Changes In Snow Water Equivalent Research Articles

Projected Changes in Snow Water Equivalent over the Tibetan Plateau under Global Warming of 1.5° and 2°C

AbstractSnow water equivalent (SWE) is a critical parameter for characterizing snowpack, which has a direct influence on the hydrological cycle, especially over high terrain. In this study, SWE from 18 coupled model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is validated against the Canadian Sea Ice and Snow Evolution Network (CanSISE) SWE. The model simulations under RCP8.5 and RCP4.5 are employed to investigate projected changes in spring/winter SWE over the Tibetan Plateau (TP) under global warming of 1.5° and 2°C. Most CMIP5 models overestimate the CanSISE SWE. A decrease in mean spring/winter SWE for both RCPs over most regions of the TP is predicted in the future, with most significant reductions over the western TP, consistent with pronounced warming in that region. This is supported by strong positive correlations between SWE and mean temperature in the future in both seasons. Compared with the preindustrial period, spring/winter SWE over the TP under global warming of 1.5° and 2°C will reduce significantly, at faster rates than over China as a whole and the Northern Hemisphere. SWE changes over the TP do not show a simple elevation dependency under global warming of 1.5° and 2°C, with maximum changes in the elevation band of 4000–4500 m. Moreover, there are also strong positive correlations between projected SWE and historical mean SWE, indicating that the initial conditions of SWE are an important parameter of future SWE under specific global warming scenarios.

Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar

AbstractCalifornia depends on snow accumulation in the Sierra Nevada for its water supply. Snowfall is measured by a combination of snow pillows, snow courses, and rain gauges. However, the paucity of locations of these measurements, particularly at high elevations, can introduce artifacts into precipitation estimates that are detrimental for hydrologic forecasting. To reduce errors, we need high‐resolution, spatially complete measurements of precipitation. Remotely sensed snow depth and snow water equivalent (SWE), with retrieval time scales that resolve storms, could help mitigate this problem in snow‐dominated watersheds. Since 2013, National Aeronautics and Space Administration's Airborne Snow Observatory (ASO) has measured snow depth in the Tuolumne basin of California's Sierra Nevada to advance streamflow forecasting through improved estimates of SWE. In early April 2015, two flights 6 days apart bracketed a single storm. The work herein documents a new use for ASO and presents a methodology to directly measure the spatial variability of frozen precipitation. In an end‐to‐end analysis, we also compare gauge‐interpolated and dynamically downscaled estimates of precipitation for the given storm with that of the ASO change in SWE. The work shows that the extension of ASO operations to additional storms could benefit our understanding of mountain hydrometeorology by delivering observations that can truly evaluate the spatial distribution of snowfall for both statistical and numerical models.

Snow cover accumulation and melting measurements taken using new automated loggers at three study locations

Snow water equivalent (SWE) exactly reflects the hydrological importance of accumulated snow cover. Taking manual measurement is both time consuming and laborious, and one major disadvantage of them is the sporadic information they give about SWE trends during the winter season. This disadvantage is eliminated through the use of an automated device capable of monitoring temporal changes of SWE practically continuously. This study investigated the testing of a new SWE monitoring device installed at three study locations over three winter seasons (2014–2017). The device measures SWE by weighing snow cover and is equipped with a fencing component expected to reduce measurement errors due to snow bridging. It is also equipped with an ultrasonic snow depth and a snowmelt seepage sensors. The evaluation indicated that an hourly interval for collecting SWE data may not be sufficient for providing an accurate description of snow cover trends and for tracking winter hydrology during rain-on-snow events. Changeable winters increase the chance of measurement errors and complicate making evaluations of snow cover. The installed component did not fully prevent snow bridging, and the occurrence of predisposed climatic conditions was site specific. Apart from snow bridging events, the measurement errors did not exceed 7 mm, i.e. maximally from 7 to 26% of SWE. Snowmelt at each device was accelerated by up to one day. The risk of the influence of ground freeze on the snowmelt seepage sensor and measurement apparatus was seen during low snow cover and occurred mainly at the lowest altitude. The calculated evaporation from snow in each particular season ranged between 3.9% and 8.3%; the average values of evaporation increased with decreasing altitude.

Accuracy evaluation of snow water equivalent global data: the case of the Northern Dvina River basin

The article presents a comprehensive multilevel accuracy evaluation of global snow water equivalent (SWE) datasets performed for the territory of the Northern Dvina River basin, where snow plays an important role in the formation of spring runoff and the increase in winter runoff during recent decades due to ongoing climate change. Six global snow water equivalent datasets, available on the National Snow and Ice Data Center website (all currently available global SWE data), were used in this study. Snow survey data of the Roshydromet observation network were used as ground-based observations. The accuracy of dataset values of SWE was evaluated by comparison with ground observations in several successive stages. In the first stage, the global SWE values were compared with the observational data for several selected weather stations, i. e., the dataset values of SWE belonging to a particular grid cell were compared with the snow survey values of a weather station located in this cell. In the second stage, the SWE values averaged over one of the main subbasins of the Northern Dvina, the Small Northern Dvina River basin, were analyzed. The final stage was the SWE comparison for the entire Northern Dvina River basin. The methodology for ranking data depending on their accuracy based on comparison with ground-based observations of several basic parameters of quantitative changes in SWE during periods of formation and before the start of melting (maximum SWE) was specially developed to implement the accuracy evaluation and SWE comparison. A comprehensive comparative analysis performed based on the developed methodology revealed that the accuracy of global SWE data increases with the investigation area (i.e., the volume of information used) for which the comparison is being carried out. The closest to the observed SWE values for all datasets were obtained for the whole Northern Dvina River basin. The ranking methodology and the implemented analysis helped to determine the most promising global SWE data for future use.

Comparative Analysis of Snowfall Accumulation and Gauge Undercatch Correction Factors from Diverse Data Sets: In Situ, Satellite, and Reanalysis

Despite its importance for hydrology and water resources, accurate estimation of snowfall rate over snow-covered regions has remained a major observational challenge from both in-situ and remote sensing instruments. Snowfall accumulation can be measured by either accumulating snowfall estimates or measuring snowpack properties such as Snow Water Equivalent (SWE) and mass. By focusing on snowfall over snow accumulation period and using case studies and long-term average (2003 to 2015) over CONUS, this study compares snowfall accumulation from gauge stations (using GPCC and PRISM products), satellite products (GPCP and the suite of IMERG products), and reanalysis (ERA-interim, ERA5, and MERRA-2). Changes in SWE based on the recent UA-SWE product together with mass change observation from GRACE were used for assessment of precipitation products. We also investigated two popular gauge undercatch correction factors (CFs) used to mitigate precipitation undercatch in GPCC and GPCP. The results show that snow accumulation from most of the products is bounded by GPCC with and without correction, highlighting the critical importance of selecting proper CFs for gauge-undercatch correction. The CF based on Legates and Willmott method was found to be more consistent with the SWE-based analysis than CF based on the Fuchs method. Reanalysis show very similar spatial pattern among themselves, but represent large variation in simulating snow accumulation, with ERA-interim showing the least accumulation and MERRA-2 showing the highest accumulation and closest to the snow accumulation suggested by SWE.

Taliks: A Tipping Point in Discontinuous Permafrost Degradation in Peatlands

AbstractTaliks (perennially thawed soil in a permafrost environment) are generally found beneath water bodies or wetlands, and their development and evolution in other environments is poorly documented. Sustained isolated taliks between seasonally frozen surface soils and permafrost have been observed at the Scotty Creek Research Station in the discontinuous permafrost region of the Northwest Territories, Canada. These taliks have been expanding both vertically and laterally over the past decade of monitoring. The main controls on expansion are thought to be (1) the availability of energy, determined by incoming radiation and advective heat flux, (2) the ability to transfer this energy to the freezing/thawing front, determined by the thermal conductivity (soil properties and moisture content), and (3) the presence and thickness of the snowpack. These controls are investigated using data collected in the field to inform a 1‐D coupled thermodynamic freeze‐thaw and unsaturated flow model. The model was successfully used to represent observed thaw rates in different parts of the landscape. It is found that high soil moisture, deeper snowpacks, and warmer or faster advective flow rates all contribute to accelerated talik growth and subsequent permafrost degradation. Simulations show that slight perturbations of available energy or soil properties, such as an increase in average surface temperature of 0.5°C or a 1‐cm change in snow water equivalent, can lead to talik formation, highlighting the vulnerability of this landscape to changes in climate or land cover.

Quantifying and Diagnosing Sources of Uncertainty in Midcentury Changes in North American Snowpack from NARCCAP

Abstract The NARCCAP RCM–GCM ensemble is used to explore the uncertainty in midcentury projections of snow over North America that arise when multiple RCMs are used to downscale multiple GCMs. Various snow metrics are examined, including snow water equivalent (SWE), snow cover extent (SCE), snow cover duration (SCD), and the timing of the snow season. Simulated biases in baseline snow characteristics are found to be sensitive to the choice of RCM and less influenced by the driving GCM. By midcentury, domain-averaged SCE and SWE are projected to decrease in all months of the year. However, using multiple RCMs to downscale multiple GCMs inflates the uncertainty in future projections of both SCE and SWE, with projections of SWE being more uncertain. Spatially, the RCMs show winter SWE decreasing over most of North America, except north of the Arctic rim, where SWE is projected to increase. SCD is also projected to decrease with both a later start and earlier termination of the snow season. For all metrics considered, the magnitude of the climate change signal varies across the RCMs. The ensemble spread is large over the western United States, where the RCMs disagree on the sign of the change in SWE in some high-elevation regions. Future projections of snow (both magnitude and spatial patterns) are more similar between simulations performed with the same RCM than the simulations driven by the same GCM. This implies that climate change uncertainty is not sufficiently explored in experiments performed with a single RCM driven by multiple GCMs.

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

Mountain snowpack response to different levels of warming

Temperature variability impacts the distribution and persistence of the mountain snowpack, which critically provides snowmelt-derived water resources to large populations worldwide. Warmer temperatures decrease the amount of montane snow water equivalent (SWE), forcing its center of mass to higher elevations. We use a unique multivariate probabilistic framework to quantify the response of the 1 April SWE volume and its centroid to a 1.0 to 2.0 °C increase in winter air temperature across the Sierra Nevada (United States). A 1.0 °C increase reduces the probability of exceeding the long-term (1985-2016) average rangewide SWE volume (15.7 km3) by 20.7%. It correspondingly is 60.6% more likely for the centroid to be higher than its long-term average (2,540 m). We further show that a 1.5 and 2.0 °C increase in the winter temperature reduces the probability of exceeding the long-term average SWE volume by 31.0% and 41.1%, respectively, whereas it becomes 79.3% and 89.8% more likely that the centroid will be higher than 2,540 m for those respective temperature changes. We also characterize regional variability across the Sierra Nevada and show that the northwestern and southeastern regions of the mountain range are 30.3% and 14.0% less likely to have 1 April SWE volumes exceed their long-term average for a 1.0 °C increase about their respective average winter temperatures. Overall, the SWE in the northern Sierra Nevada exhibits higher hydrologic vulnerability to warming than in the southern region. Given the expected increases in mountain temperatures, the observed rates of change in SWE are expected to intensify in the future.

On distinguishing snowfall from rainfall using near‐surface atmospheric information: Comparative analysis, uncertainties and hydrologic importance

The accurate estimation of precipitation phase has broad applications. In this study, we compared the skill of using various atmospheric variables and their combinations as predictors in accurately identifying surface precipitation phase, determined uncertainties associated with commonly used fixed temperature thresholds, and explored the sensitivity of hydrologic model output to uncertainty in precipitation phase using two case‐studies. The results suggest that among all single predictors, wet‐bulb temperature yields the highest skill score for determining precipitation phase and can reduce uncertainties due to regional differences, especially compared to the commonly used near‐surface air temperature. However, addition of good‐quality near‐surface wind speed measurement to dew‐point temperature and pressure showed slightly higher skill than wet‐bulb temperature. We showed that the scale mismatch between temperature from stations and gridded products can cause large uncertainties in determining precipitation phase, especially in regions with rugged topography. Such uncertainties need to be considered when the relationships developed based on station data are applied to remote‐sensing observations and model‐generated data to separate rain from snowfall. The sensitivity of hydrologic model outputs to uncertainty in precipitation phase delineation was also assessed over two major basins in California by modifying default near‐surface temperatures used in the Variable Infiltration Capacity (VIC) model. It was found that regional and scaling uncertainties in determining temperature thresholds can largely influence the accuracy of simulated downstream runoff and snow water equivalent (SWE) (e.g. up to 40% change in SWE for 2 °C shift in temperature threshold). Therefore, to reduce simulation uncertainties, it is important to improve rain–snow partitioning methods, consider regional variabilities in determining temperature thresholds, and perform the analysis at the highest possible resolutions to mitigate scale‐related uncertainties.

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

AbstractThe Colorado River Basin is a fundamentally important river for society, ecology, and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent, and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model. We combine global sensitivity analysis with a space‐filling Latin Hypercube Sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance‐based approach. We find that snow‐dominated regions are much more sensitive to uncertainties in VIC parameters. Although baseflow and runoff changes respond to parameters used in previous sensitivity studies, we discover new key parameter sensitivities. For instance, changes in runoff and evapotranspiration are sensitive to albedo, while changes in snow water equivalent are sensitive to canopy fraction and Leaf Area Index (LAI) in the VIC model. It is critical for improved modeling to narrow uncertainty in these parameters through improved observations and field studies. This is important because LAI and albedo are anticipated to change under future climate and narrowing uncertainty is paramount to advance our application of models such as VIC for water resource management.

Recent Evidence of Large-Scale Receding Snow Water Equivalents in the European Alps

Abstract Snow plays a critical role in the water cycle of many mountain regions and heavily populated areas downstream. In this study, changes of snow water equivalent (SWE) time series from long-term stations in five Alpine countries are analyzed. The sites are located between 500 and 3000 m above mean sea level, and the analysis is mainly based on measurement series from 1 February (winter) and 1 April (spring). The investigation was performed over different time periods, including the last six decades. The large majority of the SWE time series demonstrate a reduction in snow mass, which is more pronounced for spring than for winter. The observed SWE decrease is independent of latitude or longitude, despite the different climate regions in the Alpine domain. In contrast to measurement series from other mountain ranges, even the highest sites revealed a decline in spring SWE. A comparison with a 100-yr mass balance series from a glacier in the central Alps demonstrates that the peak SWEs have been on a record-low level since around the beginning of the twenty-first century at high Alpine sites. In the long term, clearly increasing temperatures and a coincident weak reduction in precipitation are the main drivers for the pronounced snow mass loss in the past.

Remote Sensing of Snow Water Equivalent Using P-Band Coherent Reflection

A proof-of-concept experiment was carried out to demonstrate the feasibility of retrieving snow water equivalent (SWE) using P-band signals of opportunity. The fundamental observation is the change in the phase of the reflected waveforms as related to the change in SWE. Through theoretical modeling it was found that the change in SWE was approximately linearly dependent on the change in phase. This was verified by retrieving SWE data collected and processed from a tower-based experiment at Fraser, CO, USA. A linear regression was performed on measured phase and in situ SWE. The correlation was found to be 0.94 and root mean square deviation was found to be 7.5 mm.

Sensitivity of Alpine3D modeled snow cover to modifications in DEM resolution, station coverage and meteorological input quantities

This study presents a comprehensive sensitivity and uncertainty assessment of important input parameters on the Alpine3D modeled snow water equivalent (SWE) for two different alpine catchments. Horizontal resolution of the DEM grid, station coverage and several meteorological input quantities were modified. Decreasing the horizontal resolution from 25 m to 1000 m leads to a 10% higher SWE. Modifications in the spatial coverage of meteorological stations influences the SWE up to 20%. Modifications of meteorological input quantities within some plausible ranges lead to changes in SWE up to 30%. The results demonstrate that Alpine3D input uncertainties are in general in the same range as the typical measurement uncertainty of SWE and the uncertainty of the typical scenario spread of GCM-RCM ensemble runs.

Regional change in snow water equivalent–surface air temperature relationship over Eurasia during boreal spring

Present study investigates local relationship between surface air temperature and snow water equivalent (SWE) change over mid- and high-latitudes of Eurasia during boreal spring. Positive correlation is generally observed around the periphery of snow covered region, indicative of an effect of snow on surface temperature change. In contrast, negative correlation is usually found over large snow amount area, implying a response of snow change to wind-induced surface temperature anomalies. With the seasonal retreat of snow covered region, region of positive correlation between SWE and surface air temperature shifts northeastward from March to May. A diagnosis of surface heat flux anomalies in April suggests that the snow impact on surface air temperature is dominant in east Europe and west Siberia through modulating surface shortwave radiation. In contrast, atmospheric effect on SWE is important in Siberia and Russia Far East through wind-induced surface sensible heat flux change. Further analysis reveals that atmospheric circulation anomalies in association with snowmelt over east Siberia may be partly attributed to sea surface temperature anomalies in the North Atlantic and the atmospheric circulation anomaly pattern associated with snowmelt over Russia Far East has a close association with the Arctic Oscillation.

Projected 21st century changes in snow water equivalent over Northern Hemisphere landmasses from the CMIP5 model ensemble

Abstract. Changes in snow water equivalent (SWE) over Northern Hemisphere (NH) landmasses are investigated for the early (2016–2035), middle (2046–2065) and late (2080–2099) 21st century using a multi-model ensemble from 20 global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The multi-model ensemble was found to provide a realistic estimate of observed NH mean winter SWE compared to the GlobSnow product. The multi-model ensemble projects significant decreases in SWE over the 21st century for most regions of the NH for representative concentration pathways (RCPs) 2.6, 4.5 and 8.5. This decrease is particularly evident over the Tibetan Plateau and North America. The only region with projected increases is eastern Siberia. Projected reductions in mean annual SWE exhibit a latitudinal gradient with the largest relative changes over lower latitudes. SWE is projected to undergo the largest decreases in the spring period where it is most strongly negatively correlated with air temperature. The reduction in snowfall amount from warming is shown to be the main contributor to projected changes in SWE during September to May over the NH.

North American Climate in CMIP5 Experiments: Part III: Assessment of Twenty-First-Century Projections*

AbstractIn part III of a three-part study on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) models, the authors examine projections of twenty-first-century climate in the representative concentration pathway 8.5 (RCP8.5) emission experiments. This paper summarizes and synthesizes results from several coordinated studies by the authors. Aspects of North American climate change that are examined include changes in continental-scale temperature and the hydrologic cycle, extremes events, and storm tracks, as well as regional manifestations of these climate variables. The authors also examine changes in the eastern North Pacific and North Atlantic tropical cyclone activity and North American intraseasonal to decadal variability, including changes in teleconnections to other regions of the globe. Projected changes are generally consistent with those previously published for CMIP3, although CMIP5 model projections differ importantly from those of CMIP3 in some aspects, including CMIP5 model agreement on increased central California precipitation. The paper also highlights uncertainties and limitations based on current results as priorities for further research. Although many projected changes in North American climate are consistent across CMIP5 models, substantial intermodel disagreement exists in other aspects. Areas of disagreement include projections of changes in snow water equivalent on a regional basis, summer Arctic sea ice extent, the magnitude and sign of regional precipitation changes, extreme heat events across the northern United States, and Atlantic and east Pacific tropical cyclone activity.

Modelling changes in grassland hydrological cycling along an elevational gradient in the Alps

ABSTRACTThe effects of elevation on surface water fluxes in dry alpine grassland ecosystems were investigated along an elevational transect between 1000 and 2000 m above sea level established in the Vinschgau/Venosta valley, a relatively dry region in the Italian Alps. The GEOtop‐dv hydrological model was employed in point‐scale mode to model the effects of the elevation gradient on snow water equivalent (SWE), soil water content (θ), evapotranspiration (ET), aboveground biomass (Bag) and water use efficiency (WUE) in different climatic conditions. Results show that SWE decreased strongly with decreasing elevation but was also affected by the interannual variability of meteorological drivers. During warmer years, the magnitude of changes in SWE was mitigated at higher altitudes while exacerbated below 1500 m. θ dynamics indicated that water stress conditions for vegetation currently occur at 1000 m each year, while only a warmer and drier year caused drought at 1500 m, and no water stress was found at 2000 m.ET, Bag and WUE did not decrease with elevation but showed a maximum at an intermediate elevation around 1500 m because of the contrasting trends of a shorter vegetation season at higher elevations and water stress at lower elevations, where, in fact, irrigation is needed to maintain grassland productivity. A simulation based on long‐term climatic conditions in combination with a sensitivity analysis of precipitation change showed that this effect is more pronounced during drier years, while for the wettest years, ET tended to decrease with increasing elevation. Taking these findings together, this study suggests that in relatively dry climatic conditions, mountain areas generally act as ‘water towers’ above 1500 m. Quantifying this critical threshold and its likely future variation under climate change scenarios is a challenge for water resource research in the Alpine region and can help stakeholders in planning future mitigation strategies. Copyright © 2014 John Wiley & Sons, Ltd.

Differences in the Potential Hydrologic Impact of Climate Change to the Athabasca and Fraser River Basins of Canada with and without Considering Shifts in Vegetation Patterns Induced by Climate Change

AbstractThe research objectives are to estimate differences between the potential impact of climatic change to the Athabasca River basin (ARB) and Fraser River basin (FRB) of Canada with and without considering shifts in vegetation patterns induced by climate change and how much the difference will depend on vegetation types and climate. The hydrologic effects of vegetation shifts on ARB and FRB were estimated by applying the Mapped Atmosphere–Plant–Soil System (MAPSS) simulated results based on the Intergovernmental Panel on Climate Change’s First and Second Assessment Report general circulation model (GCM) scenarios to the modified Interaction Soil–Biosphere–Atmosphere (MISBA) scheme. According to MAPSS, vegetation shifts in mountainous regions of FRB are expected to be dominated by conifer/broadleaf competition, while in ARB, climate projections of MAPSS predicted a southern expansion of the boreal forest. Because of differences in sublimation, there is a tendency for more snow to accumulate in open grassland than forests. Furthermore, changes to simulated mean annual maximum snowpack, runoff, and basin area covered by grassland are positively correlated to each other. Generally, a 4% increase in snow water equivalent (SWE) results in a 1% increase in mean annual runoff. These relationships hold true in both basins over a wide range of GCM-projected climate conditions and vegetation responses, suggesting that most changes in mean annual flow can be attributed to changes in SWE. Because of the different modeling approaches between MAPSS and MISBA, it seems that the treatment of these processes in vegetation and hydrologic models should be similar before conclusions can be drawn from various stand-alone simulations. Ideally, a land surface scheme should be coupled with a vegetation model in future studies.

Evaluating global snow water equivalent products for testing land surface models

This paper compares three global snow water equivalent (SWE) products, SSM/I (NSIDC), AMSR-E (NSIDC) and Globsnow (v1.0, ESA) to each other, snow covered area (SCA), ground measures of snow depth and meteorological data in an attempt to determine which might be most suitable for testing and developing land surface models. Particular attention is paid to which gives the most accurate peak accumulation, seasonal SWE changes and first and last dates of snow cover.SSM/I and AMSR-E are pure earth observation (EO) derived products whilst Globsnow is a combination of EO and ground data. The results suggest that the pure EO products can saturate in deeper snow (SWE>80–150mm), can show spurious features during melt and can overestimate SWE due to strong thermal gradients and erroneous forest cover correction factors. Along with the comparison to ground data (only a single point) this suggests that Globsnow is the more accurate product for determining peak accumulation and seasonal SWE cycle.The snow start and end dates of the three SWE products were compared to an optically derived SCA (MOD10C1, taken as truth) and found to give large errors of snow start date (root mean square error of 20+ days, though SSM/I was correct on average). The snow end dates had lower errors (a bias of 1–6days) although the spread was still on the order of three weeks.During the investigation, occasional abrupt changes in Globsnow were observed (in the v1.0 and v1.2 daily and v1.2 weekly products). These only occurred in around 1% of cases examined and seem to be spurious. Care should be taken to corrector avoid these jumps if using Globsnow to validate land surface models or in an assimilation scheme.