore and Prowse, 1988; Cohen et al., 2015; Würzer et al., 2016; Li et al., 2019; Juras et al., 2021; López-Moreno et al., 2021), and is more frequent in maritime mid-latitude mountains vs. continental, or arctic locations (López-Moreno et al., 2021). Most ROS events do not cause flooding (i.e., Merz and Blöschl, 2003; Yang et al., 2020). But, when floods do arise—they can be historic (e.g., Kattelmann, 1997; Marks et al., 1998; Rössler et al., 2014; Garvelmann et al., 2015; Pomeroy et al., 2016).

This review covers the physical processes governing ROS for midlatitude mountain snowpacks and touches on flood-generating mechanisms. Conceptually, ROS floods exist on a continuum—on one end, rainfall on a snow-dominated landscape producing TWI, and on the other, TWI resulting from both rainfall and snowmelt. Improvements to ROS flood forecasting skill is predicated on knowing the contributions of both fluxes as they impact the timing, duration, and volume of runoff (Tarasova et al., 2019). To help process-based discussion we introduce two new terms— “passive” vs. “active” snowpacks. Passive snowpacks convey rainfall but provide minimal melt, whereas active snowpacks readily melt, enhancing TWI.

Conditions for a Large ROS Flood

Many past ROS floods have been largely rainfall driven (Kattelmann, 1997; Merz and Blöschl, 2003; Wayand et al., 2015; Li et al., 2019). A good example of a more “extreme” snowmelt contribution would be Henn et al. (2020) who found that snowmelt enhanced TWI by 37% in California’s Feather River. But, broadly speaking, what drives a large ROS runoff response?

Simply put, a large ROS runoff response requires an anomalously low snowline, a large snow-covered area (SCA) and prolonged, high-intensity rainfall over most of the SCA. A seasonally low snowline can result from either a preceding cold storm, or within a single storm (Hatchett et al., 2016)—but both scenarios lead to a large SCA. Prolonged, high intensity rainfall can occur under a range of atmospheric conditions. These include: atmospheric rivers (ARs; e.g., Guan et al., 2016; Ralph et al., 2020), cloud microphysical processes such as seeder-feeder mechanisms and localized convection (e.g., Rössler et al. (2014)), enhanced mid-tropospheric moisture fluxes (e.g., Kaplan et al. (2012)), and orographic effects (Houze, 2012). We note that these atmospheric conditions operate in unison or independently, but their identification is fundamental to understanding the spatiotemporal variability in the basin-wide runoff response.

While atmospheric conditions favor prolonged, high-intensity precipitation for a large runoff response, surface processes can either amplify or dampen the runoff. Primary factors include: the snow energy balance, preferential flow, and the antecedent snowpack and soil conditions (Figure 1; Dunne and Black, 1971; Berris and Harr, 1987; Kattelmann, 1997; Singh et al., 1997; Marks et al., 1998; Kattelmann and Dozier, 1999; Rössler et al., 2014; Guan et al., 2016). An active snowpack driven by a large positive energy balance can bolster rainfall driven TWI—particularly across ephemeral (i.e., transient), low-elevation snow with little cold content (Jennings et al., 2018). However, without a saturated landscape and a snowpack with established preferential flow routing (see Section 4), runoff responses could still be dampened and/or delayed (Kattelmann, 1989; McGurk and Marsh, 1995; Singh et al., 1997; Garvelmann et al., 2015; Würzer et al., 2016).

FIGURE 1

FIGURE 1. (A) A conceptual diagram of the key drivers for a large runoff response for a ROS event. Key ingredients include a low snowline with a large SCA combined with a high intensity (i.e., heavy), long duration rainfall over the majority of the SCA. Modulating factors that can enhance runoff volumes include saturated snow and soils, and a positive energy balance during the event that results in snowmelt contributions to TWI (i.e., an active snowpack). (B) a histogram of available data on snowmelt vs. rainfall contributions to TWI from 10 studies covering 203 ROS events from the following papers: Beaudry and Golding (1983), Bergman (1983), Marks et al. (1998), Rössler et al. (2014), Garvelmann et al. (2015), Wayand et al. (2015), Pomeroy et al. (2016), Corripio and López-Moreno (2017), Mateo-Lázaro et al. (2019), and Henn et al. (2020). Note: not all events are independent, ROS definitions vary by study, and spatial scales represented range from point to basin using either observations or models. Moving forward we would encourage new studies to record the percent contributions to TWI to facilitate future intercomparison studies. (C) the evolution of the snowline in California’s Sierra Nevada (focused on the Tuolumne) using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) obtained from Worldview (https://worldview.earthdata.nasa.gov/). The first image (ci) demonstrates how a cold storm can initially exacerbate ROS flood risk by increasing SCA beyond traditional mountain regions and expanding ephemeral vs. seasonal (Petersky and Harpold, 2018) snowpack areas. However, flood risk abated as ephemeral snow melted away (cii), dampening some of its potentially “active” impact prior to ROS that occurred after 5 February. (ciii) Shows the snow cover retreat after ROS. This sequence highlights the importance of storm sequencing in ROS flooding—had the 5 February storm occurred earlier, the flood risk would have been far greater. The snow seasonality metric and snowlines were estimated from data obtained via the Snow Property Inversion from Remote Sensing product [SPIReS; Bair et al. (2021)].

Many midlatitude snow-dominated basins have extensive forest coverage, and vegetation structure/density are known to influence snow accumulation via interception, reduced wind effects, and an altered energy balance (Storck et al., 2002; Lundquist et al., 2013; Broxton et al., 2015; Stevens, 2017). Given that ROS involves some of the same processes, it would follow that vegetation should also exert control on resulting ROS runoff. However, results from field studies remain inconclusive. For example, some studies suggest that ROS produces more outflow from glades (forest open areas) compared to forests (Beaudry and Golding, 1983; Berris and Harr, 1987; Storck et al., 2002), while others observed little difference (Kattelmann, 1987; Berg et al., 1991; Garvelmann et al., 2015). A good discussion can be found in Garvelmann et al. (2015)—but, more research is needed.

Passive vs Active Snowpacks

While our binary classification (passive vs. active) is by design simple, we note that snowpacks exist on a spectrum that can vary both temporally and spatially (Figure 2). For example, at a point location, antecedent snowpack conditions (e.g., the pre-event cold content) coupled with the meteorological conditions will initially drive a singular response (passive or active), however a shift in the meteorological drivers (i.e., a change in weather fronts) could elicit a switch. From a basin perspective, snowpacks are likely composed of a transition from active to passive snowpacks because of elevation-driven temperature differences—particularly when basins encompass large topographic relief. Forest coverage and/or structure will only increase the variability in the snowpack response. Regardless, both snowpack modes (passive vs. active) can dampen and/or delay TWI through an unsatisfied irreducible water content, but only active snowpacks will amplify TWI.

FIGURE 2

FIGURE 2. A conceptual diagram connecting meteorological drivers for active vs. passive snowpacks to snowpack liquid water routing and finally the speed of water outflow. Note: for a more detailed description of hydraulic barriers with regards to water routing within the snowpack see Webb et al. (2018a).

Passive Snowpacks

We define a passive snowpack as one that primarily conveys rainfall (i.e., TWI contributions of snowmelt $\leq 10 %)$ . The minor snowmelt contribution is principally the result of advective heat—a small component of the energy balance (Trubilowicz and Moore, 2017). Because snowmelt can be small, SWE can remain the same, or even increase, when comparing pre- and post-ROS event water equivalents due to liquid water storage within the pack, and/or intermittent snowfall. Algorithms looking for a decreasing snow depth and/or SWE to indicate ROS might miss a passive snowpack. Passive snowpacks are maintained through a combination of: 1) a large cold content; 2) insufficient meteorological drivers to induce melt; and/or 3) preferential flow paths.

Regular snowfall events build cold content (Jennings et al., 2018), enabling a seasonal, deep snowpack to buffer modest meteorological drivers (Haleakala et al., 2021). Shallower snowpacks (i.e., ephemeral snow) are more at risk of melt during ROS. However, the presence of preferential flow—even for an ephemeral snowpack—can modulate the impacts of sensible heat due to rainfall energy. Preferential flow can move large volumes of water through small pores (between 3 and 8% of a cross-sectional area according to McGurk and Marsh, 1995), essentially “insulating” the rest of the pack (Marsh and Woo, 1984; Kattelmann, 1985; McGurk and Marsh, 1995; Schneebeli, 1995). While we study active snowpacks because of their large flooding impacts (i.e., Marks et al. (1998), Rössler et al. (2014), and Henn et al. (2020)), passive snowpacks may be equally potent flood drivers (see Appendix A in Wayand et al., 2015).

Active Snowpacks

Active snowpacks produce relatively large snowmelt volumes (i.e., contributing $≫ 10 %$ to TWI). In short, a satisfied cold content and a positive energy balance will shift a passive snowpack to an active one generating snowmelt. Key components of the energy balance will be discussed.

Turbulent Fluxes

The turbulent heat fluxes—particularly the latent heat flux—are dominant drivers of ROS melt. Warm and wet winter storms, often atmospheric rivers (AR), feature high specific humidity and above-average wind speeds (Ralph et al., 2004; Ralph et al., 2005; Gimeno et al., 2014; Hatchett et al., 2017). These conditions favor the latent heat flux, with energy release taking two forms: 1) the refreezing of rain within the snowpack; and 2) condensation at the snow’s surface (DeWalle and Rango, 2008). It was 2) that drove large floods after a 1996 AR in the Pacific Northwest, with 60–90% of the energy for snowmelt attributed to condensation (Marks et al., 1998). The literature is rich with other case studies demonstrating the potency of this energy balance term e.g., Berris and Harr (1987), Rössler et al. (2014) and Garvelmann et al. (2015). Nonetheless, a key modulating factor is vegetation (i.e., Berris and Harr, 1987; Marks et al., 1998), so forest density and coverage need to be carefully considered when attributing snowmelt to the turbulent fluxes.

Downwelling Longwave Radiation

Another important, but less-recognized energy balance driver of ROS melt, is downwelling longwave radiation. During ROS, longwave contributes to active snowmelt via warm, moist atmospheric conditions (Garvelmann et al., 2014; Bilish et al., 2018; Li et al., 2019). Over the continental US, Li et al. (2019) demonstrated that net radiation (dominated by longwave) is the leading source of energy (68%) for ROS snowmelt in the mountainous Western US. The study is supported by Mazurkiewicz et al. (2008) who also found that ROS snowmelt was governed by longwave contributions in the Pacific Northwest—a surprise given that the turbulent fluxes were hypothesized to be a key driver.

Ground Heat Flux

Historically, studies have often neglected the ground heat flux due to near- or below-freezing surface temperatures (DeWalle and Rango, 2008). However, during fall and spring near-surface soil temperatures can exceed 0°C and contribute to basal layer snowmelt. Pomeroy et al. (2016) found that a late-season snowpack was primarily melted by the ground heat flux during a 2013 Canadian Rockies ROS flood. They reasoned summer ROS should be considered “distinct” from winter ROS.

Advected (Sensible) Heat due to Rain

Lastly, advected (sensible) heat from percolating rain into snow (a porous media) has the potential to induce melt (Kattelmann, 1997; DeWalle and Rango, 2008; Li et al., 2019). However, in British Columbia, Trubilowicz and Moore (2017) showed advected heat contributed <10% to total energy consumed for melt over 286 ROS events (10 years period). However, the calculation neglects preferential flow (Marsh and Woo, 1985; McGurk and Marsh, 1995; Singh et al., 1997; Eiriksson et al., 2013), which if present further diminishes the role of advected heat. Nonetheless, prolonged, high-intensity rainfall has potential for non-negligible advected heat, so the term should not be overlooked.

Snow Hydraulic Conductivity and Outflow Generation

The rate of liquid water transmission through snow governs outflow timing and magnitude. Despite being studied—from theoretical (e.g., Colbeck, 1972; Colbeck, 1973; Colbeck, 1975; 1976; 1978), to experimental (e.g., Colbeck, 1974; Singh et al., 1997; Juras et al., 2017), and observational standpoints (e.g., Marsh and Woo, 1984; Conway and Benedict, 1994; McGurk and Marsh, 1995; Kattelmann and Dozier, 1999; Eiriksson et al., 2013; Rücker et al., 2019)—we have yet to fully grasp the complexities of water routing through snow. Much of the “unknown” lies in the variability of the snow’s stratigraphy and the complex exchanges of water and energy between layers.

Matrix Flow vs Preferential Flow

Percolation within a porous material is driven by a fluid’s potential energy but modulated by conductance (following Darcy’s Law). In the absence of a hydraulic barrier, vertical energy gradients (i.e., gravity) induce vertical flow. Vertical flow falls into two classes of mechanisms: 1) matrix flow; and 2) preferential flow (Schneebeli, 1995; Waldner et al., 2004). Under matrix flow, an even wetting front propagates following Darcy’s law of flow—mathematically simplistic. Preferential flow, on the other hand, consists of spatially heterogeneous saturated “preferential flow paths” that exploit density differences and conveys water due to a lower porosity (Marsh and Woo, 1984; Kattelmann, 1985; McGurk and Marsh, 1995; Schneebeli, 1995). Dye tracer studies have revealed that: 1) elements of both matrix (usually at the surface), and preferential flow (usually deeper) can be present (e.g., Conway and Benedict (1994)), and that 2) preferential flow can “pool” or generate lateral flow if hydraulic barriers (either ice lenses or capillary barriers) are present (Conway and Benedict, 1994; Pfeffer and Humphrey, 1996; Singh et al., 1997; Kattelmann and Dozier, 1999; Albert and Perron, 2000; Waldner et al., 2004; Eiriksson et al., 2013; Würzer et al., 2017; Katsushima et al., 2020). Importantly, despite preferential flow only representing a small volume of the overall snowpack (between 3 and 8% as measured by McGurk and Marsh, 1995), this conveyance mechanism cannot be overlooked as it can heavily influence basin outflow response times (Eiriksson et al., 2013; Würzer et al., 2017). But, because preferential flow is predicated on fine-scale differences in density and porosity (which shift with grain metamorphism)—prediction is challenging (Colbeck, 1978; Schneebeli, 1995; Avanzi et al., 2019). Nonetheless, continued use of dye tracer studies and new techniques like near-infrared hyperspectral imagery will help address knowledge gaps (e.g., Donahue et al., 2022). Important questions include: when does preferential flow form, and how spatially heterogenous are these features?

Rainfall Simulation Experiments

Rainfall simulation experiments attempt to improve our understanding of water routing through the whole snowpack, although results can be difficult to compare (Conway and Benedict, 1994; Singh et al., 1997; Eiriksson et al., 2013; Juras et al., 2017). For example, Singh et al. (1997) found “conditioned” snowpacks, i.e., isothermal snowpacks with a high liquid water content (LWC; 6.8%) due to previous rainfall, generate rapid runoff through preferential flow. Nonetheless, in contrast to Singh et al. (1997); Juras et al. (2017) found that highly-stratified mid-winter snowpacks (LWC of 0.9%) generated a faster outflow response due to preferential flow when compared with three isothermal snowpacks (mean LWC of 3.7%). Despite these contrary findings, stratified snowpacks appear more likely to generate preferential flow, yielding faster outflow response times. However, the initial snow depth and LWC will act as secondary controls regulating the response times (Würzer et al. (2016). We advocate for more rainfall simulation experiments as they bolster natural ROS studies, but can be conducted on our own time, and across different snow environments (i.e., elevation, aspect and vegetation classes). While these experiments are time intensive and difficult to perform, this should not deter their usage.

Modeling and Observations: Suggestions

Because mountain ROS environments are arguably one of the most complex encountered, parsing (and forecasting) active vs. passive snowpacks requires a full, physically-based characterization of the energy balance, as opposed to a degree day model—temperature alone does not drive ROS melt (Qi et al., 2017). With reasonable meteorological forcing data, energy balance models are capable of realistic estimates of snow state, i.e., the “bulk” cold content (Jennings et al., 2018). However, the quality of model output is largely predicated on the quality of the meteorological forcing—which can be poor even in observation-rich regions like California’s Sierra Nevada (Lundquist et al., 2019; Terzago et al., 2020). Denser, and improved station networks to capture complex meteorological drivers would greatly enhance our modeling capabilities.

While the meteorological environment during ROS is difficult to observe, arguably harder still are the exchanges of water and energy within the snowpack itself. To represent these processes, models require multi-layer snowpacks, with both Darcy and Richard’s equations for flow, and ways to incorporate preferential flow (i.e., Wever et al. (2016)). But our physical understanding of these processes and their variability is still in its infancy. Advancing modeling efforts requires more rigorous measurements, e.g., TWI from networks of lysimeters (Marsh and Woo, 1985; Kattelmann, 2000; Eiriksson et al., 2013; Webb et al., 2018b; Rücker et al., 2019), observations of stratigraphy and measurements of LWC (e.g., Koch et al. (2019), Mavrovic et al. (2020); Eole and Michel (2021), Capelli et al. (2022)). This “measurement space” needs clever, new engineering solutions to achieve a predictive understanding of ROS.

Final Thoughts

Impactful ROS floods are rare, unique, and occur during varied meteorological conditions with swings in flood driving mechanisms. The causative components for ROS floods (rainfall vs. snowmelt) can vary storm-by-storm depending on whether a snowpack is passive or active, and the routing mechanisms at play. Moving forward, we first encourage a more vigorous debate on the definition(s) of ROS—having agreed upon definitions will foster better intercomparison studies and understanding of future ROS risk. Second, we encourage funding for established and novel measurements and improved networking across systems (Hatchett et al., 2020). Specifically, we suggest: 1) improved and denser observation networks for the phase, temperature, and intensity of precipitation; and 2) the development of quickly deployable and movable instruments to measure the snow energy balance, snow cold content, and snowpack flow regimes. These new observations would help to constrain model physics with regards to passive vs. active snowpacks, and ensure extreme events are skillfully forecast for the correct physically-based reasons (Kirchner, 2006).

Author Contributions

WB conceptualized the paper and wrote the first draft. KH, BH, and MP provided revisions. MP supervised the project.

Funding

WB drafted the review based on funds from UCAR (subaward 001987), and NOAA (NOAA-OAR-OWAQ-2019-2005820). Publication fees were paid by the UCAR subaward.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors greatly appreciate the constructive feedback and insightful suggestions from the reviewer (CT) and from Anne Heggli.

References

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