Document Type : Research Paper
The Kan River Basin, located in the northwest of Tehran, Iran, is one of the most flood-prone sub-catchments draining from the Alborz mountains into an increasingly urbanized metropolitan area. In recent decades, accelerated land-use change, unregulated urban sprawl, and channelization of the main river corridor have significantly altered the hydrological equilibrium of the basin. These transformations have increased the peak discharges and reduced natural storage and infiltration capacities, thereby intensifying flood risk in the densely populated downstream zones—particularly within Tehran’s Districts 21 and 22.
Urban floods in Tehran have repeatedly caused severe socioeconomic damage. The destructive flood events of March 2014 and April 2019 in the Kan basin not only inflicted infrastructural losses but also revealed the absence of a reliable flood-hazard zoning plan supported by integrated modeling and GIS-based risk assessment. Consequently, the present study was designed to address these limitations by employing a combined hydrological–hydraulic modeling framework capable of simulating flood hydrographs, spatial inundation patterns, and flow dynamics for a range of design flood scenarios.
The main objectives of this study are summarized as follows:
o estimate design floods for various return periods using long-term hydrometric and rainfall records from the Kan watershed (1969–2025).
To calibrate and validate a rainfall-runoff model (HEC-HMS) capable of reproducing historic flood hydrographs with high accuracy.
To couple the resulting hydrographs with a two-dimensional hydraulic model (HEC-RAS 2D) for detailed floodplain mapping.
To perform sensitivity analysis on the Manning roughness coefficient and rainfall intensity to evaluate modeling uncertainty.
To provide management-oriented recommendations for flood risk mitigation and urban planning.
Given the basin’s mountainous–urban dual character, this integrated modeling approach offers a suitable basis for both quantitative flood prediction and qualitative spatial zoning of flood-prone areas.
The Kan basin covers approximately 230 km² and extends from the high-elevation headwaters (~3800 m a.s.l.) near Tochal peak to the low-elevation urban plains (~1200 m a.s.l.) entering Tehran’s western districts. The dominant land cover types include mountainous rangeland in the upper catchment, agricultural terraces in mid-sections, and dense urban development downstream. Average annual rainfall ranges from 500 to 800 mm in the upstream part, decreasing to about 300 mm in the metropolitan zone. The river’s main tributaries include Solqan and Sangan branches, both monitored by hydrometric stations whose daily discharge data served as the foundation for the present analysis.
The study used rainfall, discharge, and temperature data from Iran’s Ministry of Energy (Regional Water Authority of Tehran) for the period 1969–2025. Additional spatial layers included a 30 m Shuttle Radar Topography Mission (SRTM) DEM, land use/land cover maps (2024), and soil hydrologic groups from the Iranian Soil and Water Research Institute. The modeling environment comprised HEC-HMS 4.11 for hydrological simulation and HEC-RAS 6.4 for hydraulic computations, both integrated within ArcGIS 10.8 for pre- and post-processing tasks.
Rainfall–runoff transformation was modeled using the SCS-Curve Number (CN) method for rainfall loss estimation and the Clark Unit Hydrograph for runoff routing. Parameter calibration was based on historical flood events (particularly the 2014 flood), considering observed hydrographs at the Solqan station. Model efficiency was evaluated using Nash–Sutcliffe Efficiency (NSE), coefficient of determination (R²), and Percent Bias (PBIAS).
After calibration, synthetic design storms were generated for 25-, 50-, and 100-year return periods using the Log-Pearson Type III distribution. These input storms produced discharge hydrographs serving as boundary conditions for the hydraulic model.
Flood routing and flow fields were simulated in two dimensions using the finite-volume scheme of HEC-RAS 2D, which solves the shallow water equations (Saint-Venant) on an unstructured computational mesh with cells ranging from 10 to 30 m. The model incorporated river bathymetry, DEM-derived terrain, and roughness coefficients representing both natural and urban surfaces. The roughness coefficient (Manning’s n) varied spatially from 0.025 (concrete channels) to 0.045 (vegetated flood plains).
Model boundary conditions were defined based on the outflow hydrographs derived from HEC-HMS, while the downstream boundary adopted normal depth estimates. Simulation outputs included water-surface elevations, depth rasters, velocity maps, and flood extent polygons for each design discharge scenario.
Sensitivity tests were conducted to quantify the influence of:
Manning’s coefficient (±20%)
Rainfall intensity (±20%), representing potential climate change scenarios
Digital Elevation Model (DEM) resolution (10 m vs. 30 m)
Each modification was implemented individually to evaluate the response in maximum flow depth, flood extent, and average velocity.
Flood frequency analysis indicated design discharges of approximately 100 m³/s, 130 m³/s, and 150 m³/s for the 25‑, 50‑, and 100‑year return periods, respectively, at the Solqan outlet. The results show a clear increase in flood magnitude with increasing return period, reflecting the expected hydrological behavior of the basin. The recorded peak discharge of 139.3 m³/s during the 2014 flood is comparable to the estimated 100‑year design discharge, suggesting reasonable consistency between the observed extreme event and the statistically derived flood frequency estimates.
Validation against observed hydrographs produced average NSE = 0.84, R² = 0.88, and PBIAS = ±10%, indicating overall “good-to-excellent” performance. Timing of the simulated peak closely matched observations, differing by less than one hour. This level of agreement confirms that the combination of SCS-CN and Clark UH methods effectively captures the basin’s rainfall–runoff response, even under rapid runoff conditions typical of mountainous catchments with limited infiltration.
The 2D hydraulic model successfully reproduced known flood extents and provided detailed maps of depth and velocity distributions. The results demonstrated pronounced expansions of flood inundation with increasing return period:
|
Return Period |
Mean Flow Depth (m) |
Max Flow Velocity (m/s) |
Flood Extent (km²) |
|
25-year |
0.8–1.5 |
1.5–3.0 |
15–18 |
|
50-year |
1.2–2.2 |
2.0–4.0 |
20–23 |
|
100-year |
1.8–3.0 |
3.0–5.0 |
25–29 |
These results clearly identify Tehran’s Districts 21 and 22 as high-risk areas, where the lower gradient and urban encroachment have reduced the effective flow conveyance capacity. Peak velocities surpassing 4 m/s, combined with depths above 2 meters, suggest substantial potential for structural damage and erosion.
Comparative evaluation with previous studies (Hosseini & Sadeghi, 2025; Nasiri et al., 2025) revealed strong agreement in the order of magnitude and spatial distribution of flood hazards, though the present model, covering the entire watershed, reported slightly larger inundation areas—attributable to the inclusion of upstream contributing zones.
Sensitivity simulations revealed the Manning coefficient as the dominant factor influencing flood behavior. Increasing n by 20% increased water depth by ≈22% and flood area by ≈13%, while reducing flow velocity by ≈20–25%. Conversely, a 20% reduction in n caused faster, shallower flows with a 25–30% increase in average velocity.
Rainfall intensity also exerted a strong effect: a 20% increase in rainfall led to 25–40% deeper inundation and up to 35% wider flood extent. DEM resolution improvement (30 m → 10 m) produced moderate changes (10–18% variation in depth), implying that hydraulic roughness and rainfall uncertainty dominate over terrain discretization within this configuration.
These findings are consistent with international research indicating that uncertainty in roughness and hydrological forcing contributes the largest share of variance in flood modeling outputs.
The integrated modeling framework implemented in this study proved robust and efficient for flood hazard assessment in the Kan basin. Major conclusions were summarized as follows:
Reliable Reproduction of Historical Events:
The coupled HEC-HMS / HEC-RAS 2D models achieved high accuracy in simulating observed flood events, reinforcing their suitability for use in both data-scarce and urbanized mountainous basins.
High Flood Hazard for Urban Downstream Zones:
The 100-year flood scenario demonstrated that downstream areas—especially within the lower 10 km reach of the Kan river—are subjected to flow depths >2.5 m and velocities >4 m/s. These conditions can cause severe damage to infrastructure and pose significant risk to human life.
Critical Influence of Roughness and Climate Factors:
The hydrodynamic response of the system is most sensitive to Manning’s n and rainfall intensity. Anticipated increases in rainfall extremes due to climate change could markedly expand flood hazards, potentially doubling the current inundation area within upcoming decades.
Management Priorities:
Revision of Floodplain Boundaries: Updating legal river margins using new two-dimensional maps to restrict future urban development in high-risk areas.
Structural Measures: Upgrading bridge openings, river channels, and culverts to accommodate design discharges of 150 m³/s.
Non-Structural Measures: Enhancing flood warning systems, public awareness, and emergency response protocols within Tehran’s western districts.
Upstream Retention and Land Management: Implementing check dams, detention basins, and reforestation initiatives to reduce peak flow generation in the mountainous headwaters.
Data and Monitoring Improvement: Establishing high-resolution rainfall and flow monitoring networks, as well as LiDAR-based topographic surveys to reduce model uncertainty.
Future work should integrate climate model projections and socio-hydrological datasets to simulate flood risk under compound hazard scenarios (rainfall–landuse–urbanization dynamics). The inclusion of sediment transport and debris flow modules could further refine hazard assessments in steep tributaries.
In conclusion, the study underscores the necessity of adopting integrated, process-based flood modeling frameworks for hazard zoning in urbanized mountain basins. The results provide scientific evidence to guide floodplain management, land-use planning, and climate-resilient infrastructure design in Tehran and similar cities facing rapid expansion along natural drainage corridors.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, visualization, supervision, project administration: Morteza Shokri. The author has read and agreed to the published version of the manuscript.
Declaration of Generative AI and AI-assisted technologies in the writing process
During the preparation of this work, the author used ChatGPT (OpenAI) in order to improve the English writing and language clarity of the manuscript. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.
Hydrological and rainfall data used in this study were obtained from the Tehran Regional Water Authority and are subject to institutional access restrictions. Processed modeling outputs and derived datasets are available from the corresponding author upon reasonable request.
The authors acknowledge the Tehran Regional Water Authority for providing hydrological data and technical support. Appreciation is also extended to colleagues who contributed to GIS processing and model validation.
This study does not involve human participants or animal subjects. All data were obtained from authorized institutional sources. The authors declare no conflict of interest and confirm that the research was conducted in accordance with ethical standards for scientific publication.
The author declares no conflict of interest.
