Jennifer A. Curtis Christian E. Torgersen Mousa Diabat Marina F. Marcelli Erick R. Burns Shawn J. Wheelock Andrea Slotke 20210331 raster digital data, , vector digital data, tabular digital data Reston, VA U.S. Geological Survey https://doi.org/10.5066/P9DMJYT7 Marina F. Marcelli Erick R. Burns L. J. Patrick Muffler Andrew Meigs Jennifer A. Curtis Christian E. Torgersen 20221216 publication Hydrogeology Journal vol. 31, issue 2 n/a Springer Science and Business Media LLC ppg. 219-240 https://doi.org/10.1007/s10040-022-02545-x This dataset includes georeferenced high-resolution, airborne thermal infrared (TIR) imagery, a polyline shapefile of the channel centerline, and a tabular file with longitudinal stream temperature profiles for Hat Creek, California. The two aerial TIR surveys were conducted with a helicopter by NV5 Geospatial (formerly Quantum Spatial, Inc.) and are published as two raster mosaics in GeoTiff format with a resolution of 0.5 m. The TIR mosaics and longitudinal stream temperature profiles contain corrected surface temperatures in degrees C (multiplied by 10 to create an unsigned integer pixel type). The TIR dataset encompasses a 64.6-km reach of Hat Creek that extends from 50 m upstream of the confluence with Lost Creek to 50 m downstream of the confluence with the Pit River. The TIR surveys were collected during the afternoon of August 24, 2018, and the morning of August 25, 2018. The two TIR surveys were calibrated using continuous temperature loggers deployed at 12 in-stream locations distributed longitudinally throughout the survey area. A channel centerline was manually digitized within a geographic information system (GIS), and stream temperatures for longitudinal profiles were automatically sampled along the channel centerline from the TIR imagery. Sampled temperatures for the longitudinal profiles were manually filtered to remove measurements of non-water surfaces. The stream temperatures were plotted against channel distance upstream from the mouth of Hat Creek to create longitudinal stream temperature profiles, which were used to interpret groundwater discharge patterns. This data release contains two raster mosaic files with radiant temperatures of the water surface, a channel centerline shapefile, and a comma-separated value file with two longitudinal stream temperature profiles. These datasets were created as part of a U.S. Geological Survey study to assess spatial patterns of groundwater discharge. The intended uses of these data include, but are not limited to, assessments of thermal heterogeneity, sources of cold-water discharge, and geologic controls on surface water and groundwater interactions within the Hat Creek basin. Hat Creek is a tributary to the Pit River located in northeastern California. Discharge from cold-water springs contributes ~10% of the average annual flow to the Pit River, which flows into Shasta Reservoir and represents an integral component of the Central Valley Project and California’s surface-water supply. The Hat Creek study is part of a larger regional groundwater study for the Northwest Volcanic Aquifer Study Area (NVASA) funded by the USGS Water Availability and Use Study Program (WAUSP) https://www.usgs.gov/centers/or-water/science/hydrogeologic-and-geothermal-conditions-northwest-volcanic-aquifers?qt-science_center_objects=0#qt-science_center_objects This dataset can be opened using Esri ArcGIS or equivalent geographic information system software. 20180824 20180825 ground condition Complete None planned -121.5866 -121.4114 40.9899 40.6132 Hat Creek, California ISO 19115 Topic Category boundaries USGS Thesaurus remote sensing water quality water resources hydrology ecology groundwater and surface-water interaction Theme stream temperature hydrological processes thermal infrared springs USGS Metadata Identifier USGS:5f5fd56682ce3550e3bff46d Geographic Names Information System (GNIS): Hat Creek Lassen County California None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Any use of trade, firm, or product names in this metadata record or in this dataset is for descriptive purposes only and does not imply endorsement by the U.S. Government. Jennifer A Curtis U.S. Geological Survey, SOUTHWEST REGION Geologist mailing address

716 UNIT E W Cedar Street

Eureka CA 95501 US 707-442-3169 jacurtis@usgs.gov NV5 Geospatial (formerly QSI Inc.) 110 NE Circle Blvd, Ste 126 Corvallis, OR 97330 541-752-1204 Data were processed on a computer running MS Windows10. Software packages used for data processing included Agisoft Photoscan1.2, ESRI ArcMap10.6, and FLIR ResearchIR1.50.3. The FLIR SC6000 LWIR sensor (8–9.2 microns; Quantum Well infrared photodetector) has an absolute temperature accuracy of ±2 degrees C or ±2% of the reading accuracy over a temperature range of -20 to 350 degrees C. The attribute accuracy was evaluated using the mean absolute error (MAE) and root-mean-squared error (RMSE) which were quantified as the difference between the georeferenced and corrected radiant temperatures and temperatures recorded using 12 in-stream sensors. The calculated MAE for the 24-Aug-2018 survey was 0.2 degrees C and 0.3 degrees C for 25-Aug-2018. The calculated RMSE for the 24-Aug-2018 survey was 0.3 degrees C and 0.4 degrees C for 25-Aug-2018. Thermal infrared sensors measure TIR energy emitted at the water’s surface. Since water is essentially opaque to TIR wavelengths, the sensor is only measuring the water’s surface temperatures. Thermal infrared data accurately represents bulk water temperatures where the water column is thoroughly mixed; however, vertical temperature differences caused by thermal stratification can form in reaches with little or no mixing. Variable water surface conditions (i.e., riffle versus pool), slight changes in viewing angle, and variable background terrestrial temperatures (i.e., shaded or not) can result in differences in calculated radiant temperatures within the same image or between consecutive images. Within narrow channels, there are fewer stream pixels and a greater number of mixed pixels that include non-water features such as rocks, wood, and vegetation. Consequently, narrower channels, relative to the pixel size, may have higher variability and inaccuracies in the measured radiant temperatures as more ‘mixed pixels’ are sampled. Thermal signatures within the expected range for surface surfacewater were not present along the Hat Creek channel centerline between river km 14.16 and 13.37, which made the Hat Creek centerline undetectable. This condition is related to low water (<0.5 m3/s) in this reach during the thermal survey, which may have been influenced by irrigation diversions. During the TIR survey, streamflow increased by approximately 6 m3/s downstream of the Rising River confluence (km 13.37) at which point the Hat Creek centerline was again detectable in the thermal images. The stated temperature range for the raster mosaics includes the radiant temperatures of objects outside the margins of the stream. Surfaces such as metal roofs or pipes or deciduous trees have thermal emissivities that differ from liquid water. A single emissivity is used to determine the relation between the radiant temperature and the kinetic temperature of the object. Temperature data collected using in-stream sensors were used to correct radiant temperatures and to estimate kinetic stream temperature. Because radiant temperatures were only adjusted to stream temperature, the temperatures of surfaces outside the stream margins may be inaccurate. See the following publications for more detailed descriptions of airborne thermal IR remote sensing of streams and image interpretation: Handcock, R.N., Torgersen, C.E., Cherkauer, K.A., Gillespie, A.R., Tockner, K., Faux, R.N., Tan, J. and Carbonneau, P.E., 2012. Thermal infrared remote sensing of water temperature in riverine landscapes. Fluvial remote sensing for science and management, 1(2012), pp.85-113. https://doi.org/10.1002/9781119940791 Torgersen, C.E., Faux, R.N., McIntosh, B.A., Poage, N.J. and Norton, D.J., 2001. Airborne thermal remote sensing for water temperature assessment in rivers and streams. Remote Sensing of Environment, 76(3), pp.386-398. https://doi.org/10.1016/S0034-4257(01)00186-9 Cells outside the data collection area have a value of 0. The horizontal accuracy of the dataset (±10 m) was evaluated using Applanix Virtual Smartbase data. A virtual base station was computed from a network of surrounding reference stations using high-accuracy carrier-phase differential GNSS. The TIR images were orthorectified in Photoscan using camera orientations logged by the onboard global positioning system (GPS) and the inertial measurement unit. The study objective was to create a longitudinal stream temperature profile. The thermal IR imagery is intended to be used as a base layer for creating a longitudinal stream temperature profile, and the horizontal accuracy varies by ±10 m due to the high amount of distortion in the lens of the thermal imager. Areas toward the edge of an individual image frame will likely be less spatially accurate than nadir views. This dataset does not contain vertical information. Quantum Spatial, Inc. 20181129 raster digital data Digital and/or Hardcopy 20180824 ground condition 08242018 TIR raster Base data Quantum Spatial, Inc. 20181129 raster digital data Digital and/or Hardcopy 20180825 ground condition 08252018 TIR raster Base data Quantum Spatial, Inc. 20181129 vector digital data Data file published with this data release. Digital and/or Hardcopy 20180825 ground condition Channel Centerline supporting data A total of 14 in-stream sensors were deployed at sites distributed longitudinally along Hat Creek during the thermal survey. These continuous data loggers collected temperature data during the survey period at 10-minute intervals for use in TIR image calibration. Only 12 of the sensors produced data that met the accuracy criteria for calibration. 20181129 Thermal Infrared images were collected using a FLIR SC6000 LWIR sensor (8–9.2 microns; Quantum Well infrared photodetector mounted to a Bell Jet Ranger helicopter. The SC6000 is a calibrated radiometer with internal non-uniformity correction and drift compensation. The sensor is contained in a composite fiber enclosure attached to the underside of the aircraft. 20181129 The aircraft was flown longitudinally along the stream corridor to position the river in the center of each image frame. The afternoon flight on 24-Aug-2018 occurred between 16:30 and 19:00 (PDT) and the morning flight on 25-Aug-2018 occurred between 7:00 and 9:30 (PDT). A supplementary flight was conducted on 25-Aug-2018 at 18:20-19:00 for a reach that was missed during acquisition the previous day. In a few sections of the river, it was necessary to make multiple passes to cover the channel due to the width and/or sinuosity. The TIR sensor was set to acquire images at a rate of 1 image per second (1 Hz) resulting in at least 60% vertical overlap between images. Flight altitudes were selected to optimize resolution while providing an image ground footprint wide enough to capture the active channel with the stream occupying 30 – 60% of the image. A target flight altitude of 300-400 m above ground level was planned for the Hat Creek acquisition to achieve a ground sample distance of ≤ 0.5 m. Thermal infrared images were recorded directly from the sensor to an on-board computer as raw counts which were then converted to radiant temperatures. The individual images were referenced with time, position, and heading information provided by a global positioning system (GPS). 20181129 To accurately solve for image position (geographic coordinates x, y, z), the positional coordinates of the airborne sensor and the attitude of the aircraft were recorded continuously throughout the data collection missions. The position and altitude of the aircraft were measured twice per second (2 Hz) by an onboard differential GPS unit. Also pitch, roll, and yaw (heading) were measured 200 times per second (200 Hz) from an onboard inertial measurement unit (IMU). To allow for post-processing correction and calibration, aircraft/sensor position and attitude data are indexed by GPS time. 20181129 A series of corrections is applied to the aircraft trajectory and orientation using Applanix Virtual Smartbase data. The virtual base station is computed from a network of surrounding reference stations using high-accuracy carrier-phase differential GNSS. Image timestamps were linked to the corrected trajectory to define the position and orientation of the sensor during each imaging event. Image location data and calibrated TIR images were imported into Agisoft Photoscan 1.2 and a mosaic of the TIR imagery was created using automatically generated tie-points and ground control data from reference base maps. No color balancing or seam feathering is used during the mosaic process to preserve the original temperature values of the TIR imagery. 20181129 A TIR camera does not directly measure surface temperatures. These sensors capture an image of infrared radiation emitted from a surface that can be corrected to produce an accurate image of surface temperature. Response characteristics of the TIR sensor were measured in a laboratory environment. Response curves are used to relate the raw digital numbers recorded by the sensor to emitted radiance from a black body. The raw TIR images collected during the Hat Creek surveys initially contain digital numbers which are then converted to radiance temperatures based on the FLIR factory calibration. The calculated radiant temperatures are adjusted based on the kinetic temperatures recorded at each ground control location (in-stream temperature sensors). NV5 Geospatial used the FLIR ResearchIR v1.50.3 software package to perform this adjustment, which corrects for path length attenuation (caused by atmospheric temperature, relative humidity, the altitude of the camera above the surface, and the reflected radiant temperature) and the emissivity of natural water (0.98). This process produces corrected images with accurate in-stream temperatures. The in-stream temperatures were used as validation points to calculate MAE and RMSE of the corrected radiant stream temperatures. The in-stream water temperatures collected at the time of image acquisition were compared to radiant values, representing the median of ten points sampled from the image at the in-stream sensor location. 20181129 A stream centerline shapefile was manually digitized using ArcMap10.6 from the thermal mosaics. As the streams were digitized off the thermal imagery, care was taken to avoid as many non-water features as possible; however, due to the nature of the streams, aquatic vegetation, boulders, wood, bridges, and other obstructions could not always be avoided in narrow passages. 20181129 Channel Centerline Longitudinal stream temperature profiles were developed with a nested sampling design for calculating the median, mean, maximum, minimum, and standard deviation of water temperature at 10-m intervals along the channel centerline. Using ArcMap10.6, equidistant points were created along the channel centerline at an interval of 10 m. Due to the nature of automated sampling, some sample points inevitably fell on bridges or obvious non-water features skewing the temperatures. The points were reviewed, and points overlying non-water features were removed. Statistics for stream temperature for each sample point were then calculated from 10 subsamples (i.e., pixels) spaced at 0.5-m intervals along a 4-m section of the stream centerline that was centered on the sample point. The resulting median temperatures for the sample points were plotted versus river kilometer to define longitudinal stream temperature profiles for the 24-Aug-2018 and 25-Aug-2018 surveys. Channel Centerline 08242018 TIR raster 08252018 TIR raster 20200301 Universal Transverse Mercator 10 0.9996 -123.0 0.0 500000.0 0.0 coordinate pair 0.6096 0.6096 meters North_American_Datum_1983 GRS_1980 6378137.0 298.257222101 Airborne_thermal_infrared_mosaic_Hat_Creek_California_08242018_16bit.tif Raster geospatial data file. Producer Defined Value Unique corrected surface temperatures in degrees C (multiplied by 10 to create an unsigned integer pixel type) contained in each raster cell. Producer Defined 0 NoData Producer defined 0.0 864.0 Airborne_thermal_infrared_mosaic_Hat_creek_California_08252018_16bit.tif Raster geospatial data file. Producer Defined Value Unique corrected surface temperatures in degrees C (multiplied by 10 to create an unsigned integer pixel type) contained in each raster cell. Producer Defined 0 NoData Producer defined 0.0 866.0 Channel_Centerline_Hat_Creek_California_2018.shp Attribute Table Table containing attribute information associated with the data set. Producer Defined FID Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. Shape Feature geometry. ESRI Coordinates defining the features. Name Place name Producer Defined Hat Creek, CA Place Name Producer defined Length_km Channel centerline length in kilometers. Producer Defined 64.5731 64.5731 Stream_temperature_long_profiles_Hat_Creek_California_2018.csv Comma Separated Value (CSV) file containing data. Producer Defined GNIS_NAME Place Name Producer Defined Hat Creek, CA Place Name Producer defined Easting UTM coordinate (or the x-coordinate) that measures the distance in meters to a "false easting", which is uniquely defined in each UTM zone. Producer Defined 619555 633628 Northing UTM coordinate (or the y-coordinate) that measures the distance in meters to the equator. Producer Defined 4497018 4538606 River_meters River distance in meters Producer Defined 0 64570 kilometers 082418_Median Median stream temperature in degrees C during the afternoon TIR survey on 082418. Producer Defined 8.5 18.7 082518_Median Median stream temperature in degrees C during the morning TIR survey on 082518 Producer Defined 5.3 16.2 The entity and attribute information provided here describes the tabular data associated with the data set. Please review the detailed descriptions that are provided (the individual attribute descriptions) for information on the values that appear as fields/table entries of the data set. The entity and attribute information was generated by the individual and/or agency identified as the originator of the data set. Please review the rest of the metadata record for additional details and information. U.S. Geological Survey GS ScienceBase mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P9DMJYT7 None 20230427 Jennifer A. Curtis U.S. Geological Survey, SOUTHWEST REGION Research Geologist mailing address

716 UNIT E W Cedar Street

Eureka CA 95501 US 707-442-3169 jacurtis@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999