Abstract
“Rainfall threshold” is considered as one of the evolving flood forecasting approaches. When the cumulative rainfall depth for a given initial soil moisture condition intersects the corresponding moisture curve, the peak discharge is expected to be equal or greater than the threshold discharge for flooding at the target site. Besides the total rainfall depth, spatial and temporal distribution of rainfall can influence the peak discharge and the time to peak. In the few past studies on the extraction of rainfall threshold curves for flood forecasting, the rainfall assumed to be uniform in space whereas the temporal distribution was subjected to certain assumptions. In the present study, the spatial distribution of rainfall was simulated with the Monte Carlo (MC) method and the mean Huff pattern for all rainfall durations was imposed for the temporal distribution. For each of the MC run, the random weight assigned to every sub-watershed follows the pdf of weights in historical rainfall events. The HEC–HMS model with two different infiltration methods namely SCS–CN and Green–Ampt and Muskingum river routing were adopted as the hydrologic model. After the calibration and validation of the model for Madarsoo watershed in Golestan province in Northeastern Iran, the MC simulations were performed for 1, 2, 6 and 12 h durations. The outputs from the SCS–CN method exhibit only a slight increase in threshold values with respect to duration and was not in the range of our expectations from watershed response, i.e. the rainfalls with greater durations should be greater in depth to produce a specific peak discharge. For the Green–Ampt infiltration method, the rainfall thresholds with 50% probability associated with the critical discharge under dry soil moisture condition were 44.5, 49.0, 64.2 and 94.6 mm for 1, 2, 6 and 12 h durations, respectively. Results for July 2001 flooding revealed that the cumulative rainfall intersected all 10%, 50% and 90% rainfall threshold curves but for July 2005 flooding the 10% curve was only intersected by the cumulative rainfall curve. The advantage of MC-derived rainfall threshold curves in real-time operations is that decision-makers have the flexibility to adopt a curve more consistent with flood observations in the region.
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References
Amadio P, Mancini M, Menduni G, Rabuffetti D, Ravazzani G (2003) A real-time flood forecasting system based on rainfall thresholds working on the Arno Watershed: definition and reliability analysis. In: Proceedings of the 5th EGS Plinius Conference held at Ajaccio, Corsica, France
Bell VA, Moore RJ (2000) The sensitivity of catchment runoff models to rainfall data at different spatial scales. Hydrol Earth Syst Sci 4(4):633–667
Bobba AGh, Singh VP, Bengtsson L (1996) Application of first-order and Monte Carlo analysis in watershed water quality models. Water Resour Manage 10:219–240
Burges SJ, Lettenmaier DP (1975) Probability methods in stream quality management. Water Resour Bull 11(1):115–130
Changnon SA (ed) (1996) Review of the great flood of 1993: causes, impacts, and responses. Westview, Boulder
Clark CO (1945) Storage and the unit hydrograph. Trans ASCE 110:1419–1446
Day GN (1985) Extended streamflow forecasting using NWSRFS. J Water Resour Plan Manage 111(2):157–170
De Roo A, Gouweleeuw B, Thielen J, Bates P et al. (2003) Development of a European flood forecasting system. Int J River Basin Manage 1(1):49–59
Di Baldassarre G, Laio F, Montanari A (2009) Design flood estimation using model selection criteria. Phys Chem Earth 34(10–12):606–611
Downton MW, Pielke RA Jr. (2001) Discretion without accountability: politics, flood damage, and climate. Nat Hazards Rev 2(4):157–166
Georgakakos KP (2006) Analytical results for operational flash flood guidance. J Hydrol 317:81–103
Goubanova K, Li L (2007) Extremes in temperature and precipitation around the Mediterranean basin in an ensemble of future climate scenarios simulations. Gobal Planet Change 57:27–42
Green WH, Ampt GA (1911) Studies on soil physics. J Agric Sci 4(1):1–24
Haan ChT (2002) Statistical methods in hydrology Ch. 16: probabilistic methods for uncertainty, risk and reliability. Iowa State Press, Iowa
Higgins A, Archer A, Archer S (2008) A stochastic non-linear programming model for a multi-period water resource allocation with multiple objectives. Water Resour Manage 22:1445–1460
Huff FA (1990) Time distribution of heavy rainstorm in Illinois. Department of Energy and Natural Resources
Krause FA (1999) Modeling the flood hydrology of wetlands using HEC–HMS. M.S thesis, University of California, 144 pp
Laio F, Cramer-von Mises (2004) Anderson–Darling goodness of fit tests for extreme value distributions with unknown parameters. Water Resour Res 40:W09308. doi:10.1029/2004WR003204
Laio F, Di Baldassarre G, Montanari A (2009) Model selection techniques for the frequency analysis of hydrological extremes. Water Resour Res 45:7. doi:10.1029/2007WR006666
Maidment DR (1993) Handbook of hydrology: chapter 5: infiltration and soil water movement. McGraw-Hill, New York
Martina MLV, Todini E, Libralon A (2006) A Bayesian decision approach to rainfall thresholds based flood warning. Hydrol Earth Syst Sci 10:413–426
Milly PCD, Wetherald RT, Dunne KA, Delworth TL (2002) Increasing risk of great floods in a changing clime. Nature 415:514–517
Montesarchio V, Lombardo F, Napolitano F (2009) Rainfall thresholds and flood warning: an operative case study. Nat Hazards Earth Syst Sci 9:135–144
Norbiato D, Borga M, Esposti SD, Gaume E, Anquetin S (2008) Flash flood warning based on rainfall thresholds and soil moisture conditions: an assessment for gauged and ungauged basins. J Hydrology 362:274–290
Ntelekos AA, Goergakakos KP, Krajewski WF (2006) On the uncertainties of flash flood guidance: towards probabilistic forecasting of flash floods. J Hydrometeorol 7(5):896–915
Ogden FL, Julien PY (1993) Runoff sensitivity to temporal and spatial rainfall variability at runoff plane and small basin scales. Water Resour Res 29(8):2589–2597
O’Connell P (2005) Interactive comment on (A Bayesian decision approach to rainfall thresholds based flood warning) by M. L. V. Martina et al., Hyrol. Earth Syst Sci Discuss 2:S1368–S1371
Pielke RA Jr, Downton W, Miller JZ Barnard (2002) Flood damage in the Unites States, 1926–200: a reanalysis of National Weather Service estimates. UCAR, Boulder
Pilon PJ (2004) Guidelines for reducing flood losses. United Nations International Strategy for Disaster Reduction (UN/ISDR), Palais des Nations, Ch 1211 Geneva, Switzerland
Ponce VM, Hawkins RH (1996) Runoff curve number: has it reached maturity? J Hydrol Eng ASCE 1(1):11–18
Saghafian B, Julién PY, Ogden FL (1995) Similarity in catchment response 1. Stationary rainstorms. Water Resour Res 31:1533–1541
SCS (1956, 1971) Hydrology, National Engineering Handbook, Supplement A, Section 4, Chapter 10, Soil Conservation Service, USDA, Washington, D.C.
Thielen J, Bartholmes J, Ramos M-H, de Roo A (2009) The European Flood Alert System—part 1: concepts and development. Hydrol Earth Syst Sci 13:125–140
UNEP (2002) Early warning, forecasting and operational flood risk monitoring in Asia (Bangladesh, China and India). A Technical Report of Project (GT/1010-00-04): Division of Early Warning and Assessment (DEWA), United Nations Environment Programme (UNEP), P.O. Box 30552, Nairobi, Kenya
USACE (2000) HEC–HMS hydrologic modeling system user’s manual, Hydrologic Engineering Center, Davis
USACE (2006) HEC_RAS river analysis system user’s manual, Hydrologic Engineering Center, Davis
Werner M, Reggiani P, DE Roo AD, Bates P, Sprokkereef E (2005) Flood forecasting and warning at the river basin and at the European scale. Nat Hazards 36:25–42
Wu S, Li J, Huang GH (2008) Characterization and evaluation of elevation data uncertainty in water resources modeling with GIS. Water Resour Manage 22:959–972
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Golian, S., Saghafian, B. & Maknoon, R. Derivation of Probabilistic Thresholds of Spatially Distributed Rainfall for Flood Forecasting. Water Resour Manage 24, 3547–3559 (2010). https://doi.org/10.1007/s11269-010-9619-7
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DOI: https://doi.org/10.1007/s11269-010-9619-7