Articles | Volume 16, issue 9
https://doi.org/10.5194/tc-16-3489-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-3489-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Sep 2022
Research article |
| 01 Sep 2022
| 01 Sep 2022
Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Quebec, Canada
Marie-Amélie Boucher
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Quebec, Canada
Simon Lachance-Cloutier
Quebec Ministère de l’Environnement et de la Lutte contre les Changements Climatiques, Quebec City, Quebec, Canada
Richard Turcotte
Quebec Ministère de l’Environnement et de la Lutte contre les Changements Climatiques, Quebec City, Quebec, Canada
Pierre-Yves St-Louis
Quebec Ministère de l’Environnement et de la Lutte contre les Changements Climatiques, Quebec City, Quebec, Canada
Related authors
Konstantin F. F. Ntokas, Jean Odry, Marie-Amélie Boucher, and Camille Garnaud
Hydrol. Earth Syst. Sci., 25, 3017–3040, https://doi.org/10.5194/hess-25-3017-2021, https://doi.org/10.5194/hess-25-3017-2021, 2021
Short summary
Short summary
This article shows a conversion model of snow depth into snow water equivalent (SWE) using an ensemble of artificial neural networks. The novelty is a direct estimation of SWE and the improvement of the estimation by in-depth analysis of network structures. The usage of an ensemble allows a probabilistic estimation and, therefore, a deeper insight. It is a follow-up study of a similar study over Quebec but extends it to the whole area of Canada and improves it further.
Jean-Luc Martel, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, François Brissette, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Simon Lachance-Cloutier, Gabriel Rondeau-Genesse, and Louis-Philippe Caron
EGUsphere, https://doi.org/10.5194/egusphere-2024-2134, https://doi.org/10.5194/egusphere-2024-2134, 2024
Short summary
Short summary
This study explores six methods to improve the ability of Long Short-Term Memory (LSTM) neural networks to predict peak streamflows, crucial for flood analysis. By enhancing data inputs and model techniques, the research shows LSTM models can match or surpass traditional hydrological models in simulating peak flows. Tested on 88 catchments in Quebec, Canada, these methods offer promising strategies for better flood prediction.
Jean-Luc Martel, François Brissette, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Gabriel Rondeau-Genesse, and Louis-Philippe Caron
EGUsphere, https://doi.org/10.5194/egusphere-2024-2133, https://doi.org/10.5194/egusphere-2024-2133, 2024
Short summary
Short summary
This study compares Long Short-Term Memory (LSTM) neural networks with traditional hydrological models to predict future streamflow under climate change. Using data from 148 catchments, it finds that LSTM models, which learn from extensive data sequences, perform differently and often better than traditional hydrolgical models. The continental LSTM model, which includes data from diverse climate zones, is particularly effective for understanding climate impacts on water resources.
Alireza Amani, Marie-Amélie Boucher, Alexandre R. Cabral, Vincent Vionnet, and Étienne Gaborit
EGUsphere, https://doi.org/10.5194/egusphere-2024-1277, https://doi.org/10.5194/egusphere-2024-1277, 2024
Short summary
Short summary
Accurately estimating groundwater recharge using numerical models is particularly difficult in cold regions with snow and soil freezing. This study evaluated a physics-based model against high-resolution field measurements. Our findings highlight a need for a better representation of soil freezing processes, offering a roadmap for future model development. This leads to more accurate models to aid water resources management decisions in cold climates.
Valérie Jean, Marie-Amélie Boucher, Anissa Frini, and Dominic Roussel
Hydrol. Earth Syst. Sci., 27, 3351–3373, https://doi.org/10.5194/hess-27-3351-2023, https://doi.org/10.5194/hess-27-3351-2023, 2023
Short summary
Short summary
Flood forecasts are only useful if they are understood correctly. They are also uncertain, and it is difficult to present all of the information about the forecast and its uncertainty on a map, as it is three dimensional (water depth and extent, in all directions). To overcome this, we interviewed 139 people to understand their preferences in terms of forecast visualization. We propose simple and effective ways of presenting flood forecast maps so that they can be understood and useful.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
Short summary
Short summary
Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Jing Xu, François Anctil, and Marie-Amélie Boucher
Hydrol. Earth Syst. Sci., 26, 1001–1017, https://doi.org/10.5194/hess-26-1001-2022, https://doi.org/10.5194/hess-26-1001-2022, 2022
Short summary
Short summary
The performance of the non-dominated sorting genetic algorithm II (NSGA-II) is compared with a conventional post-processing method of affine kernel dressing. NSGA-II showed its superiority in improving the forecast skill and communicating trade-offs with end-users. It allows the enhancement of the forecast quality since it allows for setting multiple specific objectives from scratch. This flexibility should be considered as a reason to implement hydrologic ensemble prediction systems (H-EPSs).
Konstantin F. F. Ntokas, Jean Odry, Marie-Amélie Boucher, and Camille Garnaud
Hydrol. Earth Syst. Sci., 25, 3017–3040, https://doi.org/10.5194/hess-25-3017-2021, https://doi.org/10.5194/hess-25-3017-2021, 2021
Short summary
Short summary
This article shows a conversion model of snow depth into snow water equivalent (SWE) using an ensemble of artificial neural networks. The novelty is a direct estimation of SWE and the improvement of the estimation by in-depth analysis of network structures. The usage of an ensemble allows a probabilistic estimation and, therefore, a deeper insight. It is a follow-up study of a similar study over Quebec but extends it to the whole area of Canada and improves it further.
Rachel Bazile, Marie-Amélie Boucher, Luc Perreault, and Robert Leconte
Hydrol. Earth Syst. Sci., 21, 5747–5762, https://doi.org/10.5194/hess-21-5747-2017, https://doi.org/10.5194/hess-21-5747-2017, 2017
Short summary
Short summary
Meteorological forecasting agencies constantly work on pushing the limit of predictability farther in time. However, some end users need proof that climate model outputs are ready to be implemented operationally. We show that bias correction is crucial for the use of ECMWF System4 forecasts for the studied area and there is a potential for the use of 1-month-ahead forecasts. Beyond this, forecast performance is equivalent to using past climatology series as inputs to the hydrological model.
Simon Matte, Marie-Amélie Boucher, Vincent Boucher, and Thomas-Charles Fortier Filion
Hydrol. Earth Syst. Sci., 21, 2967–2986, https://doi.org/10.5194/hess-21-2967-2017, https://doi.org/10.5194/hess-21-2967-2017, 2017
Short summary
Short summary
In this study we set the basis of an alternative framework to replace the popular cost-loss ratio for the economic assessment of flood forecasting systems. The C-L ratio implicitly considers the decision maker to be risk-neutral, whereas it is rarely the case in real-life emergency situations. Instead of the cost-loss ratio, we propose using a utility function. We show that the decision-maker’s level of risk aversion is a crucial factor in the assessment of the economic value of flood forecasts.
Antoine Thiboult, François Anctil, and Marie-Amélie Boucher
Hydrol. Earth Syst. Sci., 20, 1809–1825, https://doi.org/10.5194/hess-20-1809-2016, https://doi.org/10.5194/hess-20-1809-2016, 2016
Short summary
Short summary
Issuing a good hydrological forecast is challenging because of the numerous sources of uncertainty that lay in the description of the hydrometeorological processes. Several modeling techniques are investigated in this paper to assess how they contribute to the forecast quality. It is shown that the best modeling approach uses several dissimilar techniques that each tackle one source of uncertainty.
Related subject area
Discipline: Snow | Subject: Data Assimilation
Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment
A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment
Esteban Alonso-González, Simon Gascoin, Sara Arioli, and Ghislain Picard
The Cryosphere, 17, 3329–3342, https://doi.org/10.5194/tc-17-3329-2023, https://doi.org/10.5194/tc-17-3329-2023, 2023
Short summary
Short summary
Data assimilation techniques are a promising approach to improve snowpack simulations in remote areas that are difficult to monitor. This paper studies the ability of satellite-observed land surface temperature to improve snowpack simulations through data assimilation. We show that it is possible to improve snowpack simulations, but the temporal resolution of the observations and the algorithm used are critical to obtain satisfactory results.
Gaia Piazzi, Guillaume Thirel, Lorenzo Campo, and Simone Gabellani
The Cryosphere, 12, 2287–2306, https://doi.org/10.5194/tc-12-2287-2018, https://doi.org/10.5194/tc-12-2287-2018, 2018
Short summary
Short summary
The study focuses on the development of a multivariate particle filtering data assimilation scheme into a point-scale snow model. One of the main challenging issues concerns the impoverishment of the particle sample, which is addressed by jointly perturbing meteorological data and model parameters. An additional snow density model is introduced to reduce sensitivity to the availability of snow mass-related observations. In this configuration, the system reveals a satisfying performance.
Cited articles
Addor, N. and Melsen, L.: Legacy, Rather Than Adequacy, Drives the Selection of
Hydrological Models, Water Resour. Res., 55, 378–390,
https://doi.org/10.1029/2018WR022958, 2019. a
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005. a
Bengtsson, T., Bickel, P., and Li, B.: Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems, Probability and statistics: Essays in honor of David A. Freedman, Institute of Mathematical Statistics Collections, 2, 316–334, https://doi.org/10.1214/193940307000000518, 2008. a
Bergeron, O.: Grilles climatiques quotidiennes du Programme de surveillance du climat du Québec, version 1.2 – Guide d’utilisation, Tech. Rep., Ministère du Développement durable, de l’Environnement et de la Lutte contre les changements climatiques, Direction du suivi de l’état de l’environnement, Québec, 33 pp., ISBN 978-2-550-73568-7, 2015. a
Beven, K. J.: Rainfall-runoff modelling: the primer, 2nd edn., John Wiley & Sons, ISBN 978-0-470-71459-1, 2012. a
Boucher, M.-A.: Meteorological inputs, Harvard Dataverse [data set], https://doi.org/10.7910/DVN/BXXRHL,
2021a. a
Boucher, M.-A.: HYDROTEL Snow Model Parameters, Harvard Dataverse [data set], https://doi.org/10.7910/DVN/RJSZIP,
2021b. a
Boucher, M.-A.: Historical Snow Simulation (Open Loop), Harvard Dataverse [data set],
https://doi.org/10.7910/DVN/CJYMCV, 2021c. a
Boucher, M.-A.: Snow Observation Data, Harvard Dataverse [data set], https://doi.org/10.7910/DVN/NPB1JY,
2021d. a
Burgess, T. M. and Webster, R.: Optimal Interpolation and Isarithmic
Mapping of Soil Properties, J. Soil Sci., 31, 315–331,
https://doi.org/10.1111/j.1365-2389.1980.tb02084.x, 1980. a
Cantet, P., Boucher, M.-A., Lachance-Coutier, S., Turcotte, R., and Fortin, V.:
Using a particle filter to estimate the spatial distribution of the snowpack
water equivalent, J. Hydrometeorol., 20, 577–594,
https://doi.org/10.1175/JHM-D-18-0140.1, 2019. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., and Wilby, R.: The
Schaake Shuffle: A Method for Reconstructing Space–Time
Variability in Forecasted Precipitation and Temperature Fields,
J. Hydrometeorol., 5, 243–262,
https://doi.org/10.1175/1525-7541(2004)005<0243:TSSAMF>2.0.CO;2, 2004. a, b, c, d
Clark, M., Kavetski, D., and F., F.: Pursuing the method of multiple working
hypotheses for hydrological modeling, Water Resour. Res., 47, W09301,
https://doi.org/10.1029/2010WR009827, 2011. a
Clark, M. P., Rupp, D. E., Woods, R. A., Zheng, X., Ibbitt, R. P., Slater,
A. G., Schmidt, J., and Uddstrom, M. J.: Hydrological data assimilation with
the ensemble Kalman filter: Use of streamflow observations to update
states in a distributed hydrological model, Adv. Water Resour., 31,
1309–1324, https://doi.org/10.1016/j.advwatres.2008.06.005, 2008. a
Cluzet, B., Lafaysse, M., Cosme, E., Albergel, C., Meunier, L.-F., and Dumont, M.: CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework, Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, 2021. a, b, c
Doesken, N. J. and Judson, A.: The snow booklet: A guide to the science,
climatology, and measurement of snow in the United States, 2nd edn., Colorado State
University Publications & Printing, ISBN 0-9651056-2-8, 1997. a
Douc, R. and Cappe, O.: Comparison of resampling schemes for particle
filtering, in: ISPA 2005. Proceedings of the 4th International
Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia, 15–17 September 2005, pp.
64–69, https://doi.org/10.1109/ISPA.2005.195385, iSSN: 1845-5921, 2005. a
Essery, R., Morin, S., Lejeune, Y., and B Ménard, C.: A comparison of 1701
snow models using observations from an alpine site, Adv. Water
Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013. a
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J.
Geophys. Res.-Oceans, 99, 10143–10162,
https://doi.org/10.1029/94JC00572, 1994. a
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and
practical implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003. a
Fortin, V., Abaza, M., Anctil, F., and Turcotte, R.: Why Should Ensemble
Spread Match the RMSE of the Ensemble Mean?, J.
Hydrometeorol., 15, 1708–1713, https://doi.org/10.1175/JHM-D-14-0008.1, 2014. a
Gordon, N. J., Salmond, D. J., and Smith, A. F. M.: Novel approach to
nonlinear/non-Gaussian Bayesian state estimation, IEE Proc. F, 140, 107–113, https://doi.org/10.1049/ip-f-2.1993.0015,
1993. a, b, c, d
Goïta, K., Walker, A. E., and Goodison, B. E.: Algorithm development for the
estimation of snow water equivalent in the boreal forest using passive
microwave data, Int. J. Remote Sens., 24, 1097–1102,
https://doi.org/10.1080/0143116021000044805, 2003. a
Hock, R., Rees, G., Williams, M. W., and Ramirez, E.: Contribution from
glaciers and snow cover to runoff from mountains in different climates,
Hydrol. Process., 20, 2089–2090, https://doi.org/10.1002/hyp.6206, 2006. a
Larue, F., Royer, A., De Sève, D., Roy, A., and Cosme, E.: Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada, Hydrol. Earth Syst. Sci., 22, 5711–5734, https://doi.org/10.5194/hess-22-5711-2018, 2018. a
Leisenring, M. and Moradkhani, H.: Snow water equivalent prediction using
Bayesian data assimilation methods, Stoch. Env. Res.
Risk A., 25, 253–270, https://doi.org/10.1007/s00477-010-0445-5, 2011. a, b, c
Li, L. and Simonovic, S. P.: System dynamics model for predicting floods from
snowmelt in North American prairie watersheds, Hydrol. Process.,
16, 2645–2666, https://doi.org/10.1002/hyp.1064, 2002. a
Magnusson, J., Winstral, A., Stordal, A. S., Essery, R., and Jonas, T.:
Improving physically based snow simulations by assimilating snow depths using
the particle filter, Water Resour. Res., 53, 1125–1143,
https://doi.org/10.1002/2016WR019092, 2017. a, b
Marks, D., Domingo, J., Susong, D., Link, T., and Garen, D.: A spatially
distributed energy balance snowmelt model for application in mountain basins,
Hydrol. Process., 13, 1935–1959,
https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1935::AID-HYP868>3.0.CO;2-C,
1999. a
Matheson, J. E. and Winkler, R. L.: Scoring rules for continuous probability
distributions, Manage. Sci., 22, 1087–1096, 1976. a
MELCC: Surveillance du climat, https://www.environnement.gouv.qc.ca/climat/surveillance/index.asp, last access: 14 January 2019. a
Moradkhani, H., Hsu, K.-L., Gupta, H., and Sorooshian, S.: Uncertainty
assessment of hydrologic model states and parameters: Sequential data
assimilation using the particle filter, Water Resour. Res., 41, W05012,
https://doi.org/10.1029/2004WR003604, 2005. a
Odry, J.: TheDroplets/Snow_spatial_particle_filter: First release of the
codes for the spatial particle filter, Zenodo [code], https://doi.org/10.5281/zenodo.5531771, 2021. a
Odry, J., Boucher, M. A., Cantet, P., Lachance-Cloutier, S., Turcotte, R., and
St-Louis, P. Y.: Using artificial neural networks to estimate snow water
equivalent from snow depth, Can. Water Resour. J., 45, 252–268,
https://doi.org/10.1080/07011784.2020.1796817, 2020. a, b, c, d
Ohmura, A.: Physical Basis for the Temperature-Based Melt-Index
Method, J. Appl. Meteorol. Clim., 40, 753–761,
https://doi.org/10.1175/1520-0450(2001)040<0753:PBFTTB>2.0.CO;2, 2001. a
Penny, S. G. and Miyoshi, T.: A local particle filter for high-dimensional geophysical systems, Nonlin. Processes Geophys., 23, 391–405, https://doi.org/10.5194/npg-23-391-2016, 2016. a
Poterjoy, J.: A Localized Particle Filter for High-Dimensional Nonlinear
Systems, Mon. Weather Rev., 144, 59–76,
https://doi.org/10.1175/MWR-D-15-0163.1, 2016. a
Schaefli, B., Hingray, B., and Musy, A.: Climate change and hydropower production in the Swiss Alps: quantification of potential impacts and related modelling uncertainties, Hydrol. Earth Syst. Sci., 11, 1191–1205, https://doi.org/10.5194/hess-11-1191-2007, 2007. a
Snyder, C., Bengtsson, T., and Morzfeld, M.: Performance Bounds for
Particle Filters Using the Optimal Proposal, Mon. Weather
Rev., 143, 4750–4761, https://doi.org/10.1175/MWR-D-15-0144.1, 2015. a
Thiboult, A. and Anctil, F.: On the difficulty to optimally implement the
ensemble Kalman filter: An experiment based on many hydrological models and
catchments, J. Hydrol., 529, 1147–1160, 2015. a
Turcotte, R., Fortin, L.-G., Fortin, V., Fortin, J.-P., and Villeneuve, J.-P.:
Operational analysis of the spatial distribution and the temporal evolution
of the snowpack water equivalent in southern Québec, Canada, Hydrol.
Res., 38, 211–234, https://doi.org/10.2166/nh.2007.009, 2007. a, b, c
Uboldi, F., Lussana, C., and Salvati, M.: Three-dimensional spatial
interpolation of surface meteorological observations from high-resolution
local networks, Meteorol. Appl., 15, 331–345,
https://doi.org/10.1002/met.76, 2008. a
World Meteorological Organization: Guide to Instruments and Methods of
Observation. Volume I – Measurement of Meteorological Variables, WMO-No. 8, vol. 1, first edn. published in 1950, World Meteorological Organization, Geneva, ISBN 978-92-63-10008-5, 2018. a
Zhang, Y.-F. and Yang, Z.-L.: Estimating uncertainties in the newly developed
multi-source land snow data assimilation system, J. Geophys.
Res.-Atmos., 121, 8254–8268,
https://doi.org/10.1002/2015JD024248, 2016. a
Short summary
The research deals with the assimilation of in-situ local snow observations in a large-scale spatialized snow modeling framework over the province of Quebec (eastern Canada). The methodology is based on proposing multiple spatialized snow scenarios using the snow model and weighting them according to the available observations. The paper especially focuses on the spatial coherence of the snow scenario proposed in the framework.
The research deals with the assimilation of in-situ local snow observations in a large-scale...
