Document Type : Research Article
Authors
1
Department of Physical Geography, Faculty of Geography and Environmental Planning, University of Sistan and Baluchestan, Zahedan, Iran
2
Department of Water Engineering, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
Abstract
The selection of flood events and determination of their characteristics (such as start and end times, peak discharge, volume, and duration) are crucial initial steps in flood analysis. In this study, to accurately extract key flood information, the automated Peaks Over Threshold (POT) model was used for flood sampling, and an automated method based on the Master Recession Curve (MRC) analysis was developed to determine flood characteristics. This process was implemented using a graphical user interface (GUI) and a toolbox in MATLAB. Results demonstrated that the proposed method performs well in the Kajo River basin, despite its variable hydrological regime. The developed toolbox can be easily applied to other watersheds for flood sampling and event description, thereby helping to reduce uncertainties in subsequent flood analyses, such as multivariate frequency and trend analyses. The practical applications of this approach in flood management, hydraulic structure design, flood risk assessment, and climate change studies in river basins are significant. However, the method's dependence on the quality and continuity of daily flow data, the need to adjust threshold parameters and independence criteria in basins with different hydroclimatic conditions, and challenges related to accurate baseflow separation are notable limitations of this research. For future studies, it is recommended that this method be validated in basins with diverse hydrological regimes, including snow-dominated basins and areas affected by human activities such as dam construction.
Introduction
Floods rank among the most devastating natural disasters, causing extensive financial and human losses worldwide, including in Iran. Climate change and the increased frequency of extreme climatic events have further underscored the importance of accurate flood analysis and risk management. The core of any flood analysis whether multivariate frequency analysis, trend assessment, or hydraulic structure design depends on the precise identification of individual flood events and the quantitative extraction of their key characteristics (flood features). These features typically include the event's start and end time, peak discharge (peak), total direct runoff volume, and flood duration. Conventional methods for extracting these features are often based on manual or semi-automated procedures that rely on the analyst's subjective judgment, posing challenges such as time consumption, lack of full reproducibility, and difficulty in transferability to different watersheds. Particularly when dealing with large volumes of streamflow time-series data, these methods become inefficient and prone to introducing systematic uncertainties in subsequent analytical stages. The two main methodological challenges in this path are the objective and optimal selection of flood events from continuous data series and the accurate separation of the flood hydrograph from baseflow to calculate the true volume. The common approach for flood sampling is the Peaks Over Threshold method, which itself heavily depends on determining an optimal threshold and criteria for event independence. On the other hand, simplified methods such as connecting a straight line between start and end points are often used to define event bounds and calculate volume, which lack sufficient accuracy. Therefore, the development of an automatic, objective, and generic method that can standardize the complete process of event selection and feature extraction with minimal user intervention is a research and operational necessity. This study aims to address this methodological gap by proposing a comprehensive automatic framework and testing it in a basin with a complex and variable hydrological regime the Kajo River basin in southeastern Iran. The Kajo basin, influenced by two distinct rainfall systems (Mediterranean and Monsoonal), the presence of an upstream dam, and a history of destructive floods, provides an ideal testing ground for evaluating the efficiency and robustness of the proposed method.
Material and Methods
This research was conducted using an integrated and automated methodological framework that covers all stages from flood event selection to feature extraction. The study area is the Kajo River basin in southeastern Iran, with an approximate area of 6,178 square kilometers. The primary data used were daily streamflow time series from two hydrometric stations, Chandokan (located in the upstream section) and Pirsohrab (located in the downstream section), over a 50-year period. The methodological core is based on two main pillars. The first stage is the automatic selection of flood events using the Peaks Over Threshold model. To eliminate the inherent subjectivity in threshold determination, an automated algorithm based on the Anderson-Darling goodness-of-fit test was implemented. This algorithm systematically evaluates candidate thresholds and selects as optimal the threshold for which the exceedance series shows the best fit with the Generalized Pareto Distribution. Simultaneously, the well-known independence criteria from the United States Water Resources Council were applied to distinguish independent events, and the validity of the final extracted flood sample was evaluated using Kendall's correlation coefficient test and the calculation of the Dispersion Index. The second stage is the automatic identification of characteristics for each flood event, which itself comprises two key processes: first, the start and end times of each flood are determined algorithmically based on ratios of the peak discharge and a fixed time window; subsequently, for the precise separation of the net flood hydrograph from baseflow and calculation of the actual volume, the Master Recession Curve method is employed. In this step, using the matching strip method, the master recession curve is extracted from the entire data period and fitted with Wittenberg's analytical equation. Deep baseflow is defined as the average of minimum flows during the dry period and is used as a baseline. Finally, this complex methodological framework is implemented in a user-friendly toolbox with a graphical interface in the MATLAB software environment, named SFE_IFC, to make the analysis process fully automated and executable on large datasets.
Results and Discussion
The application of the proposed framework to the Kajo River basin data yielded significant quantitative and qualitative results. The optimal flood thresholds for the Chandokan (upstream) and Pirsohrab (downstream) stations were calculated as 52 and 177.6 m³/s, respectively. This significant difference clearly illustrates the impact of increasing basin area and hydrological changes along the river course. Statistical evaluations confirmed the correct performance of the algorithm; Dispersion Index values close to one and non-significant Kendall's correlation coefficients at the 95% confidence level validated the assumption of event independence and the adequacy of the modeling. Over the 50-year period, 102 flood events were automatically identified at the Chandokan station and 82 events at the Pirsohrab station. Analysis of the extracted features revealed a strong positive correlation between flood volume and its duration. The coefficient of determination for this relationship was approximately 0.61 for the Pirsohrab station and 0.36 for the Chandokan station. This result indicates that flood volume is a primary determinant of the flood duration period in this basin. One of the important findings of this research was elucidating the role of different rainfall regimes on flood characteristics. Results showed that approximately 84% of floods at Pirsohrab and 65% at Chandokan occurred during the Mediterranean rainfall season (cold season). The visual outputs from the toolbox (separated hydrographs) clearly demonstrated that the proposed method can correctly identify start and end times for both single-peak and complex multi-peak floods. Furthermore, the fit of the master recession curve with a coefficient of determination greater than 0.9 proved the method's high accuracy in modeling the recession limb and, consequently, in calculating volume. Despite the construction of the Zirdan Dam upstream, the observation of large floods downstream (at the Pirsohrab station) indicates the intensity of flood processes in this basin and the necessity for considering complementary management measures.
Conclusion
This study successfully developed and validated a generic automatic method for the objective selection of flood events and the accurate extraction of their characteristics from daily streamflow data. The presented framework, by integrating an automatic optimal threshold selection algorithm within the Peaks Over Threshold model and the Master Recession Curve method, systematically addresses two major methodological problems in flood analysis. Implementing this framework in a user-friendly MATLAB toolbox has transformed it into a powerful operational tool for researchers and engineers, capable of drastically reducing analysis time while simultaneously enhancing reproducibility and transparency. Applying the method in the Kajo River basin not only proved its efficacy in a complex hydrological environment influenced by two climatic regimes but also provided valuable hydrological insights into the occurrence patterns and characteristics of floods in this important basin, including the dominance of cold-season floods in terms of frequency and the superiority of warm-season floods in terms of volume and duration. The findings emphasize the urgent need for focused attention on flood management and the development of flood control infrastructure in basins similar to Kajo. While the proposed method represents an important step toward standardization, its dependence on the quality and continuity of input data and the need for empirical adjustment of some parameters for basins with vastly different conditions remain its limitations. For future studies, validation of the method in basins with diverse hydroclimatic regimes, its integration with radar rainfall data and rainfall-runoff models for causal analysis, and investigation of the impacts of climate change on flood characteristics using this framework are recommended.
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