Kamali et al. (2026) Evaluating the impacts of green infrastructure on urban runoff attributes using detailed fine-scale hydrologic modeling

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2026
• Date: 2026-01-16
• Authors: Meysam Kamali, Husnain Tansar, Ebrahim Ahmadisharaf, Nasrin Alamdari
• DOI: 10.1016/j.ejrh.2026.103128

Research Groups

• Environment and Water Research Center, Sharif University of Technology, Tehran, Iran
• Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Tallahassee, FL, United States
• Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, FL, United States

Short Summary

This study evaluated the performance of individual and combined green infrastructures (GIs) on urban runoff volume, peak, and time-to-peak using detailed fine-scale hydrologic modeling in the 374 km² Hillsborough Watershed, Florida. It found that combined GIs significantly outperform individual GIs in runoff reduction, and fine-scale modeling reveals spatial variability in GI effectiveness that is masked by coarser watershed-scale approaches.

Objective

• To investigate the impact of individual and combined green infrastructures (bio-retention [BR], grassed swale [GS], green roof [GR], rain barrel [RB], cistern [CS], and infiltration trench [IT]) on urban runoff peak, volume, and time-to-peak using detailed fine-scale hydrologic simulations.
• To evaluate GI performance across two scales (fine-scale subwatershed and full watershed) under various rainfall intensities (2- to 200-year return periods).
• To explore how subwatershed characteristics (drainage area, imperviousness, and ground slope) influence GI effectiveness in runoff reduction.

Study Configuration

• Spatial Scale: Hillsborough Watershed (374 km²) in Midwest Florida, modeled with 3800 subwatersheds (mean area: 0.098 km²) and over 18,000 hydraulic structures.
• Temporal Scale: 24-hour design rainfall events with return periods of 5-, 10-, 25-, 50-, 100-, and 200-year. Simulation duration was 48 hours with a computational timestep of 1 minute.

Methodology and Data

• Models used: Storm Water Management Model (SWMM) version 5.2. Infiltration was modeled using the Natural Resources Conservation Service’s (NRCS’) Curve Number (CN) method, and flow routing used the dynamic wave method.
• Data sources:
  • Design rainfall depths derived from NOAA Atlas 14 (Tampa WSCMO AP, NOAA station ID: 08–8788) with NRCS type II temporal distribution.
  • Subwatershed characteristics including drainage area, land use/land cover (LULC), surface imperviousness, ground slope, and hydrologic soil groups.
  • GI design parameters adopted from existing literature.
  • The SWMM model was originally developed by CH2M HILL Engineers Inc. (2016) and recalibrated by Mayou et al. (2024).
  • Suitability-based GI locations were pre-screened from Hoque et al. (2024).

Main Results

• At the fine-scale under a 5-year rainfall event, Infiltration Trench (IT) showed the best individual performance, reducing runoff volume by 26.8–29.1 % and runoff peak by 78.6–82.4 %. Cisterns (CS) reduced runoff volume by 27.1–30.5 %.
• A combined scenario of Bio-retention (BR), Grassed Swale (GS), IT, and CS mitigated up to 78.6 % of runoff volume and 98.3 % of runoff peak for a 5-year rainfall event, outperforming individual GI scenarios.
• GI effectiveness decreased with increasing rainfall intensity, as GIs' infiltration and storage capacities were fully utilized during more intense events.
• Subwatersheds with greater imperviousness benefited most from BR, RB, CS, and IT in terms of runoff volume reductions. Drainage area showed a strong correlation with runoff peak in GS (Pearson's r = -0.79) and runoff volume in GR (Pearson's r = 0.69). Ground slope showed no meaningful correlation with runoff attributes.
• Rain barrels (RB) and CS demonstrated the best performance in delaying time-to-peak, with delays up to 28.0 % and 22.2 %, respectively. Green roofs (GR) showed reduced efficiency in delaying time-to-peak during more intense rainfall events.
• Fine-scale modeling revealed significant spatial variability in GI performance, identifying subwatersheds with disproportionately high or low effectiveness that were obscured in full watershed-scale models.

Contributions

• This study is the first to simultaneously evaluate GI performance, optimal GI type/area selection, and spatial prioritization across a large urban watershed (374 km²) with an unprecedented degree of spatial discretization (3800 subwatersheds) and hydraulic detail (over 18,000 hydraulic structures).
• It highlights the critical importance of detailed fine-scale hydrologic modeling for accurately assessing localized GI performance, capturing phenomena like hydrograph desynchronization and local peak amplification that are missed by coarser models.
• Provides novel insights into the relationships between subwatershed characteristics (imperviousness, drainage area, ground slope) and GI effectiveness in mitigating runoff attributes.
• Offers practical guidance for urban planners and stormwater managers to prioritize highly impervious subwatersheds for GI retrofits and to consider hybrid green-gray infrastructure solutions for managing extreme rainfall events.

Funding

• Florida State University
• Tampa Bay Estuary Program’s Tampa Bay Environmental Restoration Fund

Citation

@article{Kamali2026Evaluating,
  author = {Kamali, Meysam and Tansar, Husnain and Ahmadisharaf, Ebrahim and Alamdari, Nasrin},
  title = {Evaluating the impacts of green infrastructure on urban runoff attributes using detailed fine-scale hydrologic modeling},
  journal = {Journal of Hydrology Regional Studies},
  year = {2026},
  doi = {10.1016/j.ejrh.2026.103128},
  url = {https://doi.org/10.1016/j.ejrh.2026.103128}
}