ABSTRACT:

This supports the article entitled "Understanding spatiotemporal patterns and drivers of urban flooding using municipal reports". This resource includes the R codes and data (imperviousness, rainfall, flood reports, topography, census data) used for the analysis.

This resource contains information from the “City of Denver Open Data Catalog” (http://data.denvergov.org).

ABSTRACT:

This supports the submission of the article entitled "Urbanization of grasslands in the Denver area affects streamflow responses to rainfall events". This resource includes the R codes and data (streamflow, rainfall, snow) data used in the analysis across watersheds.

ABSTRACT:

The data shared here are presented in:
Knight, K.L.; Hou, G.; Bhaskar, A.S.; Chen, S. Assessing the Use of Dual-Drainage Modeling to Determine the Effects of Green Stormwater Infrastructure on Roadway Flooding and Traffic Performance. Water 2021, 13, 1563. https://doi.org/10.3390/w13111563

Summary:

I. INPUT FILES

Input data including: stormwater data, DEM, study area outline, service requests, recurring flood locations, precipitation data, and streamflow data.
Project files including Pre GSI model, 4 GSI scenario models, and validation model. Pre- and post-processing scripts including: LID application spreadsheet,
stormwater data correction, 1D and 2D output data processing. Includes description of labeling method for output data files.
The coordinate system of all project files and output data: NAD83 Colorado Central State Plane (US feet)

Stormwater network data (storm manholes, storm inlets, storm sewer mains, streams, and storm water detention and water quality areas)
was acquired from the City and County of Denver Open Data catalog (https://www.denvergov.org/opendata)

DEM data (1-meter and 3-meter resolution) was acquired from the National Elevation Dataset (NED) using the United States Geologic Survey (USGS)
The National Map (TNM) Download Client (https://apps.nationalmap.gov/downloader/#/)

Study area outline and the bounding layer that delineates roadways from surrounding area are in NAD83 Colorado Central State Plane (US Feet).

Other landuse data (building outlines, impervious area, street centerlines) was acquired from the City and County of Denver Open Data catalog
(https://www.denvergov.org/opendata).

Street polygons were produced from the street centerlines data and a buffer representing 1/2 the street width determined from the street centerline
attributes of lane numbers and roadway type.

Citizen service requests and known areas of recurring flooding datasets are not publically available, for more information contact Dr. Aditi Bhaskar

Precipitation data was downloaded from USGS at 5 raingages. data files include date, time, and 5-minute precipitation data in inches.

Streamflow data was downloaded from USGS 06711575. Data files include date, time, and 5-minute streamflow data in cubic feet per second.

The LID inputs for each subcatchment utilized a single representative 'GSI unit' based on the design of a street planter bioswale from the City and
County of Denver Ultra Urban Report. The LID input for each subcatchment for 1%, 2.5%, 3.5%, and 5% GSI scenarios are included in the table. There are
no LIDs applied to the Pre GSI or Validation scenarios.

II. PCSWMM FILES

PCSWMM project files include the '.inp' file and the relevant project file folder that contains the input layers for each PCSWMM project. The name
of the project file folder and the '.inp' file are the same and need to be located in the same folder to run simulations. Input layers in the project
file folders can be edited and viewed in ArcMap as well, but it is not recommended to directly edit PCSWMM input layers in ArcMap. Rather, create a
copy of the desired layer, edit in ArcMap, open the copy in PCSWMM, and update the PCSWMM input layer using the 'import GIS/CAD' tool.

III. MATLAB FILES

The raw stormwater network data from the City and County of Denver was filled and corrected using the methods summarized in Appendix A of the Thesis
document. The purpose of this data pre-processing was to fill and correct the missing stormwater network data and convert all known data into the
proper formatting for input into PCSWMM. All data is projected into NAD83 Colorado Central State Plane (US feet) coordinate system and clipped to the
study boundary.

The hydrograph outputs from the above scenarios were processed using MATLAB. The output streamflow data for each scenario was compared to the observed
hydrograph at USGS streamgage 06711575. Additionally, the calibration and validation model outputs were analyzed compared to the observed streamflow data
including statistical analysis. All precipitation data is in inches; all streamflow data is in cubic feet per second.

IV. ROAD NETWORK

These are data used for the GIS road network in the traffic modeling by Guangyang Hou (guangyanghou1986@gmail.com).

ABSTRACT:

This supports the submission of the article entitled "Lawn irrigation contributions to semi-arid urban baseflow based on water-stable isotopes". This resource includes the delineated watersheds in the Denver, Colorado area sampled for water stable isotopes (d2H, d18O), the isotope values, the uncertainty calculations, R codes, the Google Earth Engine land cover rasters, and water provider shapefiles.

ABSTRACT:

These are a set of codes to accompany the Water Resources Research submission by Bhaskar et al.

The following codes are run in this order:
1. GAGESII_subset_urbanizing.R

This codes identifies urbanizing watersheds and associated peak 20-year periods of urbanization from GAGES-II based on the criteria of:
1. Drainage area < 200 km2
2. 20 years of completely gap-free daily flow
3. Unaffected by regulation or diversion
4. Housing density increased by > 40% during period of analysis
5. Housing density > 200 housing units/km2 at end of analysis period
6. Imperviousness > 20% in 2012

2. selection_of_reference_gages.R

This code picks a reference gage from GAGES-II reference gages for each urbanizing gage based on the minimization of a function with 4 criteria: distance, similar drainage area, similar precipitation, and similar geology.

3. compare_urbanizing_gage_trends.R

This code calculates quantile-Kendall trend slopes for just the urbanizing gages.

4. gages_reference_vs_urbanizing.R

This code calculates quantile-Kendall trend slopes for the urbanizing and reference gages, subtracts them, creates plots, and has regression analysis. This code uses gages_urbanizing_2019-11-27_Plotting.groups.csv to group urbanizing gages into regional groups for plotting purposes.