Zacytuj

Andréassian, V., Perrin, C., Michel, C., Usart-Sanchez, I., Lavarbe, J., 2001. Impact of imperfect knowledge on the efficiency and the parameters of watershed models. Journal of Hydrology, 205, 1–4, 206–223. http://dx.doi.org/10.1016/S0022-1694(01)00437-1.10.1016/S0022-1694(01)00437-1Open DOISearch in Google Scholar

Ardia, D., Mullen, K.M., Peterson, B.G., Ulrich, J., 2015. DE-optim: Diferential evolution in R. Version 2.2-3.Search in Google Scholar

Bai, P., Liu, X., Liang, K., Liu, C., 2015. Comparison of performance of twelve monthly water balance models in different climatic catchments of China. Journal of Hydrology, 529, 1030–1040. DOI: 10.1016/j.jhydrol.2015.09.015.10.1016/j.jhydrol.2015.09.015Open DOISearch in Google Scholar

Bergström, S., 1995. The HBV model. In: Sing, V.P. (Ed.): Computers Models of Watershed Hydrology. Water. Resour. Publ., pp. 443–476.Search in Google Scholar

Beven, K.J., 2005. Rainfall-runoff modelling: Introduction. In: Anderson, M.G. (Ed): Encyclopedia of Hydrological Sciences, Wiley, Chichester, pp. 1857–1868.10.1002/0470848944.hsa130Search in Google Scholar

Brath, A., Montanari, A., Toth, E., 2004. Analysis of the effects of different scenarios of historical data availability on the calibration of a spatially-distributed hydrological model. Journal of Hydrology, 291, 3–4, 232–253. http://dx.doi.org/10.1016/j.jhydrol.2003.12.044.10.1016/j.jhydrol.2003.12.044Open DOISearch in Google Scholar

Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A., 1984. Classification and Regression Trees. The Wadsworth and Brooks-Cole Statistics-Probability Series. Taylor & Francis, 368 p. ISBN: 0412048418, 9780412048418.Search in Google Scholar

Brigode, P., Oudin, L., Perrin, C., 2013. Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change? Journal of Hydrology, 476, 410–425. http://dx.doi.org/10.1016/j.jhydrol.2012.11.012.10.1016/j.jhydrol.2012.11.012Open DOISearch in Google Scholar

Ceola, S., Arheimer, B., Baratti, E., Blöschl, G., Capell, R., Castellarin, A., Freer, J., Han, D., Hrachowitz, M., Hundecha, Y., Hutton, C., Lindström, G., Montanari, A., Nijzink, R., Parajka, J., Toth, E., Viglione, A., and Wagener, T., 2015. Virtual laboratories: new opportunities for collaborative water science. Hydrol. Earth Syst. Sci., 19, 2101–2117. DOI: 10.5194/hess-19-2101-2015.10.5194/hess-19-2101-2015Open DOISearch in Google Scholar

Chiew, F.H.S., Teng, J., Vaze, J., Post, D.A., Perraud, J.M., Kirono, D.G.C., Viney, N.R., 2009. Estimating climate change impact on runoff across southeast Australia: Method, results, and implications of the modeling method, Water Re-sour. Res., 45, W10414. DOI: 10.1029/2008WR007338.10.1029/2008WR007338Open DOISearch in Google Scholar

Coron, L., Andréassian, V., Bourqui, M., Perrin, C., Hendrickx, F., 2011. Pathologies of hydrological model used in changing climatic conditions: a review. Hydro-climatology: Variability and change. In: Proceedings of IUGG2011 symposium J-H02, Melbourne, Australia.Search in Google Scholar

Coron, L., Andréassian, V., Perrin, C., Lerat, J., Vaze, J., Bourqui, M., Hendrickx, F., 2012. Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments. Water Resour. Res., 48, W05552. DOI: 10.1029/2011WR011721.10.1029/2011WR011721Open DOISearch in Google Scholar

Coron, L., Andréassan, V., Perrin, C., Bourqui, M., Hendrickx, F., 2014. On the lack of robustness of hydrologic models regarding water balance simulation: a diagnostic approach applied to three models of increasing complexity on 20 mountainous catchments. Hydrol. Earth Syst. Sci., 18, 727–746. DOI: 10.5194/hess-18-727-2014.10.5194/hess-18-727-2014Open DOISearch in Google Scholar

Das, T., Bárdossy, A., Zehe, E., He, Y., 2008. Comparison of conceptual model performance using different representations of spatial variability. J. Hydrol., 356, 106–118.10.1016/j.jhydrol.2008.04.008Search in Google Scholar

Farkas, C., Kværnø, S.H., Engebretsen, A., Barneveld, R., Deelstra, J., 2016. Applying profile and catchment-based mathematical models for evaluating the run-off from a Nordic catchment. J. Hydrol. Hydromech., 64, 3, 218–225. DOI: 10.1515/johh-2016-0022.10.1515/johh-2016-0022Open DOISearch in Google Scholar

Fenicia, F., Kavetski, D., Savenije, H.H.G., 2011. Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development. Water Resour. Res., 47, W11510. DOI: 10.1029/2010wr010174.10.1029/2010wr010174Open DOISearch in Google Scholar

Finger, D., Heinrich, G., Gobiet, A., Bauder, A., 2012. Projections of future water resources and their uncertainty in a glacierized catchment in the Swiss Alps and the subsequent effects on hydropower production during the 21st century. Water Resour. Res., 48, 02521. DOI: 10.1029/2011WR010733, 2012.10.1029/2011WR0107332012Open DOISearch in Google Scholar

Fowler, K.J.A., Peel, M.C., Western, A.W., Zhang, L., Peterson, T.J., 2016. Simulating runoff under changing climate conditions: Revising an apparent deficiency of conceptual rainfall-runoff models. Water. Resour. Res., 52, 1820–1846. DOI: 10.1002/2015WR018068.10.1002/2015WR018068Open DOISearch in Google Scholar

Gaál, L., Szolgay, J., Kohnová, S., Parajka, J., Merz, R., Viglione, A., Blöschl, G., 2012. Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology. Water Resour. Res., 48, W04511. DOI: 10.1029/2011WR011509.10.1029/2011WR011509Open DOISearch in Google Scholar

Iorgulescu, I., Beven, K.J., 2004. Nonparametric direct mapping of rainfall-runoff relationships: An alternative approach to data analysis and modeling? Water Resour. Res., 40, W08403. DOI: 10.1029/2004WR003094.10.1029/2004WR003094Open DOISearch in Google Scholar

Klemeš, V., 1986. Dilettantism in hydrology: Transition or destiny? Water Resour. Res., 22, 9, 177–188.10.1029/WR022i09Sp0177SOpen DOISearch in Google Scholar

Kuentz, A., Arheimer, B., Hundecha, Y., Wagener, T., 2016. Understanding hydrologic variability across Europe through catchment classification. Hydrol. Earth Syst. Sci. Discuss., 21, 6, 1–28. DOI: 10.5194/hess-2016-428.10.5194/hess-2016-428Open DOISearch in Google Scholar

Magand, C., Ducharne, A., Le Moine, N., Brigode, P., 2015. Parameter transferability under changing climate: case study with a land surface model in the Durance watershed, France. Hydrological Sciences Journal, 60, 7–8, 1408–1423. DOI: 10.1080/02626667.2014.993643.10.1080/02626667.2014.993643Open DOISearch in Google Scholar

Merz, R., Blöschl, G., 2004. Regionalisation of catchment model parameters. Journal of Hydrology. 27, 95–123. DOI: 10.1002/hyp.6253.10.1002/hyp.6253Open DOISearch in Google Scholar

Merz, R., Blöschl, G., Parajka, J., 2009. Scale effects in conceptual hydrological modelling. Water Resour. Res., 45, W09405. DOI: 10.1029/2009WR007872.10.1029/2009WR007872Open DOISearch in Google Scholar

Merz, R., Parajka, J., Blöschl, G., 2011. Time stability of catchment model parameters: Implications for climate impact analyses. Water. Resour. Res., 47, 1015–1031. DOI: 10.1029/2010WR009505.10.1029/2010WR009505Open DOISearch in Google Scholar

Nash, J.E. Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I-A discussion of principles, Journal of Hydrology, 10, 3, 282–290. DOI: 10.1016/0022-1694(70)90255-6.10.1016/0022-1694(70)90255-6Open DOISearch in Google Scholar

Nester, T., Kirnbauer, R., Gutknecht, D., Blöschl, G., 2011. Climate and catchment controls on the performance of regional flood simulations. Journal of Hydrology, 340–356. http://dx.doi.org/10.1016/j.jhydrol.2011.03.028.10.1016/j.jhydrol.2011.03.028Open DOISearch in Google Scholar

Nester, T., Komma, J., Blöschl, G., 2016. Real time forecasting in the Upper Danube basin. J. Hydrol. Hydromech., 64, 4, 404–414. DOI: 10.1515/johh-2016-0033.10.1515/johh-2016-0033Open DOISearch in Google Scholar

Nijzink, R.C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H.H.G., Hrachowitz, M., 2016. The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models. Hydrol. Earth Syst. Sci., 20, 1151–1176. DOI:10.5194/hess-20-1151-2016.10.5194/hess-20-1151-2016Open DOISearch in Google Scholar

Osuch, M., Romanowicz, R.J., Booij, M.J., 2015. The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics. Hydrological Sciences Journal, 60, 7–8, 1299–1316. DOI: 10.1080/02626667.2014.967694.10.1080/02626667.2014.967694Open DOISearch in Google Scholar

Oudin, L., Perrin, C., Mathevet, T., Andréassian, V., and Michel, C., 2006. Impact of biased and randomly corrupted inputs on the efficiency and the parameters of watershed models. J. Hydrol., 320, 1–2, 62–83. DOI: 10.1016/j.jhydrol.2005.07.016.10.1016/j.jhydrol.2005.07.016Open DOISearch in Google Scholar

Parajka, J., Blöschl, G., 2008. The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models. Journal of Hydrology, 358, 3–4, 240–258. https://doi.org/10.1016/j.jhydrol.2008.06.006.10.1016/j.jhydrol.2008.06.006Open DOISearch in Google Scholar

Parajka, J., Merz, R., Blöschl, G., 2005. A comparison of regionalisation methods for catchment model parameters. Hydrol. Earth Syst. Sci., 9, 157–171. DOI: 10.5194/hess-9-157-2005.10.5194/hess-9-157-2005Open DOISearch in Google Scholar

Parajka, J., Merz, R., Blöschl, G., 2007. Uncertainty and multiple calibration in regional water balance modelling case study in 320 Austrian catchments. Hydrol. Process, 21, 435–446. DOI: 10.1002/hyp.6253.10.1002/hyp.6253Open DOISearch in Google Scholar

Pechlivanidis, I.G., Arheimer, B., 2015. Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case. Hydrol. Earth Syst. Sci., 19, 4559–4579. DOI: 10.5194/hess-19-4559-2015.10.5194/hess-19-4559-2015Open DOISearch in Google Scholar

Pebesma, E.J., 2001. Gstat User’s Manual. Dep. of Phys. Geogr., Utrecht Univ., Utrecht, The Netherlands.Search in Google Scholar

Perrin, C., Michel, C., Andréassian, V., 2001. Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments. J. Hydrol., 242, 275–301. https://doi.org/10.1016/S0022-1694(00)00393-0.10.1016/S0022-1694(00)00393-0Open DOISearch in Google Scholar

Perrin, C., Michel, C., Andréassian, V., 2003. Improvement of a parsimonious model for streamflow simulation. J. Hydrol., 279, 275–289. DOI: 10.1016/s0022-1694(03)00225-7. Perrin, C., Oudin, L., Andréassian, V., Rojas-Serna, C., Michel, C., Mathevet, T., 2007. Impact of limited streamflow data on the efficiency and the parameters of rainfall-runoff models. Hydrol. Sci. J., 52, 1, 131. http://dx.doi.org/10.1623/hysj.52.1.131.10.1016/s0022-1694(03)00225-7....-...2007.streamflowrainfall-runoffmodels.Hydrol.Sci.J.,52,1,131.http://dx.doi.org/10.1623/hysj.52.1.131Open DOISearch in Google Scholar

Perrin, C., Andréassian, V., Rojas-Serna, C., Mathevet, T., Le Moine, N., 2008. Discrete parameterization of hydrological models: Evaluating the use of parameter sets libraries over 900 catchments. Water Resour. Res., 44, W08447. DOI: 10.1029/2007WR006579.10.1029/2007WR006579Open DOISearch in Google Scholar

Poncelet, C., Merz, R., Parajka, J., Oudin, L., Andréassian, V., Perrin, C., 2017. Process-based interpretation of conceptual hydrological model performance using a multinational catchment set. Water Resource Research. DOI: 10.1002/2016WR019991.10.1002/2016WR019991Open DOISearch in Google Scholar

R Development Core Team, 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.Search in Google Scholar

Saft, M., Western, A.W., Zhang, L., Peel, M.C., Potter, N.J., 2015. The influence of multiyear drought on the annual rainfall-runoff relationship: An Australian perspective. Water Re-sour. Res., 51, 2444–2463. DOI: 10.1002/2014WR015348.10.1002/2014WR015348Open DOISearch in Google Scholar

Saft, M., Peel, M.C., Western, A.W., Zhang, L., 2016. Predicting shifts in rainfall-runoff partitioning during multiyear drought: Roles of dry period and catchment characteristics. Water Resour. Res., 52. DOI: 10.1002/2016WR019525.10.1002/2016WR019525Open DOISearch in Google Scholar

Schaefli, B. Gupta, H.V., 2007. Do Nash values have value? Hydrol. Process., 21, 2075–2080. DOI: 10.1002/hyp.6825. Seibert, J., 2003. Reliability of model predictions outside calibration conditions. Nordic Hydrology, 34, 477–492.10.1002/hyp.6825.J.2003..34477492Open DOISearch in Google Scholar

Seibert, M., Merz, B., Apel, H., 2016. Seasonal forecasting of hydrological drought in the Limpopo basin: A comparison of statistical methods. Hydrol. Earth Syst. Sci. Discuss. DOI: 10.5194/hess-2016-4, 2016.10.5194/hess-2016-42016Open DOISearch in Google Scholar

Seifert, D., Sonnenborg, T.O., Refsgaard, J.C., Højberg, A.L., Troldborg, L., 2012. Assessment of hydrological model predictive ability given multiple conceptual geological models. Water Resour. Res., 48, W06503. DOI: 10.1029/2011WR011149.10.1029/2011WR011149Open DOISearch in Google Scholar

Seiler, G., Anctil, F., Perrin, C., 2012. Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions. Hydrol. Earth Syst. Sci., 16, 4, 1171–1189. http://dx.doi.org/10.5194/hess-16-1171-2012.10.5194/hess-16-1171-2012Open DOISearch in Google Scholar

Sleziak, P., Szolgay, J., Hlavčová, K., Parajka, J., 2016a. The impact of the variability of precipitation and temperatures on the efficiency of a conceptual rainfall-runoff model. Slovak Journal of Civil Engineering, 24, 4, 1–7. DOI: 10.1515/sjce-2016-0016.10.1515/sjce-2016-0016Open DOISearch in Google Scholar

Sleziak, P., Szolgay, J., Hlavčová, K., Parajka, J., 2016b. Assessment of the performance of a hydrological model in relation to selected climatic characteristics. In: Proc. 16th International Multidisciplinary Scientific GeoConference SGEM 2016, Book 3 Vol. 3, pp. 43–52. DOI: 10.5593/SGEM2016/HB33/S02.006.10.5593/SGEM2016/HB33/S02.006Open DOISearch in Google Scholar

Stauer, J.J., Stensvold, K.A., Gregory, M.B., 2010. Determination of biologically significant hydrologic condition metrics in urbanizing watersheds: an empirical analysis over a range of environmental settings. Hydrobiologia, 654, 1, 27–55. DOI: 10.1007/s10750-010-0362-0.10.1007/s10750-010-0362-0Open DOISearch in Google Scholar

Sun, W., Wang, Y., Wang, G., Cui, X., Yu, J., Zuo, D., Xu, Z., 2017. Physically based distributed hydrological model calibration based on a short period of streamflow data: case studies in four Chinese basins. Hydrol. Earth Syst. Sci., 21, 251–265. DOI: 10.5194/hess-21-251-2017.10.5194/hess-21-251-2017Open DOISearch in Google Scholar

Therneau, T., Atkinson, B., Ripley, B., 2017. Recursive partitioning and regression trees. Version 4.1-11.Search in Google Scholar

van Esse, W.R., Perrin, C., Booij, M.J., Augustijn, D.C.M., Fenicia, F., Kavetski, D., Lobligeois, F., 2013. The influence of conceptual model structure on model performance: a comparative study from 273 French catchments. Hydrol. Earth Syst. Sci., 17, 4227–4239. DOI: 10.5194/hess-17-4227-2013.10.5194/hess-17-4227-2013Open DOISearch in Google Scholar

van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., Srinivasan, R., 2006. A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydrology, 324, 10–23.10.1016/j.jhydrol.2005.09.008Search in Google Scholar

Valent, P., Szolgay, J., 2012. Assessment of the uncertainties of a conceptual hydrologic model by using artificially generated flows. Slovak Journal of Civil Engineering, 20, 4, 35–43. DOI: https://doi.org/10.2478/v10189-012-0020-9.10.2478/v10189-012-0020-9Open DOISearch in Google Scholar

Vaze, J., Post, D.A., Chiew, F.H.S., Perraud, J.M., Viney, N.R., Teng, J., 2010. Climate nonstationarity – Validity of calibrated rainfall-runoff models for use in climatic changes studies. J. Hydrol., 394, 3–4, 447–457. DOI: 10.1016/j.jhydrol.2010.09.018.10.1016/j.jhydrol.2010.09.018Open DOISearch in Google Scholar

Viglione, A., Parajka, J., Rogger, M., Salinas, J.L., Laaha, G., Sivapalan, M., Blöschl, G., 2013. Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria. Hydrol. Earth Syst. Sci., 17, 2263–2279. DOI: 10.5194/hess-17-2263-2013.10.5194/hess-17-2263-2013Open DOISearch in Google Scholar

Viglione, A., Parajka, J., 2014. TUWmodel: Lumped hydrological model for educational purposes. Version 0.1-4. https://cran.r-project.org/web/packages/TUWmodel/index.html.Search in Google Scholar

Viviroli, D., Zappa, M., Schwanbeck, J., Gurtz, J., Weingartner, R., 2009. Continuous simulation for flood estimation in un-gauged mesoscale catchments of Switzerland – Part I: Modelling framework and calibration results. Journal of Hydro-logy, 377, 191–207. https://doi.org/10.1016/j.jhydrol. 2009.08.023.10.1016/j.jhydrol.2009.08.023Open DOISearch in Google Scholar

Wang-Erlandsson, L., Bastiaanssen, W.G.M., Gao, H., Jager-meyer, J., Senay, G.B., van Dijk, A.I.J.M., Guerschman, J.P., Keys, P.W., Gordon, L.J., Savenije, H.H.G., 2016. Global root zone sorage capacity from satellite-based evaporation. Hydrol. Earth Syst. Sci., 20, 1459–1481. www.hydrol-earth-syst-sci.net/20/1459/2016/.10.5194/hess-20-1459-2016Open DOISearch in Google Scholar

Wilby, R.L., 2005. Uncertainty in water resource model parameters used for climate change impact assessment. Hydrol. Processes, 19, 16, 3201–3219.10.1002/hyp.5819Search in Google Scholar

eISSN:
0042-790X
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Engineering, Introductions and Overviews, other