Forensic Analyses of Two Failed Rain Gardens in a Tract Development in Glasgow, Delaware

Garza, Pablo R.; Welker, Andrea L.; Rife, Sarah

doi:10.1061/9780784480175.005

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…It should be noted that reduced Ksat values over time due to clogging were observed in some vegetated GSI systems such as infiltration basins (Natarajan & Davis, ) and rain gardens (Garza, Welker, & Rife, ; Gulliver et al, ). However, factors such as improper siting and design, excess loadings of sediment inflow, and inadequate maintenance were usually the main cause of clogging in these systems.…”

Section: Causes Of Temporal and Spatial Variability Of Field Ksatmentioning

confidence: 99%

“…The United States Department of Agriculture (USDA) textural soil classification is mostly used for engineering purposes, including soil media specification in GSI systems (e.g., Pennsylvania Department of Environmental Protection [PA DEP], 2006) because of its simplicity (García‐Gaines & Frankenstein, ; Garza et al, ). This classification system is solely based on soil grain size distribution.…”

Section: Causes Of Temporal and Spatial Variability Of Field Ksatmentioning

confidence: 99%

“…However, the USDA classification system may not be effective in predicting infiltration performance because the within‐class variations for each soil texture are often greater than the between‐class variations. Also, the system does not consider the quality of the fine fraction of the soil (Lee, Traver, & Welker, ; Garza et al, ). Other soil properties such as plasticity (as determined by the Atterberg limits and utilized in the Unified Soil Classification System) are important in predicting Ksat and reducing the Ksat‐related uncertainties in GSI design and analysis (Benson, Zhai, & Rashad, ; Zukowski, ).…”

Section: Causes Of Temporal and Spatial Variability Of Field Ksatmentioning

confidence: 99%

“…A forensic analysis of two failed rain gardens in Glasgow, Delaware, was performed by Garza et al () that used laboratory measured Ksat and PI for soil samples from the rain gardens and native soils. The native soils were nonplastic and had a higher Ksat than the rain gardens, which contained significant clay contents (PI of 13 and 16).…”

Section: Causes Of Temporal and Spatial Variability Of Field Ksatmentioning

confidence: 99%

“…Similarly, Zukowski et al () found a trend of decreasing Ksat with increasing PI at a GSI site in Willow Grove, PA, i.e., two samples with the lowest Ksat had the highest PI values (8.28 and 7.35) and the sample with the lowest PI (3.1) showed the highest Ksat. To consider the effect of particle size and soil behaviour on Ksat, Garza et al () recommended using the Unified Soil Classification System in addition to the widely‐used USDA soil texture classes.…”

Section: Causes Of Temporal and Spatial Variability Of Field Ksatmentioning

confidence: 99%

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Temporal and spatial variation of infiltration in urban green infrastructure

Ebrahimian

Sample-Lord

Wadzuk

et al. 2019

Hydrological Processes

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Infiltration is the primary mechanism in green stormwater infrastructure (GSI) systems to reduce the runoff volume from urbanized areas. Soil hydraulic conductivity is most important in influencing GSI infiltration rates. Saturated hydraulic conductivity (Ksat) is a critical parameter for GSI design and post-construction performance. However, Ksat measurement in the field is problematic due to temporal and spatial variability and measurement errors. This review paper focuses on a comparison of methods for in-situ Ksat measurement and the causes of temporal and spatial variations of Ksat within GSI systems. Automated infiltration testing methods, such as the Modified Philip-Dunne (MPD) and SATURO infiltrometers, show promise for efficient Ksat measurements. Soil Ksat values can change over time and substantially vary throughout a GSI, which can be attributed to multiple factors, including but not limited to temperature changes, soil composition and properties, soil compaction level, plant root morphology and distribution, biological and macrofauna activities in the soil, inflow sediment characteristics, quality of infiltrating water, and measurement errors. There is evidence that infiltration rates in vegetated urban GSI systems are sustained given an appropriate GSI design, reasonable concentration of suspended sediments in the inflow runoff, and routine maintenance procedures.These observations indicate that clogging can be counteracted by processes that tend to increase the soil hydraulic conductivity (e.g., plant root and biological activities). This self-sustainability underlines that infiltration-based GSI systems are a reliable long-term stormwater management solution. Recommendations on how to incorporate the temporal changes of Ksat in GSI design and on obtaining a spatiallyrepresentative Ksat for the GSI design are presented. K E Y W O R D Sgreen infrastructure, hydraulic conductivity, infiltration, infiltrometer, saturated hydraulic conductivity, soil, stormwater, urban areas

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Section: Causes Of Temporal and Spatial Variability Of Field Ksatmentioning

confidence: 99%

Section: Causes Of Temporal and Spatial Variability Of Field Ksatmentioning

confidence: 99%

Section: Causes Of Temporal and Spatial Variability Of Field Ksatmentioning

confidence: 99%

Section: Causes Of Temporal and Spatial Variability Of Field Ksatmentioning

confidence: 99%

Section: Causes Of Temporal and Spatial Variability Of Field Ksatmentioning

confidence: 99%

See 3 more Smart Citations

Temporal and spatial variation of infiltration in urban green infrastructure

Ebrahimian

Sample-Lord

Wadzuk

et al. 2019

Hydrological Processes

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The Potential of Stormwater Management Strategies and Artificial Intelligence Modeling Tools to Improve Water Quality: A Review

Ramovha,

Chadyiwa,

Ntuli

et al. 2024

Water Resour Manage

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Stormwater management modeling tools have been utilized to enhance stormwater operating systems, assess modeling system efficiency, and evaluate the impacts of urban growth on stormwater runoff and water quality. This review explores the potential of stormwater management strategies and Artificial Intelligence modeling tools in enhancing water quality. The study focuses on evaluating stormwater modeling tools for planning and improving stormwater systems, assessing modeling efficiency, and understanding the impacts of new development on stormwater runoff and water quality. Various stormwater modeling tools are compared to aid in water management in urban and rural settings, which is crucial due to increasing storm intensity from climate change. The review debates the advantages and limitations of different modeling tools, particularly in modeling stormwater quantity and quality under different scenarios. It also examines tools used for predicting and analysing stormwater runoff during storm events in diverse locations. The assessment of modeling tools is centred on their support for Green Infrastructure (GI) practices, considering factors like modeling accuracy, data availability, and requirements. The study highlights the importance of these tools in managing water in urban areas and safeguarding water sources during stormwater events. Notably, the accuracy and efficacy of stormwater modeling tools are influenced by input data quality, calibration methods, and standardization metrics, with the widely used Stormwater Management Model (SWMM) being a common modeling tool.

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