Filling missing stormwater infrastructure attributes data for hydrologic-hydraulic (SWMM) model development

Effective hydrologic-hydraulic model development such as U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM) depends on the data availability and data completeness of as-built stormwater infrastructure data. The infrastructure data gaps affect accurate process representation in model causing output uncertainty, error and bias, which further affect model construction, parameterization and its reliable use. However, complete stormwater infrastructure data are often not available due to data sharing restrictions or data gaps occurring from errors of omission (i.e., infrastructure components not being recorded) and error of commission (i.e., assignment of incorrect data). This algorithm, created in R, fills the missing stormwater infrastructure attribute-values data in accordance with the available design standards and modeling practice. It can be adopted to fill missing stormwater infrastructure attributes data for any size of SWMM model. This algorithm can also be implemented to randomly sample, using Monte Carlo sampling approach, the effects of missing attribute-values for different parameters of conduits and junctions such as diameter, roughness and depth.

For details about this work readers are referred to:

1). Shrestha, A., Mascaro, G., & Garcia, M. (2022). Effects of stormwater infrastructure data completeness and model resolution on urban flood modeling. Journal of Hydrology, 607, 127498. https://doi.org/10.1016/j.jhydrol.2022.127498
2). Shrestha, A. (2022). Advances in Urban Flood Management: Addressing Data Uncertainty, Data Gaps and Adaptation Planning (Doctoral dissertation, Arizona State University). https://search.proquest.com/openview/b79c1eb133e93ea0a07b6147fe7feff6/1?pq-origsite=gscholar&cbl=18750&diss=y

For GitHub link to this repository, readers are referred to:
1). https://github.com/ashish-shrs/filling_missing_data_for_swmm/tree/main