Adam Winstral

Comparison of Snow Deposition, the Snow Cover Energy Balance, and Snowmelt at Two Sites in a Semiarid Mountain Basin

Abstract Significant differences in snow deposition, development of the seasonal snow cover, and the timing of melt can occur over small spatial distances because of differences in topographically controlled wind exposure and canopy cover. To capture important intrabasin hydrological processes related to heterogeneous snow cover and energy inputs, models must explicitly account for these differences. The “SNOBAL” point snow cover energy and mass balance model is used to evaluate differences in snow cover energy and mass balance at two sites in a small headwater drainage of the Reynolds Creek Experimental Watershed (RCEW) in the Owyhee Mountains of southwestern Idaho. Though these sites are separated by only 350 m, they are located in distinctly different snow cover regimes. The “ridge” site (elevation 2097 m) is located on a broad shelf on the southern ridge of RCEW, and the “grove” site (elevation 2061 m) is sheltered by topography and forest canopy in a grove of aspen and fir trees just in the lee of th...

Simulating snowmelt processes during rain-on-snow over a semi-arid mountain basin

Abstract In the Pacific Northwest of North America, significant flooding can occur during mid-winter rain-on-snow events. Warm, wet Pacific storms caused significant floods in the Pacific Northwest in February 1996, January 1997 and January 1998. Rapid melting of the mountain snow cover substantially augmented discharge during these flood events. An energy-balance snowmelt model is used to simulate snowmelt processes during the January 1997 event over a small headwater basin within the Reynolds Creek Experimental Watershed located in the Owyhee Mountains of southwestern Idaho, U.S.A. This sub-basin is 34% forested (12% fir, 22% aspen and 66% mixed sagebrush (primarily mountain big sagebrush)). Data from paired open and forested experimental sites were used to drive the model. Model-forcing data were corrected for topographic and vegetation canopy effects. The event was preceded by cold, stormy conditions that developed a significant snow cover over the sub-basin. The snow cover at sites protected by forest cover was slightly reduced, while at open sites significant snowmelt occurred. The warm, moist, windy conditions during the flooding event produced substantially higher melt rates in exposed areas, where sensible- and latent-heat exchanges contributed 60–90% of the energy for snowmelt. Simulated snow-cover development and ablation during the model run closely matched measured conditions at the two experimental sites. This experiment shows the sensitivity of snowmelt processes to both climate and land cover, and illustrates how the forest canopy is coupled to the hydrologic cycle in mountainous areas.

Comparing Simulated and Measured Sensible and Latent Heat Fluxes over Snow under a Pine Canopy to Improve an Energy Balance Snowmelt Model

Abstract During the second year of the NASA Cold Land Processes Experiment (CLPX), an eddy covariance (EC) system was deployed at the Local Scale Observation Site (LSOS) from mid-February to June 2003. The EC system was located beneath a uniform pine canopy, where the trees are regularly spaced and are of similar age and height. In an effort to evaluate the turbulent flux calculations of an energy balance snowmelt model (SNOBAL), modeled and EC-measured sensible and latent heat fluxes between the snow cover and the atmosphere during this period are presented and compared. Turbulent fluxes comprise a large component of the snow cover energy balance in the premelt and ripening period (March–early May) and therefore control the internal energy content of the snow cover as melt accelerates in late spring. Simulated snow cover depth closely matched measured values (RMS difference 8.3 cm; Nash–Sutcliff model efficiency 0.90), whereas simulated snow cover mass closely matched the few measured values taken during...

Evaluating snow models with varying process representations for hydrological applications

Much effort has been invested in developing snow models over several decades, resulting in a wide variety of empirical and physically based snow models. For the most part, these models are built on similar principles. The greatest differences are found in how each model parameterizes individual processes (e.g., surface albedo and snow compaction). Parameterization choices naturally span a wide range of complexities. In this study, we evaluate the performance of different snow model parameterizations for hydrological applications using an existing multimodel energy-balance framework and data from two well-instrumented alpine sites with seasonal snow cover. We also include two temperature-index snow models and an intensive, physically based multilayer snow model in our analyses. Our results show that snow mass observations provide useful information for evaluating the ability of a model to predict snowpack runoff, whereas snow depth data alone are not. For snow mass and runoff, the energy-balance models appear transferable between our two study sites, a behavior which is not observed for snow surface temperature predictions due to site-specificity of turbulent heat transfer formulations. Errors in the input and validation data, rather than model formulation, seem to be the greatest factor affecting model performance. The three model types provide similar ability to reproduce daily observed snowpack runoff when appropriate model structures are chosen. Model complexity was not a determinant for predicting daily snowpack mass and runoff reliably. Our study shows the usefulness of the multimodel framework for identifying appropriate models under given constraints such as data availability, properties of interest and computational cost.

The Use of Similarity Concepts to Represent Subgrid Variability in Land Surface Models: Case Study in a Snowmelt-Dominated Watershed

This paper develops a multivariate mosaic subgrid approach to represent subgrid variability in land surface models (LSMs). Thek-meansclustering isusedtotakeanarbitrarynumber ofinput descriptors and objectively determine areas of similarity within a catchment or mesoscale model grid box. Two different classifications of hydrologic similarity are compared: an a priori classification, where clusters are based solely on known physiographic information, and an a posteriori classification, where clusters are defined based on high-resolution LSM simulations. Simulations from these clustering approaches are compared to high-resolution gridded simulations, as well as to three common mosaic approaches used in LSMs: the ‘‘lumped’’ approach (no subgrid variability), disaggregation by elevation bands, and disaggregation by vegetation types in two subcatchments. All watershed disaggregation methods are incorporated in the Noah Multi-Physics (Noah-MP) LSM and applied to snowmelt-dominated subcatchments within the Reynolds Creek watershed in Idaho. Results demonstrate that the a priori clustering method is able to capture the aggregate impact of finescale spatial variability with O(10) simulation points, which is practical for implementation into an LSM scheme for coupled predictions on continental‐global scales. The multivariate a priori approach better represents snow cover and depth variability than the univariate mosaic approaches, critical in snowmelt-dominated areas. Catchment-averaged energy fluxes are generally within 10%‐15% for the high-resolution and a priori simulations, while displaying more subgrid variability than the univariate mosaic methods. Examination of observed and simulated streamflow time series shows that the a priori method generally reproduces hydrograph characteristics better than the simple disaggregation approaches.

Evaluation of NEXRAD Radar Precipitation Products for Natural Resource Applications

Abstract Timing and amount of precipitation are principal drivers of most rangeland processes, but the availability of rainfall-gauge data over extensive rangelands, particularly in the western United States, is limited. The National Weather Service (NWS), Department of Defense, and Federal Aviation Administration operate a network of Doppler radar stations that produce hourly rainfall estimates, at approximately 16-km2 resolution, with nominal coverage of 96% of the conterminous United States. Internal utilization of these data by the three agencies is primarily for the detection and modeling of extreme weather events. The usefulness of these data for external hydrologic and natural resource applications is limited by a lack of tools for decoding and georeferencing digital precipitation data products. We modified NWS source code to produce decoding and georeferencing tools and used them to evaluate radar precipitation data for the Boise (CBX) radar relative to gauges in the Snake River Plain of southwestern Idaho for the period January 1998 to May 2004. The relationship between radar and gauge precipitation estimates changed after a revision of radar-processing protocols in 2002 and 2003. Cumulative radar precipitation estimates made prior to November 2002 underestimated gauge readings by 50%–60%. Subsequent radar data overestimated cumulative gauge precipitation by 20%–40%. The radar, however, detected precipitation during significantly fewer hours than were detected by the gauge network both before and after programming changes. Additional modification of NWS precipitation-processing procedures might improve accessibility and utility of these data for rangeland management and natural resource modeling applications. Currently available data can still be very useful for estimating high-intensity events that greatly affect processes such as soil erosion and flooding.

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Repeated Light Detection and Ranging (LiDAR) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km LiDAR-derived dataset of snow depths, collected during the 5 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the U.S. National Weather Service, combines a physically-based energy-and10 mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1-km resolution over the coterminous United States. Independent validation data is scarce due to the assimilating nature of SNODAS, com15 pelling the need for an independent validation dataset with substantial geographic coverage. Within twelve distinctive 500×500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during 20 each of the CLPX-2 LiDAR acquisitions. This supplied a dataset for constraining the uncertainty of upscaled LiDAR estimates of snow depth at the 1-km SNODAS resolution, resulting in a root-mean-square difference of 13 centimeters. Upscaled LiDAR snow depths were then compared to the 25 SNODAS estimates over the entire study area for the dates of the LiDAR flights. The remotely-sensed snow depths provided a more spatially continuous comparison dataset and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three 30 distinct areas where the differences between LiDAR observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty.

Assessing the Sensitivities of a Distributed Snow Model to Forcing Data Resolution

AbstractHighly heterogeneous mountain snow distributions strongly affect soil moisture patterns; local ecology; and, ultimately, the timing, magnitude, and chemistry of stream runoff. Capturing these vital heterogeneities in a physically based distributed snow model requires appropriately scaled model structures. This work looks at how model scale—particularly the resolutions at which the forcing processes are represented—affects simulated snow distributions and melt. The research area is in the Reynolds Creek Experimental Watershed in southwestern Idaho. In this region, where there is a negative correlation between snow accumulation and melt rates, overall scale degradation pushed simulated melt to earlier in the season. The processes mainly responsible for snow distribution heterogeneity in this region—wind speed, wind-affected snow accumulations, thermal radiation, and solar radiation—were also independently rescaled to test process-specific spatiotemporal sensitivities. It was found that in order to a...

Statistical downscaling of gridded wind speed data using local topography

AbstractWinds, particularly high winds, strongly affect snowmelt and snow redistribution. High winds during rain-on-snow events can lead to catastrophic flooding while strong redistribution events in mountain environments can generate dangerous avalanche conditions. To provide adequate warnings, accurate wind data are required. Yet, mountain wind fields exhibit a high degree of heterogeneity at small spatial lengths that are not resolved by currently available gridded forecast data. Wind data from over 200 stations across Switzerland were used to evaluate two forecast surface wind products (~2- and 7-km horizontal resolution) and develop a statistical downscaling technique to capture these finer-scaled heterogeneities. Wind exposure metrics derived from a 25-m horizontal resolution digital elevation model effectively segregated high, moderate, and low wind speed sites. Forecast performance was markedly compromised and biased low at the exposed sites and biased high at the sheltered, valley sites. It was a...

Assessment of the Timing of Daily Peak Streamflow during the Melt Season in a Snow-Dominated Watershed

AbstractPrevious studies have shown that gauge-observed daily streamflow peak times (DPTs) during spring snowmelt can exhibit distinct temporal shifts through the season. These shifts have been attributed to three processes: 1) melt flux translation through the snowpack or percolation, 2) surface and subsurface flow of melt from the base of snowpacks to streams, and 3) translation of water flux in the streams to stream gauging stations. The goal of this study is to evaluate and quantify how these processes affect observed DPTs variations at the Reynolds Mountain East (RME) research catchment in southwest Idaho, United States. To accomplish this goal, DPTs were simulated for the RME catchment over a period of 25 water years using a modified snowmelt model, iSnobal, and a hydrology model, the Penn State Integrated Hydrologic Model (PIHM). The influence of each controlling process was then evaluated by simulating the DPT with and without the process under consideration. Both intra- and interseasonal variabil...