Articles | Volume 28, issue 5
https://doi.org/10.5194/hess-28-1127-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-1127-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow
Dipti Tiwari
CORRESPONDING AUTHOR
Université de Sherbrooke, Département de génie civil et de génie du bâtiment, 2500 Bd de l'Université, Sherbrooke, QC J1K 2R1, Canada
Mélanie Trudel
Université de Sherbrooke, Département de génie civil et de génie du bâtiment, 2500 Bd de l'Université, Sherbrooke, QC J1K 2R1, Canada
Robert Leconte
Université de Sherbrooke, Département de génie civil et de génie du bâtiment, 2500 Bd de l'Université, Sherbrooke, QC J1K 2R1, Canada
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Cited articles
Abaza, M., Anctil, F., Fortin, V., and Turcotte, R.: Sequential streamflow assimilation for short-term hydrological ensemble forecasting, J. Hydrol., 519, 2692–2706, https://doi.org/10.1016/j.jhydrol.2014.08.038, 2014. a
Abaza, M., Anctil, F., Fortin, V., and Turcotte, R.: Exploration of sequential streamflow assimilation in snow dominated watersheds, Adv. Water Resour., 86, 414–424, 2015. a
Adeyeri, O., Laux, P., Arnault, J., Lawin, A., and Kunstmann, H.: Conceptual hydrological model calibration using multi-objective optimization techniques over the transboundary Komadugu-Yobe basin, Lake Chad Area, West Africa, Journal of Hydrology: Regional Studies, 27, 100655, https://doi.org/10.1016/j.ejrh.2019.100655, 2020. a
Ala-Aho, P., Autio, A., Bhattacharjee, J., Isokangas, E., Kujala, K., Marttila, H., Menberu, M., Meriö, L. J., Postila, H., Rauhala, A., and Ronkanen, A. K.: What conditions favor the influence of seasonally frozen ground on hydrological partitioning? A systematic review, Environ. Res. Lett., 16, 043008, https://doi.org/10.1088/1748-9326/abe82c, 2021. a
Augas, J., Abbasnezhadi, K., Rousseau, A. N., and Baraer, M.: What is the trade-off between snowpack stratification and simulated snow water equivalent in a physically-based snow model?, Water, 12, 3449, https://doi.org/10.3390/w12123449, 2020. a
Barrett, A.: National Operational Hydrologic Remote Sensing Snow Data Assimilation System (SNODAS) products at NSIDC, Special Rep. 11, NSIDC, Boulder, CO, 19 pp., https://nsidc.org/sites/default/files/nsidc_special_report_11.pdf (last access: 4 March 2024), 2003. a
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., and Wood, E. F.: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, 2017. a
Bergeron, J., Royer, A., Turcotte, R., and Roy, A.: Snow cover estimation using blended MODIS and AMSR-E data for improved watershed-scale spring streamflow simulation in Quebec, Canada, Hydrol. Process., 28, 4626–4639, 2014. a
Bouda, M., Rousseau, A. N., Gumiere, S. J., Gagnon, P., Konan, B., and Moussa, R.: Implementation of an automatic calibration procedure for HYDROTEL based on prior OAT sensitivity and complementary identifiability analysis, Hydrol. Process., 28, 3947–3961, 2014. a
Budhathoki, S., Rokaya, P., Lindenschmidt, K.-E., and Davison, B.: A multi-objective calibration approach using in-situ soil moisture data for improved hydrological simulation of the Prairies, Hydrolog. Sci. J., 65, 638–649, 2020. a
Buttle, J. M., Allen, D. M., Caissie, D., Davison, B., Hayashi, M., Peters, D. L., Pomeroy, J. W., Simonovic, S., St-Hilaire, A., and Whitfield, P. H.: Flood processes in Canada: Regional and special aspects, Can. Water Resour. J., 41, 7–30, 2016. a
Casson, D. R., Werner, M., Weerts, A., and Solomatine, D.: Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed, Hydrol. Earth Syst. Sci., 22, 4685–4697, https://doi.org/10.5194/hess-22-4685-2018, 2018. a
CDS: ERA5-Land, https://cds.climate.copernicus.eu/, last access: 4 March 2024. a
Chai, T. and Draxler, R. R.: Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature, Geosci. Model Dev., 7, 1247–1250, https://doi.org/10.5194/gmd-7-1247-2014, 2014. a
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A.: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review, Water Resour. Res., 47, https://doi.org/10.1029/2011WR010745, 2011. a
Clow, D. W., Nanus, L., Verdin, K. L., and Schmidt, J.: Evaluation of SNODAS snow depth and snow water equivalent estimates for the Colorado Rocky Mountains, USA, Hydrol. Process., 26, 2583–2591, 2012. a
Demirel, M. C.: SPAEF version 2.0, GitHub. GEUS, Copenhagen, Denmark, Zenodo [code], https://doi.org/10.5281/zenodo.5861253, 2020. a, b
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: Effect of different uncertainty sources on the skill of 10 day ensemble low flow forecasts for two hydrological models, Water Resour. Res., 49, 4035–4053, 2013. a
Demirel, M. C., Mai, J., Mendiguren, G., Koch, J., Samaniego, L., and Stisen, S.: Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model, Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, 2018. a, b, c
Derksen, C., King, J., Belair, S., Garnaud, C., Vionnet, V., Fortin, V., Lemmetyinen, J., Crevier, Y., Plourde, P., Lawrence, B., and van Mierlo, H.: Development of the terrestrial snow mass mission, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 614–617, IEEE, https://doi.org/10.1109/IGARSS47720.2021.9553496, 2021. a
DeWalle, D. R. and Rango, A.: Principles of Snow Hydrology, Cambridge University Press, ISBN 978-0-521-82362-3, 2008. a
Di Marco, N., Avesani, D., Righetti, M., Zaramella, M., Majone, B., and Borga, M.: Reducing hydrological modelling uncertainty by using MODIS snow cover data and a topography-based distribution function snowmelt model, J. Hydrol., 599, 126020, https://doi.org/10.1016/j.jhydrol.2021.126020, 2021. a
Duethmann, D., Peters, J., Blume, T., Vorogushyn, S., and Güntner, A.: The value of satellite-derived snow cover images for calibrating a hydrological model in snow-dominated catchments in Central Asia, Water Resour. Res., 50, 2002–2021, 2014. a
Efstratiadis, A. and Koutsoyiannis, D.: One decade of multi-objective calibration approaches in hydrological modelling: a review, Hydrolog. Sci. J., 55, 58–78, 2010. a
Eini, M. R., Massari, C., and Piniewski, M.: Satellite-based soil moisture enhances the reliability of agro-hydrological modeling in large transboundary river basins, Sci. Total Environ., 873, 162396, https://doi.org/10.1016/j.scitotenv.2023.162396, 2023. a
Finger, D., Vis, M., Huss, M., and Seibert, J.: The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments, Water Resour. Res., 51, 1939–1958, 2015. a
Fortin, J.-P., Moussa, R., Bocquillon, C., and Villeneuve, J.-P.: Hydrotel, un modèle hydrologique distribué pouvant bénéficier des données fournies par la télédétection et les systèmes d'information géographique, Revue des sciences de l'eau, 8, 97–124, 1995. a
Fortin, J.-P., Turcotte, R., Massicotte, S., Moussa, R., Fitzback, J., and Villeneuve, J.-P.: Distributed watershed model compatible with remote sensing and GIS data. I: Description of model, J. Hydrol. Eng., 6, 91–99, 2001. a
Fossey, M., Rousseau, A. N., and Savary, S.: Assessment of the impact of spatio-temporal attributes of wetlands on stream flows using a hydrological modelling framework: a theoretical case study of a watershed under temperate climatic conditions, Hydrol. Process., 30, 1768–1781, 2016. a
Frampton, A., Painter, S. L., and Destouni, G.: Permafrost degradation and subsurface-flow changes caused by surface warming trends, Hydrogeol. J., 21, 271, https://doi.org/10.1007/s10040-012-0938-z, 2013. a
GloH20: MSWEP, https://www.gloh2o.org/mswep/, last access: 4 March 2024. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, 2009. a
Hanzer, F., Helfricht, K., Marke, T., and Strasser, U.: Multilevel spatiotemporal validation of snow/ice mass balance and runoff modeling in glacierized catchments, The Cryosphere, 10, 1859–1881, https://doi.org/10.5194/tc-10-1859-2016, 2016. a
Harshburger, B. J., Humes, K. S., Walden, V. P., Blandford, T. R., Moore, B. C., and Dezzani, R. J.: Spatial interpolation of snow water equivalency using surface observations and remotely sensed images of snow-covered area, Hydrol. Process., 24, 1285–1295, 2010. a
Hiemstra, C. A., Liston, G. E., and Reiners, W. A.: Snow redistribution by wind and interactions with vegetation at upper treeline in the Medicine Bow Mountains, Wyoming, USA, Arct. Antarct. Alp. Res., 34, 262–273, 2002. a
Hojatimalekshah, A., Uhlmann, Z., Glenn, N. F., Hiemstra, C. A., Tennant, C. J., Graham, J. D., Spaete, L., Gelvin, A., Marshall, H.-P., McNamara, J. P., and Enterkine, J.: Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning, The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, 2021. a
Huot, P.-L., Poulin, A., Audet, C., and Alarie, S.: A hybrid optimization approach for efficient calibration of computationally intensive hydrological models, Hydrolog. Sci. J., 64, 1204–1222, 2019. a
info-climat MELCCFP: Ministère de l’Environnement et de la Lutte contre les changements climatiques,Données du Réseau de surveillance du climat du Québec, Direction de la qualité de l’air et du climat, Québec, https://www.environnement.gouv.qc.ca/climat/surveillance/index.asp (last access: 25 February 2024), 2020. a
Jafarov, E. E., Coon, E. T., Harp, D. R., Wilson, C. J., Painter, S. L., Atchley, A. L., and Romanovsky, V. E.: Modeling the role of preferential snow accumulation in through talik development and hillslope groundwater flow in a transitional permafrost landscape, Environ. Res. Lett., 13, 105006, https://doi.org/10.1088/1748-9326/aadd30, 2018. a
Jahanpour, M., Tolson, B. A., and Mai, J.: PADDS algorithm assessment for biobjective water distribution system benchmark design problems, J. Water Res. Pl., 144, 04017099, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000875, 2018. a
King, F., Erler, A. R., Frey, S. K., and Fletcher, C. G.: Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada, Hydrol. Earth Syst. Sci., 24, 4887–4902, https://doi.org/10.5194/hess-24-4887-2020, 2020. a, b
Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, https://doi.org/10.1029/2005WR004362, 2006. a
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a
Leach, J. M., Kornelsen, K. C., and Coulibaly, P.: Assimilation of near-real time data products into models of an urban basin, J. Hydrol., 563, 51–64, 2018. a
Liu, Z., Yin, J., and E. Dahlke, H.: Enhancing Soil and Water Assessment Tool Snow Prediction Reliability with Remote-Sensing-Based Snow Water Equivalent Reconstruction Product for Upland Watersheds in a Multi-Objective Calibration Process, Water, 12, 3190, https://doi.org/10.3390/w12113190, 2020. a
Lucas-Picher, P., Arsenault, R., Poulin, A., Ricard, S., Lachance-Cloutier, S., and Turcotte, R.: Application of a High-Resolution Distributed Hydrological Model on a US-Canada Transboundary Basin: Simulation of the Multiyear Mean AnnualHydrograph and 2011 Flood of theRichelieu River Basin, J. Adv. Model. Earth Sy., 12, e2019MS001709, https://doi.org/10.1029/2019MS001709, 2020. a
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., and Moisander, M.: GlobSnow v3.0 snow water equivalent (SWE), Pangaea, https://doi.org/10.1594/PANGAEA.911944, 2020. a
Markhali, S. P., Poulin, A., and Boucher, M.-A.: Spatio-temporal discretization uncertainty of distributed hydrological models, Hydrol. Process., 36, e14635, https://doi.org/10.1002/hyp.14635, 2022. a
Marsh, P., Pomeroy, J., Pohl, S., Quinton, W., Onclin, C., Russell, M., Neumann, N., Pietroniro, A., Davison, B., and McCartney, S.: Snowmelt processes and runoff at the arctic treeline: ten years of MAGS research, Cold Region Atmospheric and Hydrologic Studies. The Mackenzie GEWEX Experience: Volume 2: Hydrologic Processes, 97–123, https://doi.org/10.1007/978-3-540-75136-6_6, Springer, Berlin, Heidelberg, 2008. a
Mohammed, A. A., Pavlovskii, I., Cey, E. E., and Hayashi, M.: Effects of preferential flow on snowmelt partitioning and groundwater recharge in frozen soils, Hydrol. Earth Syst. Sci., 23, 5017–5031, https://doi.org/10.5194/hess-23-5017-2019, 2019. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
NSIDC: SNODAS, https://nsidc.org/data/g02158/versions/1, last access: 4 March 2024. a
NOHRSC: SNODAS (Snow Data Assimilation System)- Data Products at National Operational Hydrologic Remote Sensing Center, National Snow and Ice Data Center, Version 1, https://doi.org/10.7265/N5TB14TC, 2004. a
Oreiller, M., Nadeau, D. F., Minville, M., and Rousseau, A. N.: Modelling snow water equivalent and spring runoff in a boreal watershed, James Bay, Canada, Hydrol. Process., 28, 5991–6005, 2014. a
Parajka, J. and Blöschl, G.: The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models, J. Hydrol., 358, 240–258, 2008. a
Rees, W.: Comparing the spatial content of thematic maps, Int. J. Remote Sens., 29, 3833–3844, 2008. a
Riley, J. P., Israelsen, E. K., and Eggleston, K. O.: Some approaches to snowmelt prediction, in: The Role of snow and ice in hydrology: proceedings of the Banff Symposia, vol. 2, 956–971, NS/WMO/IAHS/2,UNESCO, https://unesdoc.unesco.org/ark:/48223/pf0000009641 (last access: 4 March 2024), 1972. a
Roach, J., Griffith, B., Verbyla, D., and Jones, J.: Mechanisms influencing changes in lake area in Alaskan boreal forest, Glob. Change Biol., 17, 2567–2583, 2011. a
Rousseau, A. N., Fortin, J.-P., Turcotte, R., Royer, A., Savary, S., Quévy, F., Noël, P., and Paniconi, C.: PHYSITEL, a specialized GIS for supporting the implementation of distributed hydrological models, Water News – Official Magazine of the Canadian Water Resources Association, 31, 18–20, 2011. a
Roy, A., Royer, A., and Turcotte, R.: Improvement of springtime streamflow simulations in a boreal environment by incorporating snow-covered area derived from remote sensing data, J. Hydrol., 390, 35–44, 2010. a
Schumann, G.-P., Neal, J. C., Voisin, N., Andreadis, K. M., Pappenberger, F., Phanthuwongpakdee, N., Hall, A. C., and Bates, P. D.: A first large-scale flood inundation forecasting model, Water Resour. Res., 49, 6248–6257, 2013. a
Seiller, G., Anctil, F., and Perrin, C.: Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions, Hydrol. Earth Syst. Sci., 16, 1171–1189, https://doi.org/10.5194/hess-16-1171-2012, 2012. a
Singh, V. P. and Woolhiser, D. A.: Mathematical modeling of watershed hydrology, J. Hydrol. Eng., 7, 270–292, 2002. a
Stisen, S., Soltani, M., Mendiguren, G., Langkilde, H., Garcia, M., and Koch, J.: Spatial patterns in actual evapotranspiration climatologies for Europe, Remote Sens.-Basel, 13, 2410, https://doi.org/10.3390/rs13122410, 2021. a
Terink, W., Lutz, A. F., Simons, G. W. H., Immerzeel, W. W., and Droogers, P.: SPHY v2.0: Spatial Processes in HYdrology, Geosci. Model Dev., 8, 2009–2034, https://doi.org/10.5194/gmd-8-2009-2015, 2015. a
Thornton, J. M., Brauchli, T., Mariethoz, G., and Brunner, P.: Efficient multi-objective calibration and uncertainty analysis of distributed snow simulations in rugged alpine terrain, J. Hydrol., 598, 126241, https://doi.org/10.1016/j.jhydrol.2021.126241, 2021. a
Tolson, B. A. and Jahanpour, M.: Incorporating Decision-Maker Preferences into the PADDS Multi-Objective Optimization Algorithm for the Design of Water Distribution Systems:(179), in: WDSA/CCWI Joint Conference Proceedings, vol. 1, Queen’s University, Kingston, Ontario, Canada, 23–25 July, https://ojs.library.queensu.ca/index.php/wdsa-ccw/article/download/12384/7980/23328 (last access: 4 March 2024), 2018. a
Tolson, B. A. and Shoemaker, C. A.: Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resour. Res., 43, https://doi.org/10.1029/2005WR004723, 2007. a, b, c
Towner, J., Cloke, H. L., Zsoter, E., Flamig, Z., Hoch, J. M., Bazo, J., Coughlan de Perez, E., and Stephens, E. M.: Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin, Hydrol. Earth Syst. Sci., 23, 3057–3080, https://doi.org/10.5194/hess-23-3057-2019, 2019. a
Troin, M. and Caya, D.: Evaluating the SWAT's snow hydrology over a Northern Quebec watershed, Hydrol. Process., 28, 1858–1873, 2014. a
Turcotte, R., Fortin, J.-P., Rousseau, A. N., Massicotte, S., and Villeneuve, J.-P.: Determination of the drainage structure of a watershed using a digital elevation model and a digital river and lake network, J. Hydrol., 240, 225–242, 2001. a
Turcotte, R., Lacombe, P., Dimnik, C., and Villeneuve, J.-P.: Prévision hydrologique distribuée pour la gestion des barrages publics du Québec, Can. J. Civil. Eng., 31, 308–320, 2004. a
Woo, M.-k. and Young, K. L.: Modeling arctic snow distribution and melt at the 1 km grid scale, Hydrol. Res., 35, 295–307, https://doi.org/10.2166/nh.2004.0022, 2004. a
Wrzesien, M. L., Durand, M. T., Pavelsky, T. M., Howat, I. M., Margulis, S. A., and Huning, L. S.: Comparison of methods to estimate snow water equivalent at the mountain range scale: A case study of the California Sierra Nevada, J. Hydrometeorol., 18, 1101–1119, 2017. a
Xiang, Y., Chen, J., Li, L., Peng, T., and Yin, Z.: Evaluation of eight global precipitation datasets in hydrological modeling, Remote Sens.-Basel, 13, 2831, https://doi.org/10.3390/rs13142831, 2021. a
Short summary
Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Calibrating hydrological models with multi-objective functions enhances model robustness. By...