Improving Runoff Simulations using Satellite-observed Time-series of Snow Covered Area

2003 ◽  
Vol 34 (4) ◽  
pp. 281-294 ◽  
Author(s):  
R.V. Engeset ◽  
H-C. Udnæs ◽  
T. Guneriussen ◽  
H. Koren ◽  
E. Malnes ◽  
...  
Keyword(s):  
Time Series ◽  
Radar Sensors ◽  
Noaa Avhrr ◽  
Runoff Forecasting ◽  
Snow Covered Area ◽  
Covered Area ◽  
Satellite Sensors ◽  
Major Floods ◽  
Using Data ◽  
Southern Norway

Snowmelt can be a significant contributor to major floods, and hence updated snow information is very important to flood forecasting services. This study assesses whether operational runoff simulations could be improved by applying satellite-derived snow covered area (SCA) from both optical and radar sensors. Currently the HBV model is used for runoff forecasting in Norway, and satellite-observed SCA is used qualitatively but not directly in the model. Three catchments in southern Norway are studied using data from 1995 to 2002. The results show that satellite-observed SCA can be used to detect when the models do not simulate the snow reservoir correctly. Detecting errors early in the snowmelt season will help the forecasting services to update and correct the models before possible damaging floods. The method requires model calibration against SCA as well as runoff. Time-series from the satellite sensors NOAA AVHRR and ERS SAR are used. Of these, AVHRR shows good correlation with the simulated SCA, and SAR less so. Comparison of simultaneous data from AVHRR, SAR and Landsat ETM+ for May 2000 shows good inter-correlation. Of a total satellite-observed area of 1,088 km2, AVHRR observed a SCA of 823 km2 and SAR 720 km2, as compared to 889 km2 using ETM+.

scholarly journals Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

2017 ◽  
Author(s):  
Hanneke Luijting ◽  
Dagrun Vikhamar-Schuler ◽  
Trygve Aspelien ◽  
Mariken Homleid
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Water Resource Management ◽  
Snow Accumulation ◽  
Surface Model ◽  
Snow Melt ◽  
Grid Spacing ◽  
Snow Covered Area ◽  
Covered Area ◽  
Southern Norway

Abstract. In Norway, thirty percent of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of approximately 1 km over an area in southern Norway for two years (01 September 2014–31 August 2016), using two different forcing data sets: 1) hourly meteorological forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing), and 2) gridded hourly observations of temperature and precipitation (1 km grid spacing) in combination with the meteorological forecasts from AROME MetCoOp. We present an evaluation of the modeled snow depth and snow cover, as compared to point observations of snow depth and to MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snow melt. The results are promising. Both experiments are capable of simulating the snow pack over the two winter seasons, but there is an overestimation of snow depth when using only meteorological forecasts from AROME MetCoOp, although the snow-covered area throughout the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season, showing that assimilation of snow depth observations into SURFEX/Crocus might be necessary when using only meteorological forecasts as forcing. When using gridded observations, the simulation of snow depth is significantly improved, which shows that using a combination of gridded observations and meteorological forecasts to force a snowpack model is very useful and can give better results than only using meteorological forecasts. There is however an underestimation of snow ablation in both experiments. This is mainly due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snow melt and biases in the forcing data.

scholarly journals Ensemble-based assimilation of fractional snow covered area satellite retrievals to estimate snow distribution at a high Arctic site

2017 ◽  
Author(s):  
Kristoffer Aalstad ◽  
Sebastian Westermann ◽  
Thomas Vikhamar Schuler ◽  
Julia Boike ◽  
Laurent Bertino
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Coefficient Of Variation ◽  
Snow Water Equivalent ◽  
Mean Squared Error ◽  
High Arctic ◽  
Sensing Applications ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Model

Abstract. Snow, with high albedo, low thermal conductivity and large water holding capacity strongly modulates the surface energy and water balance, thus making it a critical factor in high-latitude and mountain environments. At the same time, already at medium spatial resolutions of 1 km, estimating the average and subgrid variability of the snow water equivalent (SWE) is challenging in remote sensing applications. In this study, we demonstrate an ensemble-based data assimilation scheme to estimate peak SWE distributions at such scales from a simple snow model driven by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (that is accessible from satellite products) to pre-melt SWE, while at the same time obtaining the subgrid scale distribution. Subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fractional snow covered area (fSCA) by means of the snow model. Assimilation of satellite-derived fSCA hence facilitates the constrained estimation of the average SWE and coefficient of variation, while taking into account uncertainties in both the model and assimilated data sets. Our method makes use of the ensemble-smoother with multiple data assimilation (ES-MDA) combined with analytical Gaussian anamorphosis to assimilate time series of MODIS and Sentinel-2 fSCA retrievals. The scheme is applied to high-Arctic sites near Ny Ålesund (79° N, Svalbard, Norway) where in-situ observations of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak subgrid SWE distributions on most of the occasions considered. Through the ES-MDA assimilation, the root mean squared error (RMSE) for the fSCA, peak mean SWE and subgrid coefficient of variation is improved by around 75 %, 60 % and 20 % respectively when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13 respectively. By comparing the performance of the ES-MDA to that of other ensemble-based batch smoother schemes, it was found that the ES-MDA either outperforms or at least nearly matches the performance of the other schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

scholarly journals Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

2018 ◽  
Vol 12 (1) ◽  
pp. 247-270 ◽  
Author(s):  
Kristoffer Aalstad ◽  
Sebastian Westermann ◽  
Thomas Vikhamar Schuler ◽  
Julia Boike ◽  
Laurent Bertino
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Critical Factor ◽  
Field Measurements ◽  
Sensing Applications ◽  
Snow Covered Area ◽  
Covered Area ◽  
Satellite Retrievals ◽  
Snow Model

Abstract. With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

scholarly journals Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

2018 ◽  
Vol 12 (6) ◽  
pp. 2123-2145 ◽  
Author(s):  
Hanneke Luijting ◽  
Dagrun Vikhamar-Schuler ◽  
Trygve Aspelien ◽  
Åsmund Bakketun ◽  
Mariken Homleid
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Surface Model ◽  
Observation Data ◽  
Grid Spacing ◽  
Data Set ◽  
Snow Covered Area ◽  
Covered Area ◽  
Southern Norway

Abstract. In Norway, 30 % of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of 1 km over an area in southern Norway for 2 years (1 September 2014–31 August 2016). Experiments were carried out using two different forcing data sets: (1) hourly forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing) including post-processed temperature (500 m grid spacing) and wind, and (2) gridded hourly observations of temperature and precipitation (1 km grid spacing) combined with meteorological forecasts from AROME MetCoOp for the remaining weather variables required by SURFEX/Crocus. We present an evaluation of the modelled snow depth and snow cover in comparison to 30 point observations of snow depth and MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snowmelt. Both experiments are capable of simulating the snowpack over the two winter seasons, but there is an overestimation of snow depth when using meteorological forecasts from AROME MetCoOp (bias of 20 cm and RMSE of 56 cm), although the snow-covered area in the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season. When using gridded observations, the simulation of snow depth is significantly improved (the bias for this experiment is 7 cm and RMSE 28 cm), but the spatial snow cover distribution is not well captured during the melting season. Underestimation of snow depth at high elevations (due to the low elevation bias in the gridded observation data set) likely causes the snow cover to decrease too soon during the melt season, leading to unrealistically little snow by the end of the season. Our results show that forcing data consisting of post-processed NWP data (observations assimilated into the raw NWP weather predictions) are most promising for snow simulations, when larger regions are evaluated. Post-processed NWP data provide a more representative spatial representation for both high mountains and lowlands, compared to interpolated observations. There is, however, an underestimation of snow ablation in both experiments. This is generally due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snowmelt and biases in the forcing data.

scholarly journals Remote Sensing of Snow in High Mountain Basins in Norway

1985 ◽  
Vol 6 ◽  
pp. 250-251 ◽  
Author(s):  
T. Andersen ◽  
N. Haakensen
Keyword(s):  
High Mountain ◽  
Snow Melt ◽  
Mountain River ◽  
Snow Storage ◽  
Run Off ◽  
Snow Covered Area ◽  
Covered Area ◽  
Vital Interest ◽  
Snow Conditions ◽  
Southern Norway

Information on snow conditions in high mountain river basins is of vital interest for flood predictions and power production. Based on techniques derived for mapping of snow cover from digital NOAA-data, relations are established between snow covered area and remaining snow storage for three basins in southern Norway. Together with estimates of the precipitation and information on maximum accumulated snow, the relation can be useful in run-off predictions for the snow-melt period.

scholarly journals Adaptation to climate change in Kazakhstan using data from space monitoring of snowmelt

2020 ◽  
Vol 223 ◽  
pp. 03006
Author(s):  
Aknur Zholdasbek ◽  
Azamat Kauazov
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Temporal Distribution ◽  
Snow Melting ◽  
Adaptation To Climate Change ◽  
Snow Covered Area ◽  
Covered Area ◽  
Using Data ◽  
Seasonal Snow Cover

The present article is concerned with the applied aspects of applying the results of space monitoring of snow cover, in particular, it is proposed to present the results of the analysis in the form of specialized bulletins. The purpose of this publication is to present the available results of space monitoring of snow cover in Kazakhstan as an element of adaptation to climate change. A three-level system of space monitoring of snow cover is proposed, which includes three technological complexes: operational mapping of snow cover boundaries; monitoring of seasonal snow cover dynamics; analysis of long-term snow cover dynamics. A map of snow melting in Kazakhstan in 2020, the dynamics of long-term changes of snow covered area, as well as methods for analyzing the spatial- temporal distribution of snow cover and formats of special bulletins are presented. It is most appropriate to present the results of space monitoring of snow cover in a complex, maximally generalized form (product). The results of the work can be applied in the scientific, industrial and educational spheres to adapt and increase resistance.

scholarly journals Remote Sensing of Snow in High Mountain Basins in Norway

1985 ◽  
Vol 6 ◽  
pp. 250-251
Author(s):  
T. Andersen ◽  
N. Haakensen
Keyword(s):  
High Mountain ◽  
Snow Melt ◽  
Mountain River ◽  
Snow Storage ◽  
Run Off ◽  
Snow Covered Area ◽  
Covered Area ◽  
Vital Interest ◽  
Snow Conditions ◽  
Southern Norway

Information on snow conditions in high mountain river basins is of vital interest for flood predictions and power production. Based on techniques derived for mapping of snow cover from digital NOAA-data, relations are established between snow covered area and remaining snow storage for three basins in southern Norway. Together with estimates of the precipitation and information on maximum accumulated snow, the relation can be useful in run-off predictions for the snow-melt period.

scholarly journals Dynamical properties of the spatial distribution of snow

2003 ◽  
Vol 7 (5) ◽  
pp. 744-753 ◽  
Author(s):  
T. Skaugen ◽  
S. Beldring ◽  
H.-C. Udnæs
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Snow Water Equivalent ◽  
Atmospheric Model ◽  
Skewed Distribution ◽  
Temperature Fields ◽  
Simulation Exercise ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Abstract. A simulation exercise has been performed to study the temporal development of snow covered area and the spatial distribution of snow-water equivalent (SWE). Special consideration has been paid to how the properties of the spatial statistical distribution of SWE change as a response to accumulation and ablation events. A distributed rainfall-runoff model at resolution 1 x 1 km2 has been run with time series of precipitation and temperature fields of the same spatial resolution derived from the atmospheric model HIRLAM. The precipitation fields are disaggregated and the temperature fields are interpolated. Time series of the spatial distribution of snow-water equivalent and snow-covered area for three seasons for a catchment in Norway is generated. The catchment is of size 3085 km2 and two rectangular sub-areas of 484 km2 are located within the larger catchment. The results show that the shape of the spatial distribution of SWE for all three areas changes during winter. The distribution is very skewed at the start of the accumulation season but then the skew decreases and, as the ablation season sets in, the spatial distribution again becomes more skewed with a maximum near the end of the ablation season. For one of the sub-areas, a consistently more skewed distribution of SWE is found, related to higher variability in precipitation. This indicates that observed differences in the spatial distribution of snow between alpine and forested areas can result from differences in the spatial variability of precipitation. The results obtained from the simulation exercise are consistent with modelling the spatial distribution of SWE as summations of a gamma distributed variable. Keywords: Snow, SWE, spatial distribution, simulated hydrometeorological fields

Estimation of snow covered area for an urban catchment using image processing and neural networks

2003 ◽  
Vol 48 (9) ◽  
pp. 155-164 ◽  
Author(s):  
B.V. Matheussen ◽  
S.T. Thorolfsson
Keyword(s):  
Image Processing ◽  
Time Series ◽  
Snow Cover ◽  
Winter Season ◽  
Digital Camera ◽  
Urban Catchment ◽  
Aerial Photos ◽  
Snow Covered Area ◽  
Covered Area ◽  
Urban Catchments

This paper presents a method to estimate the snow covered area (SCA) for small urban catchments. The method uses images taken with a digital camera positioned on top of a tall building. The camera is stationary and takes overview images of the same area every fifteen minutes throughout the winter season. The images were read into an image-processing program and a three-layered feed-forward perceptron artificial neural network (ANN) was used to calculate fractional snow cover within three different land cover types (road, park and roofs). The SCA was estimated from the number of pixels with snow cover relative to the total number of pixels. The method was tested for a small urban catchment, Risvollan in Trondheim, Norway. A time series of images taken during spring of 2001 and the 2001-2002 winter season was used to generate a time series of SCA. Snow covered area was also estimated from aerial photos. The results showed a strong correlation between SCA estimated from the digital camera and the aerial photos. The time series of SCA can be used for verification of urban snowmelt models.

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