REFERENCES

• Alfieri, L., et al., 2011. A staggered approach to flash flood forecasting—case study in the Cévennes region. Advances in Geosciences, 29, 13–20.
   Google Scholar
• Bàrdossy, A. and Li, J., 2008. Geostatistical interpolation using copulas. Water Resources Research, 44, W07412.
   Web of Science ®Google Scholar
• Beven, K.J., et al., 2005. The uncertainty cascade in flood forecasting. In: P. Balbanis, D. Lambroso, and P. Samuels, eds. Innovation, advances and implementation of flood forecasting technology. Tromsø: ACTIF, abstract, p. 33 , paper 9p on CD (ISBN 978-1-898485-12-4).
   Google Scholar
• Beven, K.J., Smith, P.J., and Wood, A., 2011. On the colour and spin of epistemic error (and what we might do about it). Hydrology and Earth System Sciences, 15, 3123–3133.
   Web of Science ®Google Scholar
• Bogner, K. and Pappenberger, F., 2011. Multiscale error analysis, correction, and predictive uncertainty estimation in a flood forecasting system. Water Resources Research, 47, W07524.
   PubMed Web of Science ®Google Scholar
• Braun, L., 1985. Simulation of snowmelt–runoff in lowland and lower Alpine regions of Switzerland. Zürich: ETH, Zürcher Geographische Schriften, Heft 21 [Available from: Institute for Atmospheric and Climate Science ETH, Winterthurerstr. 190, CH-8057 Zürich].
   Google Scholar
• Cloke, H.L. and Pappenberger, F., 2009. Ensemble flood forecasting: a review. Journal of Hydrology, 375, 613–626.
   Web of Science ®Google Scholar
• Dietrich, J., et al., 2009. Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions. Natural Hazards and Earth System Sciences, 9, 1529–1540.
   Web of Science ®Google Scholar
• Duband, D., 1970. Reconnaissance dynamique de la forme des situations météorologiques. Application à la prevision quantitative des précipitations. Thesis (PhD), Faculté des Sciences de Paris, France.
   Google Scholar
• Fan, J. and Gijbels, I., 1996. Local polynomial modelling and its applications. London: Chapman and Hall.
   Google Scholar
• Germann, U. and Zawadki, I., 2002. Scale dependence of the predictability if precipitation from continental radar images. Part I: Description of the methodology. Monthly Weather Review, 130, 2859–2873.
   Web of Science ®Google Scholar
• Germann, U., Zawadki, I., and Turner, B., 2006a. Predictability of precipitation from continental radar images. Part IV: Limits to prediction. Journal of Atmospheric Sciences, 63, 2092–2108.
   Web of Science ®Google Scholar
• Germann, U., et al., 2006b. Radar precipitation measurement in a mountainous region. Quarterly Journal of the Royal Meteorological Society, 132, 1669–1692.
   Web of Science ®Google Scholar
• Hall, P., Rodney, C.L.W., and Qiwei, Y., 1999. Methods for estimating a conditional distribution. Journal of the American Statistical Association, 94, 154–163.
   Web of Science ®Google Scholar
• Hanson, B.E., 2004. Nonparametric estimation of smooth conditional distributions [online]. University of Wisconsin. Available from: http://www.ssc.wisc.edu/~bhansen/papers/cdf.html [ Accessed 5 April 2013]
   Google Scholar
• Herr, H.D. and Krzysztofowicz, R., 2010. Bayesian ensemble forecast of river stages and ensemble size requirements. Journal of Hydrology, 387, 151–164.
   Web of Science ®Google Scholar
• Jakeman, A. and Young, P., 1979. Refined instrumental variable methods of recursive time-series analysis. 2. Multivariable systems. International Journal of Control, 29, 621–644.
   Web of Science ®Google Scholar
• Joe, H., 1997. Multivariate models and dependence concepts. London: Chapman and Hall.
   Google Scholar
• Keef, C., Svensson, C., and Tawn, J. A., 2009. Spatial dependence in extreme river flows and precipitation for Great Britain. Journal of Hydrology, 378, 240–252.
   Web of Science ®Google Scholar
• Kelly, K.S. and Krzysztofowicz, R., 2000. Precipitation uncertainty processor for probabilistic river stage forecasting, Water Resources Research, 36 (9), 2643–2653.
   Web of Science ®Google Scholar
• Krzysztofowicz, R., 1999. Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resources Research, 35, 2739–2750.
   Web of Science ®Google Scholar
• Lees, M.J., et al., 1994. An adaptive flood warning scheme for the River Nith at Dumfries. In: W. Watts and J. Watts, eds. Second international conference on river flood hydraulics. Chichester: John Wiley & Sons Ltd.
   Google Scholar
• Mandapaka, P.V., et al., 2012. Can Lagrangian extrapolation of radar fields be used for precipitation nowcasting over complex Alpine orography? Weather and Forecasting, 27, 28–49.
   Web of Science ®Google Scholar
• Marsigli, C., et al., 2005. The COSMO-LEPS mesoscale ensemble system: validation of the methodology and verification. Nonlinear Processes in Geophysics, 12, 527–536.
   Web of Science ®Google Scholar
• Marsigli, C., Montani, A., and Paccangnella, T., 2008. A spatial verification method applied to the evaluation of high-resolution ensemble forecasts. Meteorological Applications, 15, 125–143.
   Web of Science ®Google Scholar
• Montanari, A. and Brath, A., 2004. A stochastic approach for assessing the uncertainty of rainfall–runoff simulations. Water Resources Research, 40, W01106.
   Web of Science ®Google Scholar
• Nelson, R.B., 1998. An introduction to copulas. Berlin: Springer-Verlag, Lecture Notes in Statistics 139.
   Google Scholar
• Obled, C., Bontron, G., and Garcon, R., 2002. Quantitative precipitation forecasts: a statistical adaptation of model outputs through an analogues sorting approach. Atmospheric Research, 63, 303–324.
   Web of Science ®Google Scholar
• Panziera, L. and Germann, U., 2010. The relationship between airflow and orographic precipitation on the southern side of the Alps as revealed by weather radar. Quarterly Journal of the Royal Meteorological Society, 136, 222–238.
   Web of Science ®Google Scholar
• Panziera, L., et al., 2011. NORA—Nowcasting of Orographic Rainfall by means of Analogues. Quarterly Journal of the Royal Meteorological Society, 137, 2106–2123.
   Web of Science ®Google Scholar
• Ranzi, R., Zappa, M., and Bacchi, B., 2007. Hydrological aspects of the Mesoscale Alpine Programme: findings from field experiments and simulations. Quarterly Journal of the Royal Meteorological Society, 133, 867–880.
   Web of Science ®Google Scholar
• Romanowicz, R.J., Young, P.C., and Beven, K.J., 2006. Data assimilation and adaptive forecasting of water levels in the River Severn catchment, United Kingdom. Water Resources Research, 42, W06407.
   Web of Science ®Google Scholar
• Romanowicz, R.J., et al., 2008. A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting. Advances in Water Resources, 31, 1048–1056.
   Web of Science ®Google Scholar
• Rossa, A., et al., 2011. The COST 731 Action: a review on uncertainty propagation in advanced hydro-meteorological forecast systems. Atmospheric Research, 100 (2–3),150–167.
   Web of Science ®Google Scholar
• Rotach, M.W., et al., 2009. MAP D-PHASE real-time demonstration of weather forecast quality in the Alpine region. Bulletin of the American Meteorological Society, 90 (9), 1321–1336.
   Web of Science ®Google Scholar
• Salvadori, G. and De Michele, C., 2007. On the use of copulas in hydrology: theory and practice. Journal of Hydrologic Engineering, 12, 369–380.
   Web of Science ®Google Scholar
• Sancetta, A. and Satchell, S., 2004. The Bernstein copula and its applications to modeling and approximations of multivariate distributions. Econometric Theory, 20, 535–562.
   Web of Science ®Google Scholar
• Schaake, J.C., et al., 2007. HEPEX: The hydrological ensemble prediction experiment. Bulletin of the American Meteorological Society, 88, 1541–1547.
   Web of Science ®Google Scholar
• Schweppe, F.C., 1965. Evaluation of likelihood functions for Gaussian signals. IEEE Transactions on Information Theory, 11, 61–70.
   Web of Science ®Google Scholar
• Sirangelo, B., Versace, P., and De Luca, D.L., 2007. Rainfall nowcasting by at site stochastic model PRAISE. Hydrology and Earth System Sciences, 11, 1341–1351.
   Web of Science ®Google Scholar
• Sklar, A., 1959. Fonctions de repartition á n dimensions et leurs marges. Publications de l’Institut de Statistique de l’Univiversite de Paris, 8, 229–231.
   Google Scholar
• Smith, P.J. and Beven, K.J., 2010. Forecasting river levels during flash floods using data based mechanistic models, online data assimilation and meteorological forecasts. In: Proceedings of the British Hydrological Society international symposium, 1 July 2010, Newcastle. London: British Hydrological Society.
   Google Scholar
• Smith, P.J., et al., 2008. The provision of site specific flood warnings using wireless sensor networks. In: P. Samuels, S. Huntingdon, W. Allsop, and J. Harrop, eds. Flood risk management: research and practice. Balkema: CRC Press, 212. Abstract, paper on CD. ISBN 978-0-415-48507-4.
   Google Scholar
• Smith, P.J., et al., 2012. Flash flood forecasting using data based mechanistic models and radar rainfall forecasts. In: R.J. Moore, S.J. Cole, and A.J. Illingworth, eds. Weather radar and hydrology. Wallingford: IAHS Press, IAHS Publ. 351, 562–567.
   Google Scholar
• Smith, P.J., Beven, K., and Horsburgh, K., 2013. Data based mechanistic modelling of tidally affected river reaches for flood warning purposes: an example on the River Dee, UK. Quarterly Journal of the Royal Meteorological Society, 139 (671), 340–349.
   Web of Science ®Google Scholar
• Song, S. and Singh, V.P., 2010. Frequency analysis of droughts using the Plackett copula and parameter estimation by genetic algorithm. Stochastic Environmental Research and Risk Assessment, 24, 783–805.
   Web of Science ®Google Scholar
• Thielen, J., et al., 2009. Monthly-, medium-, and short-range flood warning: testing the limits of predictability. Meteorological Applications, 16, 77–90.
   Web of Science ®Google Scholar
• Todini, E., 2008. A model conditional processor to assess predictive uncertainty in flood forecasting. International Journal of River Basin Management, 6 (2), 123–137.
   Google Scholar
• Weerts, A.H., Winsemius, H.C., and Verkade, J.S., 2011. Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales). Hydrology and Earth System Sciences, 15, 255–265.
   Web of Science ®Google Scholar
• Wilks, D.S., 1995. Statistical methods in the atmospheric sciences. New York: Academic Press.
   Google Scholar
• Wöhling, T., Lennartz F., and Zappa, M., 2006. Updating procedure for flood forecasting with conceptual HBV-type models. Hydrology and Earth System Sciences, 10, 783–788.
   Web of Science ®Google Scholar
• Young, P.C., 2001. Data-based mechanistic modelling and validation of rainfall–flow processes. In: M.G. Anderson and P.D. Bates, eds. Model validation: Perspectives in hydrological science. Chichester: John Wiley & Sons Ltd., 117–161.
   Google Scholar
• Young, P.C., 2002. Advances in real-time flood forecasting. Philosophical Transactions of the Royal Society (A), 360 (1796), 1433–1450.
   Web of Science ®Google Scholar
• Young, P.C., 2003. Top-down and data-based mechanistic modelling of rainfall–flow dynamics at the catchment scale. Hydrological Processes, 17 (11), 2195–2217.
   Web of Science ®Google Scholar
• Young, P.C., 2006. The data-based mechanistic approach to the modelling, forecasting and control of environmental systems. Annual Reviews in Control, 30 (2), 169–182.
   Web of Science ®Google Scholar
• Young, P.C. and Beven, K.J., 1994. Data-based mechanistic modeling and the rainfall–flow nonlinearity. Environmetrics, 5, 335–363.
   Web of Science ®Google Scholar
• Young, P.C., Castelletti, A., and Pianosi, F., 2007. The data-based mechanistic approach in hydrological modelling. In: A. Castelletti and R.S. Sessa, eds. Topics on system analysis and integrated water resource management. Amsterdam: Elsevier, 27–48.
   Google Scholar
• Young, P.C., Chotai, A., and Beven, K.J., 2004. Data-based mechanistic modelling and the simplification of environmental systems. In: J. Wainwright and M. Mullgan, eds. Environmental modelling: finding simplicity in complexity. Chichester: John Wiley & Sons Ltd., 371–388.
   Google Scholar
• Young, P. and Jakeman, A., 1979. Refined instrumental variable methods of recursive time-series analysis. 1. Single input, single output systems. International Journal of Control, 29, 1–30.
   Web of Science ®Google Scholar
• Young, P., Jakeman, A., and McMurtrie, R., 1980. An instrumental variable method for model order identification. Automatica, 16, 281–294.
   Web of Science ®Google Scholar
• Young, P.C., McKenna, P., and Bruun, J., 2001. Identification of non-linear stochastic systems by state dependent parameter estimation. International of Journal of Control, 74 (18), 1837–1857.
   Web of Science ®Google Scholar
• Zappa, M., et al., 2008. MAP D-PHASE: real-time demonstration of hydrological ensemble prediction systems. Atmospheric Science Letters, 9 (2), 80–87.
   Web of Science ®Google Scholar
• Zappa, M., et al., 2011. Superposition of three sources of uncertainties in operational flood forecasting chains. Atmospheric Research, 100 (2–3), 246–262.
   Web of Science ®Google Scholar