HESS Hydrology and Earth System Sciences HESS Hydrol. Earth Syst. Sci. 1607-7938 Copernicus Publications Göttingen, Germany 10.5194/hess-17-3159-2013 Application of a model-based rainfall-runoff database as efficient tool for flood risk management Brocca L. https://orcid.org/0000-0002-9080-260X 1 Liersch S. https://orcid.org/0000-0003-2778-3861 2 Melone F. 1 Moramarco T. 1 Volk M. https://orcid.org/0000-0003-0064-8133 3 Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany UFZ – Helmholtz Centre for Environmental Research, Department Computational Landscape Ecology, Leipzig, Germany 06 08 2013 17 8 3159 3169 Copyright: © 2013 L. Brocca et al. 2013 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://hess.copernicus.org/articles/17/3159/2013/hess-17-3159-2013.html The full text article is available as a PDF file from https://hess.copernicus.org/articles/17/3159/2013/hess-17-3159-2013.pdf

A framework for a comprehensive synthetic rainfall-runoff database was developed to study catchment response to a variety of rainfall events. The framework supports effective flood risk assessment and management and implements simple approaches. It consists of three flexible components, a rainfall generator, a continuous rainfall-runoff model, and a database management system. The system was developed and tested at two gauged river sections along the upper Tiber River (central Italy). One of the main questions was to investigate how simple such approaches can be applied without impairing the quality of the results. The rainfall-runoff model was used to simulate runoff on the basis of a large number of rainfall events. The resulting rainfall-runoff database stores pre-simulated events classified on the basis of the rainfall amount, initial wetness conditions and initial discharge. The real-time operational forecasts follow an analogue method that does not need new model simulations. However, the forecasts are based on the simulation results available in the rainfall-runoff database (for the specific class to which the forecast belongs). Therefore, the database can be used as an effective tool to assess possible streamflow scenarios assuming different rainfall volumes for the following days. The application to the study site shows that magnitudes of real flood events were appropriately captured by the database. Further work should be dedicated to introduce a component for taking account of the actual temporal distribution of rainfall events into the stochastic rainfall generator and to the use of different rainfall-runoff models to enhance the usability of the proposed procedure.

 References Asquith, W. H., Thompson, D. B., Cleveland, T. G., and Fang, X.: Synthesis of Rainfall and Runoff Data used for Texas Department of Transportation Research Projects 0-4193 and 0-4194, US Geological Survey, Open File Report 2004-103, p. 50, 2004. Bae, D. H., Georgakakos, K. P., and Nanda, S. K.: Operational forecasting with real-time databases, J. Hydraul. Div.-ASCE, 121, 49–60, 1995. Barbetta, S., Moramarco, T., Brocca, L., Franchini, M., and Melone, F.: Confidence interval of real-time forecast stages provided by the STAFOM-RCM model: the case study of the Tiber River (Italy), Hydrol. Process., online first, <a href="http://dx.doi.org/10.1002/hyp.9613">https://doi.org/10.1002/hyp.9613</a>, 2012. Berni, N., Brocca, L., Giustarini, L., Pandolfo, C., Stelluti, M., Melone, F., Moramarco, T.: Coupling hydrological and hydraulic modeling for a reliable flood risk mitigation activities in the Upper-Medium Tiber River basin, Geophys. Res. Abstr., EGU2009-9498-3, EGU General Assembly 2009, Vienna, Austria, 2009a. Berni, N., Pandolfo, C., Ponziani, F., Stelluti, M., and Viterbo, A.: Umbria Region Forecasting/Decision Support fr Hydraulic Risk Mitigation Purposes, in: Proceedings of the Tenth International Conference on Computing and Control in the Water Industry, CRC Press, Sheffield (UK), 6 pp., 2009b. Beven, K. J. and Binley, A. M.: The future of distributed models: model calibration and predictive uncertainty, Hydrol. Process., 6, 279–298, 1992. Bras, R. L. and Rodriguez-Iturbe, I.: Random function and hydrology, Addison-Wesley Publishing Company, 1985. Brocca, L., Melone, F., and Moramarco, T.: On the estimation of antecedent wetness conditions in rainfall-runoff modelling, Hydrol. Process., 22, 629–642, 2008a. Brocca, L., Melone, F., Moramarco, T., and Singh, V. P.: A continuous rainfall-runoff model as tool for the critical hydrological scenario assessment in natural channels, in: Taniguchi, M., Burnett, W. C., Fukushima, Y., Haigh, M., and Umezawa, Y., From headwater to the ocean. Hydrological changes and managements, Taylor & Francis Group, London, 175–179, 2008b. Brocca, L., Melone, F., Moramarco, T., and Singh, V. P.: Assimilation of observed soil moisture data in storm rainfall-runoff modelling, J. Hydrol. Eng., 14, 153–165, 2009. Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., and Hasenauer, S.: Improving runoff prediction through the assimilation of the ASCAT soil moisture product, Hydrol. Earth Syst. Sci., 14, 1881–1893, <a href="http://dx.doi.org/10.5194/hess-14-1881-2010">https://doi.org/10.5194/hess-14-1881-2010</a>, 2010. Brocca, L., Melone, F., and Moramarco, T.: Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting, Hydrol. Process., 25, 2801–2813, 2011a. Brocca, L., Melone, F., Moramarco, T., Penna, D., Borga, M., Matgen, P., and Heitz, S.: Investigation of the hydrologic response of three experimental basins across Europe, Journal for Land Management, Food and Environment, 62, 31–37, 2011b. Camici, S., Tarpanelli, A., Brocca, L., Melone, F., and Moramarco, T.: "Design soil moisture" estimation by comparing continuous and storm-based rainfall-runoff modelling, Water Resour. Res., 47, W05527, <a href="http://dx.doi.org/10.1029/2010WR009298">https://doi.org/10.1029/2010WR009298</a>, 2011. Chao, Z., Hua-sheng, H., Wei-min, B., and Luo-ping, Z.: Robust recursive estimation of auto-regressive updating model parameters for real-time flood forecasting, J. Hydrol., 349, 376–382, 2008. Cloke, H. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, 2009. Cowpertwait, P. S. P.: Further developments of the Neyman-Scott clustered point process for modelling rainfall, Water Resour. Res., 27, 1431–1438, 1991a. Cowpertwait, P. S. P.: The stochastic generation of rainfall time series, Ph.D. Thesis, University of Newcastle upon Tyne, 1991b. Cowpertwait, P. S. P., O'Connell, P. E., Metcalfe, A. V., and Mawdsley, J. A.: Stochastic point process modelling of rainfall. I. Single-site fitting and validation, J. Hydrol., 175, 17–46, 1996. Damle, C. and Yalcin, A.: Flood prediction using Time Series Data Mining, J. Hydrol., 333, 305–316, 2007. de Roo, A. P. J., Gouweleeuw, B., Thielen, J., Bartholmes, J., Bongioannini-Cerlini, P., Todini, E., Bates, P. D., Horritt, M., Hunter, N., Beven, K., Pappenberger, F., Heise, E., Rivin, G., Hils, M., Hollingsworth, A., Holst, B., Kwadijk, J., Reggiani, P., Van Dijk, M., Sattler, K., and Sprokkereef, E.: Development of a European flood forecasting system, Int. J. River Basin Manage., 1, 49–59, 2003. Gabellani, S., Boni, G., Ferraris, L., von Hardenberg, J., and Provenzale, A.: Propagation of uncertainty from rainfall to runoff: A case study with a stochastic rainfall generator, Adv. Water Resour., 30, 2061–2071, 2007. Georgakakos, K. P., Carpenter, T. M., Cramer, J. A., Sperfslage, J. A., Sweeney, T. L., and Fread, D. L.: National system for threshold runoff estimation, in: Engineering Hydrology, edited by: Kuo, C. Y., American Society of Civil Engineers, New York, 952–957, 1993. Hartigan, J. and Wong, M.: Algorithm AS 136: A K-Means Clustering Algorithm, J. Roy. Stat. Soc. C-App., 28, 100–108, 1979. Hashemi, A. M., Franchini, M., and O'Connell, P. E.: Climatic and basin factors affecting the flood frequency curve: PART I – A simple sensitivity analysis based on the continuous simulation approach, Hydrol. Earth Syst. Sci., 4, 463–482, <a href="http://dx.doi.org/10.5194/hess-4-463-2000">https://doi.org/10.5194/hess-4-463-2000</a>, 2000. Hoffmann, L., Idrissi, A. E., Pfister, L., Hingray, B., Guex, F., Musy, A., Humbert, J., Drogue, G., and Leviandier, T.: Development of regionalized hydrological models in an area with short hydrological observation series, River Res. Appl., 20, 243–254, 2004. Hurst, H. E.: Long-term storage capacity of reservoirs, T. Am. Soc. Eng., 116, 770–799, 1951. Jakeman, A. J., Littlewood, I. G., and Whitehead, P. G.: Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments, J. Hydrol., 117, 275–300, 1993. Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, <a href="http://dx.doi.org/10.1029/2005WR004362">https://doi.org/10.1029/2005WR004362</a>, 2006. Krzysztofowicz, R.: Integrator of uncertainties for probabilistic river stage forecasting: precipitation-dependent model, J. Hydrol., 249, 69–85, 2001. Liersch, S. and Volk, M.: A rainfall-runoff database to support flood risk management, in: Proceedings of the iEMSs Fourth Biennial Meeting: International Congress on Environmental Modelling and Software (iEMSs 2008), edited by: Sànchez-Marrè, M., Béjar, J., Comas, J., Rizzoli, A. E., and Guariso, G., International Environmental Modelling and Software Society, Barcelona, Catalonia, 494–502, 2008. Matgen, P., Fenicia, F., Heitz, S., Plaza, D., de Keyser, R., Pauwels, V. R. N., Wagner, W., and Savenije, H.: Can ASCAT-derived soil wetness indices reduce predictive uncertainty in well-gauged areas? A comparison with in-situ soil moisture gauges in an assimilation application, Adv. Water Resour., 44, 49–65, 2012. Montanari, A., Rosso, R., and Taqqu, M. S.: Fractionally differenced ARIMA models applied to hydrologic time series: identification, estimation and simulation, Water Resour. Res., 33, 1035–1044, 1997. Moretti, G. and Montanari, A.: Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model, Hydrol. Earth Syst. Sci., 12, 1141–1152, <a href="http://dx.doi.org/10.5194/hess-12-1141-2008">https://doi.org/10.5194/hess-12-1141-2008</a>, 2008. Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models, Part 1 – a discussion of principles, J. Hydrol., 10, 282–290, 1970. Perrin, C., Oudin, L., Andreassian, V., Rojas-Serna, C., Michel, C., and Mathevet, T.: Impact of limited streamflow data on the efficiency and the parameters of rainfall-runoff models, Hydrolog. Sci. J., 52, 131–151, 2007. Reggiani, P. and Weerts, A.: A Bayesian approach to decisionmaking under uncertainty: An application to real-time forecasting in the river Rhine, J. Hydrol., 356, 56–69, 2008. Rodriguez-Iturbe, I., Febres De Power, B., and Valdes, J. B.: Rectangular pulses point process models for rainfall: analysis of empirical data, J. Geophys. Res., 92, 9645–9656, 1987. Romanowicz, R. J., Young, P. C., Beven, K. J., and Pappenberger, F.: A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting, Adv. Water Resour., 31, 1048–1056, 2008. Smith, K. and Ward, R.: Floods: Physical Processes and Human Impacts, John Wiley, Chichester, 1998. Tiwari, M. K. and Chatterjee, C.: Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs), J. Hydrol., 382, 20–33, 2010. Van Steenbergen, N. and Willems, P.: Method for testing the accuracy of rainfall- runoff models in predicting peak flow changes due to rainfall changes, in a climate changing context, J. Hydrol., 414–415, 425–434, 2012. Vaze, J., Post, D. A., Chiew, F. H. S., Perraud, J. M., Viney, N. R., and Teng, J.: Climate nonstationarity – Validity of calibrated rainfall-runoff models for use in climatic change studies, J. Hydrol., 394, 447–457, 2010. Villarini, G., Krajewski, W. F., Ntelekos, A. A., Georgakakos, K. P., and Smith, J. A.: Towards probabilistic forecasting of flash floods: the combined effects of uncertainty in radar-rainfall and flash flood guidance, J. Hydrol., 394, 275–284, 2010. Werner, M., Reggiani, P., De Roo, A., Bates, P., and Sprokkereef, E.: Flood forecasting and warning at the River Basin and at the European Scale, Natural Hazards, 36, 25–42, 2005.