ABSTRACT:

Data and code associated with the publication "Pervasive changes in stream intermittency across the United States" by Samuel C. Zipper et al., published in Environmental Research Letters. Link to paper: https://doi.org/10.1088/1748-9326/ac14ec

When using this dataset, please cite the published paper::
Zipper, S. C., Hammond, J. C., Shanafield, M., Zimmer, M., Datry, T., Jones, C. N., … Allen, D. C. (2021). Pervasive changes in stream intermittency across the United States. Environmental Research Letters, 16(8), 084033. https://doi.org/10.1088/1748-9326/ac14ec

Abstract for paper:
Non-perennial streams are widespread, critical to ecosystems and society, and the subject of ongoing policy debate. Prior large-scale research on stream intermittency has been based on long-term averages, generally using annually aggregated data to characterize a highly variable process. As a result, it is not well understood if, how, or why the hydrology of non-perennial streams is changing. Here, we investigate trends and drivers of three intermittency signatures that describe the duration, timing, and dry-down period of stream intermittency across the continental United States (CONUS). Half of gages exhibited a significant trend through time in at least one of the three intermittency signatures, and changes in no-flow duration were most pervasive (41% of gages). Changes in intermittency were substantial for many streams, and 7% of gages exhibited changes in annual no-flow duration exceeding 100 days during the study period. Distinct regional patterns of change were evident, with widespread drying in southern CONUS and wetting in northern CONUS. These patterns are correlated with changes in aridity, though drivers of spatiotemporal variability were diverse across the three intermittency signatures. While the no-flow timing and duration were strongly related to climate, dry-down period was most strongly related to watershed land use and physiography. Our results indicate that non-perennial conditions are increasing in prevalence over much of CONUS and binary classifications of ‘perennial’ and ‘non-perennial’ are not an accurate reflection of this change. Water management and policy should reflect the changing nature and diverse drivers of changing intermittency both today and in the future.

ABSTRACT:

Equus Beds Groundwater Management District No. 2 (GMD2) was formally established in 1975 and was the second of five such local management districts in Kansas authorized under the Groundwater Management District Act of 1972. GMD2 overlies the Equus Beds aquifer, a groundwater system in south-central Kansas that represents the easternmost portion of the much larger High Plains aquifer (HPA), which in turn covers parts of South Dakota, Wyoming, Nebraska, Colorado, Kansas, New Mexico, Oklahoma, and Texas. Like much of the HPA, irrigation is the dominant water use, although the Equus Beds aquifer is also a primary water source for large municipal allocations, such as for the cities of Wichita and Hutchinson, along with other significant industrial uses. The management goal of GMD2 is to balance groundwater withdrawals with annual recharge to prevent unsustainable groundwater mining while also protecting from and remediating groundwater contamination

ABSTRACT:

This resource contains data from stream temperature, intermittency, and conductivity (STIC) loggers placed in the South Fork of Kings Creek in Konza Prairie (KS, USA) for May 21, 2021 to December 31, 2022 at 15 minute resolution as part of the NSF-funded Aquatic Intermittency effects on Microbiomes in Streams (AIMS) project (award IOA #2019603).

The file KNZ_SiteInfo.csv contains site locations for each sensor and has three fields:
- siteID = name of site
- lat = latitude of site
- long = longitude of site

The files KNZ_AllSTICsCleaned_20210522-20211231.csv and KNZ_AllSTICsCleaned_20220101-20221231.csv contain the 2021 and 2022 data, respectively, and have the following fields:
- datetime = date and time of reading (time zone = UTC).
- siteID = name of site for linking to file KNZ_SiteInfo.csv
- SN = serial number of STIC logger used for that reading.
- sublocation = "HS" or "LS". "HS" indicates the STIC was placed at a high spot on the thalweg, and a wet reading is interpreted as an indicator of a flowing surface water connection within the stream network. "LS" indicates the STIC was placed as a low spot in the thalweg, and a wet reading is interpreted as an indicator of water present in a pool feature.
- condUncal = uncalibrated (raw) conductivity output from the STIC
- tempC = temperature [degrees Celsius]
- SpC = specific conductviity in us/cm
- wetdry = binary classification of "wet" (water present) or "dry" (no water present) for that timestep
- qual_rating = qualitative data rating crit (described below)
- QAQC = flags from data QAQC process (described below)

Due to data logger errors, maintenance, etc. there are not data for all sites at all timesteps.

qual_rating description:
- "excellent" = STIC was (1) calibrated prior to deployment, and (2) stayed operational throughout 95% of the download period, and (3) was not displaced from streambed (i.e., the external electrodes were within 1 cm from stream bed at the time of download indicating minimal erosion/deposition), and (4) data from sensor roughly agree with field observations of wet/dry (i.e., >1000 Lux sensor reading on day of removal corresponds to field observations of water at STIC).
- "good" = (1) STIC stayed operational throughout the entire download period, and (2) the external electrodes were within 1 cm from stream bed at the time of download, and (3) data from sensor roughly agree with field observations of wet/dry, but (4) the STIC was not calibrated prior to deployment.
- "fair" = (1) STIC stayed operational throughout 75% or more of the download period, and (2) data roughly agree with field observations, and/or (3) the external electrodes were between 1-3 cm from streambed at the time of download.
- "poor" = (1) STIC stayed operational throughout less than 75% of the download period, and/or (2) the external electrodes were >3 cm from streambed at the time of download, and/or (3) data does NOT agree with field observations.

QAQC description:
- "N" = application of calibration curve resulted in negative value for SpC; this was changed to a value of 0.
- "A" = wetdry reading flagged as a potential anomaly (i.e., short period of dry surroudned by long period of wet or vice versa)
An empty field here indicates no flags were generated. If multiple flags were generated, they were concatenated.

ABSTRACT:

This resource contains data from stream temperature, intermittency, and conductivity (STIC) loggers placed at the Youngmeyer Ranch field station (KS, USA) for July 16, 2021 to December 31, 2022 at 15 minute resolution as part of the NSF-funded Aquatic Intermittency effects on Microbiomes in Streams (AIMS) project (award IOA #2019603).

The file YMR_SiteInfo.csv contains site locations for each sensor and has three fields:
- siteID = name of site
- lat = latitude of site
- long = longitude of site

The files YMR_AllSTICsCleaned_20210716-20211231.csv and YMR_AllSTICsCleaned_20220101-20221231.csv contain the 2021 and 2022 data, respectively, and have the following fields:
- datetime = date and time of reading (time zone = UTC).
- siteID = name of site for linking to file KNZ_SiteInfo.csv
- SN = serial number of STIC logger used for that reading.
- condUncal = uncalibrated (raw) conductivity output from the STIC
- tempC = temperature [degrees Celsius]
- SpC = specific conductviity in us/cm
- wetdry = binary classification of "wet" (water present) or "dry" (no water present) for that timestep
- qual_rating = qualitative data rating crit (described below)
- QAQC = flags from data QAQC process (described below)

Due to data logger errors, maintenance, etc. there are not data for all sites at all timesteps.

qual_rating description:
- "excellent" = STIC was (1) calibrated prior to deployment, and (2) stayed operational throughout 95% of the download period, and (3) was not displaced from streambed (i.e., the external electrodes were within 1 cm from stream bed at the time of download indicating minimal erosion/deposition), and (4) data from sensor roughly agree with field observations of wet/dry (i.e., >1000 Lux sensor reading on day of removal corresponds to field observations of water at STIC).
- "good" = (1) STIC stayed operational throughout the entire download period, and (2) the external electrodes were within 1 cm from stream bed at the time of download, and (3) data from sensor roughly agree with field observations of wet/dry, but (4) the STIC was not calibrated prior to deployment.
- "fair" = (1) STIC stayed operational throughout 75% or more of the download period, and (2) data roughly agree with field observations, and/or (3) the external electrodes were between 1-3 cm from streambed at the time of download.
- "poor" = (1) STIC stayed operational throughout less than 75% of the download period, and/or (2) the external electrodes were >3 cm from streambed at the time of download, and/or (3) data does NOT agree with field observations.

QAQC description:
- "N" = application of calibration curve resulted in negative value for SpC; this was changed to a value of 0.
- "A" = wetdry reading flagged as a potential anomaly (i.e., short period of dry surroudned by long period of wet or vice versa)
An empty field here indicates no flags were generated. If multiple flags were generated, they were concatenated.

ABSTRACT:

This resource contains data from stream temperature, intermittency, and conductivity (STIC) loggers placed at the Oka' Yanahli preserve (OK, USA) for February 15 to October 25, 2022 at 15 minute resolution as part of the NSF-funded Aquatic Intermittency effects on Microbiomes in Streams (AIMS) project (award IOA #2019603).

The file OKA_SiteInfo.csv contains site locations for each sensor and has three fields:
- siteID = name of site
- lat = latitude of site
- long = longitude of site

The file OKA_AllSTICsCleaned_20220215-20221025.csv contains the data and has the following fields:
- datetime = date and time of reading (time zone = UTC).
- siteID = name of site for linking to file KNZ_SiteInfo.csv
- SN = serial number of STIC logger used for that reading.
- condUncal = uncalibrated (raw) conductivity output from the STIC
- tempC = temperature [degrees Celsius]
- SpC = specific conductviity in us/cm
- wetdry = binary classification of "wet" (water present) or "dry" (no water present) for that timestep
- qual_rating = qualitative data rating crit (described below)
- QAQC = flags from data QAQC process (described below)

Due to data logger errors, maintenance, etc. there are not data for all sites at all timesteps.

qual_rating description:
- "excellent" = STIC was (1) calibrated prior to deployment, and (2) stayed operational throughout 95% of the download period, and (3) was not displaced from streambed (i.e., the external electrodes were within 1 cm from stream bed at the time of download indicating minimal erosion/deposition), and (4) data from sensor roughly agree with field observations of wet/dry (i.e., >1000 Lux sensor reading on day of removal corresponds to field observations of water at STIC).
- "good" = (1) STIC stayed operational throughout the entire download period, and (2) the external electrodes were within 1 cm from stream bed at the time of download, and (3) data from sensor roughly agree with field observations of wet/dry, but (4) the STIC was not calibrated prior to deployment.
- "fair" = (1) STIC stayed operational throughout 75% or more of the download period, and (2) data roughly agree with field observations, and/or (3) the external electrodes were between 1-3 cm from streambed at the time of download.
- "poor" = (1) STIC stayed operational throughout less than 75% of the download period, and/or (2) the external electrodes were >3 cm from streambed at the time of download, and/or (3) data does NOT agree with field observations.

QAQC description:
- "N" = application of calibration curve resulted in negative value for SpC; this was changed to a value of 0.
- "A" = wetdry reading flagged as a potential anomaly (i.e., short period of dry surroudned by long period of wet or vice versa)
An empty field here indicates no flags were generated. If multiple flags were generated, they were concatenated.

ABSTRACT:

This file releases data and code for the manuscript:

Zipper, S., Kastens, J., Foster, T., Wilson, B.B., Melton, F., Grinstead, A., Deines, J., Butler, J., Marston, L., 2024. Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties at field, water right, and regional scales. Agricultural Water Management 303:109036. https://doi.org/10.1016/j.agwat.2024.109036

Please cite this manuscript if you use these data/code.

Manuscript abstract:
Irrigated agriculture is the dominant user of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively irrigated portion of the United States. We compared irrigation calculations based on an ensemble of satellite-driven ET models from OpenET with reported groundwater withdrawals from hundreds of farmer irrigation application records and a statewide flowmeter database at three spatial scales (field, water right group, and management area). At the field scale, we found that ET-based calculations of irrigation agreed best with reported irrigation when the OpenET ensemble mean was aggregated to the growing season timescale (bias = 1.6% to 4.9%, R2 = 0.53 to 0.74), and agreement between calculated and reported irrigation was better for multi-year averages than for individual years. At the water right group scale, linking pumping wells to specific irrigated fields was the primary source of uncertainty. At the management area scale, calculated irrigation exhibited similar temporal patterns as flowmeter data but tended to be positively biased with more interannual variability. Disagreement between calculated and reported irrigation was strongly correlated with annual precipitation, and calculated and reported irrigation agreed more closely after statistically adjusting for annual precipitation. The selection of an ET model was also an important consideration, as variability across ET models was larger than the potential impacts of conservation measures employed in the region. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in soil moisture, deep percolation, and runoff; careful verification of irrigated area and well-field linkages; and conducting application-specific evaluations of uncertainty.