Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

Hourly Precipitation Data (HPD) is digital data set DSI-3240, archived at the National Climatic Data Center (NCDC). The primary source of data for this file is approximately 5,500 US National Weather Service (NWS), Federal Aviation Administration (FAA), and cooperative observer stations in the United States of America, Puerto Rico, the US Virgin Islands, and various Pacific Islands. The earliest data dates vary considerably by state and region: Maine, Pennsylvania, and Texas have data since 1900. The western Pacific region that includes Guam, American Samoa, Marshall Islands, Micronesia, and Palau have data since 1978. Other states and regions have earliest dates between those extremes. The latest data in all states and regions is from the present day. The major parameter in DSI-3240 is precipitation amounts, which are measurements of hourly or daily precipitation accumulation. Accumulation was for longer periods of time if for any reason the rain gauge was out of service or no observer was present. DSI 3240_01 contains data grouped by state; DSI 3240_02 contains data grouped by year.

The Baltimore radar rainfall dataset was developed from a multi-sensor analysis combining radar rainfall estimates from the Sterling, VA WSR88D radar (KLWX) with measurements from a collection of ground based rain gages. The archived data have a 15-minute time resolution and a grid resolution of 0.01 degree latitude/longitude (approximately 1 km x 1 km); 15-minute rainfall accumulations for each grid are in mm. The dataset spans 22 years, 2000-2021, and covers an area of approximately 4,900 km^2 (70 by 70 grids, each with approximate area of 1 km^2) surrounding the Baltimore, MD metropolitan area (Figure 1). The rainfall data cover the six months from April to September of each year. This is the period with most intense sub-daily rainfall and the period for which radar measurements are most accurate. Figure 1 illustrates the climatological analyses of mean annual frequency of days with at least 1 hour rainfall exceeding 25 mm. The striking spatial variability of convective rainfall is illustrated in Figure 2 by the April-September climatology of annual lightning strikes.

As with many long-term environmental data sets, sensor technology has changed during the time period of the archive. The Sterling, VA WSR88D radar underwent a hardware upgrade from single polarization to dual polarization in 2012. Prior to the upgrade, rainfall was estimated using a conventional radar-reflectivity algorithm (HydroNEXRAD) which converts reflectivity measurements in polar coordinates from the lowest sweep to rainfall estimates on a 0.01 degree latitude-longitude grid at the surface (see Seo et al. 2010 and Smith et al. 2012 for details on the algorithm). The polarimetric upgrade introduced new measurements into the radar-rainfall algorithm. In addition to reflectivity, the operational rainfall product, Digital Precipitation Rate (DPR), directly uses differential reflectivity and specific differential phase shift measurements to estimate rainfall (https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00708; see also Giangrande and Ryzhkov 2008). Details of the algorithm structure and parameterization for the DPR radar-rainfall estimates have been modified during the 10-year period of the data set.

A storm-based (daily) multiplicative mean field bias has been applied to both datasets. The mean field bias is computed as the ratio of daily rain gage rainfall at a point to daily radar rainfall for the bin that contains the gage. The rain gage dataset is compiled from rain gages in the Baltimore metropolitan region and surrounding areas and includes gages acquired from both Baltimore City and Baltimore County, and the Global Historical Climatology Network daily (GHCNd). Mean field bias improves rainfall estimates and diminishes the impacts of changing measurement procedures.

The dataset has been archived in 2 formats: netCDF gridded rainfall, 1 file for each 15-minute time period, and csv or excel format point rainfall (1 point at the center of each grid) in a timeseries format with 1 file per calendar month covering the entire 70x70 domain. The csv files are in folders organized by calendar year. The first five columns in each file represent year, month, day, hour, and minute and can be combined to generate a unique date-time value for each time step. Each additional column is a complete time series for the month and represents data from one of the 1-km2 grid cells in the original data set.

The latitude and longitude coordinates for each pixel in the grid are provided. The latitude and longitude represent the centroid of the cell, which is square when represented in latitude and longitude coordinates and rectangular when represented in other distance-based coordinate systems such as State Plane or Universal Transverse Mercator. There are 4900 pixels in the domain. In order to visualize the data using GIS or other software, the user needs to associate each column in the annual rainfall file with the latitude and longitude values for that grid cell number.

These data may be subject to modest revision or reformatting in future versions. The current version is version 2.0 and is being offered to users who wish to explore the data. We will revise this document as needed.

The NOAA Cooperative Observer Program (COOP) 15-Minute Precipitation Data consists of quality controlled precipitation amounts, which are measurements of 15 minute accumulation of precipitation, including rain and snow for approximately 2,000 observing stations around the country, and several U.S. territories in the Caribbean and Pacific operated or managed by the NOAA National Weather Service (NWS). Stations are primary, secondary, or cooperative observer sites that have the capability to measure precipitation at 15 minute intervals. This dataset contains 15-minute precipitation data (reported 4 times per hour, if precipitation occurred) for U.S. stations along with selected non-U.S. stations in U.S. territories and associated nations. It includes major city locations and many small town locations. Daily total precipitation is also included as part of the data record. The dataset period of record is from May 1970 to December 2013. The dataset is archived by the NOAA National Centers for Environmental Information (NCEI).

Temperature and rainfall data for St. John USVI. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Based on Tsounis and Edmunds (In press), Ecosphere:\u00a0

Physical environmental conditions were characterized using three features that are well-known to affect coral reef community dynamics (described in Glynn 1993, Rogers 1993, Fabricius et al. 2005): seawater temperature, rainfall, and hurricane intensity. Together, these were used to generate seven dependent variables describing physical environmental features. Seawater temperature was recorded at each site every 15-30 min using a variety of logging sensors (see Edmunds 2006 for detailed information on the temperature measurement regime). Seawater temperature was characterized using five dependent variables calculated for each calendar year: mean temperature, maximum temperature, and minimum temperature (all averaged by day and month for each year), as well as the number of days hotter than 29.3 deg C (\u201chot days\u201d), and the number of days with temperatures greater than or equal to 26.0 deg C (\u201ccold days\u201d). The temperature defining "hot days" was determined by the coral bleaching threshold for St. John ("%5C%22http://www.coral.noaa.gov/research/climate-change/coral-%0Ableaching.html%5C%22">http://www.coral.noaa.gov/research/climate-change/coral- bleaching.html), and the temperature defining "cold days" was taken as 26.0 deg C which marks the lower 12th percentile of all daily temperatures between 1989 and 2005 (Edmunds, 2006). The upper temperature limit was defined by the local bleaching threshold, and the lower limit defined the 12th\u00a0percentile of local seawater temperature records (see Edmunds 2006 for details). Rainfall was measured at various locations around St. John (see\u00a0http://www.sercc.com) but often on the north shore (courtesy of R.\u00a0Boulon) (see Edmunds and Gray 2014). To assess the influence of hurricanes, a categorical index of local hurricane impact was employed, with the index based on qualitative estimates of wave impacts in Great Lameshur Bay as a function of wind speed, wind direction, and distance of the nearest approach of each hurricane to the study area (see Gross and Edmunds 2015). Index values of 0 were assigned to years with no hurricanes, 0.5 to hurricanes with low impacts, and 1 for hurricanes with high impacts, and years were characterized by the sum of their hurricane index values. awards_0_award_nid=55191 awards_0_award_number=DEB-0841441 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=0841441&HistoricalAwards=false awards_0_funder_name=National Science Foundation awards_0_funding_acronym=NSF awards_0_funding_source_nid=350 awards_0_program_manager=Saran Twombly awards_0_program_manager_nid=51702 awards_1_award_nid=562085 awards_1_award_number=OCE-1332915 awards_1_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1332915 awards_1_funder_name=NSF Division of Ocean Sciences awards_1_funding_acronym=NSF OCE awards_1_funding_source_nid=355 awards_1_program_manager=David L. Garrison awards_1_program_manager_nid=50534 awards_2_award_nid=562593 awards_2_award_number=DEB-1350146 awards_2_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=1350146 awards_2_funder_name=NSF Division of Environmental Biology awards_2_funding_acronym=NSF DEB awards_2_funding_source_nid=550432 awards_2_program_manager=Betsy Von Holle awards_2_program_manager_nid=701685 cdm_data_type=Other comment=Physical Data G. Tsounis and P. Edmunds, PIs Version 10 November 2016 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.664755 infoUrl=https://www.bco-dmo.org/dataset/664254 institution=BCO-DMO instruments_0_acronym=PrecipGauge instruments_0_dataset_instrument_description=Measured rainfall instruments_0_dataset_instrument_nid=664662 instruments_0_description=measures rain or snow precipitation instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/381/ instruments_0_instrument_name=Precipitation Gauge instruments_0_instrument_nid=671 instruments_0_supplied_name=Precipitation gauge instruments_1_dataset_instrument_description=Measured seawater temperature instruments_1_dataset_instrument_nid=664661 instruments_1_description=Records temperature data over a period of time. instruments_1_instrument_name=Temperature Logger instruments_1_instrument_nid=639396 instruments_1_supplied_name=Temperature logger metadata_source=https://www.bco-dmo.org/api/dataset/664254 param_mapping={'664254': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/664254/parameters people_0_affiliation=California State University Northridge people_0_affiliation_acronym=CSU-Northridge people_0_person_name=Peter J. Edmunds people_0_person_nid=51536 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=California State University Northridge people_1_affiliation_acronym=CSU-Northridge people_1_person_name=Dr Georgios Tsounis people_1_person_nid=565353 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Hannah Ake people_2_person_nid=650173 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=St. John LTREB,VI Octocorals projects_0_acronym=St. John LTREB projects_0_description=Long Term Research in Environmental Biology (LTREB) in US Virgin Islands: From the NSF award abstract: In an era of growing human pressures on natural resources, there is a critical need to understand how major ecosystems will respond, the extent to which resource management can lessen the implications of these responses, and the likely state of these ecosystems in the future. Time-series analyses of community structure provide a vital tool in meeting these needs and promise a profound understanding of community change. This study focuses on coral reef ecosystems; an existing time-series analysis of the coral community structure on the reefs of St. John, US Virgin Islands, will be expanded to 27 years of continuous data in annual increments. Expansion of the core time-series data will be used to address five questions: (1) To what extent is the ecology at a small spatial scale (1-2 km) representative of regional scale events (10's of km)? (2) What are the effects of declining coral cover in modifying the genetic population structure of the coral host and its algal symbionts? (3) What are the roles of pre- versus post-settlement events in determining the population dynamics of small corals? (4) What role do physical forcing agents (other than temperature) play in driving the population dynamics of juvenile corals? and (5) How are populations of other, non-coral invertebrates responding to decadal-scale declines in coral cover? Ecological methods identical to those used over the last two decades will be supplemented by molecular genetic tools to understand the extent to which declining coral cover is affecting the genetic diversity of the corals remaining. An information management program will be implemented to create broad access by the scientific community to the entire data set. The importance of this study lies in the extreme longevity of the data describing coral reefs in a unique ecological context, and the immense potential that these data possess for understanding both the patterns of comprehensive community change (i.e., involving corals, other invertebrates, and genetic diversity), and the processes driving them. Importantly, as this project is closely integrated with resource management within the VI National Park, as well as larger efforts to study coral reefs in the US through the NSF Moorea Coral Reef LTER, it has a strong potential to have scientific and management implications that extend further than the location of the study. The following publications and data resulted from this project: 2015 Edmunds PJ, Tsounis G, Lasker HR (2015) Differential distribution of octocorals and scleractinians around St. John and St. Thomas, US Virgin Islands. Hydrobiologia. doi: 10.1007/s10750-015-2555-zoctocoral - sp. abundance and distributionDownload complete data for this publication (Excel file) 2015 Lenz EA, Bramanti L, Lasker HR, Edmunds PJ. Long-term variation of octocoral populations in St. John, US Virgin Islands. Coral Reefs DOI 10.1007/s00338-015-1315-xoctocoral survey - densitiesoctocoral counts - photoquadrats vs. insitu surveyoctocoral literature reviewDownload complete data for this publication (Excel file) 2015 Privitera-Johnson, K., et al., Density-associated recruitment in octocoral communities in St. John, US Virgin Islands, J.Exp. Mar. Biol. Ecol. DOI 10.1016/j.jembe.2015.08.006octocoral recruitmentDownload complete data for this publication (Excel file) 2014 Edmunds PJ. Landscape-scale variation in coral reef community structure in the United States Virgin Islands. Marine Ecology Progress Series 509: 137–152. DOI 10.3354/meps10891. Data at MCR-VINP. Download complete data for this publication (Excel file) 2014 Edmunds PJ, Nozawa Y, Villanueva RD. Refuges modulate coral recruitment in the Caribbean and Pacific. Journal of Experimental Marine Biology and Ecology 454: 78-84. DOI: 10.1016/j.jembe.2014.02.00 Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Edmunds PJ, Gray SC. The effects of storms, heavy rain, and sedimentation on the shallow coral reefs of St. John, US Virgin Islands. Hydrobiologia 734(1):143-148. Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Levitan, D, Edmunds PJ, Levitan K. What makes a species common? No evidence of density-dependent recruitment or mortality of the sea urchin Diadema antillarum after the 1983-1984 mass mortality. Oecologia. DOI

On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States (i.e.,

Predicted temperature and precipitation values were generated throughout the state of Massachusetts using a stochastic weather generator (SWG) model to develop various climate change scenarios (Steinschneider and Najibi, 2022a). This data release contains temperature and precipitation statistics (SWG_outputTable.csv) derived from the SWG model under the surface warming derived from the RCP 8.5 climate change emissions scenario at 30-year moving averages centered around 2030, 2050, 2070, 2090. During the climate modeling process, extreme precipitation values were also generated by scaling previously published intensity-duration-frequency (IDF) values from the NOAA Atlas 14 database (Perica and others, 2015) by a factor per degree expected warming produced from the SWG model generator (Najibi and others, 2022; Steinschneider and Najibi, 2022b, c). These newly generated IDF values (IDF_outputTable.csv) account for expected changes in extreme precipitation driven by variations in weather associated with climate change throughout the state of Massachusetts. The data presented here were developed in collaboration with the Massachusetts Executive Office of Energy and Environmental Affairs and housed on the Massachusetts climate change clearinghouse webpage (Massachusetts Executive Office of Energy and Environmental Affairs, 2022). References: Massachusetts Executive Office of Energy and Environmental Affairs, 2022, Resilient MA Maps and Data Center at URL https://resilientma-mapcenter-mass-eoeea.hub.arcgis.com/ Najibi, N., Mukhopadhyay, S., and Steinschneider, S., 2022, Precipitation scaling with temperature in the Northeast US: Variations by weather regime, season, and precipitation intensity: Geophysical Research Letters, v. 49, no. 8, 14 p., https://doi.org/10.1029/2021GL097100. Perica, S., Pavlovic, S., St. Laurent, M., Trypaluk, C., Unruh, D., Martin, D., and Wilhite, O., 2015, NOAA Atlas 14 Volume 10 Version 3, Precipitation-Frequency Atlas of the United States, Northeastern States (revised 2019): NOAA, National Weather Service, https://doi.org/10.25923/99jt-a543. Steinschneider, S., and Najibi, N., 2022a, A weather-regime based stochastic weather generator for climate scenario development across Massachusetts: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_WGEN_20220405.pdf. Steinschneider, S., and Najibi, N., 2022b, Future projections of extreme precipitation across Massachusetts—a theory-based approach: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_IDF_Curves_Dec2021.pdf. Steinschneider, S., and Najibi, N., 2022c, Observed and projected scaling of daily extreme precipitation with dew point temperature at annual and seasonal scales across the northeast United States: Journal of Hydrometeorology, v. 23, no. 3, p. 403-419, https://doi.org/10.1175/JHM-D-21-0183.1.

Description

These data were used in the study "Flexible and Consistent Quantile Estimation for Intensity-Duration-Frequency Curves" (Fauer et al., 2021). Rainfall data were collected from stations by the German Meteorological Service (DWD) and Wupperverband (corrected data). Raw time series data from the German Meteorological Service is publicly available under https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/. Only the annual precipitation maxima over different durations are published here.

Files

yearMax.csv: This file contains aggregated rainfall data over different durations and for different stations.

meta.csv: This file contains additional information of the different stations such as longitude, latitude, altitude, temporal resolution (m=minutely, h=hourly, d=daily), group. The same group is assigned to stations which have a distance of less than 250 meters and can be treated as one station.

Abstract of the according study

We suggest a flexible parametric model for describing intensity duration frequency relationships (IDF-curves) in a consistent way, i.e., without crossing of different quantiles for a wide range of durations (1 min to 5 days). The model is based on the duration-dependent formulation of the generalized extreme value distribution (GEV). The original model shows a power-law like behaviour for the quantiles for a wide range of durations and takes care of a deviation from this scaling relation (curvature) for small durations. We extend the model with two features: i) different power-law exponents for different quantiles (multiscaling) and ii) deviation from the power-law for large durations (flattening). Based on the quantile skill score, we investigate the performance of the resulting flexible model with respect to the benefit of the individual features (curvature, multiscaling, flattening) with simulated and empirical data. We provide detailed information on the duration and probability ranges for which specific features or a systematic combination of features leads to improvements for stations in a case study area in the Wupper catchment (Germany). Our results show that allowing curvature or multiscaling improves the model only for very short or long durations, respectively, but leads to disadvantages in modeling the other duration ranges. In contrast, allowing flattening on average leads to an improvement for medium durations between 1 hour and 1 day without affecting other duration regimes. Overall, the new parametric form offers a flexible and performant model for consistently describing IDF relations over a wide range of durations.

Acknowledgements

We would like to thank the German Weather Service (DWD), and the Wupperverband, especially Marc Scheibel, for maintaining the station-based rainfall gauge and providing us with data.

This study examines the impact of climate variability on glaciers in the Olympic Mountains, Washington State, USA. The area is known for a significant precipitation gradient, with annual rainfall varying from 6.5 meters on the west-facing slopes to 0.5 meters in the northeast lowlands. We are investigating the hypothesis that past variations in glacier extent were influenced by this spatial variability in precipitation.

Our analysis relies on data from the Weather Research and Forecasting (WRF) model, captured during the Olympic Mountain Experiment (OLYMPEX) campaign of 2015-2016. This data offers a valuable source of detailed information about the region's climate dynamics.

The first dataset, "OLYMPICS_ELWHA_QUINAULT_6-HOUR_MEANS.csv," contains average precipitation data within the Elwha and Quinault basins for each 6-hour model run. This information helps to show how precipitation patterns vary within these basins over time.

The second dataset, "OLYMPEX_RAINNC_TOTAL.tif," includes the total sum of the RAINNC variable, which represents total precipitation, for the entirety of the OLYMPEX campaign. This comprehensive data provides a clear picture of overall precipitation levels during the study period.

By using these datasets, we aim to gain a better understanding of the relationship between precipitation and glacier extent in the Olympic Mountains. This knowledge is crucial for assessing the effects of climate change in this region and others with similar climate patterns.

The data presented here contain quasi-event hydrologic response characteristics for quickflow response intervals (QRIs) calculated at 57 USGS GAGES-II reference watersheds in the western US. Each row in the attached csv contains characteristics for an individual QRI at one site. QRI characteristics include duration, antecedent flow, input, input rate,peak flow, potential evapotranspiration, quickflow, and quickflow/input (effectively a runoff ratio).

Flash flooding is the top weather-related killer, responsible for an average of 140 deaths per year across the United States. Although precipitation forecasting and understanding of flash flood causes have improved in recent years, there are still many unknown factors that play into flash flooding. Despite having accurate and timely rainfall reports, some river basins simply do not respond to rainfall as meteorologists might expect. The Flash Flood Potential Index (FFPI) was developed in order to gain insight into these “problem basins”, giving National Weather Service (NWS) meteorologists insight into the intrinsic properties of a river basin and the potential for swift and copious rainfall runoff.

The contiguous US exhibits a wide variety of precipitation regimes, first, because of the wide range of latitudes and altitudes. The physiographic units with a basic meridional configuration contribute to the differentiation between east and west in the country while generating some large interior continental spaces. The frequency distribution of daily precipitation amounts almost anywhere conforms to a negative exponential distribution, reflecting the fact that there are many small daily totals and few large ones. Positive exponential curves, which plot the cumulative percentages of days with precipitation against the cumulative percentage of the rainfall amounts that they contribute, can be evaluated through the Concentration Index. The Concentration Index has been applied to the contiguous United States using a gridded climate dataset of daily precipitation data, at a resolution of 0.25°, provided by CPC/NOAA/OAR/Earth System Research Laboratory, for the period between 1956 and 2006. At the same time, other rainfall indices and variables such as the annual coefficient of variation, seasonal rainfall regimes and the probabilities of a day with precipitation have been presented with a view to explaining spatial CI patterns. The spatial distribution of the CI in the contiguous United States is geographically consistent, reflecting the principal physiographic and climatic units of the country. Likewise, linear correlations have been established between the CI and geographical factors such as latitude, longitude and altitude. In the latter case the Pearson correlation coefficient (r) between this factor and the CI is −0.51 (p-value < 0.001). For annual probability of days with precipitation and the CI there is also a significant and negative correlation, r = −0.25 (p-value < 0.001).

Fig. 8. Concentration Index values (1956–2006).

File: ci_raster_USA.tif (geoTIFF)

NOTE: After the publication of the research article we calculate the Concentration Index with the PRISM climate data set, which has a higher resolution with 4km (PRISM Climate Group, Oregon State University). Nevertheless, the temporal coverage is limited to the period from 1981 to 2017.

File: CI_PRISM_USA.tif (geoTIFF)

Fig. 4. Seasonal rainfall regimes (1956–2006) (P, spring, S, summer, A, autumn, W, winter)

File: 1) pulvio_regimes_raster_USA.tif (geoTIFF); 2) pulvio_regimes_id.csv (clasification for regimes)

Map projection details:

EPSG:2163; proj4: "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"

This data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: 10.1038/s43016-021-00372-z). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time.

This data and code archive contains the following files and folders:

• README Description: text file with this description

• flowchart.pdf Description: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper.

• runAll.sh Description: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems)

• Folder "DataRaw" Description: folder for raw data files This folder contains the following files:

• DataRaw/COWS.xlsx Description: MS-Excel file with the number of cows per county Source: USDA NASS Quickstats Observations: All available counties and years from 2002 to 2012

• DataRaw/milk_state.xlsx Description: MS-Excel file with average monthly milk yields per cow Source: USDA NASS Quickstats Observations: All available states from 1981 to 2018

• DataRaw/TMAX.csv Description: CSV file with daily maximum temperatures Source: PRISM Climate Group (spatially averaged) Observations: All counties from 1981 to 2018

• DataRaw/VPD.csv Description: CSV file with daily maximum vapor pressure deficits Source: PRISM Climate Group (spatially averaged) Observations: All counties from 1981 to 2018

• DataRaw/countynamesandID.csv Description: CSV file with county names, state FIPS codes, and county FIPS codes Source: US Census Bureau Observations: All counties

• DataRaw/statecentroids.csv Descriptions: CSV file with latitudes and longitudes of state centroids Source: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" function Observations: All states

• Folder "DataGenerated" Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).

• Folder "Results" Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).

• Folder "Figures" Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures.

• Folder "Tables" Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper.

• Folder "logFiles" Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got.

• PrepareCowsData.R Description: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses

• PrepareWeatherData.R Description: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses

• PrepareMilkData.R Description: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses

• CalcFrequenciesTHI_Temp.R Description: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state

• CalcAvgTHI.R Description: R script that calculates the average THI in each state

• PreparePanelTHI.R Description: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins

• PreparePanelTemp.R Description: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins

• PreparePanelFinal.R Description: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses

• EstimateTrendsTHI.R Description: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set

• EstimateModels.R Description: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications

• CalcCoefStateYear.R Description: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification

• SearchWeightMonths.R Description: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term

• TestModelSpec.R Description: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10

• CreateFigure1a.R Description: R script that creates subfigure a of Figure 1

• CreateFigure1b.R Description: R script that creates subfigure b of Figure 1

• CreateFigure2a.R Description: R script that creates subfigure a of Figure 2

• CreateFigure2b.R Description: R script that creates subfigure b of Figure 2

• CreateFigure2c.R Description: R script that creates subfigure c of Figure 2

• CreateFigure3.R Description: R script that creates the subfigures of Figure 3

• CreateFigure4.R Description: R script that creates the subfigures of Figure 4

• CreateFigure5_TableS6.R Description: R script that creates the subfigures of Figure 5 and Table S6

• CreateFigureS1.R Description: R script that creates Figure S1

• CreateFigureS2.R Description: R script that creates Figure S2

• CreateTableS2_S3_S7.R Description: R script that creates Tables S2, S3, and S7

• CreateTableS4_S5.R Description: R script that creates Tables S4 and S5

• CreateTableS8.R Description: R script that creates Table S8

• CreateTableS9.R Description: R script that creates Table S9

This dataset is for the Western Arlington County Rainfall Measurement Project (WACRMP), which is the author’s final project for INSC 546 Environmental Informatics at the University of Tennessee—Knoxville (UT) School of Information Sciences (SIS). The dataset consists of three CSV files, a metadata file conforming to the Federal Geographic Data Committee (FGDC) Content Standard for Digital Geospatial Metadata (CSDGM), R scripts used for calculations with the data in the CSV files, and the project's final summary report and monitoring protocol. The Western Arlington County Rainfall Measurement Project (WACRMP) is designed to measure the quantity of rainfall at a designated site in western Arlington County, Virginia from September 1, 2015 to November 20, 2015 and compare these measurements to the quantity of rainfall collected by 11 other rainfall collection sites in the northern Virginia and Washington DC metropolitan area. These 11 sites are located within several miles of western Arlington County, where there is a gap in rainfall coverage by existing rainfall collection sites associated with the National Weather Service. The results will be used to determine the daily and monthly variation in rainfall between western Arlington County and other areas in Northern Virginia and the Washington DC area and potentially inform the need for a new rainfall collection site, possibly under the auspices of the National Weather Service (NWS) Co-operative Observer (COOP) network, to be established in western Arlington County to measure rainfall and other precipitation on a year-round basis.

The U.S. Geological Survey (USGS), in cooperation with the city of Harrisonville, Missouri, assessed flooding of Muddy Creek resulting from varying precipitation magnitudes and durations, antecedent soil moisture conditions, and channel conditions. The precipitation scenarios were used to develop a library of flood-inundation maps that included a 3.8-mile reach of Muddy Creek and tributaries within and adjacent to the city. Hydrologic and hydraulic models of the upper Muddy Creek Basin were used to assess streamflow magnitudes associated with simulated precipitation amounts and the resulting flood-inundation conditions. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS; version 4.4.1) was used to simulate the amount of streamflow produced from a range of rainfall events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS; version 5.0.7) was then used to route streamflows and map resulting areas of flood inundation. The hydrologic and hydraulic models were calibrated to the September 28, 2019; May 27, 2021; and June 25, 2021, runoff events representing a range of antecedent moisture conditions and hydrologic responses. The calibrated HEC–HMS model was used to simulate streamflows from design rainfall events of 30-minute to 24-hour durations and ranging from a 100- to 0.1-percent annual exceedance probability. Flood-inundation maps were produced for USGS streamflow stages of 1.0 feet (ft), or near bankfull, to 4.0 ft, or a stage exceeding the 0.1-percent annual exceedance probability interval precipitation, using the HEC–RAS model. The consequence of each precipitation duration-frequency value was represented by a 0.5-ft increment inundation map based on the generated peak streamflow from that rainfall event and the corresponding stage at the Muddy Creek stage reference _location. Seven scenarios were developed with the HEC–HMS hydrologic model with resulting streamflows routed in a HEC-RAS hydraulic model and these scenarios varied by antecedent soil-moisture and channel conditions. The same precipitation scenarios were used in each of the seven antecedent moisture and channel conditions and the simulation results were assigned to a flood-inundation map condition based on the generated peak flow and corresponding stage at the Muddy Creek reference _location. This data release includes: 1) tables summarizing the model results including the flood-inundation map condition of each model scenario for dry (CNI; Muddy_Creek_summary_table_1_1.csv), normal (CNII; Muddy_Creek_summary_table_1_2.csv), and wet (CNIII; Muddy_Creek_summary_table_1_3.csv) antecedent soil moisture conditions (MuddyCreek_summary_tables.zip); 2) a shapefile dataset of the streamflow inundation extents at Muddy Creek reference _location stages of 1.0 to 4.0 feet (MuddyCreek_inundation_extents.zip containing MudHarMO.shp); 3) a raster dataset of the streamflow depths at Muddy Creek reference _location stages of 1.0 to 4.0 feet (MuddyCreek_inundation_depths.zip containing MudharMO_X.tif where X = 1,2,3,4,5,6,7 corresponding to inundation map stages of 1.0, 1.5 , 2.0, 2.5, 3.0, 3.5, 4.0 feet)); 4) tables of hydrologic and hydraulic model performance and calibration metrics, locations of continuous pressure transducers (PTs; MuddyCreek_PT_locations.zip) and high-water marks (HWMs; MuddCreek_HWM_locations.zip) used in assessment of model calibration and validation, and time series of pressure transducer data (MuddyCreek_PT_time_series.zip) found in MuddyCreek_model_performance_calibration_metrics.zip; 5) hydrologic and hydraulic model run files used in the simulation of dry hydrologic response conditions (CN_I conditions) and effects of proposed detention storage (MuddyCreek_dry_detention.zip); 6) hydrologic and hydraulic model run files used in the simulation and calibration of dry hydrologic response conditions (CN_I conditions) and current (2019) existing channel conditions (MuddyCreek_dry_existing_conditions.zip); 7) hydrologic and hydraulic model run files used in the simulation of normal hydrologic response conditions (CN_II conditions) and effects of cleaned culverts (MuddyCreek_normal_clean_culverts.zip); 8) hydrologic and hydraulic model run files used in the simulation of normal hydrologic response conditions (CN_II conditions) and effects of detention storage (MuddyCreek_normal_detention.zip); 9) hydrologic and hydraulic model run files used in the simulation and calibration of normal hydrologic response conditions (CN_II conditions) and current (2019) existing channel conditions (MuddyCreek_normal_existing_conditions.zip); 10) hydrologic and hydraulic model run files used in the simulation of wet hydrologic response conditions (CN_III conditions) and effects of proposed detention storage (MuddyCreek_wet_detention.zip); 11) hydrologic and hydraulic model run files used in the simulation and calibration of wet hydrologic response conditions (CN_III) and current (2019) existing channel conditions (MuddyCreek_wet_existing_conditions.zip). 12) Service definition files of the Muddy Creek water depths of inundated areas (MuddyCreek_Inundation_depths.sd) and Muddy Creek inundation area polygons (MuddyCreek_inundation_extents.sd) added on September 7, 2022.

This data release contains summary metrics describing stream stage, stream water temperature, and short-term climate conditions (daily precipitation and air temperature) for 30 streams spanning gradients of forest and pasture land uses and agricultural best management practice implementation in the Shenandoah Valley region of Virginia and West Virginia, USA. This setting is the first of four settings or "typologies" that will be assessed for the USGS Chesapeake Stream Team project. High-frequency stage and water temperature (5-minute data), and air temperature (30-minute data) were measured by the U.S. Geological Survey (USGS) from May 2021 to December 2021 and are available on the USGS National Water Information System (NWIS). Stream stage, water temperature, and air temperature data for each of the 30 sites were downloaded from NWIS. Additional daily air temperature and precipitation data were acquired from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate data website. Air temperature and water temperature data were used to compute stream water temperature metrics describing stream temperature conditions in each stream during the monitoring period. Stream stage and precipitation data were used to derive stream stage metrics describing stage conditions (a surrogate for flow) for each stream during the monitoring period. Precipitation and air temperature data from PRISM were also used to compute air temperature metrics and precipitation metrics describing climate conditions during the monitoring period. The metrics include: Air temperature metrics - Mean daily minimum, mean, and maximum air temperature Precipitation metrics - Total precipitation depth - Maximum daily precipitation depth - Average precipitation depth per days with precipitation - Frequency of precipitation days Stream stage metrics - Number of runoff events - Frequency of runoff events - Standard deviation in unit-value stage - Coefficient of variation of unit-value stage Stream water temperature metrics - Coefficient of variation of mean and maximum daily water temperatures - Number of days with temperatures of 20 or 25 degrees Celsius or greater - Duration of time above 20 or 25 degrees Celsius or greater - Maximum of seven-day moving average of daily maximum temperature - Mean of daily minimum, maximum, and daily water temperature range - A thermal sensitivity metric, which is the slope estimate from linear regression model of mean daily water temperature versus mean daily air temperature This data release contains six files: 1. "Readme.pdf": This is an expanded narrative describing the methods by which the input data were compiled and screened, and metrics were computed 2. "typology_1_temperature_stage_climate_metric_data_dictionary.csv": This file contains descriptions of each metric and the time periods for which they were computed in the “typology_1_temperature_stage_climate_summary_metrics.csv" file 3. "typology_1_temperature_stage_climate_summary_metrics.csv": This file contains stream temperature metrics, stage metrics, and climate summary metrics for each of the 30 stream sites for different time periods within the overall monitoring period. 4. "typology_1_input_data_high_frequency_temperature_and_stage.zip": This zipped folder contains 30 .csv files, which contain the high-frequency stage, water temperature, and air temperature data collected at each of the 30 stream sites. The file names include the siteID, which is the four-letter site identification listed in the "typology_1_temperature_stage_climate_summary_metrics.csv" file. 5. "typology_1_input_data_daily_climate.csv": This file contains daily climate estimates (precipitation depth, daily minimum, mean, and maximum air temperatures) from PRISM paired to each of the 30 sites. 6. "typology_1_runoff_events.csv": This file contains the stage rise and precipitation data used for some of the stage metric computations.

This data release presents truth data and benchmark results describing simulation of hydrologic drought events in the conterminous United States. This data release supports a publication (Simeone and others, 2024) which documents drought benchmarking methods and their application to the results of the National Hydrologic Model Precipitation-Runoff Modeling System v1.0 (NHM-PRMS). Truth data used were observations at U.S. Geological Survey streamgages across the conterminous United States. These data include 4662 U.S. Geological Survey streamgages with a historical period from 1984-2016. The following files are included in this data release: 1) kappa_long_nhm.csv: Benchmark results for the Cohen's kappa evaluation metrics in long table format. 2) spear_bias_dist_long_nhm.csv: Benchmark results for the Spearman's, bias, and distributional evaluation metrics in long table format. 3) ann_eval_long_nhm.csv: Benchmark results for the annual drought evaluation metrics in long table format. 4) streamflow_percentiles_nhm.zip: A zip file containing individual streamflow percentile data files used in this analysis as truth data. 5) input_data_nhm.zip: A zip file with input data for individual streamgages used for our data analysis pipeline as truth data. 6) streamflow_gages_in_study.csv: Metadata information for the 4662 U.S. Geological Survey streamgages contained in the above datasets.

This resource contains the data and scripts used for: Goeking, S. A. and D. G. Tarboton, (2022). Variable streamflow response to forest disturbance in the western US: A large-sample hydrology approach. Water Resources Research, 58, e2021WR031575. https://doi.org/10.1029/2021WR031575.

Abstract from the paper: Forest cover and streamflow are generally expected to vary inversely because reduced forest cover typically leads to less transpiration and interception. However, recent studies in the western US have found no change or even decreased streamflow following forest disturbance due to drought and insect epidemics. We investigated streamflow response to forest cover change using hydrologic, climatic, and forest data for 159 watersheds in the western US from the CAMELS dataset for the period 2000-2019. Forest change and disturbance were quantified in terms of net tree growth (total growth volume minus mortality volume) and mean annual mortality rates, respectively, from the US Forest Service’s Forest Inventory and Analysis database. Annual streamflow was analyzed using multiple methods: Mann-Kendall trend analysis, time trend analysis to quantify change not attributable to annual precipitation and temperature, and multiple regression to quantify contributions of climate, mortality, and aridity. Many watersheds exhibited decreased annual streamflow even as forest cover decreased. Time trend analysis identified decreased streamflow not attributable to precipitation and temperature changes in many disturbed watersheds, yet streamflow change was not consistently related to disturbance, suggesting drivers other than disturbance, precipitation, and temperature. Multiple regression analysis indicated that although change in streamflow is significantly related to tree mortality, the direction of this effect depends on aridity. Specifically, forest disturbances in wet, energy-limited watersheds (i.e., where annual potential evapotranspiration is less than annual precipitation) tended to increase streamflow, while post-disturbance streamflow more frequently decreased in dry water-limited watersheds (where the potential evapotranspiration to precipitation ratio exceeds 2.35).

The following scripts (R language and environment for statistical computing) produce the results, figures, and tables in this paper (in the order in which they appear in the paper; requires either running data compilation/aggregation scripts first OR using provided data files watersheds.csv and wb_filtered.csv): 1. Map_watersheds.r 2. Analysis_M-K_trend_test.r 3. analysis_M-K_quadrant_figure.r 4. analysis_timetrend_linear.r 5. analysis_regressn_w-veg.r

The following scripts (R) compile the data, aggregated from other sources prior to the analyses in the scripts listed above: 1. compilation_CAMELS.r 2. compilation_Daymet.r 3. compilation_USGS.r 4. compilation_FIA.r 5. compilation_CAMELS_Daymet_USGS.r (must run scripts #1-3 first) 6. watershed_compilation.r (must run scripts #1-5 first)

Farm ponds are a common feature of agricultural landscapes for irrigation of crops. Yet small water bodies have been ignored as reservoirs and carbon balance features despite ubiquity in the global landscape. These ponds contain surface water from precipitation and runoff, but in South Georgia, USA, groundwater supplementation is required to maintain a supply for irrigation. As part of a project to characterize water balance and quality of irrigation ponds in this landscape, data were collected describing terrain, bathymetry, inputs and outputs, and water quality from October 2021 through October 2023. The pond described in this study was located on a farm near Ty Ty, GA (Ty Ty Cooperator Farm, TCF; 31.5086980, -83.6167862). The TCF is a rotational cropping system, alternating among corn, cotton, and peanuts. During the 2022 growing season, the fields adjacent to the pond were planted with corn. Datasets include georeferenced image files (.tif) and tabular files (.csv). Image data were obtained on 27 SEP 2022 at approximately 0915 local time using a commercial, off-the-shelf unmanned aircraft system (UAS; DJI Mavic 2 Pro L1P) with an integrated digital RGB camera (Hasselblad L1D-20c_10.3 RGB with 20MP 1" CMOS sensor). The UAS was flown at 107 meters above ground level with 75% front and side overlap to capture an area of 12.99 ha. The images were used to produce an orthorectified mosaic (RMS error = 0.001 m) with 2.5 cm average ground sample distance (GSD), and a digital terrain model (DTM) with 12.5 cm GSD using 5 surveyed ground control points. A derived topobathymetric surface (12.5 cm GSD) was created by fusing bathymetric and topographic survey data. All geospatial data were projected in WGS84 / UTMZone 17N (EGM96 Geoid). Tabular data include GPS survey data points of the bathymetry and shoreline, and log files of daily water pumping into and out of the pond (acre-inches), 5-minute staff gauge levels within the pond (US feet), and 1-minute precipitation data (inches) at the farm. Water pumping, pond level and precipitation data were measured using instrumentation and data loggers operated by the USDA ARS Southeast Watershed Research Laboratory. Relevant water chemistry data were also recorded for this study, are flagged (Flag = 4) and are published separately by Pisani et al (2025) in the USDA Ag Data Commons repository, as "Water chemistry data for three agricultural ponds in the southern Coastal Plain of Georgia, USA".Datasets provided here include:1. A georeferenced orthomosaic (.tif) of aerial imagery of the study site from 27SEP2022 (1_TyTyCooperatorFarm_RGB_mosaic_20220927.tif)2. A digital terrain model (.tif) of the study site, produced from provided orthomosaic from 27SEP2022 (2_TyTyCooperatorFarm_DTM_20220927.tif)3. A fused topobathymetric surface model (.tif) of the study site (3_TyTyCooperatorFarm_DTM_20220927.tif)4. Survey data points from a bathymetric survey providing global positioning system (GPS) outputs (4_TyTyCooperatorFarm_Bathymetry_Survey_20220923_20220927.csv) NOTE: Field values for Tables 4 and 6 are nearly identical. Refer to Table 4 for values in Table 6.5. Water amounts pumped into and out of the study site pond in 2022 (5_TyTyCooperatorFarm_InOut_Pumping_Data_2022.csv)6. Survey data points from a shoreline survey providing global positioning system (GPS) outputs (6_TyTyCooperatorFarm_Shoreline_Survey_20220922.csv) NOTE: Field values for Tables 4 and 6 are nearly identical. Refer to Table 4 for values in Table 6.7. Water elevation readings from a staff gauge in the pond at the study site 2021 through 2023 (7_TyTyCooperatorFarm_Staff_Gauge_Pressure_Readings_2021_2023.csv)8. Rainfall precipitation measured at the study site during 2022 (8_TyTyCooperatorFarm_Precipitation_Rainfall_2022.csv)9. Dataset dictionary: Tabular data field values for tables 4 – 9 (9_TyTyCooperatorFarm_Data_Dictionary_Pond_Study_2022.xlsx). NOTE: Field values for Tables 4 and 6 are nearly identical. Refer to Table 4 for values in Table 6.al. Refer to Table 4 for values in Table 6.

This data release (DR) is the update of the U.S. Geological Survey - ScienceBase data release Bera (2023), with the processed data for the period October 1, 2021, through September 30, 2022. This data release describes the watershed data management (WDM) database SC22.WDM. The precipitation data are collected from a tipping-bucket rain-gage network and the hydrologic data (stage and discharge) are collected at USGS streamflow-gaging stations in and around Salt Creek watershed in DuPage County, Illinois, as described in Bera (2014). Hourly precipitation and hydrologic data for the period October 1, 2021, through September 30, 2022, are processed following the guidelines described in Bera (2014) and Murphy and Ishii (2006) and appended to SC21.WDM and renamed as SC22.WDM. Meteorological data (wind speed, solar radiation, air temperature, dewpoint temperature, and potential evapotranspiration) are copied from ARGNXX.WDM and appended to SCXX.WDM. XX represents last two digits of a water year (WY). A WY is the 12-month period, October 1 through September 30, in which it ends. Errors have been found in each of ARGNXX.WDM prior to WY23. SCXX.wdm contains erroneous meteorological data and related flag values until WY21 thereby. Meteorological data (wind speed, solar radiation, air temperature, dewpoint temperature, and potential evapotranspiration) from January 1, 1997, through September 30, 2022, are copied from ARGN23.WDM and uploaded to SC22.WDM. Bera (2024) describes the processing of the meteorological data in the database file ARGN23.WDM and Bera (2023) describes the processing of SC21.WDM. Data in dataset number (DSN) 107 and 801–810 are used in comparisons of precipitation data. DSN 107 contains hourly precipitation data collected at Argonne National Laboratory at Argonne, Illinois. DSN 801-810 contains the processed Next Generation Weather Radar (NEXRAD)-multisensor precipitation estimates (MPE) data from 10 NEXRAD–MPE subbasins in the Salt Creek watershed as described in Bera and Ortel (2018). Data in these DSNs are not quality-assured and quality-controlled. The data are downloaded and uploaded daily into a WDM database that is used for the real-time streamflow simulation system. Data from DSN 107 and 801-810 are copied from this WDM and stored in SC22.WDM. DSN 107 and 801-810 are updated with the data through September 30, 2022. Data in DSN 5400 (water-surface elevation at the quarry) and 5700 (water surface elevation at Thorndale) are copied and updated through September 30, 2022, similarly (Murphy and Ishii, 2006). The complete list of missing precipitation data periods and the nearby stations used to fill in those missing periods from October 1, 2021, through September 30, 2022, is given in Table1.csv. This file is in the comma separated values (CSV) file format and can be downloaded from this landing page. The list of snow affected days of precipitation data and the missing and estimated period of the stage and flow data in SC22.WDM database during the period October 1, 2021, through September 30, 2022, are given in the USGS annual Water Data Report at https://waterdata.usgs.gov/nwis. To open the WDM database SC22.WDM user needs to install Sara Timeseries Utility, listed in the section "Related External Resources" on this page. Table1.csv can be opened with any text editor or Microsoft Excel. References Cited: Bera, M., 2024, Meteorological Database, Argonne National Laboratory, Illinois: U.S. Geological Survey data release, https://doi.org/10.5066/P146RBHK. Bera, M., 2023, Watershed Data Management (WDM) Database (SC21.WDM) for Salt Creek Streamflow Simulation, DuPage County, Illinois, January 1, 1997, through September 30, 2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9TFCGQU. Bera, M., and Ortel, T.W., 2018, Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system: U.S. Geological Survey Open-File Report 2017–1159, 16 p., https://doi.org/10.3133/ofr20171159. Bera, M., 2014, Watershed Data Management (WDM) database for Salt Creek streamflow simulation, DuPage County, Illinois, water years 2005–11: U.S. Geological Survey Data Series 870, 18 p., https://doi.org/10.3133/ds870. Murphy, E.A., and Ishii, A.L., 2006, Watershed Data Management (WDM) Database for Salt Creek Streamflow Simulation, DuPage County, Illinois: U.S. Geological Survey Open-File Report 2006-1248, 20 p., https://pubs.usgs.gov/of/2006/1248/.