Snow Water Storage Research Articles

Characterizing Biases in Mountain Snow Accumulation From Global Data Sets

AbstractMountain snow has a fundamental role in regional water budgets through its seasonal accumulation, storage, and melt. However, characterizing snow accumulation over large regions remains difficult because of limited observational networks and the inability of available satellite instruments to remotely sense snow depth or water equivalent in mountains. Models offer some ability to estimate snow water storage (SWS) on mountain range to continental scales. Here we compare four commonly used global data sets to understand whether there is a consensus regarding mountain SWS estimates among them. The data sets—European Centre for Medium‐Range Weather Forecasts Reanalysis‐Interim, Global Land Data Assimilation System, Modern‐Era Retrospective Analysis for Research and Applications version 2, and Variable Infiltration Capacity—agree to within ±36% of the four–data set average for total global SWS. When mountain areas are extracted using a new seasonal mountain snow classification data set, the four data products have more agreement, where all are within ±21% of the seasonal SWS for mountain regions. However, when compared to high‐resolution (9 km) simulations of SWS from the Weather Research and Forecasting (WRF) regional model, the four global products differ from WRF‐estimated North American mountain snow accumulation by 40–66%, with a negative bias up to 651 km3, comparable to the annual streamflow of the Mississippi River. If we extend the North America SWS bias to global mountains, the global data sets may miss as much as 1,500 km3 of SWS, equivalent to 4% of the flow in all the world's rivers. The potential difference of SWS suggests more work must be done to characterize water resources in snow‐dominated regions, particularly in mountains.

Physically Based Modeling of the Long-Term Dynamics of Water Balance and Snow Water Storage Components in the Ob–Irtysh River Basin

The applicability of the previously developed technique for simulating the runoff hydrographs of northern rivers to the Ob River, which has largest basin area among Russian rivers and flows under severe conditions of the Western Siberia, has been investigated. The technique is based on a land surface model SWAP (Soil–Water–Atmosphere–Plants) with input data based on global data sets on land surface parameters and meteorological forcing data derived from observations at meteorological stations situated within the Ob basin. The uncertainty of Ob runoff has been evaluated. The potentialities of SWAP model in reproducing the long-term dynamics of the mean snow water storage in the Ob–Irtysh basin have also been studied.

A Reassessment of North American River Basin Cool‐Season Precipitation: Developments From a New Mountain Climatology Data Set

AbstractCharacterizing hydrological processes on large scales is challenging due to limitations of observational networks, remoting sensing platforms, and modeling techniques. Water balances have larger uncertainties in mountain regions, where orographic processes produce high spatial variability in precipitation patterns and snow accumulation. Recent work suggests current water budgets underestimate mountain snow water storage, perhaps indicating biases in modeled precipitation. We assess whether global hydroclimate data sets underestimate precipitation for six North American watersheds, ranging from 3–70% mountainous. By selecting a single representative year for each watershed, we compare relatively high‐resolution precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model with four global products: Modern‐Era Retrospective Analysis for Research and Applications, version 2, the Global Land Data Assimilation System, the Global Precipitation Climatology Project, and the Climate Research Unit's climate data set. Comparisons to WRF precipitation suggest that observation‐based gridded data products do not produce reasonable estimates of watershed‐scale cool‐season precipitation, underestimating by 1–69%. The Global Precipitation Climatology Project and the Climate Research Unit data set have average biases of −26% and −38%, respectively. The Modern‐Era Retrospective Analysis for Research and Applications version 2 and the Global Land Data Assimilation System show smaller underestimates relative to WRF (−17% and −21%, respectively), with nearly all mean bias from the mountains (underestimated by 27% and 39%) rather than the topographically simpler lowlands (underestimated by 5% and 2%). We suggest global products fail to capture orographic enhancement of precipitation, resulting in large underestimates of precipitation, snowfall, and snow water storage in mountains of selected North American watersheds, which highlights the need for more accurate precipitation estimates to accurately assess spatiotemporal variations in the water cycle.

Tree spatial patterns modulate peak snow accumulation and snow disappearance

Forests and snow covered regions frequently co-occur across the northern hemisphere. In these environments, forests are structurally and spatially complex mosaics of tree neighborhoods that are intrinsically linked to ecosystem functions. Tree and canopy structures influence snow accumulation and disappearance processes through interception and radiation attenuation. However, it is unclear if spatial heterogeneity within the forest canopy induces heterogeneity in snow accumulation and persistence. We quantitatively identified different tree neighborhoods and tested the differential effects of these within-stand neighborhoods on snow processes. Neighborhood types included individual ponderosa pine (Pinus ponderosa), Douglas-fir (Pseudotsuga menziesii) and western larch (Larix occidentalis) trees, dense overstory tree clumps, openings, and regeneration patches. Intensive measurements of snow accumulation (density and depth) and persistence (disappearance date) were made within replicate neighborhoods for three years. Overall, neighborhood type and year had a significant effect on accumulation and snow disappearance. Openings were significantly different from clumps and individuals, always accumulating more snow. Openings retained snow significantly later than clumps but were not significantly different from individuals. Within the individual tree neighborhood type, a nested species effect indicated no differences in accumulation but significant differences in disappearance between deciduous and evergreen conifers, with snow persisting longer beneath deciduous western larch. Our results suggest that canopy interception is the primary mechanism driving the accumulation phase, while snow disappearance patterns are largely a consequence of spatial variation of longwave radiation. Reducing canopy interception and longwave radiation by increasing the abundance of widely spaced single trees and small openings with silvicultural treatments should increase snow depth and duration, and thus snow water storage. Maintaining a heterogeneous canopy structure that includes tree clumps can be used to meet multiple objectives such as provision of ungulate winter range habitat, and heterogenous understory plant phenology.

Preliminary Research on GNSS-MR for Snow Depth

Snow is a critical storage component in the hydrologiccycle; accurate measurements of snow depth and snow water storage are very important for global climate research. As research and application of GNSS deepens, GNSS-MR based multipath has emerged as a new technology of remote sensing for environmental monitoring of vegetation, soil moisture, snow depth, sea levels, ,and so on. The characteristics of change in SNR data are analyzed and the fundamental principles of GNSS-MR based SNR and calculation flow chart are introduced. Experiments with GPS data collected for 17 days at the NWOT station, Colorado, verify the validity of GNSS-MR based SNR. The retrieval result was consistent with the snow depth recorder; the standard deviation was 0.07m. Results also show that the GNSS-MR for snow depth is very effective and useful for environmental monitoring with abundantglobalGNSS stations.

Modelling the Athabasca watershed snow response to a changing climate

Study regionThe Athabasca River basin (ARB) with its head-waters located within the Canadian Rockies. Study focusInvestigating the snow response of the Athabasca watershed to projected climate using the Variable Infiltration Capacity (VIC) hydrologic model and statistically downscaled future climate data from a selected set of CMIP5 GCMs forced with RCP4.5 and RCP8.5 emissions scenarios. New hydrological insights for the regionHigh resolution end-of-century projections of SWE over the Athabasca watershed show an overall decreasing trend in the mean monthly SWE over the watershed, with the largest decreases occurring in March and April, especially in the high-elevation sub-basin. There are also widespread decreases in annual maximum SWE (SWEmax), with the middle-basin showing slight increases under the RCP4.5 scenario. The dates of SWEmax are generally getting earlier, with RCP4.5 showing a less linear response than RCP8.5. Increases in early spring snowmelt are followed by decreases during the late spring and summer months mainly as a result of earlier start of snowmelt. An overall decrease in snow-cover duration of up to fifty days is projected with the largest decrease occurring in the high elevation sub-basin. Such projected declines in snow water storage and a shift to earlier peak SWE and snowmelt over the ARB have significant implications for the magnitude and timing of the watershed soil-moisture content and hydrologic regime of the Athabasca River.

Changes in seasonal snow water equivalent distribution in High Mountain Asia (1987 to 2009).

Snow meltwaters account for most of the yearly water budgets of many catchments in High Mountain Asia (HMA). We examine trends in snow water equivalent (SWE) using passive microwave data (1987 to 2009). We find an overall decrease in SWE in HMA, despite regions of increased SWE in the Pamir, Kunlun Shan, Eastern Himalaya, and Eastern Tien Shan. Although the average decline in annual SWE across HMA (contributing area, 2641 × 103 km2) is low (average, -0.3%), annual SWE losses conceal distinct seasonal and spatial heterogeneities across the study region. For example, the Tien Shan has seen both strong increases in winter SWE and sharp declines in spring and summer SWE. In the majority of catchments, the most negative SWE trends are found in mid-elevation zones, which often correspond to the regions of highest snow-water storage and are somewhat distinct from glaciated areas. Negative changes in SWE storage in these mid-elevation zones have strong implications for downstream water availability.

Snow disappearance timing is dominated by forest effects on snow accumulation in warm winter climates of the Pacific Northwest, United States

AbstractForests modify snow processes and affect snow water storage as well as snow disappearance timing. However, forest influences on snow accumulation and ablation vary with climate and topography and are therefore subject to temporal and spatial variability. We utilize multiple years of snow observations from across the Pacific Northwest, United States, to assess forest–snow interactions in the relatively warm winter conditions characteristic of maritime and transitional maritime–continental climates. We (a) quantify the difference in snow magnitude and disappearance timing between forests and open areas and (b) assess how forest modifications of snow accumulation and ablation combine to determine whether snow disappears later in the forest or in the open.We find that snow disappearance timing at 12 (out of 14) sites ranges from synchronous in the forest and open to snow persisting up to 13 weeks longer in the open relative to a forested area. By analyzing accumulation and ablation rates up to the day when snow first disappears from the forest, we find that the difference between accumulation rates in the open and forest is larger than the difference between ablation rates. Thus, canopy snow interception and subsequent loss, rather than ablation, set up longer snow duration in the open. However, at two relatively windy sites (hourly average wind speeds up to 8 and 17 m/s), differential snow disappearance timing is reversed: Snow persists 2–5 weeks longer in the forest. At the windiest sites, accumulation rates in the forest and open are similar. Ablation rates are higher in the open, but the difference between ablation rates in the forest and open at these sites is approximately equivalent to the difference at less windy sites. Thus, longer snow retention in the forest at the windiest sites is controlled by depositional differences rather than by reduced ablation rates. These findings suggest that improved quantification of forest effects on snow accumulation processes is needed to accurately predict the effect of forest management or natural disturbance on snow water resources.

Improving the Radiance Assimilation Performance in Estimating Snow Water Storage across Snow and Land-Cover Types in North America

Abstract Continental-scale snow radiance assimilation (RA) experiments are conducted in order to improve snow estimates across snow and land-cover types in North America. In the experiments, the ensemble adjustment Kalman filter is applied and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB observations are assimilated into an RA system composed of the Community Land Model, version 4 (CLM4); radiative transfer models (RTMs); and the Data Assimilation Research Testbed (DART). The performance of two snowpack RTMs, the Dense Media Radiative Transfer–Multi-Layers model (DMRT-ML), and the Microwave Emission Model of Layered Snowpacks (MEMLS) in improving snow depth estimates through RA is compared. Continental-scale snow estimates are enhanced through RA by using AMSR-E TB at the 18.7- and 23.8-GHz channels [3% (DMRT-ML) and 2% (MEMLS) improvements compared to the cases using the 18.7- and 36.5-GHz channels] and by considering the vegetation single-scattering albedo ω [2.5% (DMRT-ML) and 4.8% (MEMLS) improvements compared to the cases neglecting ω]. The contribution of TB of the vegetation canopy to TB at the top of the atmosphere is better represented by considering ω in the RA system, and improvements in the resulting snow depth are evident for the forest land-cover type (about 5%–11%) and the taiga and alpine snow classes (about 5%–11% and 4%–8%, respectively), especially in the MEMLS case. Compared to the open-loop run (0.171-m snow depth RMSE), about 7% (DMRT-ML) and 10% (MEMLS) overall improvements of the RA performance are achieved.

Future snow? A spatial-probabilistic assessment of the extraordinarily low snowpacks of 2014 and 2015 in the Oregon Cascades

Abstract. In the Pacific Northwest, USA, the extraordinarily low snowpacks of winters 2013–2014 and 2014–2015 stressed regional water resources and the social-environmental system. We introduce two new approaches to better understand how seasonal snow water storage during these two winters would compare to snow water storage under warmer climate conditions. The first approach calculates a spatial-probabilistic metric representing the likelihood that the snow water storage of 2013–2014 and 2014–2015 would occur under +2 °C perturbed climate conditions. We computed snow water storage (basin-wide and across elevations) and the ratio of snow water equivalent to cumulative precipitation (across elevations) for the McKenzie River basin (3041 km2), a major tributary to the Willamette River in Oregon, USA. We applied these computations to calculate the occurrence probability for similarly low snow water storage under climate warming. Results suggest that, relative to +2 °C conditions, basin-wide snow water storage during winter 2013–2014 would be above average, while that of winter 2014–2015 would be far below average. Snow water storage on 1 April corresponds to a 42 % (2013–2014) and 92 % (2014–2015) probability of being met or exceeded in any given year. The second approach introduces the concept of snow analogs to improve the anticipatory capacity of climate change impacts on snow-derived water resources. The use of a spatial-probabilistic approach and snow analogs provide new methods of assessing basin-wide snow water storage in a non-stationary climate and are readily applicable in other snow-dominated watersheds.

Estimating Snow Water Storage in North America Using CLM4, DART, and Snow Radiance Data Assimilation

Abstract This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting TB based on their correlations with the prior TB (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171 m RMSE), the overall snow depth estimates are improved by 1.6% (0.168 m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177 m RMSE). Significant improvement of the snow depth estimates in the rule-based RA is observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

Simulating the Impacts of Projected Climate Change on Streamflow Hydrology for the Chesapeake Bay Watershed

A gridded model was developed to simulate the hydrology of the Chesapeake Bay Watershed, the largest estuary in the United States. CMIP3 and CMIP5 climate projections were used to drive the model to assess changes in streamflow and watershed-wide hydrology. Index of agreement values indicated good model performance. Annual average temperature is projected to increase 1.9°C to 5.4°C by 2080 to 2099, with the greatest warming occurring in summer and fall in the northern part of the watershed. Annual total precipitation is projected to increase between 5.2 percent and 15.2 percent by 2080 to 2099, with the largest increases generally occurring in winter. Average evapotranspiration and rainfall are projected to increase while snowfall, snow water storage, and snowmelt decrease. Subsurface moisture is projected to decrease during the warmer months and the time to recharge increases and, in some cases, never actually occurs. Changes in annual runoff for all 346 climate projections averaged 0 percent (2020–2039), –1.5 percent (2050–2069), and –5.1 percent (2080–2099). There is a 48 percent, 52 percent, and 60 percent chance, respectively, for the future time periods that annual runoff will be less than baseline values (1950–1999). Extreme runoff projections are overwhelmingly associated with the negative end of the distribution. Runoff increases are confined to January through March and to higher elevations. This study is novel in its use of a large number of climate models, the gridded nature of the hydrologic model, and the simulation of several hydrologic variables, all of which allowed for the assessment of both uncertainty in the projections and variation across multiple spatial and temporal scales.

A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble

Abstract. We present a global water cycle reanalysis that merges water balance estimates derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission, satellite water level altimetry and off-line estimates from several hydrological models. Error estimates for the sequential data assimilation scheme were derived from available uncertainty information and the triple collocation technique. Errors in four GRACE storage products were estimated to be 11–12 mm over land areas, while errors in monthly storage changes derived from five global hydrological models were estimated to be 17–28 mm. Prior and posterior water storage estimates were evaluated against independent observations of river water level and discharge, snow water storage and glacier mass loss. Data assimilation improved or maintained agreement overall, although results varied regionally. Uncertainties were greatest in regions where glacier mass loss and subsurface storage decline are both plausible but poorly constrained. We calculated a global water budget for 2003–2012. The main changes were a net loss of polar ice caps (−342 Gt yr−1) and mountain glaciers (−230 Gt yr−1), with an additional decrease in seasonal snowpack (−18 Gt yr−1). Storage increased due to new impoundments (+16 Gt yr−1), but this was compensated by decreases in other surface water bodies (−10 Gt yr−1). If the effect of groundwater depletion (−92 Gt yr−1) is considered separately, subsurface water storage increased by +202 Gt yr−1 due particularly to increased wetness in northern temperate regions and in the seasonally wet tropics of South America and southern Africa. The reanalysis results are publicly available via www.wenfo.org/wald/.

A Bayesian hierarchical model for forecasting intermountain snow dynamics

Because of a continual increase in the demand for water as well as an ongoing regional drought, there is an imminent need to monitor and forecast water resources in the Western United States. In particular, water resources in the Intermountain West rely heavily on snow water storage. Thus, the need to improve seasonal forecasts of snowpack and considering new techniques would allow water resources to be more effectively managed throughout the entire water‐year. Many available models used in forecasting snow water equivalent (SWE) measurements require delicate calibrations. In contrast to the physical SWE models most commonly used for forecasting, we offer a statistical model. We present a data‐based statistical model that characterizes seasonal SWE in terms of a nested time series, with the large scale focusing on the inter‐annual periodicity of dominant signals and the small scale accommodating seasonal noise and autocorrelation. This model provides a framework for independently estimating mainly the temporal dynamics of SWE for the various snow telemetry sites. We use snow telemetry data from 10 stations in Utah over 34 water‐years to implement and validate this model. Copyright © 2014 John Wiley & Sons, Ltd.

Intra- and inter-annual variations in snow–water storage in data sparse desert–mountain regions assessed from remote sensing

Seasonal water flow to semi-arid to arid lands worldwide is regularly controlled by precipitation and snowmelt processes in nearby mountain ranges, where few monitoring stations exist. In this paper we present a new approach to assess the seasonal and inter-annual development of precipitation and associated snow-cover area (SCA) and snow–water storage in the Qilian Mountains of NW China. The approach addresses the spatial enhancement and calibration of remote sensing (RS) data acquired from coarse-resolution TRMM (Tropical Rainfall Measuring Mission; ~26-km resolution) and MODIS images in the development of image-surfaces of in-mountain precipitation and surface air temperature at 250-m resolution for input to a new spatially-distributed monthly snow-accumulation and snowmelt model. Spatially-enhanced precipitation and air temperature surfaces were subsequently calibrated using either geographically-weighted regression or simple linear regression and point-data from climate stations. When point-extractions from the calibrated products were compared against a new set of independent climate-station data, their respective values were comparable, giving an overall r2 of 0.87 for precipitation (RMSE=10mmmonth−1) and 0.96 for surface air temperature. With input from calibrated surfaces, monthly SCA and snow–water equivalence (SWE) in the Qilian Mountains were subsequently modeled over a 10-year period (2000–2009). Seasonal values of SCA and SWE were compared with MODIS-based (optical) and AMSR-E passive microwave-based estimates of SCA and SWE. In most cases, comparisons revealed suitable agreement across the various evaluations of SCA. Minor discrepancy between MODIS- and AMSR-E-based estimates of SCA resulted in a mean overlap of 73% when modeled SCA was compared to MODIS-based SCA and 84% overlap, when compared to AMSR-E-based SCA. Modeled and AMSR-E-based estimates of SWE at lower- to upper-mountain elevations (≤3900m above mean sea level; AMSL) compared fairly well. At elevations >3900m AMSL, discrepancies between estimates were largely attributed to an overestimation of local precipitation.

Climate change impacts on maritime mountain snowpack in the Oregon Cascades

Abstract. This study investigates the effect of projected temperature increases on maritime mountain snowpack in the McKenzie River Basin (MRB; 3041 km2) in the Cascades Mountains of Oregon, USA. We simulated the spatial distribution of snow water equivalent (SWE) in the MRB for the period of 1989–2009 with SnowModel, a spatially-distributed, process-based model (Liston and Elder, 2006b). Simulations were evaluated using point-based measurements of SWE, precipitation, and temperature that showed Nash-Sutcliffe Efficiency coefficients of 0.83, 0.97, and 0.80, respectively. Spatial accuracy was shown to be 82% using snow cover extent from the Landsat Thematic Mapper. The validated model then evaluated the inter- and intra-year sensitivity of basin wide snowpack to projected temperature increases (2 °C) and variability in precipitation (±10%). Results show that a 2 °C increase in temperature would shift the average date of peak snowpack 12 days earlier and decrease basin-wide volumetric snow water storage by 56%. Snowpack between the elevations of 1000 and 2000 m is the most sensitive to increases in temperature. Upper elevations were also affected, but to a lesser degree. Temperature increases are the primary driver of diminished snowpack accumulation, however variability in precipitation produce discernible changes in the timing and volumetric storage of snowpack. The results of this study are regionally relevant as melt water from the MRB's snowpack provides critical water supply for agriculture, ecosystems, and municipalities throughout the region especially in summer when water demand is high. While this research focused on one watershed, it serves as a case study examining the effects of climate change on maritime snow, which comprises 10% of the Earth's seasonal snow cover.

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Changes in snow accumulation and ablation following the Las Conchas Forest Fire, New Mexico, USA

ABSTRACTSeasonally, snow‐covered forests are a critical source of water in the Western United States and are subject to major disturbances, including fire, harvest, disease and insect‐caused mortality, that have relatively unknown effects on water availability. In this study, we investigated changes in winter season snow accumulation and ablation in a forest following the Las Conchas fire in the Jemez Mountains of New Mexico. We investigated two competing sets of processes that should determine the peak annual snowpack prior to snowmelt: (1) reduced interception by forest canopy results in greater new snow accumulation and (2) increased winter season ablation of the snowpack results in reduced peak snowpack volumes. These processes were evaluated with approximately 800 spatially distributed manual observations of new snow, 1500 manual observations of peak snowpack, and light detection and ranging‐derived snow depth, vegetation and terrain datasets collected prior to the fire. A single snowfall event yielded significantly larger snow depths in the post‐burn area versus the unburned area (p < 0.001), with 25% to 45% interception in the unburned area and near zero in the post‐burn area. Conversely, the peak snowpack depths were significantly larger in the unburned area compared with the post‐burn area (mean of 55 and 47 cm, respectively), despite nearly identical peak snowpacks prior to the fire (72 and 72 cm, respectively). The lack of strong vegetation controls led to less variability at peak snowpack in the post‐burn area and a shift towards topographically controlled variability, caused by differences in elevation and aspect, occurring at larger spatial scales. The unburned area had roughly 10% more water available for melt than the post‐burn area, with winter season ablation reducing snowpacks by nearly 50% prior to melt in the post‐burn area. The relative importance of shortwave radiation to the snowpack energy balance and sublimation suggests that the 10% reductions in peak snow water storage found in these north‐facing areas could be a conservative estimate for winter season ablation following fire. Further work is necessary to assess the role that topography plays in altering water partitioning following forest disturbance and the potential implications for ecological health and downstream water resources. Copyright © 2013 John Wiley & Sons, Ltd.

Snow Cover and Spring Flood Flow in the Northern Part of Western Siberia (the Poluy, Nadym, Pur, and Taz Rivers)

AbstractThe paper aims to quantitatively estimate the role of snowmelt in the spring flood flow and the redistribution of river runoff for the northern (Arctic) part of the western Siberian Plain (the rivers Poluy, Nadym, Pur, and Taz). In this region, the presence of wetlands and thermokarst lakes significantly influences the seasonal redistribution of river discharge. First the study region is described, and the snow regime from in situ observations at the Tarko-Sale meteorological station is analyzed. As Special Sensor Microwave Imager (SSM/I) estimates of snow depth for this region are lower than in situ observations, a correction of the SSM/I snow depth estimates is done using snow parameters measured on the snow transect near the meteorological station Tarko-Sale for 1991–96. This reestimated snow depth is then used to assess the volume of water stored every winter on the watersheds for 1989–2006. This snow product is compared with the spring flood streamflow estimated from in situ observations, and the regional relationship between the snow water storage and flood flow is constructed. The proportion of meltwater that does not reach the main rivers and is thus evaporated or stored by the wetlands is estimated to be on average 30% (varying from 0% to 74%). We observe an increasing trend of this value from 20%–30% in the early 1990s to 50%–60% in the mid-2000s. This increase could be attributed to several factors such as increased air temperature (leading to increased evaporation, changes in vegetation cover, and active layer depth) and also to human activity.

Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

Abstract. Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations.