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.

Discharge Frequency-magnitude Analysis of Extreme Weather Events Under Future Conditions

Lia Vergara-Pena; Felix Santiago Collazo; John Ramirez-Avila

Coastal communities in low-gradient basins face escalating flood hazard as tropical cyclones (TCs) become more frequent and intense under climate change. A key challenge in flood risk management is the resulting hazard shift, or return-period compression, in which events previously considered “exceptionally unlikely” occur more frequently. Furthermore, existing conventional hydrologic and statistical frameworks fall short at simulating unprecedented extreme events. Therefore, this study develops a semi-distributed hydrologic model (HEC-HMS) for the upper part of the St. Marys River watershed, located on the eastern border between Florida and Georgia, driven by spatiotemporal variable rainfall generated by a Parametric Precipitation Model (P-CLIPPER) to produce rainfall fields from 500 synthetic TC, for each scenario, present and future conditions. This approach enables the representation of the upper tail of the discharge distribution at very low annual exceedance probabilities (AEPs). Projected changes in Land Use/Land Cover and TC rainfall are incorporated to evaluate future climate and anthropogenic change scenarios. To quantify hazard shift, simulated peak discharges were fitted to six cumulative distribution functions, allowing the estimation of AEPs beyond the 0.1% threshold without relying solely on limited historical gauge records. The study also evaluates whether calibrated and validated hydrologic parameters remain applicable under extreme storms predicted for future scenarios. These findings support the development of flood hazard maps that account for environmental and anthropogenic changes, enhancing infrastructure reliability and long-term resilience under increasing flood frequency and intensity.

Satellite Accountability for Urban Stormwater Infrastructure: Present Capability, the Observability Budget, and the Benchmark Gap

Saurav Kumar

Urban stormwater best management practices (BMPs) receive regulatory load-reduction credit through standardized, type-averaged removal efficiencies, yet per-asset performance verification remains rare. Few sites carry sustained inflow and outflow monitoring in the most comprehensive public repository, while several US MS4 Phase II entities each manage hundreds to thousands of post-construction assets. This presentation synthesizes two recent studies that ask how far publicly available Earth observation (EO) can close that verification gap, and where it stops.

The first is an empirical pilot across 36 monitored BMPs in 12 states, observed from 2018 to 2024. Foundation-model segmentation of sub-meter imagery yields survey-grade footprints in under two minutes per site. BMP type, historically difficult to discriminate, is recoverable from learned multi-physical embeddings but not from hand-crafted spectral indices or an exhaustive search over two-band normalized-difference indices. A two-stage residualization that removes per-asset seasonality and shared regional climate response isolates within-asset spectral departures, though the resulting monitor is sensitive rather than specific, and a flagged event does not identify a cause.

The second study formalizes the limit as an observability budget: the share of attainable probabilistic forecast improvement that assimilating observed condition recovers. In a calibrated observing-system simulation experiment with ensemble Kalman assimilation, recovery scales with how much of the removal rate is routed through observable condition, from about 16 percent when the rate is hidden to 65 to 76 percent when it is fully condition-explained. Condition itself is recovered well enough to support preventive, condition-based dredging that approaches a perfect-knowledge threshold rule.

Both lines terminate at the same constraint: an absent community benchmark pairing per-asset satellite attributes with dated field-condition and performance records. The presentation closes with a three-action agenda for assembling that benchmark from records that already exist in jurisdiction-specific archives.