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Derivation of Probabilistic Thresholds of Spatially Distributed Rainfall for Flood Forecasting

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  • Saeed Golian
  • Bahram Saghafian
  • Reza Maknoon

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. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • Saeed Golian & Bahram Saghafian & Reza Maknoon, 2010. "Derivation of Probabilistic Thresholds of Spatially Distributed Rainfall for Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(13), pages 3547-3559, October.
  • Handle: RePEc:spr:waterr:v:24:y:2010:i:13:p:3547-3559
    DOI: 10.1007/s11269-010-9619-7
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    References listed on IDEAS

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    1. Andrew Higgins & Ainsley Archer & Stefan Hajkowicz, 2008. "A Stochastic Non-linear Programming Model for a Multi-period Water Resource Allocation with Multiple Objectives," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(10), pages 1445-1460, October.
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    1. Bahram Saghafian & Fatemeh Hamzekhani, 2015. "Hydrological drought early warning based on rainfall threshold," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(2), pages 815-832, November.
    2. Bahram Saghafian & Saeed Golian & Mohammad Elmi & Ruhangiz Akhtari, 2013. "Monte Carlo analysis of the effect of spatial distribution of storms on prioritization of flood source areas," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 66(2), pages 1059-1071, March.
    3. Christopher Burgess & Michael Taylor & Tannecia Stephenson & Arpita Mandal & Leiska Powell, 2015. "A macro-scale flood risk model for Jamaica with impact of climate variability," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(1), pages 231-256, August.
    4. Forood Sharifi & S. Samadi & Catherine Wilson, 2012. "Causes and consequences of recent floods in the Golestan catchments and Caspian Sea regions of Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(2), pages 533-550, March.
    5. Fränz Zeimetz & Bettina Schaefli & Guillaume Artigue & Javier García Hernández & Anton J. Schleiss, 2018. "Swiss Rainfall Mass Curves and their Influence on Extreme Flood Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2625-2638, June.
    6. Naser Dehghanian & S. Saeid Mousavi Nadoushani & Bahram Saghafian & Ruhangiz Akhtari, 2019. "Performance Evaluation of a Fuzzy Hybrid Clustering Technique to Identify Flood Source Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4621-4636, October.
    7. A. Sharafati & H. M. Azamathulla, 2018. "Assessment of Dam Overtopping Reliability using SUFI Based Overtopping Threshold Curve," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2369-2383, May.
    8. Michalis Diakakis, 2012. "Rainfall thresholds for flood triggering. The case of Marathonas in Greece," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 60(3), pages 789-800, February.
    9. Changjun Liu & Liang Guo & Lei Ye & Shunfu Zhang & Yanzeng Zhao & Tianyu Song, 2018. "A review of advances in China’s flash flood early-warning system," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(2), pages 619-634, June.
    10. Bahram Saghafian & Saeed Golian & Alireza Ghasemi, 2014. "Flood frequency analysis based on simulated peak discharges," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(1), pages 403-417, March.
    11. Shiang-Jen Wu & Chih-Tsung Hsu & Ho-Cheng Lien & Che-Hao Chang, 2015. "Modeling the effect of uncertainties in rainfall characteristics on flash flood warning based on rainfall thresholds," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 1677-1711, January.
    12. Wei Li & Jianzhong Zhou & Lu Chen & Kuaile Feng & Hairong Zhang & Changqing Meng & Na Sun, 2019. "Upper and Lower Bound Interval Forecasting Methodology Based on Ideal Boundary and Multiple Linear Regression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1203-1215, February.
    13. V. Montesarchio & F. Napolitano & M. Rianna & E. Ridolfi & F. Russo & S. Sebastianelli, 2015. "Comparison of methodologies for flood rainfall thresholds estimation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 909-934, January.
    14. Wei Li & Jianzhong Zhou & Huaiwei Sun & Kuaile Feng & Hairong Zhang & Muhammad Tayyab, 2017. "Impact of Distribution Type in Bayes Probability Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 961-977, February.

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