Simulated Snow Water Equivalent Research Articles

Effects of meteorological forcing uncertainty on high-resolution snow modeling and streamflow prediction in a mountainous karst watershed

In the mountainous Western U.S., a considerable portion of water supply originates as snowmelt passing through karst watersheds. Accurately simulating streamflow in snow-dominated, karst basins is important for water resources management. However, this has been challenging due to high spatiotemporal variability of meteorological and hydrogeological processes in these watersheds and scarcity of climate stations. To overcome these challenges, a physically based snow model is used to simulate snow processes at 100 m resolution, and the calculated snowmelt and potential evapotranspiration rates are fed into a deep learning model to simulate streamflow. The snow model was driven by meteorological variables from a regional scale Weather Research and Forecasting (WRF) model or from the North American Land Data Assimilation System (NLDAS-2). The two datasets were used both at the original resolution and downscaled to 100 m resolution based on orographic adjustments, leading to four sets of forcings. Snow model simulation results from the four sets of forcings showed large differences in simulated snow water equivalent (SWE) and snowmelt rate and timing. However, the differences were damped in simulated streamflow, as the deep learning model is partially immune to input bias and picked up different streamflow responses to snowmelt and rainfall when trained using snow model results. While the meteorological datasets considered yielded close streamflow simulation accuracy, averaging simulated streamflow from the four sets of forcings consistently achieved better performance, suggesting the value of including multiple meteorological datasets for modeling streamflow in mountainous watersheds.

Evaluation of Snow and Streamflows Using Noah-MP and WRF-Hydro Models in Aroostook River Basin, Maine

Snow influences land–atmosphere interactions in snow-dominated areas, and snow melt contributes to basin streamflows. However, estimating snowpack properties such as the snow depth (SD) and snow water equivalent (SWE) from land surface model simulations remains a challenge. This is, in part, due to uncertainties in the atmospheric forcing variables, which propagate into hydrological model predictions. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) land surface model in the NOAA’s National Water Model (NWM) version 2.0 configuration to estimate snow in a single column and subsequently the streamflow across the Aroostook River’s sub-basins in Maine for water years (WY) 2014–2016. This study evaluates how differences between two atmospheric forcing datasets, the North American Land Data Assimilation version 2 (NLDAS-2) and in situ (Station), translate into differences in the simulation of snow. NLDAS-2 was used as the meteorological forcing in the retrospective NWM 2.0 simulations. The results from the single-column study showed that differences in the simulated SWE and SD were linked to differences in the 2 m air temperature (T2m), which influenced the precipitation partitioning of rain and snow, as parameterized in Noah-MP. The negative mean bias of −0.7 K (during the accumulation period) in T2m for NLDAS-2, compared to the Station forcing, was a major factor that contributed to the positive mean bias of +52 mm on average in the peak SWE in the NLDAS-2-forced Noah-MP simulation during the study period. The higher T2m values at the Station led to higher sensible heat fluxes towards the snowpack, which led to a higher amount of net energy at the snow’s surface and melt events during the accumulation season in Station-forced Noah-MP simulations. The results from the retrospective NWM version 2.0′s simulation in the basin showed that the streamflow estimates were closer to the United States Geological Survey gage observations at the two larger sub-basins (NSE = 0.9), which were mostly forested, compared to the two smaller sub-basins (NSE ≥ 0.4), which had more agricultural land-use. This study also showed that the spring snowmelt timing was captured quite well by the timing of the decline in the simulated SWE and SD, providing an early indication of melt in most sub-basins. The simulated fractional snow cover area (fSCA) however provided less information about the changes in snow or onset of snowmelt as it was mostly binary (full snow cover in winter), which differed from the more realistic fSCA values shown by the Moderate Resolution Imaging Spectroradiometer.

Snowpack driven streamflow predictability under future climate: contrasting changes across two western Canadian river basins

Abstract Anthropogenic climate change induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the variable infiltration capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a test-bed ensemble streamflow prediction system by treating VIC simulated snow water equivalent (SWE) as a known predictor, and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041-2070 and 2071-2100 future periods against 1981-2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin, and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability.

Snow water equivalent change mapping from slope-correlated synthetic aperture radar interferometry (InSAR) phase variations

Abstract. Area-based measurements of snow water equivalent (SWE) are important for understanding earth system processes such as glacier mass balance, winter hydrological storage in drainage basins, and ground thermal regimes. Remote sensing techniques are ideally suited for wide-scale area-based mapping with the most commonly used technique to measure SWE being passive microwave, which is limited to coarse spatial resolutions of 25 km or greater and to areas without significant topographic variation. Passive microwave also has a negative bias for large SWE. Another method is repeat-pass synthetic aperture radar interferometry (InSAR) that allows measurement of SWE change at much higher spatial resolution. However, it has not been widely adopted because (1) the phase unwrapping problem has not been robustly addressed, especially for interferograms with poor coherence, and (2) SWE change maps scaled directly from repeat-pass interferograms are not an absolute measurement but contain unknown offsets for each contiguous coherent area. We develop and test a novel method for repeat-pass InSAR-based dry-snow SWE estimation that exploits the sensitivity of the dry-snow refraction-induced InSAR phase to topographic variations. The method robustly estimates absolute SWE change at spatial resolutions of < 1 km without the need for phase unwrapping. We derive a quantitative signal model for this new SWE change estimator and identify the relevant sources of bias. The method is demonstrated using both simulated SWE distributions and a 9-year RADARSAT-2 (C-band, 5.405 GHz) spotlight-mode dataset near Inuvik, Northwest Territories (NWT), Canada. SWE results are compared to in situ snow survey measurements and estimates from ERA5 reanalysis. Our method performs well in high-relief areas, thus providing complementary coverage to passive-microwave-based SWE estimation. Further, our method has the advantage of requiring only a single wavelength band and thus can utilize existing spaceborne synthetic aperture radar systems.

Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia

AbstractMountain snow is a fundamental freshwater supply in the arid regions. Climate warming alters the timing of snowmelt and shortens the snow cover duration, which greatly influences the regional climate and water management. However, a reliable estimation of snow mass in the Tianshan Mountains (TS) is still unclear due to the scarcity of extensive continuous surface observations and a complex spatial heterogeneity. Therefore, a long‐time snow simulation from 1982 to 2018 was performed in WRF/Noah‐MP to quantify snow mass in the TS, forced by ERA5 reanalysis data and real‐time updated leaf area index and green vegetation fraction. Meanwhile, March snow mass (close to the annual peak snow mass), snow cover fraction (SCF), and their associated trends were investigated in the TS. The results indicated a good accuracy of the simulated snow water equivalent (root mean square error [RMSE]: 7.82 mm/day) with a slight overestimation (2.84 mm/day). Compared with ERA5 data set, the RMSE and mean bias of the daily snow depth from WRF/Noah‐MP downscaling were significantly reduced by 95.74% and 93.02%, respectively. The climatological March snow mass measured 97.85 (±16.60) Gt in the TS and exhibited a negligible tendency during the study period. The total precipitation during the cold season controlled the variations of March snow mass. The increased precipitation in the high‐altitude regions contributed to an extensive snow mass, which could offset the loss in the TS lowland. In contrast, rapidly rising air temperature caused a significant reduction of March SCF, particularly in the Southern TS.

Dynamical downscaling of global reanalysis data for high-resolution spatial modeling of snow accumulation/melting at the central/southern Sierra Nevada watersheds

Data scarcity is one of the major constraints for hydrological modeling at a mountainous region with complex terrains. In this study, WRF (Weather Research and Forecasting model) model was configured to produce high-resolution atmospheric data for spatial modeling of snow at seven watersheds in central/southern Sierra Nevada.The configured WRF model downscaled a global reanalysis dataset into hourly time intervals and 3-km spatial grid resolution. Performance of the configured WRF model was validated by comparing basin averages of daily precipitation and daily mean temperature between the WRF simulation and data from Parameter-elevation Relationships on Independent Slopes Model (PRISM). The WRF model demonstrated satisfactory performance in simulating peaks and variations of daily precipitation and daily mean temperature. The atmospheric data which were obtained by the dynamical downscaling of Climate Forecast System Reanalysis dataset (CFSR) were then used for calibration and validation of the snow model for the seven watersheds. The dynamical downscaling and the subsequent simulation of snow were performed through the application of The Watershed Environmental Hydrology Hydro-Climate Model (WEHY-HCM) into the central/southern Sierra Nevada. The daily scale validation of the calibrated snow model showed that the simulated snow water equivalent (SWE) was mostly in agreement with observed SWE, however, tended to somewhat underestimate SWE. The snow model simulation with the precipitation input from PRISM indicated that the results using the dynamical downscaling had comparable performance with the results using PRISM precipitation, while some observation points showed improvements of the simulated SWE by using PRISM precipitation. Spatial comparison of the snow cover between the snow model simulations and Moderate Resolution Imaging Spectroradiometer (MODIS) observations showed that the snow model was capable of detecting the snow cover at high elevation bands while the snow cover detection at low elevation bands, which correspond to the boundary between snow cover and no snow cover area, was less accurate.

Evaluation of the snow cover in the Soil, Vegetation, and Snow (SVS) land surface model

AbstractThe Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at ten well-instrumented sites from the Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force-restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS.

Assessment of the accuracy of snow water equivalent calculation with the use of global numerical weather prediction models and SnoWE snowpack model (by the example of the Kama River basin)

The paper discusses the results of snow cover formation and snowmelt modeling in the Kama river basin (S = 507 km2) using two approaches previously developed by the authors. The first one is the SnoWE snowpack model developed at the Hydrometeorological Center of the Russian Federation and used in quasi-operational mode since 2015, and the second is GIS-based empirical technique which was previously implemented for the Kama river basin. Both methods are based on a combination of numerical weather prediction (NWP) models data with operational synoptic observations at the weather stations. The study was performed for the winter seasons 2018/19 and 2019/20. To assess the reliability of simulated snow water equivalent (SWE), we obtained in-situ data from 68 locations (snow survey routes) distributed over the entire area of ​​the river basin. As a result of the study, the main advantages and limitations of two methods for SWE calculation were identified. As for the maximum values of SWE, the root mean square error (RMSE) of simulated SWE ranges from 14% to 28% of the average observed SWE according to in-situ data. It was found, that the SnoWE model more reliably reproduces SWE in the lowland part of the river basin. Simultaneously, SWE was substantially underestimated according to the SnoWE model in the northern and mountainous parts of the basin,. The second method provides a more realistic estimate of the spatial distribution of SWE over the area, as well as a higher accuracy of calculation for its northern part of the river basin. The main drawback of the method is the substantial overestimation of the intensity of snowmelt and snow sublimation. Consequently, the accuracy of SWE calculations sharply decreases in the spring season. Wherein, SWE calculation accuracy in the winter season 2019/20 was substantially lower than in 2018/19 due to frequent thaws.

What is the Trade-Off between Snowpack Stratification and Simulated Snow Water Equivalent in a Physically-Based Snow Model?

In Nordic watersheds, estimation of the dynamics of snow water equivalent (SWE) represents a major step toward a satisfactory modeling of the annual hydrograph. For a multilayer, physically-based snow model like MASiN (Modèle Autonome de Simulation de la Neige), the number of modeled snow layers can affect the accuracy of the simulated SWE. The objective of this study was to identify the maximum number of snow layers (MNSL) that would define the trade-off between snowpack stratification and SWE modeling accuracy. Results indicated that decreasing the MNSL reduced the SWE modeling accuracy since the thermal energy balance and the mass balance were less accurately resolved by the model. Nevertheless, from a performance standpoint, SWE modeling can be accurate enough with a MNSL of two (2), with a substantial performance drop for a MNSL value of around nine (9). Additionally, the linear correlation between the values of the calibrated parameters and the MNSL indicated that reducing the latter in MASiN increased the fresh snow density and the settlement coefficient, while the maximum radiation coefficient decreased. In this case, MASiN favored the melting process, and thus the homogenization of snow layers occurred from the top layers of the snowpack in the modeling algorithm.

Can Convection‐Permitting Modeling Provide Decent Precipitation for Offline High‐Resolution Snowpack Simulations Over Mountains?

AbstractAccurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions.

Improving permafrost physics in the coupled Canadian Land Surface Scheme (v.3.6.2) and Canadian Terrestrial Ecosystem Model (v.2.1) (CLASS-CTEM)

Abstract. The Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component of the Canadian Earth System Model (CanESM). Here, we investigate the impact of changes to CLASS-CTEM that are designed to improve the simulation of permafrost physics. Overall, 18 tests were performed, including changing the model configuration (number and depth of ground layers, different soil permeable depth datasets, adding a surface moss layer), and investigating alternative parameterizations of soil hydrology, soil thermal conductivity, and snow properties. To evaluate these changes, CLASS-CTEM outputs were compared to 1570 active layer thickness (ALT) measurements from 97 observation sites that are part of the Global Terrestrial Network for Permafrost (GTN-P), 105 106 monthly ground temperature observations from 132 GTN-P borehole sites, a blend of five observation-based snow water equivalent (SWE) datasets (Blended-5), remotely sensed albedo, and seasonal discharge for major rivers draining permafrost regions. From the tests performed, the final revised model configuration has more ground layers (increased from 3 to 20) extending to greater depth (from 4.1 to 61.4 m) and uses a new soil permeable depths dataset with a surface layer of moss added. The most beneficial change to the model parameterizations was incorporation of unfrozen water in frozen soils. These changes to CLASS-CTEM cause a small improvement in simulated SWE with little change in surface albedo but greatly improve the model performance at the GTN-P ALT and borehole sites. Compared to the GTN-P observations, the revised CLASS-CTEM ALTs have a weighted mean absolute error (wMAE) of 0.41–0.47 m (depending on configuration), improved from >2.5 m for the original model, while the borehole sites see a consistent improvement in wMAE for most seasons and depths considered, with seasonal wMAE values for the shallow surface layers of the revised model simulation of at most 3.7 ∘C, which is 1.2 ∘C more than the wMAE of the screen-level air temperature used to drive the model as compared to site-level observations (2.5 ∘C). Subgrid heterogeneity estimates were derived from the standard deviation of ALT on the 1 km2 measurement grids at the GTN-P ALT sites, the spread in wMAE in grid cells with multiple GTN-P ALT sites, as well as from 35 boreholes measured within a 1200 km2 region as part of the Slave Province Surficial Materials and Permafrost Study. Given the size of the model grid cells (approximately 2.8∘), subgrid heterogeneity makes it likely difficult to appreciably reduce the wMAE of ALT or borehole temperatures much further.

Major factors affecting the snow simulations by the JULES in New Zealand

AbstractSnow frequently occurs over New Zealand. The seasonal snow accumulation can be about 3 m deep in early spring at some locations. Thus, for numerical weather prediction and climate modelling over New Zealand, a reliable, sophisticated snow model that can properly describe the physical processes associated with snow cover is required. In the past, owing to a lack of snow observations, modelling and verification of snow processes was limited over New Zealand. In the present study, the multilayer snow scheme of the Joint UK Land Environment Simulator (JULES)—the land surface model used in New Zealand regional weather and climate models—was used to simulate the snow water equivalent (SWE) and snow density at two sites in the South Island of New Zealand. The model captured seasonal and interannual variability in snow accumulation and melt. However, large errors in the simulated SWE (up to 300 mm) were found during snowmelt periods and in relatively warm winters when a significant amount of liquid water was present in snow. Sensitivity tests showed that errors in the simulated long wave radiation (a negative bias of about 6 W/m2), snow albedo (0.05–0.10) and air temperature for snow occurrence ( T c, about 0.5 K) were the major factors causing large errors in the simulated SWE. In addition, the simulated snow density was lower than observed. The relatively warm and humid maritime climate of New Zealand appears to make snow simulation in New Zealand more sensitive to long wave radiation, snow albedo and Tc than for continents.

Simulation of snow cover formation and melt with publication of the output data on the web map service (on the example of Kama river basin)

It is performed an assessment of the use of daily precipitation forecasts of global numerical weather prediction (NWP) models GFS (U.S.), GEM (Canada), SLAV (Russia) and ICON (Germany) as input data for snow accumulation and melt modelling in the Kama river basin for two cold seasons. It is shown, that maximum snow water equivalent (SWE) calculated on the basis of NWP models output has an error less than 27% of the measured values, in the conditions of 2017-2018 snow accumulation season. However, this is preliminary assessment, which requires verification by several seasons. It is rather difficult to conclude which model provides highest accuracy of SWE calculation, because each of them has its specific limitations. In 2018-2019 cold season, we additionally obtained ICON model data, which provides the most accurate forecast of precipitation. The simulated SWE and meltwater outflow data are published on the online web map service.

Testing Model Representations of Snowpack Liquid Water Percolation Across Multiple Climates

AbstractSnowpack liquid water percolation is a sensitive model process that is crucial for snowpack runoff forecasts yet varies in sensitivity between climates and snow seasons. Therefore, models of varied complexity developed for different climates and purposes use different percolation representations. We investigated how liquid fluxes in a multilayer snow model vary as represented by discrete representations of gravity drainage and snow density thresholds. We evaluated performance and sensitivity to nonphysical parameters using point measurements of snow water equivalent (SWE) at a maritime site in Washington, USA, and measurements of both SWE and runoff in the French and Swiss Alps. At all three locations, the gravity drainage simulations reduced parameter sensitivity and increased model performance. Average Nash‐Sutcliffe efficiency improved from 0.06 to 0.61 between density threshold and gravity drainage simulations with default parameters. The disparity in model performance was particularly evident at the maritime site (Washington, USA), where the gravity drainage peak SWE was biased by 6% (0.10 m) but density threshold peak SWE was biased by 85% (1.51 m). Simulated runoff and SWE also decreased in performance and increased in parameter sensitivity when including a widely used two‐layer percolation routine. This demonstrates the importance of testing and evaluating models across a wide range of climates, with close attention paid to warmer regions, where percolation has high parameter sensitivity. This is particularly important for global snow modeling and climate change scenarios where multiple regions and snow seasons must be adequately represented with a single model implementation.

Intercomparison of Univariate and Joint Bias Correction Methods in Changing Climate From a Hydrological Perspective

In this paper, the ability of two joint bias correction algorithms to adjust biases in daily mean temperature and precipitation is compared against two univariate quantile mapping methods when constructing projections from years 1981–2010 to early (2011–2040) and late (2061–2090) 21st century periods. Using both climate model simulations and the corresponding hydrological model simulations as proxies for the future in a pseudo-reality framework, these methods are inter-compared in a cross-validation manner in order to assess to what extent the more sophisticated methods have added value, particularly from the hydrological modeling perspective. By design, bi-variate bias correction methods improve the inter-variable relationships in the baseline period. Cross-validation results show, however, that both in the early and late 21st century conditions the additional benefit of using bi-variate bias correction methods is not obvious, as univariate methods have a comparable performance. From the evaluated hydrological variables, the added value is most clearly seen in the simulated snow water equivalent. Although not having the best performance in adjusting the temperature and precipitation distributions, quantile mapping applied as a delta change method performs well from the hydrological modeling point of view, particularly in the early 21st century conditions. This suggests that retaining the observed correlation structures of temperature and precipitation might in some cases be sufficient for simulating future hydrological climate change impacts.

A GIS-based modeling of snow accumulation and melt processes in the Votkinsk reservoir basin

Coupled hydrological and atmospheric modeling is an efficient method for snowmelt runoff forecast in large basins. We use short-range precipitation forecasts of mesoscale atmospheric Weather Research and Forecasting (WRF) model combining them with ground-based and satellite observations for modeling snow accumulation and snowmelt processes in the Votkinsk reservoir basin (184,319 km2). The method is tested during three winter seasons (2012–2015). The MODIS-based vegetation map and leaf area index data are used to calculate the snowmelt intensity and snow evaporation in the studied basin. The GIS-based snow accumulation and snowmelt modeling provides a reliable and highly detailed spatial distribution for snow water equivalent (SWE) and snow-covered areas (SCA). The modelling results are validated by comparing actual and estimated SWE and SCA data. The actual SCA results are derived from MODIS satellite data. The algorithm for assessing the SCA by MODIS data (ATBD-MOD 10) has been adapted to a forest zone. In general, the proposed method provides satisfactory results for maximum SWE calculations. The calculation accuracy is slightly degraded during snowmelt periods. The SCA data is simulated with a higher reliability than the SWE data. The differences between the simulated and actual SWE may be explained by the overestimation of the WRF-simulated total precipitation and the unrepresentativeness of the SWE measurements (snow survey).

Investigating the development of shallow snowpacks on arable land, using comprehensive field observations and spatially distributed snow modelling

Abstract Shallow (<1 m deep) snowpacks on agricultural areas are an important hydrological component in many countries, which determines how much meltwater is potentially available for overland flow, causing soil erosion and flooding at the end of winter. Therefore, it is important to understand the development of shallow snowpacks in a spatially distributed manner. This study combined field observations with spatially distributed snow modelling using the UEBGrid model, for three consecutive winters (2013–2015) in southern Norway. Model performance was evaluated by comparing the spatially distributed snow water equivalent (SWE) measurements over time with the simulated SWE. UEBGrid replicated SWE development at catchment scale with satisfactory accuracy for the three winters. The different calibration approaches which were necessary for winters 2013 and 2015 showed the delicacy of modelling the change in shallow snowpacks. Especially the refreezing of meltwater and prevention of runoff and infiltration of meltwater by frozen soils and ice layers can make simulations of shallow snowpacks challenging.

Analysis of water balance and runoff generation in high latitude agricultural fields during mild and cold winters

High-latitude conditions in northern Europe are characterised by short growing seasons (May–August) and long dormant seasons. Alternating mild and freezing conditions lead to variable snow accumulation–melt cycles affecting runoff generation, and consequently the loss of nutrients and sediments from agricultural fields. We assessed water balance in two subsurface drained clayey agricultural fields of different slopes (1% and 5%) in southern Finland to discern changes between mild and cold winters. The water balances of the two field sections were produced with a spatially distributed 3D hydrological model. Simulated snow water equivalent (SWE), drain discharge, tillage layer runoff and groundwater outflow from a 7-year period were examined during the dormant seasons (September–April) in relation to the North Atlantic Oscillation (NAO) index, which characterises phases related to mild and cold winters in northern Europe. Mild periods (positive NAO) were associated with more frequent runoff events, which were sustained throughout mild winters with lower SWE and shorter time of snow cover. Understanding and quantifying the water balance through periods of different weather patterns is essential as climate change is projected to increase the occurrence of positive NAO phases challenging the control of nutrient and sediment losses from agricultural fields.

Assimilation of AMSR-E snow water equivalent data in a spatially-lumped snow model

Summary Accurately initializing snow model states in hydrologic prediction models is important for estimating future snowmelt, water supplies, and flooding potential. While ground-based snow observations give the most reliable information about snowpack conditions, they are spatially limited. In the north-central USA, there are no continual observations of hydrologically critical snow variables. Satellites offer the most likely source of spatial snow data, such as the snow water equivalent (SWE), for this region. In this study, we test the impact of assimilating SWE data from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) instrument into the US National Weather Service (NWS) SNOW17 model for seven watersheds in the Upper Mississippi River basin. The SNOW17 is coupled with the NWS Sacramento Soil Moisture Accounting (SACSMA) model, and both simulated SWE and discharge are evaluated. The ensemble Kalman filter (EnKF) assimilation framework is applied and updating occurs on a daily cycle for water years 2006–2011. Prior to assimilation, AMSR-E data is bias corrected using data from the National Operational Hydrologic Remote Sensing Center (NOHRSC) airborne snow survey program. An average AMSR-E SWE bias of −17.91 mm was found for the study basins. SNOW17 and SAC-SMA model parameters from the North Central River Forecast Center (NCRFC) are used. Compared to a baseline run without assimilation, the SWE assimilation improved discharge for five of the seven study sites, in particular for high discharge magnitudes associated with snow melt runoff. SWE and discharge simulations suggest that the SNOW17 is underestimating SWE and snowmelt rates in the study basins. Deep snow conditions and periods of snowmelt may have introduced error into the assimilation due to difficulty obtaining accurate brightness temperatures under these conditions. Overall results indicate that the AMSR-E data and EnKF are viable and effective solutions for improving simulations of the operational forecast model.

Changes in the snow water equivalent in mountainous basins in Slovakia over recent decades

Abstract. Changes in snowpack and duration of snow cover can cause changes in the regime of snow and rain-snow induced floods. The recent IPCC report suggests that, in snow-dominated regions such as the Alps, the Carpathian Mountains and the northern parts of Europe, spring snowmelt floods may occur earlier in a future climate because of warmer winters, and flood hazards may increase during wetter and warmer winters, with more frequent rain and less frequent snowfall. The monitoring and modelling of snow accumulation and snow melting in mountainous catchments is rather complicated, especially due to the high spatial variability of snow characteristics and the limited availability of terrestrial hydrological data. An evaluation of changes in the snow water equivalent (SWE) during the period of 1961–2010 in the Upper Hron river basin, which is representative of the mountainous regions in Central Slovakia, is provided in this paper. An analysis of the snow cover was performed using simulated values of the snow water equivalent by a conceptual semi-distributed hydrological rainfall-runoff model. Due to the poor availability of the measured snow water equivalent data, the analysis was performed using its simulated values. Modelling of the SWE was performed in different altitude zones by a conceptual semi-distributed hydrological rainfall-runoff model. The evaluation of the results over the past five decades indicates a decrease in the simulated snow water equivalent and the snow duration in each altitude zone and in all months of the winter season. Significant decreasing trends were found for December, January and February, especially in the highest altitude zone.