Open Access
Issue |
E3S Web Conf.
Volume 7, 2016
3rd European Conference on Flood Risk Management (FLOODrisk 2016)
|
|
---|---|---|
Article Number | 04006 | |
Number of page(s) | 11 | |
Section | Hazard analysis and modelling | |
DOI | https://doi.org/10.1051/e3sconf/20160704006 | |
Published online | 20 October 2016 |
- Moore, R.J., 1985. The probability-distributed principle and runoff production at point and basin scales. Hydrological Sciences Journal, 30, 273–297. [CrossRef] [Google Scholar]
- Moore, R.J., 2007. The PDM rainfall-runoff model. Hydrology and Earth System Sciences, 11, 1, 483–499. [Google Scholar]
- Nielsen, S.A., Hansen, E., 1973. Numerical simulation of the rainfall-runoff process on a daily basis. Nordic Hydrology, 4, 171–190. [CrossRef] [Google Scholar]
- Abbott, M.B., Bathurst, J.C., Cunge, J.A., Oconnell, P.E., Rasmussen, J., 1986a. An Introduction to the European Hydrological System - Système Hydrologique Européen, She. 1. History and Philosophy of A Physically-Based, Distributed Modeling System. Journal of Hydrology, 87, 45–59. [CrossRef] [Google Scholar]
- Abbott, M.B., Bathurst, J.C., Cunge, J.A., Oconnell, P.E., Rasmussen, J., 1986b. An Introduction to the European Hydrological System - Système Hydrologique Européen, She. 2. Structure of A Physically-Based, Distributed Modeling System. Journal of Hydrology, 87, 61–77. [Google Scholar]
- Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R., 1998. Large area hydrologic modeling and assessment part I : Model development. Journal of American Water Research Association, 34, 1, 73–89 [Google Scholar]
- Beven, K.J., Kirkby, M.J., 1979. A Physically Based Variable Contributing Area Model of Basin Hydrology. Hydrology Sciences Bulletin 24, 43–69. [Google Scholar]
- Jakeman, A.J., Littlewood, I.G., Whitehead, P.G. 1990. Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. Journal of Hydrology, 117, 275–300. [CrossRef] [Google Scholar]
- Cole, S.J., Robson, A.J., Bell, V.A., Moore, R. J.: Model initialisation, data assimilation and probabilistic flood forecasting for distributed hydrological models, in: Geophysical Research Abstracts, 11, EGU2009-8048-3, 2009. [Google Scholar]
- Cabus, P., 2008. River flow prediction through rainfall–runoff modeling with a probability distributed model (PDM) in Flanders, Belgium. Agricultural Water Management, 95, pp. 859–868 [CrossRef] [Google Scholar]
- Dewelde, J., Verbeke, S., Quintelier, E., Cabus, P., Vermeulen, A., Vansteenkiste, T., De Jongh, I., Cauwenberghs, K., 2014.Real-time flood forecasting in Flanders. 11th International Conference on Hydroinformatics HIC 2014, New York City, USA; Conference proceedings. [Google Scholar]
- Van Steenbergen, N., Willems, P., 2012. Method for testing the accuracy of rainfall– runoff models in predicting peak flow changes due to rainfall changes, in a climate changing context. Journal of Hydrology, 414–415, 425–434. [Google Scholar]
- Van Steenbergen, N., Willems, P., 2014. Quantification of rainfall forecast uncertainty and its impact on flood forecasting. 11th International Conference on Hydroinformatics - HIC 2014, New York City, USA. [Google Scholar]
- Breuer, L., Huisman, J.A., Willems, P., Bormann, H., Bronstert, A., Croke, B.F.W., Frede, H.-G., Gräff, T., Hubrechts, L., Jakeman, A.J., Kite, G., Lanini, J., Leavesley, G., Lettenmaier, D.P., Lindström, G., Seibert, J., Sivapalan, M., Viney, N.R., 2009. Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) I: Model intercomparison with current land use. Advances in Water Resources, 32 (2), 129–146. [CrossRef] [Google Scholar]
- Viney, N.R., Bormann, H., Breuer, L., Bronstert, A., Croke, B.F.W., Frede, H., Gräff, T., Hubrechts, L., Huisman, J.A., Jakeman, A.J., Kite, G.W., Lanini, J., Leavesley, G., Lettenmaier, D.P., Lindström, G., Seibert, J., Sivapalan, M., Willems, P., 2009. Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions. Advances in Water Resources, 32 (2), 147–158. [CrossRef] [Google Scholar]
- Ludwig, R., May, I., Turcotte, R., Vescovi, L., Braun, M., Cyr, J.-F., Fortin, L.-G, Chaumont, D., Biner, S., Chartier, I., Caya, D., Mauser, W., 2009. The role of hydrological model complexity and uncertainty in climate change impact assessment. Advanced. Geosciences, 21, 63–71. [CrossRef] [Google Scholar]
- Maurer, E.-P., Brekke, L.-D., Pruitt, T., 2010. Contrasting lumped and distributed hydrology models for estimating climate change impacts on California watersheds. Journal of the American Water Resources Association, 46 (5), 1024–1035. [CrossRef] [Google Scholar]
- Van Steenbergen, N., Ronsyn, J., Willems, P., 2012. A non-parametric data-based approach for probabilistic flood forecasting in support of uncertainty communication. Environmental Modelling & Software, 33, 92–105 [CrossRef] [Google Scholar]
- Vansteenkiste, T., Tavakoli, M., Ntegeka, V., De Smedt, F., Batelaan, O., Pereira, F., Willems, P., 2014. Intercomparison of hydrological model structure and calibration approaches in climate scenario impact projections. Journal of Hydrology, 519A, 27, 743–755. [CrossRef] [Google Scholar]
- Werner, M., Cranston, M., Harrison, T., Whitfield, D., Schellekens, J., 2009. Recent developments in operational flood forecasting in England, Wales and Scotland. Meteorololgical Applications, 16, 13–22. [Google Scholar]
- De Lange, W.J., Prinsen, G.F, Hoogewoud, J.C, Veldhuizen, A.A, Verkaik, J., Oude Essink, G.H.P., van Walsum, P.E.V., Delsam, J.R, Hunink, J.C., Massop, H.Th.L., Kroon, T., 2014. An operational, multi-scale, multi-model system for for consensus based, integrated water management and policy analysis: The Netherlands Hydrological Instrument. Environmental Modelling & Software, 59, 98–108. [CrossRef] [Google Scholar]
- Martens, B., Cabus, P., De Jongh, I., Verhoest, N.E.C., 2013. Merging weather radar observations with ground-based measurements of rainfall using an adaptive multiquadric surface fitting algorithm. Journal of Hydrology, 500, 84–96 [CrossRef] [Google Scholar]
- Karssenberg, D. De Jong, K, Van der Kwast, J., 2007. Modelling landscape dynamics with Python. International Journal of Geographical Information Science, 19, 623–637. [CrossRef] [Google Scholar]
- PCRaster, January 2013. PCRaster internet site. Available online at: http://www.python.org [Google Scholar]
- Willems, P., 2014. Parsimonious rainfall–runoff model construction supported by time series processing and validation of hydrological extremes – Part 1: Step-wise model-structure identification and calibration approach. Journal of Hydrology, 510 (14), 578–590. [CrossRef] [Google Scholar]
- Willems, P., Quan, T.Q, Van den Zegel, B., De Decker, K., Pannemans, B., Gullentops, C. Buitrago, S., Blanckaert, J., Adams, R., 2013. Next Generation Tool for Flexible Hydrological Modelling – concept note. Concept-eindrapport. KU Leuven & IMDC, project L 2012 T 0001 X Perceel 2 Dijle / Vlaamse Milieumaatschappij – Afdeling Operationeel Waterbeheer, maart 2014. [Google Scholar]
- Leavesley, G.H., Restrepo, P.J., Stannard, L G., Frankoski, L.A., Sautins, A.M., 1996. The modular modeling system (MMS) - A modeling framework for multidisciplinary research and operational applications. GIS and environmental modeling: Progress and research issues, Goodchild, M. et al., eds., GIS World Books, Fort Collins, Colorado, 155–158. [Google Scholar]
- Kraft, P., Vache, K.B., Frede, H.-G., Breuer, L., 2011. A hydrological programming language extension for integrated catchment models. Environmental Modelling and Software, 26, 828–830. [CrossRef] [Google Scholar]
- Fenicia, F., Savenije, H.H.G., Matgen, P., Pfister, L., 2008. Understanding catchment behavior through stepwise model concept improvement. Water Resources Research 44, W01402þ [CrossRef] [Google Scholar]
- Duan, Q.Y., Gupta, V.K., Sorooshian, S., 1993. Shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications, 76 (3), 501–521. [Google Scholar]
- Tran, Q.Q., Willems, P., Pannemans, B., Blanckaert, J., Pereira, P., Nossent, J., Cauwenberghs, K., Vansteenkiste, T., 2015. Flexible hydrological modeling - Disaggregation from lumped catchment scale to higher spatial resolutions. Geophysical Research Abstracts 17, EGU2015-6983–1. [Google Scholar]
- Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models, I, A discussion of principles. Journal of Hydrology, 10, 282–290. [Google Scholar]
- Buytaert, W., Baez, S., Bustamante, M., Dewulf, A., 2012. Web-Based Environmental Simulation: Bridging the Gap between Scientific Modeling and Decision-Making. Environmental science & technology, 46, 1971–1976. [CrossRef] [PubMed] [Google Scholar]
- Alberti, K., de Jong, K., Karssenberg, D., A virtual globe for environmental impact assessment. European Geosciences Union, EGU General Assembly, 2014. [Google Scholar]
- Bröring, A., Maué, P., Janowicz, K., Nüst, D., Malewski, C., 2011. Semantically-Enabled Sensor Plug & Play for the Sensor Web. Sensors, 11 (8), 7568–7605. [CrossRef] [Google Scholar]
- Maué, P., Stasch, C., Athanasopoulos, G., Gerharz, L., 2011. Geospatial Standards for Web-enabled Environmental Models. Internal Journal for Spatial Data Infrastructures Research (IJSDIR), 6. [Google Scholar]
- Wolfs, V., Meert, P., Willems, P., 2015. Modular conceptual modelling approach and software for river hydraulic simulations. Environmental Modelling & Software 71, 60–77. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.