Homogenisation of a gridded snow water equivalent climatology for Alpine terrain: methodology and applications

Abstract. Gridded snow water equivalent (SWE) data sets are valuable for estimating the snow water resources and verify different model systems, e.g. hydrological, land surface or atmospheric models. However, changing data availability represents a considerable challenge when trying to derive consistent time series for SWE products. In an attempt to improve the product consistency, we first evaluated the differences between two climatologies of SWE grids that were calculated on the basis of data from 110 and 203 stations, respectively. The "shorter" climatology (2001–2009) was produced using 203 stations (map203) and the "longer" one (1971–2009) 110 stations (map110). Relative to map203, map110 underestimated SWE, especially at higher elevations and at the end of the winter season. We tested the potential of quantile mapping to compensate for mapping errors in map110 relative to map203. During a 9 yr calibration period from 2001 to 2009, for which both map203 and map110 were available, the method could successfully refine the spatial and temporal SWE representation in map110 by making seasonal, regional and altitude-related distinctions. Expanding the calibration to the full 39 yr showed that the general underestimation of map110 with respect to map203 could be removed for the whole winter. The calibrated SWE maps fitted the reference (map203) well when averaged over regions and time periods, where the mean error is approximately zero. However, deviations between the calibrated maps and map203 were observed at single grid cells and years. When we looked at three different regions in more detail, we found that the calibration had the largest effect in the region with the highest proportion of catchment areas above 2000 m a.s.l. and that the general underestimation of map110 compared to map203 could be removed for the entire snow season. The added value of the calibrated SWE climatology is illustrated with practical examples: the verification of a hydrological model, the estimation of snow resource anomalies and the predictability of runoff through SWE.

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A statistical approach to refining snow water equivalent climatologies in Alpine terrain

The Cryosphere Discussions ◽

10.5194/tcd-7-4241-2013 ◽

2013 ◽

Vol 7 (4) ◽

pp. 4241-4286 ◽

Cited By ~ 1

Author(s):

S. Jörg-Hess ◽

F. Fundel ◽

T. Jonas ◽

M. Zappa

Keyword(s):

Statistical Approach ◽

Snow Water Equivalent ◽

Winter Season ◽

Data Availability ◽

Added Value ◽

Hydrological Models ◽

Quantile Mapping ◽

Catchment Areas ◽

Snow Water ◽

The Mean

Abstract. Gridded snow water equivalent (SWE) are valuable to estimate the snow water resources and verify hydrological models and other models that consider snow as a component of the natural system. However, changing data availability represents a considerable challenge when trying to derive consistent time series for SWE products. In an attempt to improve the product consistency, we first evaluated the differences between two climatologies of SWE grids that were calculated on the basis of data from 110 and 203 stations, respectively. The "shorter" climatology (2001–2009) was produced using 203 stations (map203) and the "longer" one (1971–2009) 110 stations (map110). Relative to map203, map110 underestimated SWE, especially at higher elevations and at the end of the winter season. We tested the potential of quantile mapping to compensate for mapping errors in map110 relative to map203. During a nine-year calibration period from 2001–2009, for which both map203 and map110 were available, the method could successfully refine the spatial and temporal SWE representation in map110 by making seasonal, regional and altitude-related distinctions. Expanding the calibration to the full 39 yr showed that the general underestimation of map110 could be removed for the whole winter. The calibrated SWE maps were accurate when averaged over regions and time periods, where the mean error is approximately zero. However, deviations were observed at single grid cells and years. When we looked at three different regions in more detail, we found that the calibration had the largest effect in the region with the highest proportion of catchment areas above 2000 m a.s.l. and that the general underestimation of map110 could be removed for the entire snow season. The added value of the calibrated SWE climatology is illustrated with practical examples: the verification of a hydrological model, the estimation of snow resource anomalies and the predictability of runoff through SWE.

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Predicting natural streamflows in regulated snowmelt-driven watersheds using regionalization methods

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-9435-2013 ◽

2013 ◽

Vol 10 (7) ◽

pp. 9435-9476

Author(s):

D. Kim ◽

J. Kaluarachchi

Keyword(s):

Snow Water Equivalent ◽

Precipitation Index ◽

Data Availability ◽

Data Sets ◽

Tank Model ◽

Natural Flow ◽

Snow Water ◽

Minimal Data ◽

Canal Networks ◽

Ungauged Watersheds

Abstract. Predicting streamflows in snow-fed watersheds in the Western United States is important for water allocation. Since many of these watersheds are heavily regulated through canal networks and reservoirs, predicting expected natural flows and therefore water availability under limited data is always a challenge. This study investigates the applicability of the flow duration curve (FDC) method for predicting natural flows in gauged and ungauged snow-fed watersheds. Point snow observations, air temperature, precipitation, and snow water equivalent, are used to simulate snowmelt process with SNOW-17 model and extended to streamflow generation by a FDC method with modified current precipitation index. For regulated (ungauged) watersheds, a parametric regional FDC method is applied to reconstruct natural flow. For comparison, a simplified Tank Model is used as well. The proximity regionalization method is used to generate streamflow using the Tank Model in ungauged watersheds. The results show that the FDC method can produce acceptable natural flow estimates in both gauged and ungauged watersheds under data limited conditions. The performance of the FDC method is better in watersheds with relatively low evapotranspiration (ET). Multiple donor data sets including current precipitation index are recommended to reduce uncertainty of the regional FDC method for ungauged watersheds. In spite of its simplicity, the FDC method can perform better than the Tank Model under minimal data availability.

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Nimbus-7 SMMR Derived Global Snow Cover Parameters

Annals of Glaciology ◽

10.1017/s0260305500200736 ◽

1987 ◽

Vol 9 ◽

pp. 39-44 ◽

Cited By ~ 200

Author(s):

A.T.C. Chang ◽

J.L. Foster ◽

D.K. Hall

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Theoretical Calculations ◽

Winter Season ◽

Western China ◽

High Plains ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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On the sources of global land surface hydrologic predictability

Hydrology and Earth System Sciences ◽

10.5194/hess-17-2781-2013 ◽

2013 ◽

Vol 17 (7) ◽

pp. 2781-2796 ◽

Cited By ~ 68

Author(s):

S. Shukla ◽

J. Sheffield ◽

E. F. Wood ◽

D. P. Lettenmaier

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Snow Water Equivalent ◽

Forecast Skill ◽

Streamflow Prediction ◽

Infiltration Capacity ◽

Forecast Period ◽

Data Set ◽

Model Based ◽

Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

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Nimbus-7 SMMR Derived Global Snow Cover Parameters

Annals of Glaciology ◽

10.3189/s0260305500200736 ◽

1987 ◽

Vol 9 ◽

pp. 39-44 ◽

Cited By ~ 85

Author(s):

A.T.C. Chang ◽

J.L. Foster ◽

D.K. Hall

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Theoretical Calculations ◽

Winter Season ◽

Western China ◽

High Plains ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Snow covers about 40 million km2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

10.5194/essd-2021-160 ◽

2021 ◽

Author(s):

Vincent Vionnet ◽

Colleen Mortimer ◽

Mike Brady ◽

Louise Arnal ◽

Ross Brown

Keyword(s):

Newfoundland And Labrador ◽

Land Surface ◽

Snow Water Equivalent ◽

Water Security ◽

Monitoring And Evaluation ◽

Surface Models ◽

Snow Water ◽

The Government ◽

Different Sources

Abstract. In situ measurements of snow water equivalent (SWE) – the depth of water that would be produced if all the snow melted – are used in many applications including water management, flood forecasting, climate monitoring, and evaluation of hydrological and land surface models. The Canadian historical SWE dataset (CanSWE) combines manual and automated pan-Canadian SWE observations collected by national, provincial and territorial agencies as well as hydropower companies. Snow depth and derived bulk snow density are also included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations, and quality control records. The CanSWE dataset was compiled from 15 different sources and includes SWE information for all provinces and territories that measure SWE. Data were updated to July 2020 and new historical data from the Government of Northwest Territories, Government of Newfoundland and Labrador, Saskatchewan Water Security Agency, and Hydro Quebec were included. CanSWE includes over one million SWE measurements from 2607 different locations across Canada over the period 1928–2020. It is publicly available at https://doi.org/10.5281/zenodo.4734372 (Vionnet et al., 2021).

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Snowpack energy balance and changes in the frequency and extremity of rain-on-snow events in the warming climate

10.5194/egusphere-egu21-73 ◽

2021 ◽

Author(s):

Ondrej Hotovy ◽

Michal Jenicek

Keyword(s):

Energy Balance ◽

Air Temperature ◽

Snow Water Equivalent ◽

Hydrological Cycle ◽

Winter Season ◽

Catchment Scale ◽

The Future ◽

Snow Water ◽

The Impact ◽

Catchment Runoff

Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. Changes in the precipitation distribution and intensity, as well as a shift from snowfall to rain is expected in the future due to climate changes. As a result, rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur more frequently. Heat from liquid precipitation constitutes one of the snowpack energy balance components. Consequently, snowmelt and runoff may be strongly affected by these temperature and precipitation changes.The objective of this study is 1) to evaluate the frequency, inter-annual variability and extremity of rain-on-snow events in the past based on existing measurements together with an analysis of changes in the snowpack energy balance, and 2) to simulate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected 40 near-natural mountain catchments in Czechia with significant snow influence on runoff and with available long-time series (>35 years) of daily hydrological and meteorological variables. A semi-distributed conceptual model, HBV-light, was used to simulate the individual components of the water cycle at a catchment scale. The model was calibrated for each of study catchments by using 100 calibration trials which resulted in respective number of optimized parameter sets. The model performance was evaluated against observed runoff and snow water equivalent. Rain-on-snow events definition by threshold values for air temperature, snow depth, rain intensity and snow water equivalent decrease allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1965-2019 and to explain the role of different catchment attributes.The preliminary results show that a significant change of rain-on-snow events related to increasing air temperature is not clearly evident. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence during winter season due to a decrease in snowfall fraction. In contrast, a decrease in total number of events was observed due to the shortening of the period with existing snow cover on the ground. Modelling approach also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.

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Snow as a source of predictability in seasonal forecasts?

10.5194/egusphere-egu21-7331 ◽

2021 ◽

Author(s):

Danny Risto ◽

Bodo Ahrens ◽

Kristina Fröhlich

Keyword(s):

Latent Heat ◽

Land Surface ◽

Snow Water Equivalent ◽

Reanalysis Data ◽

Heat Fluxes ◽

Seasonal Forecasting ◽

Seasonal Forecasts ◽

Predictive Skill ◽

Temperature Forecast ◽

Snow Water

Besides the ocean, the land surface is a crucial component for predictability at (sub-)seasonal time scales. While the prediction of 2m temperature up to several months is possible for some maritime regions, continental regions lack predictive skill. Improved representation of the land surface in seasonal forecasting systems could help to close this gap. Snow cover fraction and snow water equivalent (SWE) are essential properties of the land surface. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) and melting snow cools the surface air and contributes to soil moisture. First, we analyse the dynamical relationships between snow, 2m temperature and sensible/latent heat fluxes in reanalysis data in the northern hemisphere. Then we investigate whether these relationships are also present in operational seasonal forecast models provided by Copernicus Climate Change Service (C3S). First results show that the quality of the 2m temperature forecast over continental regions drops sharply after the first forecasted month, whereas anomalies in snow water equivalent can be predicted up to several months. Forecasted anomalies in sensible and latent heat fluxes of continental land surfaces show predictive skill during winter and spring only locally in some places, which reduces potential interactions between snow/land surface and the atmosphere in the models. The goal of this ongoing work is to assess the importance of snow initialisation and parameterisation for seasonal forecasting.

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Evaluation of CLASS Snow Simulation over Eastern Canada

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0153.1 ◽

2017 ◽

Vol 18 (5) ◽

pp. 1205-1225 ◽

Cited By ~ 19

Author(s):

Diana Verseghy ◽

Ross Brown ◽

Libo Wang

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Water Equivalent ◽

Climate Model ◽

Regional Climate ◽

Eastern Canada ◽

Fractional Snow Cover ◽

Forest Regions ◽

Snow Water ◽

Snow Model

Abstract The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990–2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to −0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.

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Large scale snow water equivalent status monitoring: comparison of different snow water products in the upper Colorado Basin

Hydrology and Earth System Sciences ◽

10.5194/hess-17-5127-2013 ◽

2013 ◽

Vol 17 (12) ◽

pp. 5127-5139 ◽

Cited By ~ 6

Author(s):

G. A. Artan ◽

J. P. Verdin ◽

R. Lietzow

Keyword(s):

Energy Balance ◽

Water Content ◽

Snow Water Equivalent ◽

Weather Forecast ◽

Snow Accumulation ◽

Passive Microwave ◽

Data Sets ◽

Snow Water ◽

Trmm Precipitation ◽

Colorado Basin

Abstract. We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS (r = 0.94 and r = 0.64; d.f. = 14 – d.f. = degrees of freedom) and SNOTEL (r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE (r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values (r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the TRMM precipitation data were found to be unreliable sources for mapping SWE in the study area; both data sets lacked discernible variability of snow water content between sites as seen in the SNOTEL and SNODAS SWE data. This study will contribute to better understanding the adequacy of data from weather forecast models, TRMM, and microwave imagery for monitoring status of the snow water content.

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