Moving Toward Dynamic and Data-Driven GSI Maintenance

Wadzuk, Bridget; Gile, Bridget; Smith, Virginia; Ebrahimian, Ali; Strauss, Micah; Traver, Robert G.

doi:10.1061/jswbay.0000958

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…While this framework includes the full storm cycle, emphasis is placed on the points of saturation, system recovery, infiltration, and evapotranspiration of the soil moisture profile as these are the primary indicators of GSI performance, system health, and demonstrates system recovery. As maintenance continues to be a substantial factor in the success of stormwater management programs that rely on GSI [28], having another tool of using soil moisture to indicate when maintenance is required is extremely helpful.…”

Section: Discussionmentioning

confidence: 99%

A Soil Moisture Profile Conceptual Framework to Identify Water Availability and Recovery in Green Stormwater Infrastructure

Shakya,

Hess,

Wadzuk

et al. 2023

Hydrology

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The recovery of soil void space through infiltration and evapotranspiration processes within green stormwater infrastructure (GSI) is key to continued hydrologic function. As such, soil void space recovery must be well understood to improve the design and modeling and to provide realistic expectations of GSI performance. A novel conceptual framework of soil moisture behavior was developed to define the soil moisture availability at pre-, during, and post-storm conditions. It uses soil moisture measurements and provides seven critical soil moisture points (A, B, C, D, E, F, F″) that describe the soil–water void space recovery after a storm passes through a GSI. The framework outputs a quantification of a GSI subsurface hydrology, including average soil moisture, the duration of saturation, soil moisture recession, desaturation time, infiltration rates, and evapotranspiration (ET) rates. The outputs the framework provide were compared to the values that were obtained through more traditional measurements of infiltration (through spot field infiltration testing), ET (through a variety of methods to quantify GSI ET), soil moisture measurements (through the soil water characteristics curve), and the duration of saturation/desaturation time (through a simulated runoff test), all which provided a strong justification to the framework. This conceptual framework has several applications, including providing an understanding of a system’s ability to hold water, the post-storm recovery process, GSI unit processes (ET and infiltration), important water contents that define the soil–water relationship (such as field capacity and saturation), and a way to quantify long-term changes in performance all through minimal monitoring with one or more soil moisture sensors. The application of this framework to GSI design promotes a deeper understanding of the subsurface hydrology and site-specific soil conditions, which is a key advancement in the understanding of long-term performance and informing GSI design and maintenance.

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Section: Discussionmentioning

confidence: 99%

A Soil Moisture Profile Conceptual Framework to Identify Water Availability and Recovery in Green Stormwater Infrastructure

Shakya,

Hess,

Wadzuk

et al. 2023

Hydrology

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“…A selective approach (i.e., choosing storms that perform above or below the current scenario—similar to a climate change approach) also forecasts how the rain garden would perform if there was some change in the rain garden's behavior (Figure S3 in Supporting Information ). Forecasting performance alone could aid in maintenance planning, or in combination with real‐time performance could indicate an aberration in treatment capacity and instigate a maintenance event (Strauss & Wadzuk, 2022; Wadzuk et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

A Complete Water Balance of a Rain Garden

McGauley,

Amur,

Shakya

et al. 2023

Water Resources Research

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A bioinfiltration rain garden was retrofitted from an existing traffic island at Villanova University in 2001. It has been monitored continuously since 2003 at a 5‐min timeseries resolution and with instrumentation that would enable a water balance calculation. This 20‐year data set allows for an in‐depth analysis of the hydrologic pathways and management in the rain garden. Using physical equations and modeled data (based on real‐time measurements), a balance of all influent, stored, and effluent water within the rain garden was constructed. Analysis shows the rain garden captures 73.5% of runoff, resulting in a post‐implementation management of 86.2% of all rainfall in its watershed. In comparison to the hydrology of other land covers, implementing the rain garden resulted in the management of 37.6% more rainfall than pre‐implementation, producing a hydrological signature similar to that of cultivated land or low development levels (e.g., 30% impervious). Additionally, with the long data record, several statistical techniques were applied to determine the amount of monitoring needed for a certain level of precision in system performance assessment. For 5% uncertainty, approximately 3 years of continuous data is needed to assess performance. This analysis not only facilitates understanding the function of rain garden systems, but also provides conclusions and methodology for understanding the uncertainty associated with the extent of monitoring performed on these green stormwater infrastructure systems. These findings provide practical knowledge as monitoring of stormwater management infrastructures is becoming a more standard part of their operation.

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“…Further, there is a need to develop more sustainable data-informed maintenance programs (Houle et al 2013;Blecken et al 2017;Clary et al 2018;De-Ville et al 2018;DelGrosso et al 2019;Sharior et al 2019). Observation data present an opportunity to develop smart and sustainable data-informed SCM maintenance programs (Wadzuk et al 2021a;Wadzuk et al 2021b). Concurrent with a move toward data-informed design and maintenance, municipalities are moving toward requiring monitoring to demonstrate performance in their stormwater management plans, (e.g., Measure W in Los Angeles; Storm Water Grant Program 2019), which will create a preponderance of data that will only be leveraged with an efficient data model.…”

Section: Introductionmentioning

confidence: 99%

A Relational Data Model for Advancing Stormwater Infrastructure Management

Smith

McGauley

Newman

et al. 2023

J. Sustainable Water Built Environ.

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To date, the knowledge benefits that can result from the growing abundance of measured stormwater data have yet to be fully realized within the industry. Obstacles due to data format and storage, retrieval, and quality control have limited the size and impact of accessing known data, resulting in data that are often siloed by project or objective, which inhibits advancements in our understanding, as well as decision making and information sharing across municipalities. Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI)'s observations data model (ODM) is a relational data model designed to organize disparate types of data; however, it does not provide avenues for the storage of stormwater monitoring-specific metadata. To facilitate stormwater infrastructure data analysis, the Villanova stormwater infrastructure data model (SIDM), constructed comparably to ODM1, was created by adding and removing tables from the ODM1 structure, along with other modifications to enable efficient data access. The novel data model presented here facilitates a comprehensive spatiotemporal analysis specific to stormwater systems to overcome traditional stormwater data siloes and harness the benefits of stormwater data through efficient avenues. The data model structure of the SIDM enables stormwater-specific data to be managed efficiently not only to provide data regarding function and performance but to advance the science around stormwater planning and management, as well as knowledge sharing across researchers, utilities, and municipalities. In this paper, we describe the logic of the design, benefits of the data model, and applications to specific green stormwater infrastructure installations. This has the potential to not only provide novel insight but open the door for applications of modeling advancement in physics-based models and beyond to artificial intelligence, data analysis, and data-informed decision making in the planning, design, operation, and maintenance of stormwater systems.

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Moving Toward Dynamic and Data-Driven GSI Maintenance

Cited by 10 publications

References 30 publications

A Soil Moisture Profile Conceptual Framework to Identify Water Availability and Recovery in Green Stormwater Infrastructure

A Soil Moisture Profile Conceptual Framework to Identify Water Availability and Recovery in Green Stormwater Infrastructure

A Complete Water Balance of a Rain Garden

A Relational Data Model for Advancing Stormwater Infrastructure Management

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