scholarly journals Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

2012 ◽  
Vol 6 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
T. M. Saloranta
Keyword(s):  
Snow Water Equivalent ◽  
Statistical Evaluation ◽  
Model Performance ◽  
Spatiotemporal Variability ◽  
Spatiotemporal Resolution ◽  
Model Code ◽  
Temperature And Precipitation ◽  
Rugged Topography ◽  
Snow Model ◽  
Snow Conditions

Abstract. Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957–2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

scholarly journals Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

2012 ◽  
Vol 6 (2) ◽  
pp. 1337-1366 ◽  
Author(s):  
T. M. Saloranta
Keyword(s):  
Snow Water Equivalent ◽  
Statistical Evaluation ◽  
Model Performance ◽  
Model Fit ◽  
Spatiotemporal Variability ◽  
Spatiotemporal Resolution ◽  
Model Code ◽  
Temperature And Precipitation ◽  
Snow Model ◽  
Snow Conditions

Abstract. Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1×1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates among others snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a~thorough spatiotemporal statistical evaluation of the model performance in 1957–2011 is made using the two major sets of extensive in-situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the distribution of model fit for SWE has a clear dependency on elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet still process-based method to construct snow maps of high spatiotemporal resolution. It is especially well-suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

In search of operational snow model structures for the future – comparing four snow models for 17 catchments in Norway

2018 ◽  
Vol 49 (6) ◽  
pp. 1929-1945 ◽  
Author(s):  
Thomas Skaugen ◽  
Hanneke Luijting ◽  
Tuomo Saloranta ◽  
Dagrun Vikhamar-Schuler ◽  
Karsten Müller
Keyword(s):  
Energy Balance ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Threshold Temperature ◽  
Temperature Index ◽  
Ungauged Sites ◽  
Temperature And Precipitation ◽  
Snow Conditions ◽  
Model Structures

Abstract In order to use the best suited snow models to investigate snow conditions at ungauged sites and for a changed climate, we have tested four snow models for 17 catchments in Norway. The Crocus and seNorge models are gridded whereas the Distance Distribution Dynamics (DDD) model with its two versions, DDD_CX and DDD_EB, is catchment based. Crocus and DDD_EB use energy balance for estimating snowmelt and SeNorge and DDD_CX use temperature-index methods. SeNorge has calibrated the temperature-index against observed snowmelt, whereas DDD_CX has calibrated the temperature-index against runoff. The models use gridded temperature and precipitation at 1 h resolution for the period 2013–2016. Crocus needs additional forcing from a numerical weather prediction model, whereas DDD_EB calculates the energy-balance elements by using proxy models forced by temperature and precipitation. The threshold temperature for solid and liquid precipitation is common for all the models and equal to 0.5 °C. No corrections of precipitation or temperature are allowed. The snow simulations are validated against observed snow water equivalent (SWE) and against satellite derived snow covered area (SCA). SeNorge and DDD_EB perform best with respect to both SWE and SCA suggesting model structures suited for describing snow conditions at ungauged sites and for a changed climate.

Climate change decisive for Asia’s snow meltwater supply

2020 ◽  
Author(s):  
Philip Kraaijenbrink ◽  
Emmy Stigter ◽  
Tandong Yao ◽  
Walter Immerzeel
Keyword(s):  
Climate Change ◽  
Water Supply ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
River Basins ◽  
Large River ◽  
Future Climate Change ◽  
Seasonal Snow ◽  
Temperature And Precipitation ◽  
Snow Model

<p>Meltwater from seasonal snow provides a substantial amount of runoff to many of the rivers that originate in the high mountains of Asia, yet the importance of snow in the region as streamflow component, its changes over the past decades, and its sensitivity to future climatic changes are relatively unknown. To understand future changes in the water supply to the millions of people living downstream, a better understanding of snow dynamics at large scale is key. Using a novel snow model, forced by ERA5 climate reanalysis and calibrated by MODIS remote sensing observations, we generate daily snow water equivalent output at 0.05° resolution covering all major river basins in Asia. We show that between 1979 and 2018 significant and spatially variable changes have occurred in snow meltwater availability and its timing, with melt peaks attenuating and/or advancing in time, and snowmelt seasons shortening. Additionally, our results reveal that snowmelt is a much more important contributor to streamflow than glacier melt in many of Asia's large river basins. In a bottom-up elasticity analysis we project strong changes in snowmelt in the future under changing temperature and precipitation. Sensitivity of snowmelt to climate change varies among basins, however, and actual losses are strongly dependent on the degree of future climate change. Limiting climate change in the current century is therefore crucial in order to sustain the role of seasonal snow packs in Asia’s water supply.</p>

scholarly journals Simulating more accurate snow maps for Norway with MCMC parameter estimation method

2014 ◽  
Vol 8 (2) ◽  
pp. 1973-2003 ◽  
Author(s):  
T. M. Saloranta
Keyword(s):  
Snow Water Equivalent ◽  
Probability Distributions ◽  
Estimation Method ◽  
Model Performance ◽  
Simulation Method ◽  
Future Research ◽  
Model Parameters ◽  
Data Set ◽  
Mcmc Simulation ◽  
Snow Model

Abstract. The seNorge snow model produces daily updated maps (1 km × 1 km resolution) of snow conditions for Norway which are used by the national flood, avalanche and landslide forecasting services, among others. The snow model uses gridded observations of daily temperature and precipitation as its input forcing. In this paper the revisions made to the new seNorge snow model code (v.1.1.1) are described, and a systematic model analysis is performed by first revealing the most influential key parameters by the Extended FAST sensitivity analysis and then estimating their probability distributions by the MCMC simulation method, using 565 observations of snow water equivalent (SWE) and snow density (ρ). The MCMC simulation resulted in rather narrow posterior distributions for the four estimated model parameters, and enhanced the model performance and snow map quality significantly, mainly by removing the known significant overestimation biases in SWE and ρ. In the new model version (v.1.1.1) the Nash–Sutcliffe (NS) model performance values are now well positive (NS = 0.61 for SWE and NS = 0.30 for ρ), in contrast to the much lower negative NS-values of the previous model (v.1.1). Moreover, the model evaluation against approximately 400 000 point measurements of snow depth shows improvement in the simulated percentage of "good match"-stations (76–84% before April, and still 65% at the end of April). Future research efforts should focus on decreasing the variability in the model fit with observations (i.e. model precision) by further improvements in the seNorge snow model and its important fundament, the gridded meteorological input data set used as its forcing.

scholarly journals Assessing snow water equivalent of an Alpine catchment using snow dynamic model calibrated with satellite images

2008 ◽  
Vol 5 (6) ◽  
pp. 3129-3156
Author(s):  
C. Corbari ◽  
G. Ravazzani ◽  
J. Martinelli ◽  
M. Mancini
Keyword(s):  
Satellite Images ◽  
Snow Water Equivalent ◽  
Model Performance ◽  
Distributed Hydrological Model ◽  
Temperature Thresholds ◽  
Basin Scale ◽  
Snow Water ◽  
Snow Model ◽  
Height Data ◽  
Good Agreement

Abstract. This paper presents a simplified numerical model of snow dynamic implemented into a continuous distributed hydrological model for hydrograph simulations at basin scale. This snow model is based on air temperature thresholds that rule the snow melt and accumulation processes. A procedure to calibrate these temperature thresholds from NOAA satellite snow cover maps is discussed. We show that, for an accurate model calibration from satellite images, it is necessary to consider the presence of areas with complex topography such as mountain slopes. Snow model performance is tested both at local and basin scale on Alpine catchment. At local scale a good agreement between modelled snow dynamic and observed snow height data at snow gauge stations in the river Anza basin (Italy) is shown; at basin scale agreement between observed and simulated hydrographs at the discharge station of river Toce (Italy) is reported.

scholarly journals Snow Model Verification Using Ensemble Prediction and Operational Benchmarks

2008 ◽  
Vol 9 (6) ◽  
pp. 1402-1415 ◽  
Author(s):  
Kristie J. Franz ◽  
Terri S. Hogue ◽  
Soroosh Sorooshian
Keyword(s):  
Snow Water Equivalent ◽  
Initial Conditions ◽  
Model Performance ◽  
Hydrologic Model ◽  
Snow Accumulation ◽  
Forecast Skill ◽  
Model Verification ◽  
Ensemble Prediction ◽  
Snow Model ◽  
Rainfall Runoff Model

Abstract Hydrologic model evaluations have traditionally focused on measuring how closely the model can simulate various characteristics of historical observations. Although advancing hydrologic forecasting is an often-stated goal of numerous modeling studies, testing in a forecasting mode is seldom undertaken, limiting information derived from these analyses. One can overcome this limitation through generation, and subsequent analysis, of ensemble hindcasts. In this study, long-range ensemble hindcasts are generated for the available period of record for a basin in southwestern Idaho for the purpose of evaluating the Snow–Atmosphere–Soil Transfer (SAST) model against the current operational benchmark, the National Weather Service’s (NWS) snow accumulation and ablation model SNOW17. Both snow models were coupled with the NWS operational rainfall runoff model and ensembles of seasonal discharge and weekly snow water equivalent (SWE) were evaluated. Ensemble predictions from both the SAST and SNOW17 models were better than climatology forecasts, for the period studied. In most cases, the accuracy of the SAST-generated predictions was similar to the SNOW17-generated predictions, except during periods of significant melting. Differences in model performance are partially attributed to initial condition errors. After updating the SWE state in the snow models with the observed SWE, the forecasts were improved during the first 2–4 weeks of the forecast window and the skills were essentially equal in both forecasting systems for the study watershed. Climate dominated the forecast uncertainty in the latter part of the forecast window while initial conditions controlled the forecast skill in the first 3–4 weeks of the forecast. The use of hindcasting in the snow model analysis revealed that, given the dominance of the initial conditions on forecast skill, streamflow predictions will be most improved through the use of state updating.

scholarly journals How Does Availability of Meteorological Forcing Data Impact Physically Based Snowpack Simulations?*

2015 ◽  
Vol 17 (1) ◽  
pp. 99-120 ◽  
Author(s):  
Mark S. Raleigh ◽  
Ben Livneh ◽  
Karl Lapo ◽  
Jessica D. Lundquist
Keyword(s):  
Snow Water Equivalent ◽  
Longwave Radiation ◽  
Data Availability ◽  
Model Complexity ◽  
Shortwave Radiation ◽  
Meteorological Forcing ◽  
Temperature And Precipitation ◽  
Physically Based ◽  
Snow Model ◽  
The Impact

Abstract Physically based models facilitate understanding of seasonal snow processes but require meteorological forcing data beyond air temperature and precipitation (e.g., wind, humidity, shortwave radiation, and longwave radiation) that are typically unavailable at automatic weather stations (AWSs) and instead are often represented with empirical estimates. Research is needed to understand which forcings (after temperature and precipitation) would most benefit snow modeling through expanded observation or improved estimation techniques. Here, the impact of forcing data availability on snow model output is assessed with data-withholding experiments using 3-yr datasets at well-instrumented sites in four climates. The interplay between forcing availability and model complexity is examined among the Utah Energy Balance (UEB), the Distributed Hydrology Soil Vegetation Model (DHSVM) snow submodel, and the snow thermal model (SNTHERM). Sixty-four unique forcing scenarios were evaluated, with different assumptions regarding availability of hourly meteorological observations at each site. Modeled snow water equivalent (SWE) and snow surface temperature Tsurf diverged most often because of availability of longwave radiation, which is the least frequently measured forcing in cold regions in the western United States. Availability of longwave radiation (i.e., observed vs empirically estimated) caused maximum SWE differences up to 234 mm (57% of peak SWE), mean differences up to 6.2°C in Tsurf, and up to 32 days difference in snow disappearance timing. From a model data perspective, more common observations of longwave radiation at AWSs could benefit snow model development and applications, but other aspects (e.g., costs, site access, and maintenance) need consideration.

scholarly journals Seasonal and Ephemeral Snowpacks of the Conterminous United States

Hydrology ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 32
Author(s):  
Benjamin J. Hatchett
Keyword(s):  
United States ◽  
At Risk ◽  
New England ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Atlantic Coast ◽  
Seasonal Snow ◽  
Temperature And Precipitation ◽  
Lower Elevation ◽  
Precipitation Estimates

Snowpack seasonality in the conterminous United States (U.S.) is examined using a recently-released daily, 4 km spatial resolution gridded snow water equivalent and snow depth product developed by assimilating station-based observations and gridded temperature and precipitation estimates from PRISM. Seasonal snowpacks for the period spanning water years 1982–2017 were calculated using two established methods: (1) the classic Sturm approach that requires 60 days of snow cover with a peak depth >50 cm and (2) the snow seasonality metric (SSM) that only requires 60 days of continuous snow cover to define seasonal snow. The latter approach yields continuous values from −1 to +1, where −1 (+1) indicates an ephemeral (seasonal) snowpack. The SSM approach is novel in its ability to identify both seasonal and ephemeral snowpacks. Both approaches identify seasonal snowpacks in western U.S. mountains and the northern central and eastern U.S. The SSM approach identifies greater areas of seasonal snowpacks compared to the Sturm method, particularly in the Upper Midwest, New England, and the Intermountain West. This is a result of the relaxed depth constraint compared to the Sturm approach. Ephemeral snowpacks exist throughout lower elevation regions of the western U.S. and across a broad longitudinal swath centered near 35° N spanning the lee of the Rocky Mountains to the Atlantic coast. Because it lacks a depth constraint, the SSM approach may inform the location of shallow but long-duration snowpacks at risk of transitioning to ephemeral snowpacks with climatic change. A case study in Oregon during an extreme snow drought year (2014/2015) highlights seasonal to ephemeral snowpack transitions. Aggregating seasonal and ephemeral snowpacks to the HUC-8 watershed level in the western U.S. demonstrates the majority of watersheds are at risk of losing seasonal snow.

scholarly journals Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

scholarly journals An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models

2012 ◽  
Vol 4 (1) ◽  
pp. 13-21 ◽  
Author(s):  
S. Morin ◽  
Y. Lejeune ◽  
B. Lesaffre ◽  
J.-M. Panel ◽  
D. Poncet ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Model Performance ◽  
Short Wave ◽  
Wave Radiation ◽  
Density Profiles ◽  
The Public ◽  
Snow Water ◽  
Snowpack Properties

Abstract. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249) as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

Sign in / Sign up

Export Citation Format

Share Document