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.