Assessing the sensitivities of a distributed snow model to forcing data resolution

Highly 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 accurately simulate snowmelt in this catchment, the snow cover needed to be resolved to 100 m. Wind and wind-affected precipitation—the primary influence on snow distribution—required similar resolution. Thermal radiation scaled with the vegetation structure (~100 m), while solar radiation was adequately modeled with 100–250-m resolution. Spatiotemporal sensitivities to model scale were found that allowed for further reductions in computational costs through the winter months with limited losses in accuracy. It was also shown that these modeling-based scale breaks could be associated with physiographic and vegetation structures to aid a priori modeling decisions

Natural climate variability is an important aspect of future projections of snow water resources and rain-on-snow events

Climate projection studies of future changes in snow conditions and resulting rain-on-snow (ROS) flood events are subject to large uncertainties. Typically, emission scenario uncertainties and climate model uncertainties are included. This is the first study on this topic to also include quantification of natural climate variability, which is the dominant uncertainty for precipitation at local scales with large implications for runoff projections, for example. To quantify natural climate variability, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The climate change signal for snow water resources stands out as early as mid-century from the noise originating from the three sources of uncertainty investigated, namely uncertainty in emission scenarios, uncertainty in climate models, and natural climate variability. For ROS events, a climate change signal toward more frequent and intense events was found for an RCP 8.5 scenario at high elevations at the end of the century, consistently with other studies. However, for ROS events with a substantial contribution of snowmelt to runoff (> 20 %), the climate change signal was largely masked by sources of uncertainty. Only those ROS events where snowmelt does not play an important role during the event will occur considerably more frequently in the future, while ROS events with substantial snowmelt contribution will mainly occur earlier in the year but not more frequently. There are two reasons for this: first, although it will rain more frequently in midwinter, the snowpack will typically still be too cold and dry and thus cannot contribute significantly to runoff; second, the very rapid decline in snowpack toward early summer, when conditions typically prevail for substantial contributions from snowmelt, will result in a large decrease in ROS events at that time of the year. Finally, natural climate variability is the primary source of uncertainty in projections of ROS metrics until the end of the century, contributing more than 70 % of the total uncertainty. These results imply that both the inclusion of natural climate variability and the use of a snow model, which includes a physically based process representation of water retention, are important for ROS projections at the local scale.ISSN:1994-0416ISSN:1994-042

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

Spatial scaling of snow processes : modelling implications

Snow cover affects life on Earth in many important ways. The high reflectivity and high moisture content of snow cover affects the global energy balance, atmospheric circulation, and weather. The characteristic heterogeneity of ephemeral mountain snowpacks exhibits strong controls on hydrological, biological, and ecological processes. Accurately predicting process responses is based on knowing the volume, distribution, and state of the snow cover. Physically-based distributed snow models (DSMs) are capable of explicitly representing these vital heterogeneities and are well suited for predicting future impacts such as those associated with climate change. These models however, are currently limited by high computational demands. This research sought to reduce these computational demands and extend the limits of physically-based DSMs. In many regions, wind plays a dominant role in determining snow accumulation patterns .: New algorithms based on terrain and vegetation structure were developed that capably reproduced observed heterogeneities in mountain winds and wind-affected snow distributions. Characterizing the wind and snow patterns in this simplified manner bypassed the heavy computational demands associated with numerically solving the fluid mechanics of windflow and mass transport. The algorithms were incorporated into a mass and energy balance DSM which accurately depicted the heterogeneous accumulation and melt of the snow cover. The computational efficiency of these new algorithms enabled what was perhaps the first DSM application to include the effects of blowing and drifting snow over this large an area at this fine a temporal resolution. Model scale also plays an important role in determining computation times. It was shown that a 100 metre model scale was sufficient for characterizing mountain snow distributions and melt. Furthermore, it was determined that not all the driving processes required the same level of detail creating the potential for additional cost savings. The presented findings have substantially reduced costs and expanded the capabilities of DSMs.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Improving hydropower inflow forecasts by assimilating snow data

The study describes the depositional development and sediment partitioning in a prograding paralic Triassic succession. The deposits are associated with the advance of large prism‐scale clinoforms across a shallower platform area. Approaching the platform, the limited accommodation and associated relative higher rates of deposition generated straighter clinoforms with lower foreset angles. The vertical restriction across the platform is interpreted to have amplified the tidal signature. Sediment was redistributed from the coast into increasingly sandy delta‐front deposits, compared to offshore equivalents. The deposits comprise extensive compound dune fields of amalgamated and increasingly clean sandbodies up‐section. Rapid deposition of significant amounts of sand led to differential subsidence and growth‐faulting in the delta front, with downthrown fault blocks further amplifying the tidal energy through funnelling. A mixed‐energy environment created along‐strike variability along the delta front with sedimentation governing process‐regime. Areas of lower sedimentation were reworked by wave and storm‐action, whereas high sedimentation rates preserved fluvially dominated mouth bars. A major transgression, however, favoured tidally dominated deposits also in these areas, attributed to increasing rugosity of the coastline. Formation of an extensive subaqueous platform between the coast and delta front dampened incoming wave energy, and tidally dominated deposits dominate the near‐shore successions. Meanwhile formation of wave‐built sand‐bars atop the platform attest to continued wave influence. The strong tidal regime led to the development of a heterolithic near‐shore tidally dominated channel system, and sandier fluvial channels up‐river. The highly meandering tidal channels incising the subaqueous platform form kilometre wide successions of inclined heterolithic stratification. The fluvially dominated channels which govern deposition on the delta plain are narrower and slightly less deep, straighter, generally symmetric and filled with cleaner sands. This study provides important insight into tidal amplification and sand redistribution during shallowing on a wide shelf, along with along‐strike process‐regime variability resulting from variations in sediment influx.publishedVersio

Predicting Germination Response to Temperature. II. Three-dimensional Regression, Statistical Gridding and Iterative-probit Optimization Using Measured and Interpolated-subpopulation Data

• Background and Aims Most current thermal-germination models are parameterized with subpopulation-specific rate data, interpolated from cumulative-germination-response curves. The purpose of this study was to evaluate the relative accuracy of three-dimensional models for predicting cumulative germination response to temperature. Three-dimensional models are relatively more efficient to implement than two-dimensional models and can be parameterized directly with measured data

Persistence of topographic controls on the spatial distribution of snow depth in rugged mountain terrain

[1] We model the spatial distribution of snow depth across a wind-dominated alpine basin using a geostatistical approach with a complex variable mean. Snow depth surveys were conducted at maximum accumulation from 1997 through 2003 in the 2.3 km 2 Green Lakes Valley watershed in Colorado. We model snow depth as a random function that can be decomposed into a deterministic trend and a stochastic residual. Three snow depth trends were considered, differing in how they model the effect of terrain parameters on snow depth. The terrain parameters considered were elevation, slope, potential radiation, an index of wind sheltering, and an index of wind drifting. When nonlinear interactions between the terrain parameters were included and a multiyear data set was analyzed, all five terrain parameters were found to be statistically significant in predicting snow depth, yet only potential radiation and the index of wind sheltering were found to be statistically significant for all individual years. Of the five terrain parameters considered, the index of wind sheltering was found to have the greatest effect on predicted snow depth. The methodology presented in this paper allows for the characterization of the spatial correlation of model residuals for a variable mean model, incorporates the spatial correlation into the optimization of the deterministic trend, and produces smooth estimate maps that may extrapolate above and below measured values

Spatial Variability of SWE in Alpine Areas - How do variability patterns change with grid designs?

Estimation of the total amount of water stored as snow in a catchment area during the winter season is a major driver for successful modeling and managing of water resources as well as for accurate predictions of mass balances and changes thereof on glaciated areas. As a comprehensive measurement of the entire catchment is usually impossible, the main difficulty is to link scales. Point measurements of snow depth and density must be combined to estimate the distribution of snow water equivalent (SWE) in a slope, and various slopes are combined to estimate in the average amount of SWE in a catchment. However, especially in mountainous areas, wind redistribution in combination with variable precipitation and complex surface topography, reduce the representativeness of single point data of SWE to sometimes less than a few meters. Therefore, the estimated variability pattern will highly depend on the applied measurement grid and its spatial resolution. For the present study, we employed radar technology to increase the resolution of measurement points to tens of centimeters and less. These radar measurements were performed at three different locations: (i) a relatively low slope, high Alpine glacier in Tirol, Austria, (ii) a non glaciated, high Alpine site in SW Colorado, USA and (iii) a highly wind influenced middle elevation site in Idaho, USA. A regular grid of circles subdivides the respective measurement area in several parts. The variability patterns of the two-way travel time (TWT) of the radar signal are analyzed for each circle separately utilizing geostatistical methods. These patterns are compared with the results using different spatial resolutions and to the results of the respective probings in the circles. At site (i) the observed snow depths were very homogeneous on a scale of hundreds of meters, and the variability patterns of the radar data stay fairly constant and correspond well with the probings. Site (ii) and (iii), however, are characterized by high variabilities in snow depth on a relatively small spatial scale. Therefore, the variability pattern changed significantly with varying spatial resolutions and the probings don't correspond to the radar measurements