Moving Toward Dynamic and Data-Driven GSI Maintenance

Inspection programs offer key data on the physical site conditions that can be used in a data-driven approach, such as inspection frequency, maintenance effort outcomes, system longevity, seasonal effect, and presence of any elements in a GSI system that routinely cause issues. There are some strategies to increase the reach of limited inspection resources. For example, an inspection program based on random and unannounced spot checking rather than full coverage could encourage routine maintenance by private GSI owners while reducing the number of field inspections required (Debo and Reese 2002). To increase data collection, municipalities could shift inspection responsibilities to the property owners and certified inspectors, and require reporting structures to provide data to be used for dynamic maintenance analysis. Municipalities could implement self-reporting requirements, with a system for verification, whereby property owners are educated to perform their own GSI inspections or hire a certified inspector, and a municipal inspection is triggered by failures to submit required self-inspection results—e.g., as successfully seen in the Clean Air Act (Salzman and Thompson 2014).

Inspection programs coupled with new tools for monitoring and GSI site data collection (Fig. 1) provide new means for viewing projects in time and space. Monitoring capabilities can offer a level of ongoing inspection between physical site visits. The current explosion of technology allows for collecting information about GSI systems at ever-increasing rates and resolutions. The tools available to collect information are constantly changing and becoming more affordable. However, the variables critical to understanding GSI function and performance remain largely the same. Care should be taken during data collection that data types are standard and durable over time. All data, continuous or discrete and stored as a vector or raster, should be categorized in a manner that allows for relational mapping.

Because maintenance and inspection programs generate high volumes of data, including contributions from multiple groups, it becomes increasingly important to store data in an integrated, relational structure (Horsburgh et al. 2009). Cities and private contractors tend to use their own software platforms to store and process data, which allows program managers to select the software that best matches their preferences. However, this approach can create data silos that are unable to integrate with other data types or are inaccessible to potential collaborators, neighboring departments (K. Flynn, personal communication, 2019; L. Sherman, personal communication, 2019; T. Saldutti, personal communication, 2019; K. Vacca, personal communication, 2019), or larger agencies. Many municipalities have work order management systems that integrate and relate GSI structures linked to maintenance data, which aids in scheduling and managing work and associated field data (e.g., trash litter loads, effort data). The ability to leverage data streams throughout and across organizations can continue to improve and enrich the way data can be used and manipulated.

Horsburgh et al. (2009) proposed an observations data model (ODM), a relational data model that accommodates different types of data series while facilitating data sharing through a controlled vocabulary and format, focused on hydrologic environmental observations data. The Villanova Center for Resilient Water Systems recently launched a revised structured query language (SQL) data model based on the ODM specific to GSI monitoring, called the stormwater infrastructure data model (SIDM; Strauss and Wadzuk, forthcoming). Like the ODM, the IDM allows for analysis of a wide range of continuous and discrete GSI hydrologic and water quality data. The multidimensional structure of the IDM allows for data analysis spanning projects, locations, time, and users, while maintaining specific metadata for each value within the database. The metadata organization provides a relational structure that facilitates findability, accessibility, interoperability, and reusability (FAIR) data principles. For management of physical assets, a similar relational data model could be developed as a shared data management platform for holistic asset management, allowing for communication and collaboration across operations, maintenance, and other asset management issues.

We are presently in a data revolution, and by tapping into the data collection, modeling, analysis, and use capabilities, we can exponentially grow our ability to implement GSI and ensure its longevity. Key to this goal is to understand GSI’s dynamic performance, and more importantly, what performance failure is and how we can appropriately set risk benchmarks.

Dynamic GSI systems and performance data, along with data tools and technologies, can enhance operational decisions and cost-effectiveness of maintenance activities (Wadzuk et al. 2021). Within the municipal stormwater management community, there is a desire for improved data management and communication. Each of the data-driven tools presented here represents an exciting area for future research and potential incorporation into municipal management. These tools employ new technologies that are promising but generally not well documented for GSI asset management. The new GSI IDM, for example, constitutes a novel data management platform explicitly tailored to an environmental observations data format that would easily link with outside databases and applications. A relational data model opens the door for a wide range of other analysis techniques (e.g., GIS, ANN, MC) that can be implemented to perform risk assessments for GSI systems under existing and proposed maintenance programs. To move toward a relational data model, it is recommended to begin merging hard infrastructure data (e.g., siting and hydrologic performance) with management data (e.g., litter index and maintenance records), environmental data (e.g., climate and weather application programming interfaces) and potentially other social data (e.g., demographics, school and other community institutions) to identify existing maintenance areas of concern (Christman et al. 2018; Hosseiny et al. 2020). These areas can be assessed to determine why they require more maintenance and to find trends with nonperforming GSI, as well as to develop targeted lower-cost maintenance approaches. A simple example would be targeting fall leaf litter cleanup in areas with trees based on weather conditions, which may reduce other inspection visits and inform the required skillset of the maintenance team. Outcomes from this type of investigation can help with more efficient maintenance of systems, and can be used in planning and siting, to improve design, and provide other benefits. If a municipality chooses to develop a multidimensional relational structure that can enable different working groups to contribute data and analyze data in new ways, skilled personnel will be needed to develop and maintain this platform.

To direct data collection efforts, future work needs to be done on understanding which system variables most reliably predict failure of a GSI system. This type of investigation could link GSI hydrologic performance in response to maintenance and could help inform decisions about allocating limited maintenance resources. A key future need—in general and specific to GSI maintenance—is the need to define failure and determine the risk we are willing to accept. Collectively, there is an abundance of GSI performance data over time and space. With advancements in data management technology, all GSI stakeholders can move to better understand the reliability of their systems, which will ultimately lead to increased GSI sustainability and resiliency.