Zohar et al. (2025) Toward scalable green roofs: A critical review of hydrological design, modelling, monitoring, and future directions

Identification

• Journal: The Science of The Total Environment
• Year: 2025
• Date: 2025-12-23
• Authors: Yonatan Zohar, Angel Sussman, Fadi Kizel, Eran Friedler
• DOI: 10.1016/j.scitotenv.2025.181221

Research Groups

• Faculty of Civil and Environmental Engineering, Technion – Israel Institute of Technology, Israel

Short Summary

This review addresses the gap between extensive green roof (GR) research and their limited large-scale implementation for urban stormwater management. It synthesizes knowledge across design, hydrological modelling, and monitoring, emphasizing remote sensing (RS) as a scalable solution, and explores future directions like blue-green solar roofs (BGSRs) to foster widespread adoption.

Objective

• To bridge the knowledge gap hindering large-scale implementation of green roofs (GRs) for urban stormwater management by providing a comprehensive overview of GR hydrological assessments.
• To review critical hydrological processes, identify performance-driving parameters, evaluate existing modelling approaches, and examine current monitoring techniques and their limitations.
• To emphasize remote sensing (RS) as a promising approach for efficient and scalable monitoring and performance evaluation of GRs.
• To highlight research gaps and suggest future directions, including the integration of blue-green solar roofs (BGSRs) and RS-based hydrological indices.

Study Configuration

• Spatial Scale: Individual roof scale, plot-level, urban watershed, city-wide, and catchment scale.
• Temporal Scale: Single rainfall events, short periods, seasonal behavior, multi-year simulations, and long-term trends (decades).

Methodology and Data

• Models used:
  • Hydrological Models: Storm Water Management Model (SWMM), HYDRUS-1D/2D/3D, MIKE SHE, Characteristic Runoff Equations (CRE), Unit Hydrograph (UH), Curve Number (CN) method, process-based water balance models, conceptual reservoir models (cascaded, nonlinear, linear).
  • Evapotranspiration (ET) Models: Penman-Monteith (FAO-56, ASCE), Hargreaves (HG), Priestley–Taylor (P-T), Thornthwaite–Mather (T-M).
  • Artificial Intelligence/Machine Learning: Random Forest, Support Vector Machines, Bayes classifier.
• Data sources:
  • In-situ Measurements: Manual field inspections, soil moisture probes, flowmeters, precipitation gauges, lysimeters, spectral and thermal imaging systems, field spectroradiometers.
  • Remote Sensing: Satellite imagery (Sentinel-2, Landsat-8/9, Sentinel-1, Planetscope, Worldview-3, ZY-3), Unmanned Aerial Vehicle (UAV) imagery (RGB, multispectral, thermal).
  • Other: Meteorological data, experimental time-series data.

Main Results

• Green roofs (GRs) significantly reduce urban runoff volumes (annual retention rates from 13% to 94%) and attenuate peak flows (40% to 70% reduction).
• Hydrological performance is governed by substrate depth (e.g., 0.04 m depth showed higher retention than 0.065 m), substrate composition (e.g., crushed brick media improved retention over LECA), vegetation type (e.g., Sedum, Origanum onites), roof slope, and age.
• Blue-green roofs (BGRs) with water storage modules (e.g., 0.03 m to 0.06 m deep reservoirs) demonstrate superior runoff reduction (70–97% during extreme events) compared to conventional GRs (12%).
• Existing hydrological models (SWMM, HYDRUS) are widely used but require calibration and may have limitations in representing complex GR processes. Empirical models are site-specific.
• Evapotranspiration is the main water-loss process, accounting for approximately 50% of annual precipitation in humid regions and up to 90% in arid zones. Various models (e.g., Penman-Monteith, Priestley-Taylor) are used for ET estimation, often with crop coefficients (Kc) ranging from 0.32 to 1.67.
• Traditional GR monitoring is labor-intensive and not scalable for city-wide implementation. Remote sensing (RS) offers a scalable, repeatable, and quantitative solution for long-term monitoring.
• RS techniques, including spectral imaging (e.g., Vegetation Indices like NDVI), thermal infrared (TIR), Light Detection and Ranging (LiDAR), and Synthetic Aperture Radar (SAR), can serve as proxies for interception, evapotranspiration, and storage.
• The explicit integration of RS with GR hydrological modelling remains nascent, with few studies directly linking RS observations to quantitative runoff metrics.
• Unmanned Aerial Vehicles (UAVs) provide high spatial resolution (centimeter-level) and flexible scheduling for localized monitoring, while satellites (e.g., Sentinel-2, Landsat-8/9) offer broad spatial coverage (10–30 m resolution) and historical data, despite limitations like cloud cover and coarse pixel size.
• Future directions include developing robust hydrological RS indices ("hydro-indices"), integrating RS and in-situ data, utilizing artificial intelligence/machine learning for data analysis, and implementing multi-functional Blue-Green-Solar Roofs (BGSRs) that combine energy production, water retention, and ecological benefits.

Contributions

• Provides a comprehensive synthesis of the current state of knowledge in green roof hydrological design, modelling, and monitoring, addressing the fragmentation across disciplines.
• Identifies the lack of scalable monitoring as a critical barrier to widespread GR adoption and proposes remote sensing as a key enabling technology for city-scale, long-term performance assessment.
• Evaluates the strengths and limitations of various hydrological and evapotranspiration models, highlighting the need for improved integration of process-based and data-driven approaches.
• Explores innovative future directions, such as the development of integrated blue-green solar roofs (BGSRs) and the creation of remote sensing-based hydrological indices for continuous performance assessment.
• Outlines a pathway for transitioning GRs from isolated interventions to cornerstone strategies within resilient, multi-service urban infrastructure by emphasizing integrated workflows, robust model calibration, and uncertainty quantification.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Zohar2025Toward,
  author = {Zohar, Yonatan and Sussman, Angel and Kizel, Fadi and Friedler, Eran},
  title = {Toward scalable green roofs: A critical review of hydrological design, modelling, monitoring, and future directions},
  journal = {The Science of The Total Environment},
  year = {2025},
  doi = {10.1016/j.scitotenv.2025.181221},
  url = {https://doi.org/10.1016/j.scitotenv.2025.181221}
}