Have a personal or library account? Click to login
Multi-criteria evaluation for parameter uncertainty assessment and ensemble runoff forecasting in a snow-dominated basin Cover

Multi-criteria evaluation for parameter uncertainty assessment and ensemble runoff forecasting in a snow-dominated basin

Open Access
|Aug 2023

References

  1. Adeyeri, O.E., Laux, P., Arnault, J., Lawin, A.E., Kunstmann, H., 2020. 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.
  2. Anghileri, D., Voisin, N., Castelletti, A., Pianosi, F., Nijssen, B., Lettenmaier, D.P., 2016. Value of long‐term streamflow forecasts to reservoir operations for water supply in snow‐dominated river catchments. Water Resources Research, 52, 6, 4209–4225.
  3. Barnes, W.L., Pagano, T.S., Salomonson. V.V., 1998. Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1. IEEE Transactions on Geoscience and Remote Sensing, 36, 1088–1100.
  4. Beldring, S., 2002. Multi-criteria validation of a precipitation-runoff model. J. Hydrol., 257, 1–4, 189–211.
  5. Bergström, S., 1976. Development and application of a conceptual runoff model for Scandinavian catchments. SMHI Reports RHO, no. 7, Norrköping.
  6. Beven, K., 2006. A manifesto for the equifinality thesis. Journal of Hydrology, 320, 1–2, 18–36.
  7. Blasone, R.S., Madsen, H., Rosbjerg, D., 2007. Parameter estimation in distributed hydrological modelling: comparison of global and local optimisation techniques. Hydrology Research, 38, 4–5, 451–476.
  8. Boyle, D.P., Gupta, H.V., Sorooshian, S., 2000. Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods. Water Resources Research, 36, 12, 3663–3674.
  9. Budhathoki, S., Rokaya, P., Lindenschmidt, K.E., Davison, B., 2020. A multi-objective calibration approach using in-situ soil moisture data for improved hydrological simulation of the Prairies. Hydrological Sciences Journal, 65, 4, 638–649.
  10. Demirel, M.C., Özen, A., Orta, S., Toker, E., Demir, H.K., Ekmekcioğlu, Ö. et al., 2019. Additional value of using satellite-based soil moisture and two sources of groundwater data for hydrological model calibration. Water, 11, 10, 2083.
  11. Di Marco, N., Avesani, D., Righetti, M., Zaramella, M., Majone, B., Borga, M., 2021. Reducing hydrological modelling uncertainty by using MODIS snow cover data and a topography-based distribution function snowmelt model. Journal of Hydrology, 599, 126020.
  12. Dong, C., 2018. Remote sensing, hydrological modeling and in situ observations in snow cover research: A review. Journal of Hydrology, 561, 573–583.
  13. Duan, Q., Schaake, J., Andréassian, V., Franks, S., Goteti, G., Gupta, H.V. et al., 2006. Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. Journal of Hydrology, 320, 1–2, 3–17.
  14. Duethmann, D., Peters, J., Blume, T., Vorogushyn, S., Güntner, A., 2014. The value of satellite derived snow cover images for calibrating a hydrological model in snow dominated catchments in Central Asia. Water Resour. Res., 50, 3, 2002–2021.
  15. Efstratiadis, A., Koutsoyiannis, D., 2010. One decade of multi-objective calibration approaches in hydrological modelling: a review. Hydrological Sciences Journal–Journal Des Sciences Hydrologiques, 55, 1, 58–78.
  16. Etter, S., Addor, N., Huss, M., Finger, D., 2017. Climate change impacts on future snow, ice and rain runoff in a Swiss mountain catchment using multi-dataset calibration. Journal of Hydrology: Regional Studies, 13, 222–239.
  17. Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., Burlando, P., 2011. The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model. Water Resour. Res., 47, 7, W07519.
  18. Finger, D., Vis, M., Huss, M., Seibert, J., 2015. The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments. Water Resour. Res., 51, 4, 1939–1958.
  19. Foulon, É., Rousseau, A.N., 2018. Equifinality and automatic calibration: What is the impact of hypothesizing an optimal parameter set on modelled hydrological processes? Canadian Water Resources Journal/Revue canadienne des ressources hydriques, 43, 1, 47–67.
  20. Gafurov, A., Bárdossy, A., 2009. Cloud removal methodology from MODIS snow cover product. Hydrology and Earth System Sciences, 13, 1361–73.
  21. Hall, D.K., Riggs, G.A., Salomonson, V.V., DiGirolamo, N.E., Bayr, K.J., 2002. MODIS snow cover products. Remote Sens. Environ., 83, 1, 181–194.
  22. Hall, D.K., Riggs, G.A., Salomonson, V.V., 1995. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment, 54, 127–40.
  23. Han, P., Long, D., Han, Z., Du, M., Dai, L., Hao, X., 2019. Improved understanding of snowmelt runoff from the headwaters of China’s Yangtze River using remotely sensed snow products and hydrological modeling. Remote Sens. Environ., 224, 44–59.
  24. Her, Y., Seong, C., 2018. Responses of hydrological model equifinality, uncertainty, and performance to multi-objective parameter calibration. Journal of Hydroinformatics, 20, 4, 864–885.
  25. Häggström, M., Lindström, G., Sandoval, L.A., Vega, M.E., 1988. Application of the HBV model to the upper Río Cauca basin.
  26. Huo, J., Liu, L., 2020. Evaluation method of multiobjective functions’ combination and its application in hydrological model evaluation. Computational Intelligence and Neuroscience, 2020, Article ID: 8594727.
  27. Ji, H., Fang, G., Yang, J., Chen, Y., 2019. Multi-objective calibration of a distributed hydrological model in a highly glacierized watershed in Central Asia. Water, 11, 3, 554.
  28. Krajčí, P., Holko, L., Perdigao, R.A.P., Parajka, J., 2014. Estimation of regional snowline elevation (RSLE) from MODIS images for seasonally snow covered mountain basins. Journal of Hydrology, 519, 1769–1778.
  29. Kuban, M., Parajka, J., Tong, R., Greimeister-Pfeil, I., Vreugdenhil, M., Szolgay, J., Kohnova, S., Hlavcova, K., Sleziak, P., Brziak, A. 2022. The effects of satellite soil moisture data on the parametrization of topsoil and root zone soil moisture in a conceptual hydrological model. Journal of Hydrology and Hydromechanics, 70, 3, 295–307.
  30. Kundu, D., Vervoort, R.W., van Ogtrop, F.F., 2017. The value of remotely sensed surface soil moisture for model calibration using SWAT. Hydrol. Process., 31, 2764–2780.
  31. Li, Y., Grimaldi, S., Pauwels, V.R., Walker, J.P., 2018. Hydrologic model calibration using remotely sensed soil moisture and discharge measurements: The impact on predictions at gauged and ungauged locations. J. Hydrol., 557, 897–909.
  32. Lopez, M.G., Vis, M.J., Jenicek, M., Griessinger, N., Seibert, J., 2020. Complexity and performance of temperature-based snow routines for runoff modelling in mountainous areas in Central Europe. Hydrol. Earth Syst. Sci. Discussions (13 February 2020), 1–31.
  33. Madsen, H., 2003. Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Advances in Water Resources., 26, 2, 205–216.
  34. Magnusson, J., Winstral, A., Stordal, A.S., Essery, R., Jonas, T., 2017. Improving physically based snow simulations by assimilating snow depths using the particle filter. Water Resources Research, 53, 2, 1125–1143.
  35. Mazzoleni, M., Noh, S.J., Lee, H., Liu, Y., Seo, D.J., Amaranto, A. et al., 2018. Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods. Hydrological Sciences Journal, 63, 3, 386–407.
  36. Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE., 50, 3, 885–900.
  37. Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models: A discussion of principles. J. Hydrol., 10, 3, 282–290.
  38. Nemri, S., Kinnard, C., 2020. Comparing calibration strategies of a conceptual snow hydrology model and their impact on model performance and parameter identifiability. Journal of Hydrology, 582, 124474.
  39. Nijzink, R.C., Almeida, S., Pechlivanidis, I.G., Capell, R., Gustafssons, D., Arheimer, B. et al., 2018. Constraining conceptual hydrological models with multiple information sources. Water Resources Research, 54, 10, 8332–8362.
  40. Pan, S., Liu, L., Bai, Z., Xu, Y.P., 2018. Integration of remote sensing evapotranspiration into multi-objective calibration of distributed hydrology–soil–vegetation model (DHSVM) in a humid region of China. Water, 10, 12, 1841.
  41. Parajka, J., Merz, R., Blöschl, G., 2007. Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments. Hydrol. Process., 21, 4, 435–446.
  42. Parajka, J., Blöschl, G., 2008. The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models. J. Hydrol., 358, 3–4, 240–258.
  43. Parajka, J., Naeimi, V., Blöschl, G., Komma, J., 2009. Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria. Hydrol. Earth Syst. Sci., 13, 259–271.
  44. Rajib, M.A., Merwade, V., Yu, Z., 2016. Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture. J. Hydrol., 536, 192–207.
  45. Reynolds, J.E., Halldin, S., Xu, C.Y., Seibert, J., Kauffeldt, A., 2017. Sub-daily runoff predictions using parameters calibrated on the basis of data with a daily temporal resolution. Journal of Hydrology, 550, 399–411.
  46. Ribstein, P., 2019. Revisiting a simple degree-day model for integrating satellite data: implementation of SWE-SCA hystereses. Journal of hydrology and hydromechanics, 67, 1, 70–81.
  47. Sahraei, S., Asadzadeh, M., Unduche, F., 2020. Signature-based multi-modelling and multi-objective calibration of hydrologic models: Application in flood forecasting for Canadian Prairies. Journal of Hydrology, 588, 125095.
  48. Seibert, J., 2000. Multi-criteria calibration of a conceptual runoff model using a genetic algorithm, Hydrol. Earth Syst. Sci., 4, 215–224.
  49. Seibert, J., Vis, M., 2012. Teaching hydrological modeling with a user-friendly catchment runoff model software package. Hydrol. Earth Syst. Sci., 16, 9, 3315–3325.
  50. Seibert, J., McDonnell, J.J., Woodsmith, R.D., 2010. Effects of wildfire on catchment runoff response: a modelling approach to detect changes in snow-dominated forested catchments. Hydrology Research, 41, 5, 378–390.
  51. Silvestro, F., Gabellani, S., Rudari, R., Delogu, F., Laiolo, P., Boni, G., 2015. Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data. Hydrology and Earth System Sciences, 19, 4, 1727–1751.
  52. Sleziak, P., Holko, L., Danko, M., Parajka, J., 2020. Uncertainty in the number of calibration repetitions of a hydrologic model in varying climatic conditions. Water, 12, 9, 2362.
  53. Sorman, A.A., Yamankurt, E., 2011. Modified satellite products on snow covered area in upper Euphrates basin, Turkey. Geophys. Res. Abstr., 13, EGU2011-7887.
  54. Széles, B., Parajka, J., Hogan, P., Silasari, R., Pavlin, L., Strauss, P., Blöschl, G., 2020. The added value of different data types for calibrating and testing a hydrologic model in a small catchment. Water Resources Research, 56, 10, e2019WR026153.
  55. Şensoy, A., Uysal, G., Şorman, A.A., 2018. Developing a decision support framework for real‐time flood management using integrated models. Journal of Flood Risk Management, 11, S866–S883.
  56. Şorman, A.A., Şensoy, A., Tekeli, A.E., Şorman, A.Ü., Akyürek, Z., 2009. Modelling and forecasting snowmelt runoff process using the HBV model in the eastern part of Turkey. Hydrol. Process., 23, 7, 1031–1040.
  57. Thornton, J.M., Brauchli, T., Mariethoz, G., Brunner, P., 2021. Efficient multi-objective calibration and uncertainty analysis of distributed snow simulations in rugged alpine terrain. Journal of Hydrology, 598, 126241.
  58. Tibangayuka, N., Mulungu, D. M., Izdori, F., 2022. Performance evaluation, sensitivity, and uncertainty analysis of HBV model in Wami Ruvu basin, Tanzania. Journal of Hydrology: Regional Studies, 44, 101266.
  59. Tong, R., Parajka, J., Salentinig, A., Pfeil, I., Komma, J., Széles, B., Kubáň, M., Valent, P., Vreugdenhil, M., Wagner, W., Blöschl, G., 2021. The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model. Hydrol. Earth Syst. Sci., 25, 1389–1410.
  60. Tuo, Y., Marcolini, G., Disse, M., Chiogna, G., 2018. A multi-objective approach to improve SWAT model calibration in alpine catchments. Journal of Hydrology, 559, 347–360.
  61. Udnæs, H.C., Alfnes, E., Andreassen, L.M., 2007. Improving runoff modelling using satellite derived snow covered area. Hydrology Research., 38, 1, 21–32.
  62. Vis, M., Knight, R., Pool, S., Wolfe, W., Seibert, J., 2015. Model calibration criteria for estimating ecological flow characteristics. Water, 7, 5, 2358–2381.
  63. Wagener, T., Montanari, A., 2011. Convergence of approaches toward reducing uncertainty in predictions in ungauged basins. Water Resour. Res., 47, W06301.
  64. Wang, X.W., Xie, H.J., Liang, T. G., Huang. X.D., 2009. Comparison and validation of MODIS standard and new combination of Terra and Aqua snow cover products in northern Xinjiang, China. Hydrological Processes, 23, 419–29.
  65. Zhang, R., Liu, J., Gao, H., Mao, G., 2018. Can multi-objective calibration of streamflow guarantee better hydrological model accuracy? Journal of Hydroinformatics, 20, 3, 687–698.
  66. URL-1: www.nsdic.org/data/docs/noaa
  67. URL-2: www.wrf-model.org
DOI: https://doi.org/10.2478/johh-2023-0003 | Journal eISSN: 1338-4333 | Journal ISSN: 0042-790X
Language: English
Page range: 231 - 247
Submitted on: May 22, 2022
Accepted on: Jan 23, 2023
Published on: Aug 10, 2023
Published by: Slovak Academy of Sciences, Institute of Hydrology; Institute of Hydrodynamics, Czech Academy of Sciences, Prague
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Y. Oğulcan Doğan, A. Arda Şorman, Aynur Şensoy, published by Slovak Academy of Sciences, Institute of Hydrology; Institute of Hydrodynamics, Czech Academy of Sciences, Prague
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.