Improving Flash Flood Forecasting with a Bayesian Probabilistic Approach: A Case Study on the Posina Basin in Italy
The Flash Flood Guidance (FFG) provides the rainfall amount of a given duration necessary to cause flooding. The approach is based on the development of rainfall-runoff curves, which helps us to find out the rainfall amount that would cause flooding. An alternative approach, mostly experimented with Italian Alpine catchments, is based on determining threshold discharges from past events and on finding whether or not an oncoming flood has its magnitude more than some critical discharge thresholds found beforehand. Both approaches suffer from large uncertainties in forecasting flash floods as, due to the simplistic approach followed, the same rainfall amount may or may not cause flooding. This uncertainty leads to the question whether a probabilistic model is preferable over a deterministic one in forecasting flash floods. We propose the use of a Bayesian probabilistic approach in flash flood forecasting. A prior probability of flooding is derived based on historical data. Additional information, such as antecedent moisture condition (AMC) and rainfall amount over any rainfall thresholds are used in computing the likelihood of observing these conditions given a flash flood has occurred. Finally, the posterior probability of flooding is computed using the prior probability and the likelihood. The variation of the computed posterior probability with rainfall amount and AMC presents the suitability of the approach in decision making in an uncertain environment. The methodology has been applied to the Posina basin in Italy. From the promising results obtained, we can conclude that the Bayesian approach in flash flood forecasting provides more realistic forecasting over the FFG.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2702891Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 150
 T. Loster, “Flood Trends and Global Change.” Paper presented at the EuroConference on Global Change and Catastrophe Risk Management, HASA, Laxenburg, Austria, 6-9 June, 1999.
 WMO/GWP, WMO, GWP, IMGW (Institute of Meteorology and Water Management Poland), “Recent Experiences from Central and Eastern Europe.” Guidance on Flash Flood Management, 2007. Last accessed on 10 August 2015, www.gwp.org/Global/GWP.../Floods-guidance.pdf.
 NOAA, COMET, “Flash Flood Early Warning System Reference Guide”, 2010. Accessed on 10 August 2015 www.meted.ucar.edu/communities/hazwarnsys/ffewsrg/FF_EWS.pdf.
 G. Smith, “Detemining the Hydrologic Response of FFMP Basins to Heavy Rain by Analyzing their Physiographic Charachteristics.” Flash Flood Potential, pp 11, 2003 (Aailable from the NWS Colorado Basin River Forecast Center at http://www.cbrfc.noaa.gov/papers/ffp_wpap.pdf.
 M. Borga, E. Gaume, M. Martina, and J. Thielen, “Realtime Guidance for Flash Flood Risk Management.” FLOODsite, pp. 37-77, 2009.
 S. Reed, J. Schaake, V. Koren, D. J. Seo, M. Smith, “A statistical-distributed modeling approach for flash flood prediction”. Hydrology Laboratory, Office of Hydrologic Development National Weather Service, NOAA, Silver Spring, Maryland, 2004.
 S. Reed, S. Schaake, and Z. Zhang, “A distributed hydrologic model and threshold frequency-based method for flash flood forecasting at ungauged location.” J. Hydrol. 337, 402–420, 2007.
 B. A. Cosgrove et al. “Overview and Initial Evaluation of the Distributed Hydrological Model Threshold Frequency (DHM-TF) Flash Flood Forecasting System, NOAA Technical Report NWS 54, March 2012.
 D. R. Greene, M. D. Hudlow, “Hydrometeorological grid mapping procedures, International Symposium on Hydrometeorology, American Water Resources Associations, Denver, Colorado., 14-17 June 1982.
 R. Krzysztofowicz, “Bayesian theory of probabilistic forecasting via deterministic hydrologic model.” Water Resour. Res., 35, 2739–2750. 1999.
 V. N. Steenbergen, J. Ronsyn, P. Willems, and V. K. Eerdenbrugh, “A Data-Based Probabilistic Approach to Calculate and Visualise the Uncertainty of Flood Forecasts.” Verlag der Technischen Universitat Graz, pp. 177-182, 2010.
 M. Berti, M. Martina, S. Franceschini, S. Pignone, A. Simoni, and M. Pizziolo, “Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach.” Journal of Geophysical Research: Earth Surface, 117-4, Article ID F04006, 2012.
 M. L. V. Martina, E. Todini, and A. Libralon, “A Bayesian decision approach to rainfall thresholds based flood warning.” Hydrol. Earth Syst. Sci., 10, pp. 413-426, 2006.
 D. R. Cox, “Frequentist and Bayesian Statistics.” A Critique, N.p.: n.p., 2006. M. H. P Ambaum, “Frequentist vs Bayesian statistics— a non-statisticians view.” University of Reading, UK, 2012.
 M. H. P. Ambaum, “Frequentist vs Bayesian statistics— a non-statisticians view.” University of Reading, UK, 2012.
 A. Onisko, M. J. Druzdzel, and H. Wasyluk, “Learning Bayesian Network Parameters from Small Data Sets: Application of Noisy-OR Gates.” International Journal of Approximate Reasoning, pp. 165–182, 2001.
 Soil Conservation Service (1972) Hydrology. National Engineering Hand book, Sec. 4, U.S. Govt. Printing Office, Washington D.C.
 K. P. Georgakakos, R. Graham, R. Jubach, T. M. Modrick, E. Shamir, C. Spencer, and J. A. Sperfclage, “Global Flash Flood Guidance System.” Phase I, Technical Report #9, Hydrologic Research Center – San Diego, USAID/WMO, 2013.
 P. Sayers, L. Yuanyuan, G. G. Galloway, E. Penning-Rowsell, S. Fuxin, W. Kang, C. Yiwei, and T. L. Quesne, “Flood Risk Management”, Asian Development Bank, GIWP, UNESCO, and WWF-UK.pp.50-53, 2013.