Launce Cyber-GIS Jupyter for Water.

To create a new environment and to add the kernel to Jupyter Notebook, open the terminal and run the following:

conda create --name pyswmm conda activate pyswmm conda install -c anaconda ipykernel python -m ipykernel install --user --name pyswmm --display-name "PySWMM"

This will create a new development environment named "PySWMM", which can be selected from the dropdown menu of Jupyter Notebook. Now install the following packages:

pip install swmm5 conda install pyswmm pip install swmmio

The PySWMM kernel and its associated development environment provide all the functionality to simulate and modify (in real-time) EPA SWMM models through HydroShare Cyber-GIS.

This is the initial model for Scotts Level Branch (outlet location: https://waterdata.usgs.gov/usa/nwis/uv?01589290) developed in EPA SWMM 5.1. The watershed has six subcatchments. The subcatchments were delineated using ArcHydro and HEC-Geo-HMS (add-ins for ESRI ArcMap 10.7). The model is simulated with data from 1/1/2019 to 12/31/2020. Each subcatchment has six land cover categories, namely, Residential, Industrial, Commercial, Forest, Grass-Pasture, and Agriculture. Nitrogen, Phosphorous, and Suspended Solids (i.e., pollutant loading) are modeled using event mean concentrations (EMC) based on the U.S. Army Corps of Engineer's guidelines (Please refer to page 132 of the attached file named EMC.pdf for the detailed table).

To illustrate the application of LID Controls in SWMM 5.1, a Rain Barrel (barrel height = 36 in, area footprint = 2.3 sft, capacity = 227 L, drains over 24 hours) is designed. For each subcatchment, 500 of such Rain Barrels are implemented. The detailed design of the Rain Barrel can be found in section "2.4. Municipal RWH scenarios" of this paper: https://www.sciencedirect.com/science/article/pii/S0022169413007671

There are two models with the continuous simulation (1/1/2019 to 12/31/2020): One without any LID Control, and one with the Rain Barrels. These models are simulated on a daily basis.

There are also two models using a 24-hour 100-year design storm: One without any LID Control, and one with the Rain Barrel. These models are simulated (on an hourly basis) with a depth of 8.38 inches using the SCS Type 3 storm from HEC-HMS 4.3. The design depth is obtained from NOAA Atlas 14 (https://hdsc.nws.noaa.gov/hdsc/pfds/)

The first part of this repository includes a Python file containing two functions that utilize ESRI's ArcGIS arcpy library. Users can input shapefiles (polygons) of sub-catchments and raster files of land use land cover, and soil types. Additionally, another function allows users to input shapefiles (polylines) of the stormwater network, which include data on built material types and the age of infrastructure, to generate grouped categories of sub-catchments and stormwater conduits.

The second part of the repository contains an R file, which includes two algorithms. The first algorithm extracts time series data of nodes' flooding from a one-dimensional SWMM model and overland flood water depth from a one- and two-dimensional coupled version of the SWMM model. It then establishes a statistical relationship between the two models. The second algorithm parameterizes the SWMM 1D version using a "Genetic Algorithm" for single objective optimization in parallel computing nodes.

For details about this work readers are referred to:

1). Shrestha, A., Garcia, M. & Doerry, E. (2024). Leveraging catchment scale automated novel data collection infrastructure to advance urban hydrologic-hydraulic modeling. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2024.106046 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 links to this repository, and any updates, readers are referred to: 1). https://github.com/ashish-shrs/Parameter_grouping_for_hydrologic-hydraulic_model_calibration 2). https://github.com/ashish-shrs/Algorithm_for_novel_data_application_in_hydrologic-hydraulic_model_calibration

The Nature Conservancy and Texas A&M AgriLife Extension completed the Green Stormwater Infrastructure for Urban Resilience: Opportunity Analysis for Dallas, Texas in collaboration with the City of Dallas and The Trust for Public Land, to identify areas in Dallas where green stormwater infrastructure can most effectively enhance urban flood management – considering capacity, cost, and future impacts of climate change. The focus was on evaluating opportunities where the existing drainage network may be limited, and likely to lead to areal flooding.The priority subwatersheds and GSI opportunity layers included here are outputs from modeling and spatial analysis, which have inherent limitations and uncertainties [1]. We share these layers to facilitate community, policy, and investment considerations, and recommend they be considered together with additional data, such as: City data on channel flooding, customer service calls and upcoming streets and parks projects; FEMA floodplain maps and Community Rating System scores; and data on water quality, equity and land use types available within The Trust for Public Land’s Smart Growth for Dallas tool [2]. Data from this analysis has been integrated into TPL’s Smart Growth for Dallas Decision Support Tool.Priority Subwatersheds. These subwatersheds represent priority areas where GSI could improve stormwater drainage. These areas drain to stormwater network inlets that overflowed in study models* under a variety of rainfall events and indicate where the drainage network is undersized and likely to contribute to aerial flooding. These areas do not represent areal flood risk. (*modeled using EPA SWMM v 5.1.; see analysis sections 2.1-2.3 and 3.2).Green Stormwater Infrastructure (GSI) Opportunity Areas, for the 100-year current conditions storm. The GSI opportunity areas identified are high level and focus on the three types of GSI systems included in the study: bioretention areas, rain gardens, and rainwater harvesting cisterns falling within priority subwatersheds for current conditions storms. Opportunities exist outside of these areas and for other types of GSI. Furthermore, additional detailed feasibility studies would be required for any potential site.

High runoff from urban areas has negative impacts on receiving water bodies. In Hawai'i, this problem is exacerbated by the natural landscape, which rapidly changes from steep ridges to low-lying valleys nearshore. Maunalua Bay, a region located on the southeastern coast of O‘ahu, has been declared an impaired water body by the Hawai‘i Department of Health due to high levels of nutrients and pollutants. Nine highly urbanized watersheds feed into Maunalua Bay, and runoff during storms deposits harmful sediment and pollutants into the Bay. To improve the health of Maunalua Bay, this project utilized hydrologic modeling to determine the runoff-reduction potential of green infrastructure under climate change projections. We created hydrologic models to determine areas of high runoff to guide where to prioritize green infrastructure placement within the urbanized environment. Green infrastructure is a useful means to capture runoff before it enters a waterbody. Targeting strategic locations for runoff reduction practices, such as green infrastructure, can reduce the quantity of runoff that feeds into Maunalua Bay, improving water quality. We also conducted a climate change analysis by modeling how future precipitation projections might influence regional runoff patterns. Our results can serve to inform stormwater management practices that prioritize green infrastructure placement in high runoff locations modeled under current and future climate scenarios. Methods This data was collected from Hawaiian institutions and climate change models. The data was processed using ESRI ArcGIS, R, Python, and the Environmental Protection Agency's Stormwater Management Model (SWMM 5.1).

The Nature Conservancy and Texas A&M AgriLife Extension completed the Green Stormwater Infrastructure for Urban Resilience: Opportunity Analysis for Dallas, Texas in collaboration with the City of Dallas and The Trust for Public Land, to identify areas in Dallas where green stormwater infrastructure can most effectively enhance urban flood management – considering capacity, cost, and future impacts of climate change. The focus was on evaluating opportunities where the existing drainage network may be limited, and likely to lead to areal flooding.The priority subwatersheds and GSI opportunity layers included here are outputs from modeling and spatial analysis, which have inherent limitations and uncertainties [1]. We share these layers to facilitate community, policy, and investment considerations, and recommend they be considered together with additional data, such as: City data on channel flooding, customer service calls and upcoming streets and parks projects; FEMA floodplain maps and Community Rating System scores; and data on water quality, equity and land use types available within The Trust for Public Land’s Smart Growth for Dallas tool [2]. Data from this analysis has been integrated into TPL’s Smart Growth for Dallas Decision Support Tool.Priority Subwatersheds. These subwatersheds represent priority areas where GSI could improve stormwater drainage. These areas drain to stormwater network inlets that overflowed in study models* under a variety of rainfall events and indicate where the drainage network is undersized and likely to contribute to aerial flooding. These areas do not represent areal flood risk. (*modeled using EPA SWMM v 5.1.; see analysis sections 2.1-2.3 and 3.2).Green Stormwater Infrastructure (GSI) Opportunity Areas, for the 100-year current conditions storm. The GSI opportunity areas identified are high level and focus on the three types of GSI systems included in the study: bioretention areas, rain gardens, and rainwater harvesting cisterns falling within priority subwatersheds for current conditions storms. Opportunities exist outside of these areas and for other types of GSI. Furthermore, additional detailed feasibility studies would be required for any potential site.