HBV hydrology model

The HBV hydrology model, or Hydrologiska Byråns Vattenbalansavdelning model, is a computer simulation used to analyze river discharge and water pollution. Developed originally for use in Scandinavia,[1][2][3] this hydrological transport model has also been applied in a large number of catchments on most continents.[4][5][6]

Headwaters of the Pungwe River; HBV has been used to model this drainage basin

Discharge modelling

This is the major application of HBV, and has gone through much refinement.[7] It comprises the following routines:

  • Snow routine
  • Soil moisture routine
  • Response function
  • Routing routine

The HBV model is a lumped (or semi-distributed) bucket-type (or also called 'conceptual') catchment model that has relatively few model parameters and minimal forcing input requirements, usually the daily temperature and the daily precipitation. First the snow is calculated after defining a threshold melting temperature (TT usually 0 °C) and a parameter CMELT that reflects the equivalent melted snow for the difference of temperature. The result is divided into a liquid part that is the surface runoff and a second part that infiltrates. Second the soil moisture is calculated after defining an initial value and the field capacity (FC). Third calculation of the actual Evapotranspiration (ETPa), first by using an external model (ex: Penman) for finding the potential ETP and then fitting the result to the temperatures and the permanent wilting point(PWP) of the catchment in question. A parameter C which reflects the increase in the ETP with the differences in temperatures ( Actual Temperature and Monthly mean Temperature). The model considers the catchment as two reservoirs (S1 and S2) connected by a percolation flow, the inflow to the first reservoir is calculated as the surface runoff, which is what remains from the initial precipitations after calculating the infiltration and the evapotranspiration. The outflow from the first reservoir is divided into two separate flows (Q1 and Q2) where Q1 represents the fast flow which is triggered after a certain threshold L to be defined by the user and Q2 the intermediate flow. A constant K1 is used to find the outflows as a function of the storage in S1. To consider the percolation rate a constant Kd is used as along as the storage is S1. The outflow from the second reservoir is considered to be the groundwater flow (Q3) function of a constant K2 and the storage in S2. The total flow generated from a certain rain event is the sum of the 3 flows. The result of the model are later compared to the actual measured flow values and Nasch parameter is used to calibrate the model by changing the different parameters. The model has in total 9 parameters : TT, Cmelt, FC, C, PWP, L, K1, K2, Kd. For a good calibration of the model it is better to use Monte-Carlo simulation or the GLUE-Method to properly define the parameters and the uncertainty in the model. The model is fairly reliable but as usual the need of good input data is essential for good results. The sensitivity of the HBV model to parameter uncertainty was explored [8] revealing significant parameter interactions affecting calibration uniqueness, and some state dependence. HBV has been used for discharge modelling in many countries worldwide, including Brazil, China,[9] Iran,[10] Mozambique,[11] Sweden,[12][13][14] Switzerland[15] and Zimbabwe.[16] The HBV has also been used to simulate internal variables such as groundwater levels.[17] The model has also been used for hydrological change detection studies[18] and climate-change impact studies.[19][20]

The HBV model exists in several versions. One version, which has been especially designed for education with a user-friendly graphical user interface, is HBV light.[21]

HBV emulation is available as a part of Raven hydrologic framework. Raven is an open-source robust and flexible hydrological modelling framework, designed for application to challenging hydrological problems in academia and practice. This fully object-oriented code provides complete flexibility in spatial discretization, interpolation, process representation, and forcing function generation.

Sediment and solute modelling

The HBV model can also simulate the riverine transport of sediment and dissolved solids. Lidén simulated the transport of nitrogen, phosphorus and suspended sediment in Brazil, Estonia, Sweden and Zimbabwe.[22][23]

See also

References
  1. Bergström, S., 1976. Development and application of a conceptual runoff model for Scandinavian catchments, SMHI Report RHO 7, Norrköping, 134 pp.
  2. Bergström, S. 1995. The HBV model. In: Singh, V.P. (Ed.) Computer Models of Watershed Hydrology. Water Resources Publications, Highlands Ranch, CO., pp. 443-476.
  3. Bergström, Sten; Lindström, Göran (2015-05-26). "Interpretation of runoff processes in hydrological modelling-experience from the HBV approach". Hydrological Processes. 29 (16): 3535–3545. doi:10.1002/hyp.10510. ISSN 0885-6087. S2CID 130830725.
  4. Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil, F. and Loumagne, C. 2005. Which potential evapotranspiration input for a lumped rainfall–runoff model? Part 2—Towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling. Journal of Hydrology, 303, 290-306.
  5. Perrin, C., Michel, C. and Andréassian, V. 2001. Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments. Journal of Hydrology, 242, 275-301.
  6. Seibert, J. and Bergström, S.: A retrospective on hydrological catchment modelling based on half a century with the HBV model, Hydrol. Earth Syst. Sci., 26, 1371–1388, , 2022
  7. Lindström, G., Gardelin, M., Johansson, B., Persson, M. and Bergström, S. 1997. Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology, 201, 272-288.
  8. Abebe, N.A., F.L. Ogden, and N. Raj-Pradhan 2010. Sensitivity and uncertainty analysis of the conceptual HBV rainfall-runoff model: Implications for parameter estimation. J. Hydrol., 389(2010):301-310. .
  9. Zhang, X. and Lindström, G. 1996. A comparative study of a Swedish and a Chinese hydrological model. Water Resources Bulletin, 32, 985-994.
  10. Masih, I., Uhlenbrook, S., Ahmad, M.D. and Maskey, S. 2008. Regionalization of a conceptual rainfall runoff model based on similarity of the flow duration curve: a case study from Karkheh river basin, Iran. Geophysical Research Abstracts, SRef-ID: 1607-7962/gra/EGU2008-A-00226.
  11. Andersson, L., Hellström, S.-S., Kjellström, E., Losjö, K., Rummukainen, M., Samuelsson, P. and Wilk, J. 2006. Modelling Report: Climate change impacts on water resources in the Pungwe drainage basin. SMHI Report 2006-41, Norrköping, 92 pp.
  12. Seibert, J. 1999. Regionalisation of parameters for a conceptual rainfall-runoff model. Agricultural and Forest Meteorology, 98-99, 279-293.
  13. Seibert, J., 2003. Reliability of model predictions outside calibration conditions. Nordic Hydrology, 34, 477-492. Archived 2011-07-21 at the Wayback Machine
  14. Teutschbein, Claudia; Seibert, Jan (August 2012). "Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods". Journal of Hydrology. 456–457: 12–29. doi:10.1016/j.jhydrol.2012.05.052. ISSN 0022-1694.
  15. Addor, Nans; Rössler, Ole; Köplin, Nina; Huss, Matthias; Weingartner, Rolf; Seibert, Jan (October 2014). "Robust changes and sources of uncertainty in the projected hydrological regimes of Swiss catchments" (PDF). Water Resources Research. 50 (10): 7541–7562. doi:10.1002/2014wr015549. ISSN 0043-1397. S2CID 52837807.
  16. Lidén, R. and Harlin, J. 2000. Analysis of conceptual rainfall–runoff modelling performance in different climates. Journal of Hydrology, 238, 231-247.
  17. Seibert, J., 2000. Multi-criteria calibration of a conceptual rainfall-runoff model using a genetic algorithm. Hydrology and Earth System Sciences, 4(2), 215-224.
  18. Seibert, Jan; McDonnell, J.J. (2010). "Land-cover impacts on streamflow: A change-detection modelling approach that incorporates parameter uncertainty". Hydrological Sciences Journal. 55 (3): 316–332. doi:10.1080/02626661003683264. S2CID 26825290.
  19. Jenicek, Michal; Seibert, Jan; Staudinger, Maria (January 2018). "Modeling of Future Changes in Seasonal Snowpack and Impacts on Summer Low Flows in Alpine Catchments". Water Resources Research. 54 (1): 538–556. doi:10.1002/2017wr021648. ISSN 0043-1397. S2CID 133729782.
  20. Teutschbein, C.; Sponseller, R. A.; Grabs, T.; Blackburn, M.; Boyer, E. W.; Hytteborn, J. K.; Bishop, K. (November 2017). "Future Riverine Inorganic Nitrogen Load to the Baltic Sea From Sweden: An Ensemble Approach to Assessing Climate Change Effects". Global Biogeochemical Cycles. 31 (11): 1674–1701. doi:10.1002/2016gb005598. ISSN 0886-6236.
  21. Seibert, Jan; Vis, Marc (2012). "Teaching hydrological modelling with a user-friendly catchment-runoff-model software package". Hydrol. Earth Syst. Sci. 16 (9): 3315–3325. doi:10.5194/hess-16-3315-2012.
  22. Lidén, R., Conceptual Runoff Models for Material Transport Estimations, PhD dissertation, Lund University, Lund, Sweden (2000)
  23. Lidén, R., Harlin, J., Karlsson, M. and Rahmberg, M. 2001. Hydrological modelling of fine sediments in the Odzi River, Zimbabwe. Water SA, 27, 303-315.
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