Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States

Abstract

In snow‐dominated regions, a key source of uncertainty in hydrologic prediction and forecasting is the magnitude and distribution of snow water equivalent (SWE). With ensemble simulations, this work demonstrates that SWE variability across the mountain ranges of the western United States (represented by 246 Snow Telemetry stations) can largely be captured at the daily time scale by a simple mass and energy‐balance snow model with four physically reasonable parameters—three snow albedo parameters and one snow temperature threshold for precipitation partitioning. The model skill is lower in the maritime Pacific Northwest where SWE variability is more sensitive to errors associated with simulated energy balance (e.g., downward radiation fluxes) and the temperature‐only precipitation partitioning approach. Poor model skill in high‐altitude, windy locations in the Northern Rockies can be attributed to precipitation undercatch and underrepresented wind processes. For the purpose of large‐domain hydrologic applications, regional snow parameters were developed for eight ecoregions characterized by a distinct hydroclimatic regime across the western United States. Results suggest that regionally coherent snow parameterizations are able to capture daily variations in SWE at most Snow Telemetry stations, suggesting that areas with a similar hydroclimate share a similar snow regime. While the three albedo parameters show limited spatial variability across all regions, the regional snow temperature threshold (Ts) shows marked spatial variation correlated with relative humidity; the Ts values increase from 0.2 °C in the higher‐humidity Pacific Northwest to 4.0 °C in the colder, lower‐humidity Rocky Mountains.

Sensitivity of Snow Simulations to Different Atmospheric Forcing Data Sets in the Land Surface Model CAS‐LSM

“… c.SNOTEL In Situ SWE Data As a further evaluation, we compared the modeled SWE with the SNOTEL (Serreze et al, 1999) data, which are point scale in situ SWE observations collected by the Natural Resources Conservation Service (NRCS) in the western United States, including Alaska. Pacific Northwest National Laboratory provided BCQC SNOTEL data (Sun et al, 2019; Yan et al, 2018), consisting of bias‐corrected and quality‐controlled daily SWE data up through 30 September 2018 for 829 active stations located in the western United States and Alaska. The data are available from the Pacific Northwest National Laboratory (https://dhsvm.pnnl.gov/bcqc_snotel_data.stm).…”

“…In this study, we conducted four sensitivity simulations using the LSM for Chinese Academy of Sciences (CAS‐LSM) (Xie et al, 2018) driven by different atmospheric forcing data sets, that is, GSWP3, CRU‐NCEP, WFD/WFDEI, and Princeton. We assessed the modeled SCF, SWE, and SDP using a set of observations, including the Moderate Resolution Imaging Spectroradiometer (MODIS)‐based SCF product (Hall & Riggs, 2015), the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E)‐derived SWE product (Tedesco et al, 2004), SWE from the Snowpack Telemetry (SNOTEL) network (Serreze et al, 1999; Sun et al, 2019; Yan et al, 2018), SDP from the China Meteorological Administration (CMA), and SDP from the University of Arizona (UA; Broxton et al, 2019; Zeng et al, 2018). In addition to the detailed evaluation of our modeling results, our analysis allowed for a comparison between the four forcing data sets and the sensitivities of the simulated snow evolution to the forcing uncertainties in different snow climates.…”

“…In this study, we conducted four sensitivity simulations using the LSM for Chinese Academy of Sciences (CAS-LSM) (Xie et al, 2018) driven by different atmospheric forcing data sets, that is, GSWP3, CRU-NCEP, WFD/WFDEI, and Princeton. We assessed the modeled SCF, SWE, and SDP using a set of observations, including the Moderate Resolution Imaging Spectroradiometer (MODIS)-based SCF product (Hall & Riggs, 2015), the Advanced Microwave Scanning Radiometer for EOS (AMSR-E)-derived SWE product (Tedesco et al, 2004), SWE from the Snowpack Telemetry (SNOTEL) network (Serreze et al, 1999;Sun et al, 2019;Yan et al, 2018), SDP from the China Meteorological Administration (CMA), and SDP from 10.1029/2019JD032001…”

Next-Generation Intensity-Duration-Frequency Curves for Climate-Resilient Infrastructure Design: Advances and Opportunities

“…To extend NG-IDF curves in time and space beyond the limited coverage of the SNOTEL data, the authors (Sun et al, 2019) developed and validated regionally coherent DHSVM snow parameters. By using the BCQC SNOTEL data at 246 sites over the WUS, the authors (Sun et al, 2019) performed a generalized sensitivity test for the DHSVM snow model and identified sensitive snow parameters that control daily SWE evolution under diverse climate regimes. Regional parameters were then developed for these sensitive snow parameters for eight ecoregions (CEC, 2009) characterized by a distinct hydroclimatic regime across the WUS.…”

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

“…However, parameter values are often abstract even in physically-based models, observations may be lacking, or ones found in literature may be inappropriate. For spatially distributed snow model applications, especially when applied in very heterogeneous environments or at large scales, the problem of suitable spatial parameter aggregation arises (Sun et al, 2019).…”