انتخاب رویدادهای سیلاب و تعیین ویژگیهای آن، نخستین گامهای حیاتی برای تحلیلهای سیلاب محسوب میشوند. در این بررسی، برای بهدستآوردن دقیق اطلاعات کلیدی سیلاب، از مدل خودکار آستانهای، برای نمونهبرداری از سیلابها استفاده و یک روش خودکار برای تعیین ویژگیهای سیلاب با بهکارگیری روش تحلیل منحنی فروکش اصلی(MRC) ارائه گردید. همچنین از یک رابط کاربری گرافیکی (GUI) و جعبه ابزار برای این فرآیند در متلب استفاده شده است. نتایج نشان داد که روش پیشنهادی برای حوضه آبریز رودخانه کاجو با رژیم هیدرولوژیکی متغیر، عملکرد خوبی دارد. جعبه ابزار توسعهیافته را میتوان به راحتی در سایر حوضههای آبخیز برای نمونهبرداری سیلاب و توصیف وقایع سیلاب به کار برد. کاربرد عملی این رویکرد در تحلیلهای مدیریت سیلاب، طراحی سازههای هیدرولیکی، ارزیابی ریسک سیل و مطالعات تغییر اقلیم در حوضههای آبریز قابل توجه است. با این حال، وابستگی روش به کیفیت و پیوستگی دادههای جریان روزانه، نیاز به تنظیم پارامترهای آستانه و معیارهای استقلال در حوضههای با شرایط هیدرواقلیمی متفاوت، و چالشهای مربوط به تفکیک دقیق جریان پایه از جمله محدودیتهای قابل توجه این پژوهش محسوب میشوند. برای پژوهشهای آینده، پیشنهاد میشود این روش در حوضههای با رژیمهای هیدرولوژیکی متنوع اعتبارسنجی گردد.
Adhikari, P., Yang, H., Douglas, K. R., Kirschbaum, D. B., Gourley, J., Adler, R., & Brakenridge, G. R. (2010). A digitized global flood inventory (1998–2008): Compilation and preliminary results. Natural Hazards, 55(2), 405–422. https://doi.org/10.1007/s11069-010-9537-2
Aissia, M. A., Ben Chebana, F., Ouarda, T. B. M. J., Roy, L., Desrochers, G., & Chartier, I. (2012). Multivariate Analysis of flood characteristics in a climate change context of the watershed of the Baskatong Reservoir, Province of Qu´ebec, Canada. Hydrological Processes, 261, 130–142. https://doi.org/10.1002/hyp.8117
Arciniega-Esparza, S., Breña-Naranjo, J. A., Pedrozo-Acuña, A., & Appendini, C. M. (2017). HYDRORECESSION: A Matlab toolbox for streamflow recession analysis. Computers & Geosciences, 98, 87-92. https://doi.org/10.1016/j.cageo.2016.10.005
Aryanmanesh, J., Nazaripour, H., Mahmoodi, P., & Khosravi, P. (2024). Reconstruction of Missing Daily Streamflow Data using the MissForest Algorithm in Southern Baluchestan Basin, Iran. Journal of Watershed Management Research, 15(2), 49-64. [In Persian] https://doi.org/10.61186/jwmr.15.2.49
Bernardara, P., Mazas, F., Kergadallan, X., & Hamm, L. (2014). A two-step framework for over-threshold modelling of environmental extremes. Natural Hazards and Earth System Sciences, 14(3), 635–647. https://doi.org/10.5194/nhess-14-635-2014
Beven, K., Smith, P. J., & Wood, A. (2011). On the colour and spin of epistemic error (and what we might do about it). Hydrology and Earth System Sciences, 15(10), 3123-3133. https://doi.org/10.5194/hess-15-3123-2011
Blöschl, G., Hall, J., Viglione, A., Perdigão, R. A., Parajka, J., Merz, B., ... & Živković, N. (2019). Changing climate both increases and decreases European river floods. Nature, 573(7772), 108-111. https://doi.org/10.1038/s41586-019-1495-6
Brunner, M. I., Furrer, R., Sikorska, A. E., Viviroli, D., Seibert, J., & Favre, A. C. (2018). Synthetic design hydrographs for ungauged catchments: a comparison of regionalization methods. Stochastic Environmental Research and Risk Assessment, 32(7), 1993-2023. https://doi.org/10.1007/s00477-018-1523-3
Brunner, M. I., Hingray, B., Zappa, M., & Favre, A. C. (2019). Future trends in the interdependence between flood peaks and volumes: Hydro‐climatological drivers and uncertainty. Water Resources Research, 55(6), 4745-4759. https://doi.org/10.1029/2019WR024701
Carlotto, T., & Chaffe, P. L. B. (2019). Master recession curve parameterization tool (MRCPtool): different approaches to recession curve analysis. Computers & Geosciences, 132, 1-8. https://doi.org/ 10.1016/j.cageo.2019.06.016
Choulakian, V., & Stephens, M. A. (2001). Goodness-of-fit tests for the generalized Pareto distribution. Technometrics, 43(4), 478-484. https://doi.org/10.1198/00401700152672573
Claps, P., & Laio, F. (2003). Can continuous streamflow data support flood frequency analysis? An alternative to the partial duration series approach. Water Resources Research, 39(8). https://doi.org/10.1029/2002WR001868
Cunnane, C. (1979). A note on the Poisson assumption in partial duration series models. Water Resources Research, 15(2), 489-494. https://doi.org/10.1029/WR015i002p00489
Daneshkhah, A., Remesan, R., Chatrabgoun, O., & Holman, I. P. (2016). Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model. Journal of Hydrology, 540, 469-487. https://doi.org/10.1016/j.jhydrol.2016.06.044
Davison, A. C., & Smith, R. L. (1990). Models for exceedances over high thresholds. Journal of the Royal Statistical Society Series B: Statistical Methodology, 52(3), 393-425. https://doi.org/10.1111/j.2517-6161.1990.tb01796.x
De Bruijn, J. A., de Moel, H., Jongman, B., de Ruiter, M. C., Wagemaker, J., & Aerts, J. C. (2019). A global database of historic and real-time flood events based on social media. Scientific Data, 6(1), 311. https://doi.org/10.1038/s41597-019-0326-9
Duan, Q., Schaake, J., Andréassian, V., Franks, S., Goteti, G., Gupta, H. V., ... & Wood, E. F. (2006). Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. Journal of Hydrology, 320(1-2), 3-17. https://doi.org/10.1016/j.jhydrol.2005.07.031
Durocher, M., Burn, D. H., & Ashkar, F. (2019). Comparison of estimation methods for a nonstationary index‐flood model in flood frequency analysis using peaks over threshold. Water Resources Research, 55(11), 9398-9416. https://doi.org/10.1029/2019WR025305
Durocher, M., Mostofi Zadeh, S., Burn, D. H., & Ashkar, F. (2018). Comparison of automatic procedures for selecting flood peaks over threshold based on goodness‐of‐fit tests. Hydrological Processes, 32(18), 2874-2887. https://doi.org/10.1002/hyp.13223
Güngör, M. N., & Ozkaya, A. (2025). Baseflow index estimation using digital and minima methods in the Büyük Menderes Basin. Sustainable Water Resources Management, 11(6), 129. https://doi.org/10.1007/s40899-025-01304-6
Harville, D. A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72(358), 320-338. https://doi.org/10.2307/2286796
Helfer, F., Bernardi, F., De Barros, C. A. P., Allasia, D. G., Minella, J. P. G., Tassi, R., & Scariot, N. (2025). Enhanced baseflow separation in rural catchments: event-specific calibration of recursive digital filters with tracer-derived data. Hydrology and Earth System Sciences, 29(23), 6959-6984. https://doi.org/10.5194/hess-29-6959-2025
Heo, J. H., Shin, H., Nam, W., Om, J., & Jeong, C. (2013). Approximation of modified Anderson–Darling test statistics for extreme value distributions with unknown shape parameter. Journal of Hydrology, 499, 41-49. https://doi.org/10.1016/j.jhydrol.2013.06.008
Jeong, D. I., Sushama, L., Khaliq, M. N., & Roy, R. (2014). A copula-based multivariate analysis of Canadian RCM projected changes to flood characteristics for northeastern Canada. Climate Dynamics, 42(7), 2045-2066. https://doi.org/10.1007/s00382-013-1851-4
Karahacane, H., Meddi, M., Chebana, F., & Saaed, H. A. (2020). Complete multivariate flood frequency analysis, applied to northern Algeria. Journal of Flood Risk Management, 13(4), e12619. https://doi.org/10.1111/jfr3.12619
Kauffeldt, A., Wetterhall, F., Pappenberger, F., Salamon, P., & Thielen, J. (2016). Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level. Environmental Modelling & Software, 75, 68-76. https://doi.org/10.1016/j.envsoft.2015.09.009
Lamb, R., & Beven, K. (1997). Using interactive recession curve analysis to specify a general catchment storage model. Hydrology and Earth System Sciences, 1(1), 101-113. https://doi.org/10.5194/hess-1-101-1997
Lang, M., Ouarda, T. B., & Bobée, B. (1999). Towards operational guidelines for over-threshold modeling. Journal of Hydrology, 225(3-4), 103-117. https://doi.org/10.1016/S0022-1694(99)00167-5
Liang, B., Shao, Z., Li, H., Shao, M., & Lee, D. (2019). An automated threshold selection method based on the characteristic of extrapolated significant wave heights. Coastal Engineering, 144, 22-32. https://doi.org/10.1016/j.coastaleng.2018.12.001
Liu, Y. R., Li, Y. P., Ma, Y. A., Jia, Q. M., & Su, Y. Y. (2020). Development of a Bayesian-copula-based frequency analysis method for hydrological risk assessment–The Naryn River in Central Asia. Journal of Hydrology, 580, 124349. https://doi.org/10.1016/j.jhydrol.2019.124349
Mazas, F., & Hamm, L. (2011). A multi-distribution approach to POT methods for determining extreme wave heights. Coastal Engineering, 58(5), 385-394. https://doi.org/10.1016/j.coastaleng.2010.12.003
Mediero, L., Jim´ Enez-Alvarez, A., & Garrote, L. (2010). Design flood hydrographs from the relationship between flood peak and volume. Hydrology and Earth System Sciences, 14(12), 2495–2505. https://doi.org/10.5194/hess-14-2495-2010
Moradi, M., & Ranjbar Saadat Abadi, A. (2020). A Synoptic-Dynamic Investigation of the Flood in Sistan and Baluchestan and the Heavy Snowfall in Gilan in winter 2020. Journal of Geography and Environmental Hazards, 9(3), 227-243. https://doi.org/10.22067/geoeh.2020.67338.1000
Mostofizadeh, S., Durocher, M., Burn, D. H., & Ashkar, F. (2019). Pooled flood frequency analysis: a comparison based on peaks-over-threshold and annual maximum series. Hydrological Sciences Journal, 64(2), 121-136. https://doi.org/10.1080/02626667.2019.1577556
Nadarajah, S., & Shiau, J. T. (2005). Analysis of extreme flood events for the Pachang River, Taiwan. Water Resources Management, 19(4), 363-374. https://doi.org/10.1007/s11269-005-2073-2
Nathan, R. J., & McMahon, T. A. (1990). Evaluation of automated techniques for base flow and recession analyses. Water Resources Research, 26(7), 1465-1473. https://doi.org/10.1029/WR026i007p01465
Nazaripour, H. (2015). Development of a New Comprehensive Multivariate Aggregate Drought Index (ADI) based on Principal Component Analysis (PCA) for Hydro- Meteorological Droughts Assessment in the Southeast of Iran (Case Study: Pishin Dam Basin). Journal of Geography and Environmental Hazards, 4(3), 91-112. https://doi.org/10.22067/geo.v4i3.31626
Oktaviana, P. P., Syafei, A. D., Kuswanto, H., & Hermana, J. (2025). Temporal Probability Analysis of Flood Occurrence Using Peak Over Threshold Method for Extreme Rainfall Events. In BIO Web of Conferences (Vol. 157, p. 11002). EDP Sciences. https://doi.org/10.1051/bioconf/202515711002
Ombadi, M., Nguyen, P., Sorooshian, S., & Hsu, K. L. (2018). Developing intensity‐duration‐frequency (IDF) curves from satellite‐based precipitation: Methodology and evaluation. Water Resources Research, 54(10), 7752-7766. https://doi.org/10.1029/2018WR022929
Önöz, B., & Bayazit, M. (2001). Effect of the occurrence process of the peaks over threshold on the flood estimates. Journal of Hydrology, 244(1-2), 86-96. https://doi.org/10.1016/S0022-1694(01)00330-4
Qiu, L., Du, Z., Zhu, Q., & Fan, Y. (2017). An integrated flood management system based on linking environmental models and disaster-related data. Environmental Modelling & Software, 91, 111-126.
Ribatet, M., Sauquet, E., Grésillon, J. M., & Ouarda, T. B. (2007). A regional Bayesian POT model for flood frequency analysis. Stochastic Environmental Research and Risk Assessment, 21(4), 327-339. https://doi.org/10.1007/s00477-006-0068-z
Solari, S., & Losada, M. A. (2012). A unified statistical model for hydrological variables including the selection of threshold for the peak over threshold method. Water Resources Research, 48(10). https://doi.org/ 10.1029/2011WR011475
Solari, S., Egüen, M., Polo, M. J., & Losada, M. A. (2017). Peaks Over Threshold (POT): A methodology for automatic threshold estimation using goodness of fit p‐value. Water Resources Research, 53(4), 2833-2849. https://doi.org/10.1002/2016WR019426
Sujono, J., Shikasho, S., & Hiramatsu, K. (2004). A comparison of techniques for hydrograph recession analysis. Hydrological Processes, 18(3), 403-413. https://doi.org/10.1002/hyp.1247
Tanoue, M., Hirabayashi, Y., & Ikeuchi, H. (2016). Global-scale river flood vulnerability in the last 50 years. Scientific Reports, 6(1), 36021. https://doi.org/10.1038/srep36021
Tosunoglu, F., Gürbüz, F., & İspirli, M. N. (2020). Multivariate modeling of flood characteristics using Vine copulas. Environmental Earth Sciences, 79(19), 459. https://doi.org/10.1007/s12665-020-09199-6
Vittal, H., Singh, J., Kumar, P., & Karmakar, S. (2015). A framework for multivariate data-based at-site flood frequency analysis: Essentiality of the conjugal application of parametric and nonparametric approaches. Journal of Hydrology, 525, 658-675. https://doi.org/10.1016/j.jhydrol.2015.04.024
Yue, S. (2000). The bivariate lognormal distribution to model a multivariate flood episode. Hydrological Processes, 14(14), 2575-2588. https://doi.org/10.1002/1099-1085(20001015)
Zeng, S., Du, H., & Xia, J. (2020). Development of an interface-oriented add-in modeling framework for integrated water system simulation and its application. Environmental Modelling & Software, 134, 104840. https://doi.org/10.1016/j.envsoft.2020.104840
Zhang, Q., Zhang, L., She, D., Wan, S., Wang, G., & Zeng, S. (2021) Automatic procedure for selecting flood events identifying flood characteristics from daily streamflow data. Environmental Modelling and Software, 145, 105180. https://doi.org/10.1016/j.envsoft.2021.105180