Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
J. Olsson (2006)
Publisher: Copernicus Publications
Journal: Natural Hazards and Earth System Sciences
Languages: English
Types: Article
Subjects: [SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces, environment, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.STU] Sciences of the Universe [physics]/Earth Sciences, G, GE1-350, Geography. Anthropology. Recreation, QE1-996.5, Environmental technology. Sanitary engineering, Environmental sciences, Geology, TD1-1066
International audience; The propagation of spatio-temporal errors in precipitation estimates to runoff errors in the output from the conceptual hydrological HBV model was investigated. The study region was the Gimån catchment in central Sweden, and the period year 2002. Five precipitation sources were considered: NWP model (H22), weather radar (RAD), precipitation gauges (PTH), and two versions of a mesoscale analysis system (M11, M22). To define the baseline estimates of precipitation and runoff, used to define seasonal precipitation and runoff biases, the mesoscale climate analysis M11 was used. The main precipitation biases were a systematic overestimation of precipitation by H22, in particular during winter and early spring, and a pronounced local overestimation by RAD during autumn, in the western part of the catchment. These overestimations in some cases exceeded 50% in terms of seasonal subcatchment relative accumulated volume bias, but generally the bias was within ±20%. The precipitation data from the different sources were used to drive the HBV model, set up and calibrated for two stations in Gimån, both for continuous simulation during 2002 and for forecasting of the spring flood peak. In summer, autumn and winter all sources agreed well. In spring H22 overestimated the accumulated runoff volume by ~50% and peak discharge by almost 100%, owing to both overestimated snow depth and precipitation during the spring flood. PTH overestimated spring runoff volumes by ~15% owing to overestimated winter precipitation. The results demonstrate how biases in precipitation estimates may exhibit a substantial space-time variability, and may further become either magnified or reduced when applied for hydrological purposes, depending on both temporal and spatial variations in the catchment. Thus, the uncertainty in precipitation estimates should preferably be specified as a function of both time and space.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Alexandersson, H.: Correction of precipitation with a simple climatological approach (in Swedish), SMHI Reports Meteorology, nr. 111, 2003.
    • Arheimer, B.: Riverine Nitrogen - analysis and modelling under Nordic conditions, Kanaltryckeriet, Motala, 1998.
    • Bergstro¨ m, S.: Development and application of a conceptual runoff model for Scandinavian catchments, SMHI Reports Hydrology and Oceanography, nr. 7, 1976.
    • Bergstro¨ m, S.: The HBV model - its structure and applications, SMHI Reports Hydrology, nr. 4, 1992.
    • Bergstro¨ m, S. and Carlsson, B.: River runoff to the Baltic Sea: 1950-1990, Ambio, 23, 280-287, 1994.
    • Bergstro¨ m, S., Harlin, J., and Lindstro¨ m, G.: Spillway design floods in Sweden. I: New guidelines, Hydrol. Sci. J., 37, 505-519, 1992.
    • Brandt, M., Jutman, T., and Alexandersson, H.: The water balance of Sweden, Annual mean values 1961-1990 of precipitation, evaporation and runoff (in Swedish), SMHI Reports Hydrology, nr. 49, 1994.
    • Carpenter, T. M. and Georgakakos, K. P.: Impacts of parametric and radar rainfall uncertainty on the ensemble streamflow simulations of a distributed hydrologic model, J. Hydrol., 298, 202- 221, 2004.
    • Chaubey, I., Haan, C. T., Grunwald, S., and Salisbury, J. M.: Uncertainty in the model parameters due to spatial variability of rainfall, J. Hydrol., 220, 48-61, 1999.
    • Førland, E. J., Allerup, P., Dahlstro¨ m, B., Elomaa, E., Jonsson, J., Madsen, H., Pera¨la¨, J., Rissanan, P., Vedin, H., and Vejen, F.: Manual for operational correction of nordic precipitation data, DNMI Report nr. 24/96, Norwegian Meteorological Institute, Norway, 1996.
    • Graham, P.: Modelling runoff to the Baltic basin. Ambio, 28, 328- 334, 1999.
    • Ha¨ggmark, L., Ivarsson, K.-I., and Olofsson, P.-O.: MESAN - Mesoscale Analysis (in Swedish), SMHI Reports Meteorology and Climatology, nr. 75, 1997.
    • Ha¨ggmark, L., Ivarsson, K.-I., Gollvik, S., and Olofsson, P.-O.: Mesan, an operational mesoscale analysis system, Tellus, 52A, 2-20, 2000.
    • Hossain, F., Anagnostou, E. N., Dinku, T., and Borga, M.: Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty, Hydrol. Processes, 18, 3277-3291, 2004.
    • Johansson, B. and Chen, D.: The influence of wind and topography on precipitation distribution in Sweden: Statistical analysis and modelling, Int. J. Climatol., 23, 1523-1535, 2003.
    • Johansson, B. and Chen, D.: Estimation of areal precipitation for runoff modelling using wind data: a case study in Sweden, Clim. Res., 29, 53-61, 2005.
    • Jutman, T.:. Production of a new runoff map of Sweden. Proceedings of Nordic Hydrological Conference, 4-6 August, Alta, Norway, NHP report, nr. 30, 643-651, 1992.
    • Ka¨lle´n, E.: HIRLAM Documentation Manual, Available from SMHI, 60176 Norrko¨ ping, Sweden, 1996.
    • Koistinen, J. and Michelson, D. B.: BALTEX weather radar-based products and their accuracies, Boreal Env. Res., 7, 253-163, 2002.
    • Koistinen, J. and Puhakka, T.: An improved spatial gauge-radar adjustment technique, Preprints 20th AMS Conf. on Radar Met., 30 November-3 December, Boston, MA., 179-186, 1981.
    • Kyriakidis, P. C., Miller, N. L., and Kim, J.: Uncertainty propagation of regional climate model precipitation forecasts to hydrologic impact assessment, J. Hydrometeorol., 2, 140-160, 2001.
    • Lindstro¨ m, G.: A simple automatic calibration routine for the HBV Model, Nordic Hydrol., 28, 153-168, 1997.
    • Lindstro¨ m, G. and Rodhe, A.: Transit times of water in soil lysimeters from modelling of oxygen-18, Water, Air and Soil Pollution, 65, 83-100, 1992.
    • Lindstro¨ m, G., Johansson, B., Persson, M., Gardelin, M., and Bergstro¨m, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272-288, 1997.
    • Louis, J. F.: A parametric model of vertical eddy fluxes in the atmosphere, Bound.-Layer Meteorol., 17, 187-202, 1979.
    • Maskey, S., Guinot, V., and Price, R. K.: Treatment of precipitation uncertainity in rainfall-runoff modelling: a fuzzy set approach, Adv. Water Resour., 27, 889-898, 2004.
    • Michelson, D. B.: Systematic correction of precipitation gauge observations using analyzed meteorological variables, J. Hydrol., 290, 161-170, 2004.
    • Michelson, D. B., Andersson, T., Koistinen, J., Collier, C. G., Riedl, J., Szturc, J., Gjertsen, U., Nielsen, A., and Overgaard, S.: BALTEX Radar Data Centre products and their methodologies, SMHI Reports Meteorology and Climatology, nr. 90, 2000.
    • Nandakumar, N. and Mein, R. G.: Uncertainty in rainfall-runoff model simulations and the implications for predicting the hydrologic effects of land-use change, J. Hydrol., 192, 211-232, 1997.
    • Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models. Part I: A discussion of principles, J. Hydrol., 10, 282-290.
    • Nijssen, B. and Lettenmaier, D. P.: Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites, J. Geophys. Res., 109, D02103, doi:10.1029/2003JD003497, 2004.
    • Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P.: Numerical Recipes in FORTRAN, The Art of Scientific Computing, Second Edition, Cambridge Univ. Press, 1992.
    • Savija¨rvi, H.: Fast radiation parameterization schemes for mesoscale and short-range forecast models, J. Appl. Meteorol., 29, 437-447, 1989.
    • Sharif, H. O., Odgen, F. L., Krajewski, W. F., and Xue, M.: Numerical simulations of radar rainfall error propagation, Water Resour. Res., 38, 8, doi:10.1029/2001WR000525, 2002.
    • Sundqvist, H., Berge, E., and Kristjansson, J. E.: Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model, Mon. Wea. Rev., 117, 1641-1657, 1989.
    • Swedish Meteorological and Hydrological Institute (SMHI): Weather and Water - the Weather Year 2002 (in Swedish), Va¨der och Vatten, nr. 13, SMHI, Norrko¨ ping, Sweden, 2002.
    • Todini, E.: A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements, Hydrol. Earth Syst. Sci., 5, 187-199, 2001, http://www.hydrol-earth-syst-sci.net/5/187/2001/.
    • Unde´n, P., Rontu, L., Ja¨rvinen, H., Lynch, P., Calvo, J., Cats, G., Cuxart, J., Eerola, K., Fortelius, C., Antonio Garcia-Moya, J., Jones, C., Lenderlink, G., McDonald, A., McGrath, R., Navascues, B., Woetman Nielsen, N., Ødegaard, V., Rodriguez, E., Rummukainen, M., Ro˜o˜ m, R., Sattler, K., Hansen Sass, B., Savija¨rvi, H., Wichers Schreur, B., Sigg, R., The, H., and Tijm, A.: HIRLAM-5 Scientific Documentation, available from SMHI, 60176 Norrko¨ ping, Sweden, 2002.
  • No related research data.
  • No similar publications.