Extending the vadose zone: Characterizing the role of snow for liquid water storage and transmission in streamflow generation

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Depth‐based and radar‐based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in‐situ measurements or density models, to calculate SWE. However, in‐situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near‐coincident, lidar‐measured snow depths with ground‐penetrating radar (GPR) two‐way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in‐situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

Section: Discussionmentioning

confidence: 99%

Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

Bonnell,

McGrath,

Hedrick

et al. 2023

Self Cite

“…Wu et al (2021) Approximately 1/4 of the watersheds studied receive >30% of annual precipitation as snow, which is not accounted for in the hydrologic signatures as implemented here, yet snowmelt as an antecedent moisture source can impact streamflow response at the event scale (Hammond & Kampf, 2020). Burned areas show impacts to snow accumulation and accelerated snowmelt after wildfire (Gleason et al, 2019;Kampf et al, 2022) and snowmelt can generate both saturation and IE as flowpaths converge within the snowpack (Webb et al, 2022). The missing representation of snowmelt could explain some of the variability in our signature values.…”

Section: Timescales Of Recovery From Wildfire Impacts On Overland Flo...mentioning

confidence: 99%

A hydrologic signature approach to analysing wildfire impacts on overland flow

Bolotin,

McMillan

2024

Post‐fire flooding and debris flows are often triggered by increased overland flow resulting from wildfire impacts on soil infiltration capacity and surface roughness. Increasing wildfire activity and intensification of precipitation with climate change make improving understanding of post‐fire overland flow a particularly pertinent task. Hydrologic signatures, which are metrics that summarize the hydrologic regime of watersheds using rainfall and runoff time series, can be calculated for large samples of watersheds relatively easily to understand post‐fire hydrologic processes. We demonstrate that signatures designed specifically for overland flow reflect changes to overland flow processes with wildfire that align with previous case studies on burned watersheds. For example, signatures suggest increases in infiltration‐excess overland flow and decrease in saturation‐excess overland flow in the first and second years after wildfire in the majority of watersheds examined. We show that climate, watershed and wildfire attributes can predict either post‐fire signatures of overland flow or changes in signature values with wildfire using machine learning. Normalized difference vegetation index (NDVI), air temperature, amount of developed/undeveloped land, soil thickness and clay content were the most used predictors by well‐performing machine learning models. Signatures of overland flow provide a streamlined approach for characterizing and understanding post‐fire overland flow, which is beneficial for watershed managers who must rapidly assess and mitigate the risk of post‐fire hydrologic hazards after wildfire occurs.

Energy‐Water Asynchrony Principally Determines Water Available for Runoff From Snowmelt in Continental Montane Forests

Webb,

Knowles,

Fox

et al. 2024

Changes in the volume, rate, and timing of the snowmelt water pulse have profound implications for seasonal soil moisture, evapotranspiration (ET), groundwater recharge, and downstream water availability, especially in the context of climate change. Here, we present an empirical analysis of water available for runoff using five eddy covariance towers located in continental montane forests across a regional gradient of snow depth, precipitation seasonality, and aridity. We specifically investigated how energy‐water asynchrony (i.e., snowmelt timing relative to atmospheric demand), surface water input intensity (rain and snowmelt), and observed winter ET (winter AET) impact multiple water balance metrics that determine water available for runoff (WAfR). Overall, we found that WAfR had the strongest relationship with energy‐water asynchrony (adjusted r2 = 0.52) and that winter AET was correlated to total water year evapotranspiration but not to other water balance metrics. Stepwise regression analysis demonstrated that none of the tested mechanisms were strongly related to the Budyko‐type runoff anomaly (highest adjusted r2 = 0.21). We, therefore, conclude that WAfR from continental montane forests is most sensitive to the degree of energy‐water asynchrony that occurs. The results of this empirical study identify the physical mechanisms driving variability of WAfR in continental montane forests and are thus broadly relevant to the hydrologic management and modelling communities.