Risk and Resilience of Distributed Stormwater Infrastructure at Scale: Assessment of 8000 Catchments Acorss New York State

Omid Emamjomehzadeh; Omar Wani

This study presents a comprehensive, uncertainty-aware framework, along with an open-source code repository, for assessing the hydraulic resilience and failure risk of thousands of large culverts at the state level. The framework combines 1-meter digital elevation data, extracts catchment morphological attributes, and utilizes historical and projected rainfall and land use data to model both spatial and temporal variations in culvert performance. Our large-scale simulations determine the percentage of culverts with hydraulic capacities below the 5-year, 25-year, 50-year, and 100-year design discharge levels. Further analysis shows that culverts on main roads, with larger drainage areas, constructed more recently, or exposed to rainfall temporal pattern II, exhibit greater resilience on average. The study also finds that the exceedance risk increases over time due to changes in land use and precipitation. The magnitude of this risk increase varies significantly depending on the chosen projection scenarios. These insights provide practical guidance for infrastructure planning, enabling road managers to assess and prioritize culvert upgrades under current and future climate conditions. All data, methods, and open-source code developed in this study are publicly available to promote scalability to other regions.

Stormwater Management for Retaining Structures on Steep, Low-Permeability Terrain

Lanae Williams; Kaile Johnson; Amisha Bhandari; Sunil Bista; Rocky Talchabhadel

Infrastructure on steep slopes and low-infiltration soils are vulnerable to stormwater-induced hydrostatic pressures. This study evaluates several cost-effective drainage strategies to protect a retaining structure at a water facility situated on impervious soil in Jackson, MS (watershed area 10 km2). We explore runoff depth for various return periods, including the 100-year, 24-hour design storm employing the NRCS Curve Number method, and Rational method. The runoff depth and watershed time of concentration are used to route through the NRCS dimensionless unit hydrograph to estimate peak discharge. To safely manage this peak flow, a hybrid drainage system is proposed that integrates both surface and subsurface components. Surface channels, appropriately graded and lined, can convey the primary stormwater away from the structure, while subsurface interceptor drains can be placed at constrained to capture localized flows that could otherwise increase hydrostatic loading. This approach offers a reliable, adaptable, and economical solution for infrastructure protection on challenging terrain.

Innovative Curve Number Methodology for Estimating Per- and polyfluoroalkyl substances (PFAS) Mass Discharge in Stormwater

Hanadi Rifai; William Vines; Charles Newell

Per- and polyfluoroalkyl substances (PFAS) contamination in stormwater represents a significant environmental concern due to its potential for contaminating natural and engineered water systems. While mechanistic models for estimating runoff and pollutant mass flux have been developed, there is a lack of simple hydrologic and pollutant mass discharge tools for simulating PFAS loads from small watershed areas for use by non-expert modelers, site managers, and decision makers. This paper develops a novel decision support system (DSS), STORME-PFAS, that relies on updated curve number methodology to estimate PFAS mass flux from single storm events and annually. STORME-PFAS, in contrast with conventional DSS, integrates the databases within the modeling tools in a readily accessible interface built within Excel spreadsheets and simulates three hydrologic variants: single storm mass discharge, annual mass discharge, and discrete sampling mass discharge. The single storm event variant converts measured rainfall to modeled runoff via calculations using modified TR-55 curve number methodology. The annual runoff variant estimates runoff for a 1-year period using historic precipitation data and the Guswa curve number exponential distribution method. The modeled variants provide users with modified curve number tables using a non-standard initial abstraction ratio and guidance with data for modeling runoff from concrete surfaces. Validation against field data from a military site with measured PFAS in stormwater demonstrated correspondence with SWMM modeling for the same site. Sensitivity analyses and Monte Carlo modeling were used to develop ranges for runoff volumes and PFAS mass flux with their probability of occurrence for the modeled site.

Impact of Streamflow-Based Curve Number Calibration on Runoff Estimation and Design Storm Bias

John Ramirez-Avila; Sandra Ortega Achury; Jonathan Lasco; Jesus Ortiz; Diego Galindo; Carlos Gonzalez Murillo

The USDA NRCS Curve Number (CN) method is widely applied for runoff estimation and peak-flow design. In regional and extreme-event studies, CN calibration often relies on total streamflow because rainfall–runoff datasets with baseflow separation are scarce, while streamflow records are readily available. Although this practice is justified for design storms where baseflow is minimal, it may introduce bias when CN is intended to represent direct runoff.

CN calibration was performed using the Least Squares Error (LSE) method across 19 Mississippi and 44 Texas watersheds for λ = 0.00, 0.05, 0.20, and optimized values. Optimized λ values were consistently below the NRCS default of 0.20, with best performance near λ ≈ 0.05. Streamflow-based CNs ranged from ~59 to 78 in Mississippi and ~60 to 79 in Texas, while quickflow-based CNs ranged from ~57 to 77 and runoff-based CNs from ~58 to 77, respectively. These differences produced runoff depth overestimation of ~2–6% in Mississippi and ~7–10% in Texas for 2-year storms, with smaller relative errors for 20-year and 100-year storms. For a 10-acre drainage area (≈40,468 m²), this bias translates into ~80–170 m³ extra runoff for frequent storms and up to ~330 m³ for 100-year events, potentially affecting detention sizing and peak-flow design.

Findings demonstrate that streamflow-based CN calibration introduces systematic overestimation of runoff, particularly for smaller, frequent storms. When direct runoff governs design objectives, quickflow-based calibration and site-specific λ optimization are recommended to improve accuracy and mitigate oversizing of stormwater infrastructure.