Combining snow water equivalent data from multiple sources to estimate spatio-temporal trends and compare measurement systems

2002 ◽  
Vol 7 (4) ◽  
pp. 536-557 ◽  
Author(s):  
Mary Kathryn Cowles ◽  
Dale L. Zimmerman ◽  
Aaron Christ ◽  
David L. McGinnis
Keyword(s):  
Snow Water Equivalent ◽  
Temporal Trends ◽  
Multiple Sources ◽  
Measurement Systems ◽  
Snow Water ◽  
Spatio Temporal

scholarly journals CrocO_v1.0 : a Particle Filter to assimilate snowpack observations in a spatialised framework

2020 ◽  
Author(s):  
Bertrand Cluzet ◽  
Matthieu Lafaysse ◽  
Emmanuel Cosme ◽  
Clément Albergel ◽  
Louis-François Meunier ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Near Infrared ◽  
Snow Water Equivalent ◽  
Sources Of Information ◽  
Average Improvement ◽  
Modelling Uncertainties ◽  
Snow Water ◽  
Spatio Temporal ◽  
Snowpack Properties

Abstract. Monitoring the evolution of the snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In-situ and remotely sensed observations provide precious information on the snowpack but usually offer a limited spatio-temporal coverage of bulk or surface variables only. In particular, visible-near infrared (VIS-NIR) reflectance observations can inform on the snowpack surface properties but are limited by terrain shading and clouds. Snowpack modelling enables to estimate any physical variable, virtually anywhere, but is affected by large errors and uncertainties. Data assimilation offers a way to combine both sources of information, and to propagate information from observed areas to non observed areas. Here, we present CrocO, (Crocus-Observations) an ensemble data assimilation system able to ingest any snowpack observation (applied as a first step to the height of snow (HS) and VIS-NIR reflectances) in a spatialised geometry. CrocO uses an ensemble of snowpack simulations to represent modelling uncertainties, and a Particle Filter (PF) to reduce them. The PF is known to collapse when assimilating a too large number of observations. Two variants of the PF were specifically implemented to ensure that observations information is propagated in space while tackling this issue. The global algorithm ingests all available observations with an iterative inflation of observation errors, while the klocal algorithm is a localised approach performing a selection of the observations to assimilate based on background correlation patterns. Experiments are carried out in a twin experiment setup, with synthetic observations of HS and VIS-NIR reflectances available in only a 1/6th of the simulation domain. Results show that compared against runs without assimilation, analyses exhibit an average improvement of snow water equivalent Continuous Rank Probability Score (CRPS) of 60 % when assimilating HS with a 40-member ensemble, and an average 20 % CRPS improvement when assimilating reflectance with a 160-member ensemble. Significant improvements are also obtained outside the observation domain. These promising results open a way for the assimilation of real observations of reflectance, or of any snowpack observations in a spatialised context.

scholarly journals Spatial and temporal variability of snow depth and SWE in a small mountain catchment

2010 ◽  
Vol 4 (1) ◽  
pp. 1-30 ◽  
Author(s):  
T. Grünewald ◽  
M. Schirmer ◽  
R. Mott ◽  
M. Lehning
Keyword(s):  
Temporal Variability ◽  
Snow Depth ◽  
Energy Transport ◽  
Snow Water Equivalent ◽  
Laser Scanner ◽  
Ablation Rate ◽  
Spatial And Temporal Variability ◽  
M Cell ◽  
Snow Water ◽  
Spatio Temporal

Abstract. The spatio-temporal variability of the mountain snow cover determines the avalanche danger, snow water storage, permafrost distribution and the local distribution of fauna and flora. Using a new type of terrestrial laser scanner (TLS), which is particularly suited for measurements of snow covered surfaces, snow depth, snow water equivalent (SWE) and melt rates have been monitored in a high alpine catchment during an ablation period. This allowed for the first time to get a high resolution (2.5 m cell size) picture of spatial variability and its temporal development. A very high variability in which maximum snow depths between 0–9 m at the end of the accumulation season was found. This variability decreased during the ablation phase, although the dominant snow deposition features remained intact. The spatial patterns of calculated SWE were found to be similar to snow depth. Average daily melt rate was between 15 mm/d at the beginning of the ablation period and 30 mm/d at the end. The spatial variation of melt rates increased during the ablation rate and could not be explained in a simple manner by geographical or meteorological parameters, which suggests significant lateral energy fluxes contributing to observed melt. It could be qualitatively shown that the effect of the lateral energy transport must increase as the fraction of snow free surfaces increases during the ablation period.

scholarly journals CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework

2021 ◽  
Vol 14 (3) ◽  
pp. 1595-1614
Author(s):  
Bertrand Cluzet ◽  
Matthieu Lafaysse ◽  
Emmanuel Cosme ◽  
Clément Albergel ◽  
Louis-François Meunier ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Near Infrared ◽  
Snow Water Equivalent ◽  
Sources Of Information ◽  
Average Improvement ◽  
Modelling Uncertainties ◽  
Snow Water ◽  
Spatio Temporal ◽  
Snowpack Properties

Abstract. Monitoring the evolution of snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In situ and remotely sensed observations provide precious information on the state of the snowpack but usually offer limited spatio-temporal coverage of bulk or surface variables only. In particular, visible–near-infrared (Vis–NIR) reflectance observations can provide information about the snowpack surface properties but are limited by terrain shading and clouds. Snowpack modelling enables the estimation of any physical variable virtually anywhere, but it is affected by large errors and uncertainties. Data assimilation offers a way to combine both sources of information and to propagate information from observed areas to non-observed areas. Here, we present CrocO (Crocus-Observations), an ensemble data assimilation system able to ingest any snowpack observation (applied as a first step to the height of snow (HS) and Vis–NIR reflectances) in a spatialised geometry. CrocO uses an ensemble of snowpack simulations to represent modelling uncertainties and a particle filter (PF) to reduce them. The PF is prone to collapse when assimilating too many observations. Two variants of the PF were specifically implemented to ensure that observational information is propagated in space while tackling this issue. The global algorithm ingests all available observations with an iterative inflation of observation errors, while the klocal algorithm is a localised approach performing a selection of the observations to assimilate based on background correlation patterns. Feasibility testing experiments are carried out in an identical twin experiment setup, with synthetic observations of HS and Vis–NIR reflectances available in only one-sixth of the simulation domain. Results show that compared against runs without assimilation, analyses exhibit an average improvement of the snow water equivalent continuous rank probability score (CRPS) of 60 % when assimilating HS with a 40-member ensemble and an average 20 % CRPS improvement when assimilating reflectance with a 160-member ensemble. Significant improvements are also obtained outside the observation domain. These promising results open a possibility for the assimilation of real observations of reflectance or of any snowpack observations in a spatialised context.

scholarly journals Spatio-temporal trends in the hydroclimate of Turkey for the last decades based on two reanalysis datasets

2016 ◽  
Author(s):  
Mustafa Gokmen
Keyword(s):  
Snow Water Equivalent ◽  
Water Cycle ◽  
Eastern Mediterranean ◽  
Regional Scale ◽  
Temporal Trends ◽  
Aegean Sea ◽  
Rain Gauge ◽  
Observation Data ◽  
Spatiotemporal Trends ◽  
Snow Water

Abstract. We present a regional assessment of the spatiotemporal trends in several hydro-climate variables from 1979 to 2010 in Turkey, one of the vulnerable countries of the Eastern Mediterranean to climate change, using the two reanalysis products of ECMWF: ERA-Interim and ERA-Interim/Land, namely. The trend analysis revealed that an average warming of 1.26 °C occurred in Turkey from 1979 to 2010, with high confidence intervals (95 to 99 %) mostly. Geographically, the largest warming (up to 1.8 °C) occurred in the Western coastal areas next to Aegean Sea and in the South-East regions. The increasing trend of air temperature was confirmed by the comparisons with the measurements from several meteorological stations. With respect to the regional trends in hydrological variables, ERA-Interim and ERA-Interim/Land revealed quite different pictures: the ERA-Interim dataset indicated that there have been significant decreasing trends of precipitation, snow water equivalent and runoff in some parts of inner/South-eastern Anatolia (up to 250 mm decrease totally in the upstream of Euphrates, Kizilirmak and Seyhan basins), while ERA-Interim/Land showed none or minor trends in the same areas. Comparison of the precipitation trends by the two datasets with some rain gauge data distributed over Turkey revealed that none of the products is consistently closer to the observations. Based on the trend assessment of the hydrological trends by the two datasets and the comparisons with the observation data and other trend studies in the study area we can conclude that, except for some evapotranspiration trends over Mediterranean and Black Sea, there have not been clear and considerable trends of precipitation, snow water equivalent and runoff quantities over Turkey from 1979 to 2010, despite the considerable warming for the same period throughout the country. In this respect, we can suggest that, the impacts of global warming on the water cycle are rather unpredictable especially at regional scale.

scholarly journals Estimating relationships between snow water equivalent, snow covered area, and topography to extend the Airborne Snow Observatory dataset

2017 ◽  
Author(s):  
Dominik Schneider ◽  
Noah P. Molotch ◽  
Jeffrey S. Deems
Keyword(s):  
Resource Management ◽  
Water Resource Management ◽  
Satellite Images ◽  
Snow Water Equivalent ◽  
Spatial And Temporal Patterns ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
Spatio Temporal ◽  
To Come

Abstract. A new spatio-temporal dataset from the ongoing Airborne Snow Observatory (ASO) provides an unprecedented look at the spatial and temporal patterns of snow water equivalent (SWE) over multiple years in the Tuolumne Basin, California, USA. We found that fractional snow covered area (fSCA) significantly improves our ability to model the distribution of SWE based on relationships between SWE, fSCA, and topography. Further, the broad availability of satellite images of fSCA facilitates the transfer of these relationship to different years with minimal degradation in performance (r2 = 0.85, % MAE = 33 %, % Bias = 1 %) compared with models fit on the same day, by considering variations in SWE depth as expressed by differences in fSCA between years. The crux of this proposition is in selecting the model to transfer. We offer a method with which to select a model from another year based on the similarity in SWE distribution at existing snow pillows in the area. Comparison of the best transferred predictions and the selected predictions results in a mild decrease in r2 (0.02) and moderate increases in % MAE (15 %) and % Bias (10 %). The results motivate further refinement in the technique used to select the best model because if these dates can be identified then SWE can be modeled at accuracy levels equivalent to models generated from ASO data collected on the day of interest. Lastly, we found that models from ASO observations of anomalously low snowpacks in 2015 still transferred to other years, although the same cannot be said for the reverse. This research provides a first attempt at extending the value of ASO beyond the observations and we expect ASO will continue to provide insights for improving water resource management for years to come.

scholarly journals Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering

2019 ◽  
Author(s):  
Joel Fiddes ◽  
Kristoffer Aalstad ◽  
Sebastian Westermann
Keyword(s):  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Mass Movement ◽  
Computational Cost ◽  
Temporal Scales ◽  
Future Evolution ◽  
Spatial And Temporal Scales ◽  
Snow Water ◽  
Spatio Temporal ◽  
New Processing

Abstract. Spatial variability in high-relief landscapes is immense, and grid-based models cannot be run at spatial resolutions to explicitly represent important physical processes. This hampers the assessment of the current and future evolution of important issues such as water availability or mass movement hazards. Here, we present a new processing chain that couples an efficient subgrid method with a downscaling tool and data assimilation method with the purpose to improve numerical simulation of surface processes at multiple spatial and temporal scales in ungauged basins. The novelty of the approach is that while we add 1–2 orders of magnitude of computational cost by ensemble simulations, we save 4–5 orders of magnitude over explicitly simulating a high-resolution grid. This approach makes data assimilation at large spatio-temporal scales feasible. In addition, this approach utilises only freely available global datasets and is therefore able to run globally. We demonstrate marked improvements in estimating snow height and snow water equivalent at various experimental scales using this approach. We propose this as a suitable method for a wide variety of operational and research applications where surface models need to be run at large scales with sparse to non-existent ground observations and with the flexibility to assimilate diverse variables retrieved by EO missions.

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