ABSTRACT:

This is one of three rasters collected during summer of 2017 displaying relative stream temperature data obtained from thermal infrared images collected via a small unmanned aerial vehicle. This site was flown on July 19, 2017 at 15:30 EST. It includes 14 images with digital numbers manually adjusted in Adobe Photoshop, georeferenced, and stitched together. As this displays relative temperature, higher values (255) indicate warmer temperatures, while lower values (0) indicate cooler temperatures, but exact temperatures are not specified as the photos were digitally altered in Photoshop.

This particular site visualizes the impact of stormwater from a culvert mixing with main stem stream temperatures.

Drone imagery was flown with an Inspire 1, and thermal infrared imagery was collected with a Zenmuse XT Radiometric thermal camera. Altitude was 61 m AGL and camera yaw was 28 deg.

ABSTRACT:

This is one of three raster datasets collected during summer of 2017 displaying relative stream temperature data obtained from thermal infrared images collected via a small unmanned aerial vehicle. This site was flown on May 12, 2017 at 15:30 EST. It includes 13 images with digital numbers manually adjusted in Adobe Photoshop and then georeferenced. As this displays relative temperature, higher values (255) indicate warmer temperatures, while lower values (0) indicate cooler temperatures, but exact temperatures are not specified as the photos were digitally altered in Photoshop.

This particular site visualizes the impact of stormwater from a culvert mixing with main stem stream temperatures.

Drone imagery was flown with an Inspire 1, and thermal infrared imagery was collected with a Zenmuse XT Radiometric thermal camera. Altitude was 61 m AGL and camera yaw was 28 deg.

ABSTRACT:

This is one of three raster datasets collected during summer of 2017 displaying relative stream temperature data obtained from thermal infrared images collected via a small unmanned aerial vehicle. This site was flown on May 12, 2017 at 15:30 EST. It includes 7 images with digital numbers manually adjusted in Adobe Photoshop and then georeferenced. As this displays relative temperature, higher values (255) indicate warmer temperatures, while lower values (0) indicate cooler temperatures, but exact temperatures are not specified as the photos were digitally altered in Photoshop.

This particular site visualizes the impact of water from a natural spring mixing with main stem stream temperatures.

Drone imagery was flown with an Inspire 1, and thermal infrared imagery was collected with a Zenmuse XT Radiometric thermal camera. Altitude was 61 m AGL and camera yaw was 28 deg.

ABSTRACT:

Topographic indices calculated in support of Kelleher and McPhillips (in review). We calculated two topographic indices - absolute sink depth (m) and topographic wetness index (-) - using TauDEM (v. 5.3) software and the D-infinity flow routing algorithm.

Watersheds include those in Manhattan (CP1, CP2, M1, M2) and Staten Island (SI1, SI2). Naming convention and sites are shown in the associated manuscript. Note that processing for Baltimore is limited to the extent of each watershed that overlaps with the Baltimore city limits, though processing occurred for the entire watershed and was masked to this area.

Values were processed based on the LiDAR digital elevation models (DEM) for NYC, linked below in references (note: to make datasets comparable, the NYC DEM was coarsened to 0.91 m resolution). As presented in the associated manuscript, all topographic index values were extracted for all surfaces (e.g., bare soil, pavement, sidewalks, and vegetated areas) that excluded open water and building footprints (where topographic processing and DEM coverages are less reliable). Land cover datasets are linked below.

Naming convention for all files first specifies watershed name (NYC: cp1, cp2, m1, m2, si1, si2) followed by topographic index type (twi = topographic wetness index, sink = sink depth).

Descriptions for how each topographic index are calculated are specified in the associated manuscript. Generally, sink depths were calculated by differencing the filled and unfilled DEMs, and TWI was calculated from topographic slope and accumulated area, both processed within TauDEM (note: when negative slopes were calculated, these were replaced with very small values, e.g., 0.001).

ABSTRACT:

Topographic indices calculated in support of Kelleher and McPhillips (in review). We calculated two topographic indices - absolute sink depth (m) and topographic wetness index (-) - using TauDEM (v. 5.3) software and the D-infinity flow routing algorithm.

Watersheds include Gwynns Falls [gwynn] and Jones Falls [jones]. Naming convention and sites are shown in the associated manuscript. Note that processing for Baltimore is limited to the extent of each watershed that overlaps with the Baltimore city limits, though processing occurred for the entire watershed and was masked to this area.

Values were processed based on the LiDAR digital elevation model (DEM) for Baltimore, linked below in references. As presented in the associated manuscript, all topographic index values were extracted for all surfaces (e.g., bare soil, pavement, sidewalks, and vegetated areas) that excluded open water and building footprints (where topographic processing and DEM coverages are less reliable). Land cover datasets are linked below.

Naming convention for all files first specifies watershed name (Baltimore; gwynn, jones) followed by topographic index type (twi = topographic wetness index, sink = sink depth).

Descriptions for how each topographic index are calculated are specified in the associated manuscript. Generally, sink depths were calculated by differencing the filled and unfilled DEMs, and TWI was calculated from topographic slope and accumulated area, both processed within TauDEM (note: when negative slopes were calculated, these were replaced with very small values, e.g., 0.001).

ABSTRACT:

Headwater stream temperatures measured within the Gribble Gap headwater catchment at 19 distinct locations. The dataset contains latitude and longitude values for each site, and stream temperatures at a 10 minute interval measured during spring (March 7, 2017 through April 4, 2017) and late summer (August 23, 2017 through September 19, 2017). Observations were collected by Dr. JP Gannon (Virginia Tech) and John Morgan (undergraduate student at Western Carolina University). All observations were made using Thermochron iButtons (Model DS1920L; resolution of 0.0625°C; manufacturer accuracy of +/- 0.5°C). Many sites were instrumented in duplicate, with duplicate sensors at a site indicated by 'A' or 'B' designations. Streamflow is reported at the Gribble Gap outlet in L/s, and all water temperature values are reported in °C. Sites extend from channel heads (W600, GC200, LS180) to the watershed outlet.

ABSTRACT:

This collection includes two presentations to be used in a hydrology course to cover topics of (1) infiltration and (2) hydroecology. These resources were created as part of the NSF-funded Modular Curriculum for Hydrologic Advancement led by Dr. Thorsten Wagener.

More information can be found in:
Wagener, T., Kelleher, C., Weiler, M., McGlynn, B., Gooseff, M., Marshall, L., Meixner, T., McGuire, K., Gregg, S., Sharma, P., and Zappe, S.: It takes a community to raise a hydrologist: the Modular Curriculum for Hydrologic Advancement (MOCHA), Hydrol. Earth Syst. Sci., 16, 3405–3418, https://doi.org/10.5194/hess-16-3405-2012, 2012.

ABSTRACT:

These exercises are designed to introduce students to analyzing real-world datasets with Matlab (4 exercises) and ArcGIS (1 exercise). The activities all require students to watch a video and complete a task before class time, during which they would follow the guide to complete several different tasks. These tasks are specific to learning about Meadowbrook Creek, a first order urban stream in the Syracuse, NY area, but could easily be developed for other places and other types of datasets.

ABSTRACT:

This resource contains the data supporting the article "Monthly river temperature trends across the US confound annual changes".

This resources contains a list of sites, site classifications, and river temperature trends for sites across the United States. Trends were calculated at annual and monthly timescales. Annual trends were calculated using the Seasonal Mann-Kendall trend test; monthly trends were calculated using the Mann-Kendall trend test. Additional information describing methods is included in Kelleher et al. (2021). All trends are calculated using publicly available river temperature data from the United States Geological Survey.