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 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.

ABSTRACT:

This resource includes Stream Temperature, Intermittency, and Conductivity (STIC) data collected from the South Fork of Kings Creek within the Konza Prairie Biological Station. At the USGS gage located on the mainstem (06879560; est. 1979), Kings Creek is a 5th order intermittent stream draining 1059-ha of tallgrass prairie in the Kansas Flint Hills. These data were collected in support of the sampling goals of the Aquatic Intermittency effects on Microbiomes in Streams (AIMS) Project. These sensors were set to collect temperature and conductivity data every 15 minutes starting in May 2021, and the raw conductivity data were used to classify the timeseries into wet or dry readings at each timestep. Each .csv file is associated with a single site. Also included is a “ReadMe” file that includes author information, column descriptions, and site locations.

AIMS OSF site: https://osf.io/e7s9j/

ABSTRACT:

The following standard operating procedure (SOP) was created for the the Aquatic Intermittency effects on Microbiomes in Streams (AIMS), an NSF EPSCoR funded project (OIA 2019603) seeking to explore the impacts of stream drying on downstream water quality across Kansas, Oklahoma, Alabama, Idaho, and Mississippi. AIMS integrates datasets on hydrology, microbiomes, macroinvertebrates, and biogeochemistry in three regions (Mountain West, Great Plains, and Southeast Forests) to test the overarching hypothesis that physical drivers (e.g., climate, hydrology) interact with biological drivers (e.g., microbes, biogeochemistry) to control water quality in intermittent streams. An overview of the AIMS project can be found here: https://youtu.be/HDKIBNEnwdM

This protocol details the process for setting up, testing, deploying, downloading, relaunching, and retrieving STIC sensors to assess the presence and absence of surface water. These sensors will be used throughout the nine focal watersheds to get a spatially distributed view of stream drying patterns (AIMS Approach 1) and will inform locations for distributed seasonal sampling (AIMS Approach 2).

The "living" version of this SOP can be found on Google Docs: https://docs.google.com/document/d/1_GOR5HyaH7kxzvBhT6yi1ajTyhBhfVRI9UGYjQ043qI/edit?tab=t.0

Also included in this resource are field sheets, used when STICs were collected to record site, serial number, timing of collection, and other information important for STIC processing.

From this SOP, the following data types will be created: Time series of temperature and conductivity. [AIMS rTypes: STIC]

ABSTRACT:

The following standard operating procedure (SOP) was created for the the Aquatic Intermittency effects on Microbiomes in Streams (AIMS), an NSF EPSCoR funded project (OIA 2019603) seeking to explore the impacts of stream drying on downstream water quality across Kansas, Oklahoma, Alabama, Idaho, and Mississippi. AIMS integrates datasets on hydrology, microbiomes, macroinvertebrates, and biogeochemistry in three regions (Mountain West, Great Plains, and Southeast Forests) to test the overarching hypothesis that physical drivers (e.g., climate, hydrology) interact with biological drivers (e.g., microbes, biogeochemistry) to control water quality in intermittent streams. An overview of the AIMS project can be found here: https://youtu.be/HDKIBNEnwdM

This protocol will detail the process for calibrating and launching STIC (Stream Temperature Intermittency & Relative Conductivity) sensors.

The "living" version of this SOP can be found on Google Docs: https://docs.google.com/document/d/1gTZ5MecE8Xjp6ymhH4rB92V_i1lH93Jv/edit

From this SOP, the following data types will be created: Time series of temperature and conductivity. [AIMS rTypes: STIC]

ABSTRACT:

The following standard operating procedure (SOP) was created for the the Aquatic Intermittency effects on Microbiomes in Streams (AIMS), an NSF EPSCoR funded project (OIA 2019603) seeking to explore the impacts of stream drying on downstream water quality across Kansas, Oklahoma, Alabama, Idaho, and Mississippi. AIMS integrates datasets on hydrology, microbiomes, macroinvertebrates, and biogeochemistry in three regions (Mountain West, Great Plains, and Southeast Forests) to test the overarching hypothesis that physical drivers (e.g., climate, hydrology) interact with biological drivers (e.g., microbes, biogeochemistry) to control water quality in intermittent streams. An overview of the AIMS project can be found here: https://youtu.be/HDKIBNEnwdM.

This protocol details the process for measuring streamflow within the stream network focused largely on low-flow conditions using dilution gaging techniques.

Also included in this resource is the AIMS datasheet used when taking measurements in the field.

The "living" version of this SOP is available on Google Docs: https://docs.google.com/document/d/18mvs_aAr677eQDrwUuassMTWmjggSQxVkmkr0vgF0J4/edit?tab=t.0

From this SOP, the following data types will be created: stream width, depth, discharge (AIMS rTypes created: ENVI, DISC).

ABSTRACT:

This resource contains the data and code associated with the manuscript "STICr: An open-source package and workflow for Stream Temperature, Intermittency, and Conductivity (STIC) data" by Zipper et al.

Full citation:
[ADD HERE]

Paper abstract:
Non-perennial streams constitute over half the world’s stream miles but are not commonly included in streamflow monitoring networks. Stream Temperature, Intermittency, and Conductivity (STIC) loggers are widely used for characterizing flow presence or absence in non-perennial streams. To facilitate ‘FAIR’ (findable, accessible, interoperable, and reusable) stream intermittency science, we present an open-source R package, STICr, for processing STIC logger data. STICr includes functions to tidy data, calibrate sensors, classify data into wet/dry readings, and perform quality checks and validation. We also show a reproducible STICr-based workflow for an interdisciplinary project spanning multiple watersheds, years, and research groups. In South Fork Kings Creek (Konza Prairie, Kansas, USA), we show that stream intermittency is driven by the balance between monthly precipitation inputs, seasonal evapotranspiration fluxes, and underlying geology. Overall, STICr can be used to create FAIR stream intermittency data and enable advances in hydrologic and ecosystem science.

ABSTRACT:

The following standard operating procedure (SOP) was created for the the Aquatic Intermittency effects on Microbiomes in Streams (AIMS), an NSF EPSCoR funded project (OIA 2019603) seeking to explore the impacts of stream drying on downstream water quality across Kansas, Oklahoma, Alabama, Idaho, and Mississippi. AIMS integrates datasets on hydrology, microbiomes, macroinvertebrates, and biogeochemistry in three regions (Mountain West, Great Plains, and Southeast Forests) to test the overarching hypothesis that physical drivers (e.g., climate, hydrology) interact with biological drivers (e.g., microbes, biogeochemistry) to control water quality in intermittent streams. An overview of the AIMS project can be found here: https://youtu.be/HDKIBNEnwdM

This protocol will detail the process for calibrating and launching STIC (Stream Temperature Intermittency & Relative Conductivity) sensors.

The "living" version of this SOP can be found on Google Docs: https://docs.google.com/document/d/17nQj1tIW42W_opQpSKIezl_pxacDicHuLs5GB-OVrjE/edit?tab=t.0

From this SOP, the following data types will be created: Time series of pressure, temperature, water level, water height, water depth, and water elevation at stilling wells and piezometers [AIMS rTypes: PRES]