1,389
Views
7
CrossRef citations to date
0
Altmetric
Technical Note

Definitions of climatological and discharge days: do they matter in hydrological modelling?

ORCID Icon, , &
Pages 836-844 | Received 16 Oct 2017, Accepted 01 Mar 2018, Published online: 27 Mar 2018

References

  • Amenu, G.G. and Killingtveit, Å. (2001). Real-time inflow forecasting for GilgelGibe reservoir, Ethiopia. In: Hydropower in the New Millennium: Proceedings of the 4th International Conference Hydropower, Bergen, Norway.
  • Bastola, S. and Murphy, C., 2013. Sensitivity of the performance of a conceptual rainfall–runoff model to the temporal sampling of calibration data. Hydrology Research, 44 (3), 484–493. doi:10.2166/nh.2012.061
  • Bergström, S., 1976. Development and application of a conceptual runoff model for Scandinavian catchments. Norrköping, Sweden: SMHI, Report No. RHO 7.
  • Bergström, S., 1992. The HBV model – its structure and applications. Norrköping, Sweden: SMHI Hydrology, RH No. 4.
  • Beven, K., 2001. Rainfall–runoff modelling: the primer. Chichester: Wiley.
  • Beven, K., 2009. Environmental modelling: an uncertain future? London: Routledge.
  • Chen, H., et al., 2012. Impacts of climate change on the Qingjiang Watershed’s runoff change trend in China. Stochastic Environmental Research and Risk Assessment, 26, 847–858. doi:10.1007/s00477-011-0524-2
  • Chiew, F. and McMahon, T., 1994. Application of the daily rainfall–runoff model MODHYDROLOG to 28 Australian catchments. Journal of Hydrology, 153 (1–4), 383–416. doi:10.1016/0022-1694(94)90200-3
  • Das, T., et al., 2008. Comparison of conceptual model performance using different representations of spatial variability. Journal of Hydrology, 356 (1–2), 106–118. doi:10.1016/j.jhydrol.2008.04.008
  • Gan, T.Y., Dlamini, E.M., and Biftu, G.F., 1997. Effects of model complexity and structure, data quality, and objective functions on hydrologic modeling. Journal of Hydrology, 192 (1–4), 81–103. doi:10.1016/S0022-1694(96)03114-9
  • Georgakakos, K.P., et al. 1999. Design and tests of an integrated hydrometerorological forecast system for the operational estimation and forecasting of rainfall and streamflow in the mountainous Panama Canal watershed. San Diego, CA: Hydrologic Research Center, HRC Technical Report No. 2.
  • Girons Lopez, M. and Seibert, J., 2016. Influence of hydro-meteorological data spatial aggregation on streamflow modelling. Journal of Hydrology, 541, 1212–1220.doi:10.1016/j.jhydrol.2016.08.026
  • Häggström, M., et al. 1990. Application of the HBV model for flood forecasting in six Central American rivers. Norrköping, Sweden: SMHI Hydrology, No. 27.
  • Harlin, J. and Kung, C.-S., 1992. Parameter uncertainty and simulation of design floods in Sweden. Journal of Hydrology, 137, 209–230. doi:10.1016/0022-1694(92)90057-3
  • Hutchinson, M.F., et al., 2009. Development and testing of Canada-wide interpolated spatial models of daily minimum-maximum temperature and precipitation for 1961-2003. Journal of Applied Meteorology and Climatology, 48 (4), 725–741. doi:10.1175/2008JAMC1979.1
  • Jie, M.-X., et al., 2016. A comparative study of different objective functions to improve the flood forecasting accuracy. Hydrology Research, 47 (4), 718–735. doi:10.2166/nh.2015.078
  • Jie, M.-X., et al. 2018. Transferability of conceptual hydrological models across temporal resolutions: approach and application. Water Resources Management, 32, 1367–1381. doi:10.1007/s11269-017-1874-4
  • Kavetski, D., Fenicia, F., and Clark, M.P., 2011. Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological modeling: insights from an experimental catchment. Water Resources Research, 47, W05501. doi:10.1029/2010WR009525
  • Kobold, M. and Brilly, M., 2006. The use of HBV model for flash flood forecasting. Natural Hazards and Earth System Science, 6 (3), 407–417. doi:10.5194/nhess-6-407-2006
  • Kuczera, G., 1997. Efficient subspace probabilistic parameter optimization for catchment models. Water Resources Research, 33 (1), 177–185. doi:10.1029/96WR02671
  • Liden, R., 1999. A new approach for estimating suspended sediment yield. Hydrology and Earth System Sciences, 3 (2), 285–294. doi:10.5194/hess-3-285-1999
  • Lindström, G., et al., 1997. Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology, 201 (1), 272–288. doi:10.1016/S0022-1694(97)00041-3
  • Littlewood, I.G. and Croke, B.F.W., 2008. Data time-step dependency of conceptual rainfall – streamflow model parameters: an empirical study with implications for regionalisation. Hydrological Sciences Journal, 53 (4), 685–695. doi:10.1623/hysj.53.4.685
  • Melsen, L.A., et al., 2016. HESS opinions: the need for process-based evaluation of large-domain hyper-resolution models. Hydrology and Earth System Sciences Discussions, 20, 1069–1079. doi:10.5194/hess-20-1069-2016
  • Pianosi, F. and Wagener, T., 2016. Understanding the time-varying importance of different uncertainty sources in hydrological modelling using global sensitivity analysis. Hydrological Processes, 30 (22), 3991–4003. doi:10.1002/hyp.10968
  • Reynolds, J.E., et al., 2017. Sub-daily runoff predictions using parameters calibrated on the basis of data with a daily temporal resolution. Journal of Hydrology, 550, 399–411. doi:10.1016/j.jhydrol.2017.05.012
  • Ropelewski, C.F. and Halpert, M.S., 1987. Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Monthly Weather Review, 115 (8), 1606–1626. doi:10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2
  • Rosbjerg, D., et al., 2013. Prediction of floods in ungauged basins. In: G. Blöschl, et al., eds. Runoff prediction in ungauged basins: synthesis across processes, places and scales. Cambridge University Press, 189–226. doi:10.1017/CBO9781139235761.012
  • Seibert, J., 1999. Regionalisation of parameters for a conceptual rainfall–runoff model. Agricultural and Forest Meteorology, 98–99, 279–293. doi:10.1016/S0168-1923(99)00105-7
  • Seibert, J., 2000. Multi-criteria calibration of a conceptual runoff model using a genetic algorithm. Hydrology and Earth System Science, 4 (2), 215–224. doi:10.5194/hess-4-215-2000
  • Seibert, J. and Vis, M., 2012. Teaching hydrological modeling with a user-friendly catchment-runoff-model software package. Hydrology and Earth System Sciences, 16, 3315–3325. doi:10.5194/hess-16-3315-2012
  • USGS. 2016. U.S. Geological Survey [online]. Available from: http://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=3demg [ Accessed 7 Mar 2016]
  • Vincent, L.A., et al., 2009. Bias in minimum temperature introduced by a redefinition of the climatological day at the canadian synoptic stations. Journal of Applied Meteorology and Climatology, 48 (10), 2160–2168. doi:10.1175/2009JAMC2191.1
  • Westerberg, I.K., et al., 2014. Regional water balance modelling using flow-duration curves with observational uncertainties. Hydrol Earth Systems Sciences, 18, 2993–3013. doi:10.5194/hess-18-2993-2014
  • Wetterhall, F., et al., 2011. Effects of temporal resolution of input precipitation on the performance of hydrological forecasting. Advances in Geosciences, 29, 21–25. doi:10.5194/adgeo-29-21-2011
  • WMO, 2010. Manual on stream gauging, volume II – computation of discharge. Geneva: World Meteorological Organization, WMO-No. 104.
  • WMO, 2011. Guide to climatological practices. Geneva: World Meteorological Organization, WMO-No. 100.
  • Xu, H., et al., 2013. Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China. Journal of Hydrology, 505, 1–12. doi:10.1016/j.jhydrol.2013.09.004
  • Zeng, Q., et al., 2016. Feasibility and uncertainty of using conceptual rainfall–runoff models in design flood estimation. Hydrology Research, 47 (4), 701–717. doi:10.2166/nh.2015.069