Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Griessinger, Nena; Seibert, Jan; Magnusson, Jan; Jonas, Tobias

doi:10.5194/hess-20-3895-2016

Cited by 64 publications

(78 citation statements)

References 32 publications

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“…We use the temperature index model in this study as it needs minimal data input and is easy to implement. In fact, our results demonstrate that the temperature index model performs fairly well, consistent with some other studies [49,50]. The model performance for estimating peak surface runoff Q runoff is given in Table 2 and Figure 5B.…”

Section: Resultssupporting

confidence: 77%

Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations

2017

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Abstract:Flooding is projected to increase with climate change in many parts of the world. Floods in cold regions are commonly a result of snowmelt during the spring break-up. The peak river flow (Q peak ) for the Mackenzie River, located in northwest Canada, is modelled using the Gravity Recovery and Climate Experiment (GRACE) satellite observations. Compared with the observed Q peak at a downstream hydrometric station, the model results have a correlation coefficient of 0.83 (p < 0.001) and a mean absolute error of 6.5% of the mean observed value of 28,400 m 3 ·s −1 for the 12 study years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014). The results are compared with those for other basins to examine the difference in the major factors controlling the Q peak . It was found that the temperature variations in the snowmelt season are the principal driver for the Q peak in the Mackenzie River. In contrast, the variations in snow accumulation play a more important role in the Q peak for warmer southern basins in Canada. The study provides a GRACE-based approach for basin-scale snow mass estimation, which is largely independent of in situ observations and eliminates the limitations and uncertainties with traditional snow measurements. Snow mass estimated from the GRACE data was about 20% higher than that from the Global Land Data Assimilation System (GLDAS) datasets. The model is relatively simple and only needs GRACE and temperature data for flood forecasting. It can be readily applied to other cold region basins, and could be particularly useful for regions with minimal data.

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Section: Resultssupporting

confidence: 77%

Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations

2017

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“…Although advances in meteorological/snowpack models and simulation approaches are improving the prediction of observational data, inaccuracies remain. Many studies have highlighted the potential to improve snowpack modeling by assimilating observational data [46,89]. Satellite data enables the distribution of the snowpack over large areas to be determined, and the assimilation of such data into snowpack models has been shown to significantly improve the simulation results in theory [44].…”

Section: Future Perspectives On Distributed Snowpack Simulationsmentioning

confidence: 99%

Multi-Criteria Evaluation of Snowpack Simulations in Complex Alpine Terrain Using Satellite and In Situ Observations

et al. 2018

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Abstract:This work presents an extensive evaluation of the Crocus snowpack model over a rugged and highly glacierized mountain catchment (Arve valley, Western Alps, France) from 1989 to 2015. The simulations were compared and evaluated using in-situ point snow depth measurements, in-situ seasonal and annual glacier surface mass balance, snow covered area evolution based on optical satellite imagery at 250 m resolution (MODIS sensor), and the annual equilibrium-line altitude of glaciers, derived from satellite images (Landsat, SPOT, and ASTER). The snowpack simulations were obtained using the Crocus snowpack model driven by the same, originally semi-distributed, meteorological forcing (SAFRAN) reanalysis using the native semi-distributed configuration, but also a fully distributed configuration. The semi-distributed approach addresses land surface simulations for discrete topographic classes characterized by elevation range, aspect, and slope. The distributed approach operates on a 250-m grid, enabling inclusion of terrain shadowing effects, based on the same original meteorological dataset. Despite the fact that the two simulations use the same snowpack model, being potentially subjected to same potential deviation from the parametrization of certain physical processes, the results showed that both approaches accurately reproduced the snowpack distribution over the study period. Slightly (although statistically significantly) better results were obtained by using the distributed approach. The evaluation of the snow cover area with MODIS sensor has shown, on average, a reduction of the Root Mean Squared Error (RMSE) from 15.2% with the semi-distributed approach to 12.6% with the distributed one. Similarly, surface glacier mass balance RMSE decreased from 1.475 m of water equivalent (W.E.) for the semi-distributed simulation to 1.375 m W.E. for the distribution. The improvement, observed with a much higher computational time, does not justify the recommendation of this approach for all applications; however, for simulations that require a precise representation of snowpack distribution, the distributed approach is suggested.

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“…SCM were obtained from Terra Moderate Resolution Imaging Spectroradiometer images with a tailored topographic correction and improved ground resolution of 250 m in order to take into account the specific characteristics of mountainous areas (Notarnicola et al, 2013a(Notarnicola et al, , 2013b. The daily SWE data, being generated on the basis of 298 observational snow monitoring sites in Switzerland using a distributed snow hydrological model (Griessinger et al, 2016;Magnusson et al, 2014), were assessed using validation points following the methods of Foppa et al (2007) and further elaborated by Magnusson et al (2014). SWE grids were resampled to 250 m resolution using the Nearest Neighbor method in order to match the SCM.…”

Section: Snow Metricsmentioning

confidence: 99%

Relative Influence of Timing and Accumulation of Snow on Alpine Land Surface Phenology

et al. 2018

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Timing and accumulation of snow are among the most important phenomena influencing land surface phenology in mountainous ecosystems. However, our knowledge on their influence on alpine land surface phenology is still limited, and much remains unclear as to which snow metrics are most relevant for studying this interaction. In this study, we analyzed five snow and phenology metrics, namely, timing (snow cover duration (SCD) and last snow day), accumulation of snow (mean snow water equivalent, SWEm), and mountain land surface phenology (start of season and length of season) in the Swiss Alps during the period 2003–2014. We examined elevational and regional variations in the relationships between snow and alpine land surface phenology metrics using multiple linear regression and relative weight analyses and subsequently identified the snow metrics that showed strongest associations with variations in alpine land surface phenology of natural vegetation types. We found that the relationships between snow and phenology metrics were pronounced in high‐elevational regions and alpine natural grassland and sparsely vegetated areas. Start of season was influenced primarily by SCD, secondarily by SWEm, while length of season was equally affected by SCD and SWEm across different elevational bands. We conclude that SCD plays the most significant role compared to other snow metrics. Future variations of snow cover and accumulation are likely to influence alpine ecosystems, for instance, their species composition due to changes in the potential growing season. Also, their spatial distribution may change as a response to the new environmental conditions if these prove persistent.

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Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Cited by 64 publications

References 32 publications

Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations

Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations

Multi-Criteria Evaluation of Snowpack Simulations in Complex Alpine Terrain Using Satellite and In Situ Observations

Relative Influence of Timing and Accumulation of Snow on Alpine Land Surface Phenology

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