Full Storm Ahead: Harnessing Green Streets for Climate-Ready Watershed Management

John Riverson; Steve Carter; Chris Carandang; Benjamin Bowes

As a coastal county with densely populated urban areas, aging infrastructure, and vulnerable communities, San Mateo County faces significant risks from flooding, water quality degradation, and infrastructure strain. This presentation discusses a climate adaptation risk analysis study which was conducted for the San Mateo Countywide Sustainable Streets Master Plan, a collaborative initiative involving Caltrans and the City/County Association of Governments of San Mateo County (C/CAG) and its 21 member agencies. The analysis evaluated the effectiveness of green infrastructure (GI) integrated into sustainable street designs for enhancing the climate resiliency of roadways and storm drain systems under projected climate change scenarios. We quantified future stormwater runoff and assessed the capacity of GI to mitigate these increases using a regional model and an ensemble of ten general circulation models (GCMs) under a high-emissions pathway (RCP 8.5). Design storms ranging from 2-year to 100-year return periods were modeled to capture both frequent and extreme precipitation events. Results indicate that countywide runoff is projected to increase by 15% to 50%, with roadway runoff rising by 11% to 41%, depending on storm magnitude. The GI implementation scenario was found to meaningfully offset these impacts, particularly for smaller, more frequent storms. Notably, green streets, which are roadway corridors that incorporate GI practices within the public right-of-way to manage stormwater runoff at its source, were projected to fully offset the increased runoff from 2-year storms and reduce the increase by up to 40% for 10-year storms, which are typical design criteria for local storm drain systems. These findings suggest that using more GI can improve climate resilience and limit expensive upgrades to gray infrastructure. This analysis supports the integration of sustainable streets as a key strategy for watershed management and climate adaptation planning in urbanized regions.

Hydrologic Modeling of Permeable Paver Systems

Paul Cureton

This presentation will cover the basics of hydrologic modeling of permeable paver systems using the Modified Rational Method, the SCS Curve Number Method, and water balance equations. Calculation methods for C-value and Curve Number will be demonstrated, and a case study will be provided for a project in Williamson County, TN. Design considerations for steep slopes (> 5.0%) and soils with little to no infiltration will also be discussed. A new design parameter for permeable pavement will be introduced, Perimeter-to-Area ratio, which has been shown to reduce maintenance and improve long-term performance, and is currently be studied by a Concrete Masonry and Hardscape Association (CMHA) research grant.

Remote Sensing and Machine Learning for Green Stormwater Infrastructure Condition Assessment

Omar Hegazy; Walter McDonald; Abhiram Pamula

With the increasing use of Green Stormwater Infrastructure (GSI) to manage stormwater runoff, a significant challenge is the inspection and maintenance of GSI that are widely distributed across urban areas. Remote sensing may be one approach to overcome this challenge through broad, frequent, and spatially distributed data across an entire city that could save both time and resources over in-person inspections. However, it is unclear how to utilize reflectance data of the surface from remote sensing for concrete maintenance indicators or actions. The goal of this study is therefore to classify the land cover of GSI sites using both drone and satellite remote sensing data and translate this classification into useful maintenance indicators. To do so, we collected remote sensing data over one year at 20 GSI sites in Milwaukee, WI using a drone with a multispectral camera (2 cm resolution), as well as high-resolution satellite data (30 cm). We then applied machine-learning algorithms to classify the land cover using categories unique to seasonal land cover changes. Preliminary results indicate that summer land cover classifications (healthy plants, unhealthy plants, dead plants and organic material, and inorganic material) produce an accuracy up to 78% with satellite data and 92% with higher resolution drone data. Future work will evaluate the impact of the seasonal variation on the model outputs. Ultimately, the outcome of this work could lead to reduced cost and time required to effectively maintain GSI systems, thereby improving their overall efficiency and performance in managing stormwater runoff.

Exploring a Quantification Method for the Variability in GSI Performance Hydrographs

Kaleigh Myers; Bridget Wadzuk; Amanda Hess

Green stormwater infrastructure (GSI) mimics natural hydrology by maintaining natural processes (i.e., water retention, infiltration, and evapotranspiration). Various factors influence GSI performance, including environmental conditions (i.e., soil characteristics, ambient temperature, plant density, antecedent dry time, and seasonality) and design-related factors (e.g., specifications and construction). These factors contribute to varying GSI responses to similar rainfall events, and their performance variability should be adequately communicated for widespread acceptance. The primary objective of this research involves developing a methodology to quantify this variability regarding ponding, overflow, and cumulative outflow volume across differing antecedent dry time and percolation rates for the same event. The experiment involved three identical (as possible) loamy sand soil columns (38 cm soil, 15 cm ponding zone, 15 cm diameter). Percolation rates are pump-controlled, and a custom inflow device delivers a simulated rainfall event (15.24 mm/hour, 3-hour duration), which is performed a minimum of seven times for statistical replication. Preliminary results indicate variations among ponding depth, outflow, and overflow responses due to the construction of the columns (all three column results per individual trial) are more prominent than experimental variability (all trial results per individual column). The construction variability, likely due to minor compaction differences (i.e., ±0.2 g/cm3), can cause overflow from some columns and no overflow from others, highlighting crucial aspects of constructed GSI (influence of design/construction) and necessity to quantify the degree of variability. Future tests involve varying the antecedent dry time and percolation rates for the same event to understand the influence of ambient conditions on variability.