Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for stream temperature (Ts) temporal prediction (training in one period and predicting in another period at the same sites). However, spatial extrapolation is a well-known challenge to modelling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitored or dammed basins. Here we compiled a new benchmark dataset consisting of >400 basins across the contiguous United States in different data availability groups (DAG, meaning the daily sampling frequency) with and without major dams, and studied how to assemble suitable training datasets for predictions in basins with or without temperature monitoring. For prediction in unmonitored basins (PUB), LSTM produced a root-mean-square error (RMSE) of 1.129°C and an R2 of 0.983. While these metrics declined from LSTM's temporal prediction performance, they far surpassed traditional models' PUB values, and were competitive with traditional models' temporal prediction on calibrated sites. Even for unmonitored basins with major reservoirs, we obtained a median RMSE of 1.202°C and an R2 of 0.984. For temporal prediction, the most suitable training set was the matching DAG that the basin could be grouped into (for example, the 60% DAG was most suitable for a basin with 61% data availability). However, for PUB, a training dataset including all basins with data was consistently preferred. An input-selection ensemble moderately mitigated attribute overfitting. Our results indicate there are influential latent processes not sufficiently described by the inputs (e.g., geology, wetland covers), but temporal fluctuations can still be predicted well, and LSTM appears to be a highly accurate Ts modelling tool even for spatial extrapolation.
This data note describes the Biscuit Brook and Neversink Reservoir watershed Long-Term Monitoring Data that includes: 1) stream discharge, (1983 – 2020 for Biscuit Brook and 1937 – 2020 for the Neversink Reservoir watershed), 2) stream water chemistry, 1983-2020, at 4 stations, 3) fish survey data from 16 locations in the watershed 1990-2019, 4) soil chemistry data from 2 headwater sub-watersheds, 1993-2012, and 5) periodic stream water chemistry sampling data from 364 locations throughout the watershed, 1983-2020. The Neversink Reservoir watershed in the Catskill Mountains of New York, USA drains an area of 172.5 km2. The watershed feeds one of 6 reservoirs in New York City's West of Hudson water supply, which accounts for about 90% of the city's water supply. Biscuit Brook is a 9.63 km2 tributary sub-watershed within the Neversink Reservoir watershed.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14394","usgsCitation":"Murdoch, P.S., Burns, D., McHale, M., Siemion, J., Baldigo, B., Lawrence, G.B., George, S.D., Antidormi, M.R., and Bonville, D.B., 2021, The Biscuit Brook and Neversink Reservoir Watersheds: Long-term investigations of stream chemistry, soil chemistry, and aquatic ecology in the Catskill Mountains, New York, USA, 1983 to 2020: Hydrological Processes, v. 35, e14394, 12 p., https://doi.org/10.1002/hyp.14394.","productDescription":"e14394, 12 p.","ipdsId":"IP-126065","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":450676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14394","text":"Publisher Index Page"},{"id":389807,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Biscuit Brook and Neversink Reservoir Watersheds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.62188720703125,\n 41.840920397579936\n ],\n [\n -74.60403442382812,\n 41.85319643776675\n ],\n [\n -74.53811645507812,\n 41.881831370505594\n ],\n [\n -74.44129943847656,\n 41.92629234083705\n ],\n [\n -74.28337097167969,\n 42.007978804701\n ],\n [\n -74.278564453125,\n 42.06509700139039\n ],\n [\n -74.30671691894531,\n 42.11095834849246\n ],\n [\n -74.3341827392578,\n 42.13133052651052\n ],\n [\n -74.4049072265625,\n 42.132858175814626\n ],\n [\n -74.44747924804688,\n 42.11707068963613\n ],\n [\n -74.48867797851562,\n 42.042153895364\n ],\n [\n -74.57725524902344,\n 41.984504674276074\n ],\n [\n -74.652099609375,\n 41.9528519300999\n ],\n [\n -74.73518371582031,\n 41.89869952106346\n ],\n [\n -74.74273681640625,\n 41.86291329896065\n ],\n [\n -74.70497131347656,\n 41.81636125072054\n ],\n [\n -74.64866638183594,\n 41.81175536180908\n ],\n [\n -74.62188720703125,\n 41.840920397579936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","noUsgsAuthors":false,"publicationDate":"2021-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Murdoch, Peter S. 0000-0001-9243-505X pmurdoch@usgs.gov","orcid":"https://orcid.org/0000-0001-9243-505X","contributorId":2453,"corporation":false,"usgs":true,"family":"Murdoch","given":"Peter","email":"pmurdoch@usgs.gov","middleInitial":"S.","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":824014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824015,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McHale, Michael 0000-0003-3780-1816 mmchale@usgs.gov","orcid":"https://orcid.org/0000-0003-3780-1816","contributorId":177292,"corporation":false,"usgs":true,"family":"McHale","given":"Michael","email":"mmchale@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824016,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Siemion, Jason 0000-0001-5635-6469 jsiemion@usgs.gov","orcid":"https://orcid.org/0000-0001-5635-6469","contributorId":127562,"corporation":false,"usgs":true,"family":"Siemion","given":"Jason","email":"jsiemion@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824017,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824018,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824019,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824020,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Antidormi, Michael R. 0000-0002-3967-1173 mantidormi@usgs.gov","orcid":"https://orcid.org/0000-0002-3967-1173","contributorId":150722,"corporation":false,"usgs":true,"family":"Antidormi","given":"Michael","email":"mantidormi@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824021,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bonville, Donald B. 0000-0003-4480-9381","orcid":"https://orcid.org/0000-0003-4480-9381","contributorId":248849,"corporation":false,"usgs":true,"family":"Bonville","given":"Donald","email":"","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824022,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70227126,"text":"70227126 - 2021 - Evaluating streamwater dissolved organic carbon dynamics in context of variable flowpath contributions with a tracer-based mixing model","interactions":[],"lastModifiedDate":"2022-01-03T15:32:26.574984","indexId":"70227126","displayToPublicDate":"2021-09-23T08:09:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating streamwater dissolved organic carbon dynamics in context of variable flowpath contributions with a tracer-based mixing model","docAbstract":"This study focuses on characterizing the contributions of key terrestrial pathways that deliver dissolved organic carbon (DOC) to streams during hydrological events and on elucidating factors governing variation in water and DOC fluxes from these pathways. We made high-frequency measurements of discharge, specific conductance (SC), and fluorescent dissolved organic matter (FDOM) during 221 events recorded over 2 years within four Vermont (USA) watersheds that range in area from 0.4 to 139 km2. Using the SC measurements, together with statistical information on discharge, we separated the event hydrographs into contributions from three terrestrial pathways, which we refer to as riparian quickflow, subsurface quickflow, and slow-flow groundwater. The pathway discharges were used as input to a mixing model that closely approximated sub-hourly streamwater DOC concentrations as measured with the FDOM sensors. Subsurface quickflow, comprised of pre-event water, was the leading contributor to streamwater DOC fluxes, while riparian quickflow, comprised of event water, was the second-leading contributor to streamwater DOC fluxes, despite comprising the smallest proportion of streamflow yield among the three end-member pathways. Fixed-effects regression analysis revealed that the relationship between DOC fluxes from the end-member pathways and event magnitude was consistent across the four watersheds. This analysis also showed that DOC fluxes from the quickflow pathways increased significantly with temperature and varied inversely, but weakly, with catchment antecedent wetness. We believe that our approach, which leverages in-stream sensors that enable high-frequency measurements over extended periods, may be applicable for evaluating controls on DOC export from other watersheds within and beyond our study region.
This study integrated spatially distributed field observations and soil thermal models to constrain the impact of frozen ground on snowmelt partitioning and streamflow generation in an alpine catchment within the Niwot Ridge Long-Term Ecological Research site, Colorado, USA. The study area was comprised of two contrasting hillslopes with notable differences in topography, snow depth and plant community composition. Time-lapse electrical resistivity surveys and soil thermal models enabled extension of discrete soil moisture and temperature measurements to incorporate landscape variability at scales and depths not possible with point measurements alone. Specifically, heterogenous snowpack thickness (~0–4 m) and soil volumetric water content between hillslopes (~0.1–0.45) strongly influenced the depths of seasonal frost, and the antecedent soil moisture available to form pore ice prior to freezing. Variable frost depths and antecedent soil moisture conditions were expected to create a patchwork of differing snowmelt infiltration rates and flowpaths. However, spikes in soil temperature and volumetric water content, as well as decreases in subsurface electrical resistivity revealed snowmelt infiltration across both hillslopes that coincided with initial decreases in snow water equivalent and early increases in streamflow. Soil temperature, soil moisture and electrical resistivity data from both wet and dry hillslopes showed that initial increases in streamflow occurred prior to deep soil water flux. Temporal lags between snowmelt infiltration and deeper percolation suggested that the lateral movement of water through the unsaturated zone was an important driver of early streamflow generation. These findings provide the type of process-based information needed to bridge gaps in scale and populate physically based cryohydrologic models to investigate subsurface hydrology and biogeochemical transport in soils that freeze seasonally.
The Rio Grande flows approximately 670 miles from its headwaters in the San Juan Mountains of south-central Colorado to Fort Quitman, Texas, draining the Upper Rio Grande Basin (URGB) study area of 32,000 square miles that includes parts of Colorado, New Mexico, and Texas. Parts of the basin extend into the United Mexican States (hereafter “Mexico”), where the Rio Grande forms the international boundary between Texas and the State of Chihuahua, Mexico. The URGB was chosen as a focus area study (FAS) for the U.S. Geological Survey (USGS) National Water Census (NWC) as part of the WaterSMART initiative. The objective of the USGS NWC under WaterSMART is to focus on the technical aspects of providing information and tools to stakeholders so that they can make informed decisions on water availability.
This report contains water-use withdrawal estimates of groundwater and surface water for public-supply, self-supplied domestic, and irrigation water use for years 1985–2015 at 5-year intervals for the 22 drainage basins at the subbasin 8-digit hydrologic unit code (HUC-8) level. Data for additional categories of self-supplied industrial, mining, livestock, aquaculture, thermoelectric, and hydroelectric water use are provided in the accompanying data release to illustrate total withdrawals for the URGB. The additional category data are provided in this report only for the year 2015. Deliveries of water from public-supply systems to domestic users are included and are the only water-delivery data presented in this report. Consumptive use for irrigation is reported for all HUC-8 subbasins for 2015 and for select HUC-8s in the other years beginning in 1985 (the irrigation category includes irrigation for both crop and golf). Water transported outside of the URGB (interbasin transfers) is not included as part of the withdrawals and are not accounted for in any category of use within the URGB.
Estimated total withdrawals for all the water-use categories (including hydroelectric) in 2015 as reported in the USGS compilations in the URGB were 3,152.10 million gallons per day (Mgal/d). Surface water was the dominant source of water used in the URGB, providing about 71 percent of total withdrawals. Nearly all withdrawals were from freshwater sources; there was a small amount of saline groundwater that was used for public supply and self-supplied industrial, which were all reported in Texas. The proportions of total 2015 withdrawals from States in the URGB are 46 percent each in Colorado and New Mexico and 8 percent in Texas. A comparison of 2015 water withdrawals for the URGB—for the categories of public supply, self-supplied domestic, self-supplied industrial, thermoelectric, irrigation, livestock, mining, aquaculture, and hydroelectric—showed that irrigation is the dominant water use, at 74 percent of total withdrawals. Other water-use categories in the URGB that use about 1 percent or greater of the total water use by volume are public supply (9 percent) and self-supplied domestic and aquaculture (each about 1 percent). This report focuses on the higher volume, consumptively used categories of public supply, self-supplied domestic, and irrigation. A discussion on basin population provides context for the categories of public-supply and self-supplied domestic water use.
The population in the part of the basin in the United States grew from 1.36 to 2.26 million people between 1985 and 2015. With the city of Ciudad Juarez, Chihuahua, Mexico, included, the total population of the URGB grew from an estimated 2.01 to 3.66 million people between 1985 and 2015. The largest concentrations of population are in New Mexico, Texas, and Chihuahua, with 98 percent of the total number of people in the basin in 1985 and 99 percent of the total in 2015 residing in these states. Albuquerque, El Paso, and Ciudad Juarez are the largest cities in the basin.
Total withdrawals for public supply in the URGB averaged 277 Mgal/d from 1985 to 2015. About 60 percent of the URGB total public-supply withdrawals occurred in New Mexico, which averaged 170 Mgal/d. Groundwater provided 92 and 70 percent of the total withdrawals for public supply in 1985 and 2015, respectively. Deliveries to domestic users from public suppliers are reported for all drainage basins and years, and account for part of the total public-supply withdrawals. In the URGB, domestic deliveries from public suppliers increased from 1985 to 1995; since 2005, domestic deliveries from public supply have declined. The total populations served by public suppliers in the URGB increased by 90 percent from 1985 to 2015. In the URGB, more people were served by public-supply systems than were self-supplied, and the percentage of people on public-supply systems ranged from 81 to 92 percent from 1985 to 2015. Total domestic withdrawals in the URGB (deliveries plus self-supply withdrawals) ranged from 177.49 to 234.83 Mgal/d and peaked in 2005. Domestic use decreased from 2005 to 2010 by 17 percent and remained less than 200 Mgal/d in 2015. The per-capita daily use for the entire URGB fluctuated between the reporting years, but overall, domestic per-capita use across the basin has declined 46 percent from 145 gallons per capita daily (gpcd) in 1985 to 79 gpcd in 2015.
Total irrigation withdrawals in the URGB had a mean value of 2,767.66 Mgal/d from 1985 to 2015 and withdrawals peaked in 1995 at 3,416.84 Mgal/d. Over the 30-year period, irrigation source water in the URGB has ranged from 69 to 84 percent surface water, and the most surface water diverted in the basin for irrigation was in 1995. Groundwater withdrawals for irrigation have fluctuated but overall decreased by 13 percent between 2005 and 2015. Slightly more than one-half of total irrigation withdrawals within the URGB occurred in Colorado, with a mean of 57 percent from 1985 to 2015. From the peak of water withdrawals in 1995 to the conclusion of this study in 2015, total irrigation withdrawals across the study area decreased by 32 percent.
The total number of irrigated lands in the URGB from 1985 to 2015 had a mean of about 800 thousand acres, and more irrigated lands were consistently located in the headwaters of the URGB in the San Luis Valley, Colorado than the remainder of the study basin. In the 30-year period, Colorado had a mean of 68 percent of total irrigated lands whereas irrigated acres in New Mexico had a mean of 26 percent and the remaining 7 percent were in Texas. Since 2000, the number of irrigated acres in the URGB has fluctuated, but overall has decreased by 12 percent.
More land was irrigated with surface systems (surface irrigation includes flood, furrow, and gated pipe systems, hereafter collectively termed “surface”) in the URGB than with other irrigation system types. Across the 30-year period, from 62 to 88 percent of total irrigated lands had surface-system irrigation, and surface systems covered a mean of 69 percent of the URGB’s acres. Microirrigation systems, predominantly in New Mexico and Texas, compose 0.2 percent or less of the irrigated acres in the basin and were first reported in 1995. From 1985 to 2015, the surface systems decreased in the basin by about 38 percent, and the number of acres of sprinkler and microirrigation systems increased. Acres irrigated by sprinkler systems (predominately center pivot systems) have increased 179 percent from about 99 thousand acres in 1985 to 275 thousand acres in 2015. In this dataset, the number of sprinkler acres surpassed the number of surface irrigated acres in 2000. Within the San Luis Valley in Colorado, the acreage of surface irrigation has decreased, and sprinkler irrigation has increased over the 30-year period. In the New Mexico part of the URGB, surface irrigation is reported as the dominant system type, where irrigation by surface systems accounts for 97–98 percent of how water is provided to crops. As in New Mexico, crops in Texas are irrigated primarily by surface systems.
The mean of the mean simulated actual evapotranspiration (ETa) for crops in 2015 across the basin was highest for durum wheat at an estimated 36.00 inches per year (in/yr), and lowest for vegetables at an estimated 19.48 in/yr. Alfalfa and irrigated grass pastures mean ETa had a mean of 31.4 and 27.58 in/yr, respectively, for the basin. Pecans and peppers, both signature crops in the Rio Grande Basin, each had a mean ETa of 30.67 and 30.38 in/yr of mean. In general, mean ETa values for crops were lower in the HUCs of the Colorado San Luis Valley (13010001, 13010002, 13010003 and 13010004) which are more northerly and at higher elevations. The mean ETa for crops increased in the HUCs that are more southerly and at lower elevations in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215036","usgsCitation":"Ivahnenko, T.I., Flickinger, A.K., Galanter, A.E., Douglas-Mankin, K.R., Pedraza, D.E., and Senay, G.B., 2021, Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015: U.S. Geological Survey Scientific Investigations Report 2021–5036, 31 p., https://doi.org/10.3133/sir20215036.","productDescription":"Report: viii, 35 p.:; Data Releases","onlineOnly":"Y","ipdsId":"IP-096649","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":389160,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SQ1Y3T","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Red River Basin, 2010 and 2015"},{"id":389156,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5036/coverthb.jpg"},{"id":389157,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5036/sir20215036.pdf","text":"Report","size":"5.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5036"},{"id":389158,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SX6CJ2","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Upper Rio Grande Basin, 1985–2015"},{"id":389159,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99OIFYY","text":"USGS data release","linkHelpText":"2015 irrigated acres feature class for the Upper Rio Grande Basin, New Mexico, Texas, United States and Chihuahua, Mexico"}],"country":"United States","state":"New Mexico","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.973876953125,\n 36.96744946416934\n ],\n [\n -107.09472656249999,\n 36.53612263184686\n ],\n [\n -107.149658203125,\n 35.951329861522666\n ],\n [\n -107.75390625,\n 35.30840140169162\n ],\n [\n -108.25927734375,\n 34.95799531086792\n ],\n [\n -108.204345703125,\n 34.298068350990825\n ],\n [\n -107.852783203125,\n 33.55055114384406\n ],\n [\n -107.70996093749999,\n 33.091541548655215\n ],\n [\n -107.40234375,\n 32.20350534542368\n ],\n [\n -107.314453125,\n 31.85889704445453\n ],\n [\n -107.237548828125,\n 31.70947636001935\n ],\n [\n -106.4794921875,\n 31.793555207271424\n ],\n [\n -106.44653320312499,\n 31.98944183792288\n ],\n [\n -106.336669921875,\n 32.34284135639302\n ],\n [\n -106.226806640625,\n 32.676372772089834\n ],\n [\n -105.99609375,\n 33.0178760185549\n ],\n [\n -105.677490234375,\n 33.65120829920497\n ],\n [\n -105.413818359375,\n 34.21634468843463\n ],\n [\n -105.260009765625,\n 34.939985151560435\n ],\n [\n -105.22705078125,\n 35.27253175660236\n ],\n [\n -105.23803710937499,\n 35.7019167328534\n ],\n [\n -105.16113281249999,\n 36.146746777814364\n ],\n [\n -104.952392578125,\n 36.41244153535644\n ],\n [\n -104.886474609375,\n 36.99377838872517\n ],\n [\n -106.973876953125,\n 36.96744946416934\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Proper identification of wetlands, along with a better understanding of the hydrology of mitigated wetlands, is needed to assist with conservation efforts aimed at maintaining the productivity and ecological function (wetland mitigation) of agricultural lands. The U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service, completed a study to evaluate two models for simulating hydrologic conditions in fields with subsurface drainage to mitigated wetlands at several sites in North Dakota. These two models were evaluated as possible tools for water resource managers to use for designing wetland mitigation projects in the area in the future.
The Soil-Plant-Atmosphere-Water (SPAW) model simulates the daily hydrologic water budgets of agricultural landscapes by two linked routines, one for farm fields (field hydrology) and one for impoundments such as wetlands and ponds (pond model). The DRAINMOD model was used in conjunction with the SPAW model because although the SPAW model can be used to simulate the hydrology of small drainage basins containing wetlands, the SPAW model does not contain routines to simulate drainage, either subsurface drainage or surface (drainage ditches), that can directly affect the wetland hydrology. The wetlands in the study areas in this report are all downstream from and adjacent to drained agricultural fields. SPAW and DRAINMOD models were developed and calibrated at three study areas (study areas B, D, and S) to evaluate how the models simulated field-scale hydrologic characteristics and the water balance in wetlands from January 1, 2003, through December 31, 2018.
The SPAW model developed for study area B included five modeled fields in the field hydrology portion of SPAW that contributed inflow to one wetland simulated in the pond model portion of SPAW. Simulated wetland water depths were most similar to water depths measured at site BWET1, with an absolute mean error of 0.10 foot and a root mean square error of 0.14 foot. Site BWET2 had slightly larger errors, with an absolute mean error of 0.22 foot and a root mean square error of 0.28 foot. Simulated water depths were similar to the pattern of measured water depths at BWET1 and BWET2 from about mid-April 2018 through about mid-September 2018, but overpredicted water depths in the fall from about mid-September 2018 through about mid-October 2018.
The SPAW model developed for study area D included six modeled fields in the field hydrology portion of SPAW that contributed inflow to five wetlands connected in series in the pond model portion of SPAW. Simulated water depths compared relatively well to water depths in the five wetlands, with the absolute mean error ranging from 0.17 foot (DWET1) to 0.39 foot (DWET2), and the root mean square error ranging from 0.28 foot (DWET1) to 0.56 foot (DWET5).
The SPAW model developed for study area S included one modeled field in the field hydrology portion of SPAW that contributed inflow to one wetland in the pond model portion of SPAW. Among the SPAW models developed for the three study areas, the model for study area S had the best comparison between simulated and measured water depths, with an absolute mean error of 0.06 foot and a root mean square error of 0.10 foot.
DRAINMOD models were developed and calibrated at the three study areas and provided inflow from subsurface drainage discharge to the SPAW models for simulating water levels in wetlands in the study areas. The calibrated DRAINMOD model for study area B showed the variability of hydrologic processes in the modeled field throughout the wide range of hydrologic conditions from January 1, 2003, through December 31, 2018. In general, the discharge through the modeled subsurface drainage system was in the spring and early summer (April through June) most years, with little to no discharge later in the year. Although the subsurface drainage system in study area D was the most complex among the three study areas and was simplified into a uniform system within DRAINMOD, simulated water table depths at study area D correlated better to measured water table depths compared to results from the model applications at the other two study areas. Simulated water table depths had an absolute mean error of 0.30 foot and root mean square error of 0.37 foot at site DGW1 and an absolute mean error of 0.29 foot and a root mean square error of 0.34 foot at site DGW2. Although the subsurface drainage system in study area S was the simplest and the modeled field was the smallest among the three study areas, simulated water table depths at study area S did not correlate as well to measured water table depths compared to results from the model applications at the other two study areas.
The SPAW and DRAINMOD model applications at the three study areas in southeast North Dakota adequately simulated the hydrologic processes for fields with subsurface drainage that are connected to adjacent wetlands. However, more measured data would be needed to fully evaluate the models throughout the range of possible climatic conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215091","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service","usgsCitation":"Galloway, J.M., Tatge, W.S., and Wheeling, S.L., 2021, Evaluation of hydrologic simulation models for fields with subsurface drainage to mitigated wetlands in Barnes, Dickey, and Sargent Counties, North Dakota: U.S. Geological Survey Scientific Investigations Report 2021–5091, 58 p., https://doi.org/10.3133/sir20215091.","productDescription":"Report: vi, 58 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-128613","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":389200,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5091/images"},{"id":389199,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5091/sir20215091.xml","size":"386 kB","linkFileType":{"id":8,"text":"xml"}},{"id":389198,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":389196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5091/coverthb.jpg"},{"id":389197,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5091/sir20215091.pdf","text":"Report","size":"5.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5091"}],"country":"United States","state":"North Dakota","county":"Barnes County, Dickey County, Sargent County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-97.961,47.241],[-97.7061,47.2402],[-97.7071,47.1529],[-97.7062,47.0665],[-97.7059,46.9792],[-97.6839,46.9792],[-97.683,46.6294],[-97.81,46.6297],[-97.9059,46.6293],[-97.9357,46.6294],[-98.0349,46.6293],[-98.1889,46.6297],[-98.2868,46.63],[-98.3152,46.63],[-98.4396,46.6296],[-98.4412,46.9789],[-98.4685,46.9788],[-98.4677,47.2402],[-97.9958,47.2411],[-97.9764,47.2412],[-97.961,47.241]]],[[[-98.0095,45.9355],[-98.164,45.9356],[-98.1849,45.9355],[-98.3472,45.9355],[-98.3537,45.9355],[-98.7267,45.9373],[-98.7273,45.9373],[-99.0021,45.9393],[-99.0054,45.9393],[-99.0073,46.0262],[-99.0061,46.1132],[-99.0054,46.2002],[-99.0049,46.2822],[-98.9154,46.2821],[-98.7878,46.2805],[-98.755,46.281],[-98.6622,46.2812],[-98.5359,46.2817],[-98.5024,46.2808],[-98.2859,46.2816],[-98.2524,46.2815],[-98.1616,46.2818],[-98.1314,46.2813],[-98.0366,46.2809],[-98.009,46.2814],[-97.9096,46.2823],[-97.8826,46.2827],[-97.5333,46.2819],[-97.4063,46.2823],[-97.2833,46.2822],[-97.2615,46.2822],[-97.2618,46.196],[-97.2603,45.9985],[-97.231,45.9951],[-97.2313,45.936],[-97.3576,45.936],[-97.3773,45.936],[-97.4826,45.9359],[-97.605,45.9356],[-97.755,45.9356],[-97.9775,45.9351],[-98.0017,45.9355],[-98.0095,45.9355]]]]},\"properties\":{\"name\":\"Barnes\",\"state\":\"ND\"}}]}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Benthic samples were collected from the Sacramento–San Joaquin Delta of northern California to examine the effect of the changing hydrologic flow on the bivalves Potamocorbula and Corbicula before, during, and after the False River Barrier (hereafter, barrier) was in operation (May–November 2015). Potamocorbula moved upstream in the Sacramento River as the salinity intruded. Given the lower electrical conductivity of the San Joaquin River, Potamocorbula did not move as far upriver as it did in the Sacramento River. Potamocorbula recruits settled in the Sacramento and False Rivers, whereas Corbicula recruits were mostly found in the San Joaquin River. When the grazing rates for the two bivalves were combined, new populations of Potamocorbula plus existing Corbicula likely reduced the net growth rate of the phytoplankton in and just upstream from the Sacramento and San Joaquin River confluence region when the barrier was in place. Prior to the barrier installation, a very dry period assumably aided the success of Potamocorbula in the confluence region; nonetheless, they also responded to the increasing salinity in the Sacramento River and their population spatially expanded. Potamocorbula’s upriver incursion was stopped owing to the return of freshwater flow due to the removal of the barrier, but the adults of the species were still present at the upstream end of Decker Island in January 2016. Corbicula adults did not seem to respond to the increased salinity caused by the barrier and maintained their biomass at all locations compared to what was recorded before the barrier.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211088","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Parchaso, F., Zierdt Smith, E.L., and Thompson, J.K., 2021, Effect of the emergency drought barrier on the distribution, biomass, and grazing rate of the bivalves Corbicula fluminea and Potamocorbula amurensis, False River, California: U.S. Geological Survey Open-File Report 2021–1088, 22 p., https://doi.org/10.3133/ofr20211088.","productDescription":"vii, 22 p.","onlineOnly":"Y","ipdsId":"IP-120260","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":389246,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1088/coverthb.jpg"},{"id":389247,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1088/ofr20211088.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1088"}],"country":"United States","state":"California","otherGeospatial":"False River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.87957763671874,\n 38.005902055387075\n ],\n [\n -121.44561767578124,\n 38.005902055387075\n ],\n [\n -121.44561767578124,\n 38.232786699509965\n ],\n [\n -121.87957763671874,\n 38.232786699509965\n ],\n [\n -121.87957763671874,\n 38.005902055387075\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Water Resources, Earth System Processes Division
U.S. Geological Survey
345 Middlefield Road
Menlo Park, California, 94025
Maximum likelihood logistic regression (MLLR) models for the northeastern United States forecast drought probability estimates for water flowing in rivers and streams using methods previously identified and developed. Streamflow data from winter months are used to estimate chances of hydrological drought during summer months. Daily streamflow data collected from 1,143 streamgages from April 1, 1877, through October 31, 2018, are used to provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February. This allows estimates of outcomes from 5 to 11 months ahead of their occurrence. Models specific to the northeastern United States were investigated and updated. The MLLR models of drought stream-flow probabilities utilize the explanatory power of temporally linked water flows. Models with strong drought streamflow probability correct-classification rates were produced for streams throughout the northeastern United States. A test of northeastern United States drought streamflow probability predictions found that overall correct-classification rates for drought streamflow probabilities in the northeastern United States exceeded 97 percent when predicting July 2019 drought probability using February 2019 monthly mean streamflow data. Using hydrological drought probability estimates in a water-management context informs understandings of possible future streamflow drought conditions in the northeastern United States, provides warnings of potential future drought conditions, and aids water-management decision making and responses to changing circumstances.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215084","usgsCitation":"Austin, S.H., 2021, Forecasting drought probabilities for streams in the northeastern United States: U.S. Geological Survey Scientific Investigations Report 2021–5084, 11 p., https://doi.org/10.3133/sir20215084.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"11","ipdsId":"IP-113685","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":388741,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5084/sir20215084.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5084"},{"id":388740,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5084/coverthb.jpg"},{"id":388742,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E3SK56","text":"USGS data release","linkHelpText":"Terms, statistics, and performance measures for maximum likelihood logistic regression models estimating hydrological drought probabilities in the northeastern United States (2019)"}],"country":"United States","state":"Connecticut, Delaware, Massachusetts, Maine, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Virginia, Vermont, West 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\"}}]}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Natural disturbances maintain biological diversity and landscape heterogeneity and initiate ecosystem renewal and reorganization. However, the severity, frequency, and extent of many disturbances have increased substantially in recent decades as the result of anthropogenic climate change. Disturbances can be discrete, short-duration events, such as wildfires or hurricanes, or can exert persistent, cumulative stresses on an ecosystem (for example, ongoing warming of ocean or land surface temperatures). Landscape and ecosystem impacts can occur from a single disturbance, from several disturbances acting independently, or from the interactions of multiple, linked disturbances. Key, climate-related disturbances affecting global biomes and ecosystems include shifting temperature and hydrologic regimes (for example, warming surface temperatures and increasing aridity), increased frequency and magnitude of extreme events such as heatwaves, severe droughts, storms, and hurricanes, warming-induced permafrost thaw, and heightened wildfire activity and insect-caused tree mortality. For ecosystems and landscapes, the consequences of climate-amplified disturbance include forced poleward and upward movement of plant and animal species, widespread tree mortality and reduced forest productivity, changes in plant community structure and species distributions, reduced biodiversity, increased erosion, debris flows, wetland dynamism, declining sea ice extent, more frequent storm-driven tides and saltwater intrusion, and increased landscape flammability.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Routledge handbook of landscape ecology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Routledge","doi":"10.4324/9780429399480-11","usgsCitation":"Loehman, R.A., Friggens, M., Sherriff, R., Keyser, A.R., and Riley, K.L., 2021, Impacts of climate changes and amplified natural disturbance on global ecosystems, chap. of Routledge handbook of landscape ecology, p. 175-198, https://doi.org/10.4324/9780429399480-11.","productDescription":"24 p.","startPage":"175","endPage":"198","ipdsId":"IP-108658","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":400700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":false,"id":843150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friggens, Megan","contributorId":219865,"corporation":false,"usgs":false,"family":"Friggens","given":"Megan","email":"","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":843151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherriff, Rosemary L.","contributorId":243263,"corporation":false,"usgs":false,"family":"Sherriff","given":"Rosemary L.","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":843152,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keyser, Alisa R.","contributorId":248331,"corporation":false,"usgs":false,"family":"Keyser","given":"Alisa","email":"","middleInitial":"R.","affiliations":[{"id":49860,"text":"Univ. of New Mexico","active":true,"usgs":false}],"preferred":false,"id":843153,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riley, Karin L.","contributorId":169453,"corporation":false,"usgs":false,"family":"Riley","given":"Karin","email":"","middleInitial":"L.","affiliations":[{"id":25512,"text":"US Forest Service Fire Science Lab","active":true,"usgs":false}],"preferred":false,"id":843154,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223775,"text":"sir20215093 - 2021 - A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17","interactions":[],"lastModifiedDate":"2021-09-08T11:52:20.913559","indexId":"sir20215093","displayToPublicDate":"2021-09-07T19:13:38","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5093","displayTitle":"A Machine Learning Approach to Modeling Streamflow with Sparse Data in Ungaged Watersheds on the Wyoming Range, Wyoming, 2012–17","title":"A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17","docAbstract":"Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse.
To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow.
The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [R2]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period.
The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature.
The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance (R2=0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance (R2=0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent (R2=0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations.
The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215093","usgsCitation":"McShane, R.R., and Eddy-Miller, C.A., 2021, A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17: U.S. Geological Survey Scientific Investigations Report 2021–5093, 29 p., https://doi.org/10.3133/sir20215093.","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-117330","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":388893,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCP1AE","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input data, model output, and R scripts for a machine learning streamflow model on the Wyoming Range, Wyoming, 2012–17"},{"id":388895,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5093/sir20215093.xml","text":"Report","size":"219 kB","linkFileType":{"id":8,"text":"xml"}},{"id":388896,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5093/images"},{"id":388894,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5093/coverthb.jpg"},{"id":388892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5093/sir20215093.pdf","text":"Report","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5093"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wyoming Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.90972900390625,\n 42.09618442380296\n ],\n [\n -110.01708984374999,\n 42.09618442380296\n ],\n [\n -110.01708984374999,\n 42.68041629144619\n ],\n [\n -110.90972900390625,\n 42.68041629144619\n ],\n [\n -110.90972900390625,\n 42.09618442380296\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The travel time or “age” of groundwater affects catchment responses to hydrologic changes, geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE = 0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE = 0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4 m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9 years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and geochemistry flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.
The U.S. Geological Survey, in cooperation with the National Park Service, developed a three-dimensional groundwater-flow model for Assateague Island in eastern Maryland and Virginia to assess the effects of sea-level rise on the groundwater system. Sea-level rise is expected to increase the altitude of the water table in barrier island aquifer systems, possibly leading to adverse effects to ecosystems on the barrier islands. The potential effects of sea-level rise were evaluated by simulating groundwater conditions under sea-level-rise scenarios of 20 centimeters (cm), 40 cm, and 60 cm. Results show that as sea level rises, low-lying areas of the island originally represented as receiving freshwater recharge in the baseline scenario are inundated by saltwater. This change from freshwater recharge to saltwater decreases the overall amount of freshwater recharging the system. As the water table rises in response to the higher sea levels, freshwater flow out of the system changes, with more freshwater leaving as submarine groundwater discharge and less freshwater leaving as seeps and evapotranspiration. At the current land-surface altitude, as much as 50 percent of the island may be inundated with a 60-cm rise in sea level, and the low-lying marshes may change from freshwater to saltwater.
Groundwater levels at 32 wells were monitored for as long as 12 months between October 2014 and September 2015 on Assateague Island. Results from objective classification analysis of 14 shallow monitoring wells show two dominant processes affecting groundwater levels in two different settings on the island. On the western side of the island, between the primary dune and the inland bays, water levels clearly respond to precipitation events. This side of the island is more protected from ocean tides and typically is more vegetated than the eastern side. On the eastern side of the island, between the Atlantic Ocean and the primary dune, water levels clearly respond to tidal events. Specific conductance was measured at four wells, two on the western part of the island and two on the eastern part of the island. Specific conductance values in the two wells west of the primary dune show episodic decreases, coinciding with precipitation events. Specific conductance values in the two wells on the eastern side of the primary dune show episodic increases, coinciding with high-tide events. These high frequency monitoring data are intended to aid in designing a monitoring network that can document both short-term and long-term hydrologic processes on Assateague Island National Seashore.
This study uses a modeling approach consistent with models developed for Gateway National Recreation Area, Sandy Hook Unit (New Jersey) and Fire Island National Seashore (New York). Combined, these models are meant to improve the regional capabilities for predicting climate-change effects on barrier islands and provide resource managers with a common set of tools for adaptation and mitigation of potentially adverse effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205104","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fleming, B.J., Raffensperger, J.P., Goodling, P.J., and Masterson, J., 2021, Simulated effects of sea-level rise on the shallow, fresh groundwater system of Assateague Island, Maryland and Virginia: U.S. Geological Survey Scientific Investigations Report 2020–5104, 62 p., https://doi.org/10.3133/sir20205104.","productDescription":"Report: viii, 62 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094959","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":387028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AJOLRK","text":"USGS data release","linkHelpText":"MODFLOW-NWT model with SWI2 used to evaluate the water-table response to sea-level rise and change in recharge, Assateague Island, Maryland and Virginia"},{"id":387027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5104/sir20205104.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5104"},{"id":387026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5104/coverthb.jpg"},{"id":387041,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205117","text":"Scientific Investigations Report 2020–5117","linkHelpText":"- Simulation of Water-Table and Freshwater/Saltwater Interface Response to Climate-Change-Driven Sea-Level Rise and Changes in Recharge at Fire Island National Seashore, New York"},{"id":387040,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205080","text":"Scientific Investigations Report 2020–5080","linkHelpText":"- Simulation of Water-Table Response to Sea-Level Rise and Change in Recharge, Sandy Hook Unit, Gateway National Recreation Area, New Jersey"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.42388916015625,\n 37.87376937332855\n ],\n [\n -75.3826904296875,\n 37.83473402375478\n ],\n [\n -75.30441284179688,\n 37.88027325525864\n ],\n [\n -75.15335083007812,\n 38.11727165830543\n ],\n [\n -75.12039184570312,\n 38.29101446582335\n ],\n [\n -75.17120361328125,\n 38.22847167526397\n ],\n [\n -75.28793334960938,\n 38.0513353697269\n ],\n [\n -75.3826904296875,\n 37.93769926732864\n ],\n [\n -75.42388916015625,\n 37.87376937332855\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
The spread of the invasive and fire-adapted buffelgrass (Cenchrus ciliaris L.) threatens desert ecosystems by competing for resources, increasing fuel loads, and creating wildfire connectivity. The Rincon Mountain District of Saguaro National Park addressed this natural resource threat with the use of glyphosate-based herbicides (GBHs). In 2010, the Rincon Mountain District initiated an aerial restoration plan to control dense buffelgrass patches in remote areas and implemented a trial project to evaluate the effects of aerial restoration techniques that included the helicopter application of GBHs. In 2014, more than 250 acres of buffelgrass in the Rincon Mountain District were treated with the aerial application of GBHs. This widespread aerial application of GBHs continued through 2018, but the potential transport and effects to aquatic ecosystems were unknown.
In 2015–18, the U.S. Geological Survey, in cooperation with the National Park Service, studied the occurrence, distribution, fate, and transport of glyphosate in surface water and sediments derived from areas that were treated during past and current aerial herbicide applications. Three watersheds, treated with different regimens of GBHs, were sampled for glyphosate and the primary metabolite of glyphosate, aminomethylphosphonic acid (AMPA), during various hydrologic flow conditions. Water and aquatic sediment were collected from three watersheds, each in a different stage of application during the U.S. Geological Survey study. The unnamed watershed above the Loma Verde Trailhead referred to by the National Park Service as “Loma Verde canyon” had received no aerial treatment since 2014, whereas the Box Canyon watershed was aerially treated every year beginning in 2014. The Madrona Canyon watershed was first sprayed in 2016 and aerial application continued once a year though the entirety of the study. In addition, terrestrial soil samples were sampled from areas sprayed to understand dissipation rates and herbicide transport via sediments washing away during rainfall runoff. The concentrations present in water and sediment samples were compared to ecological benchmarks and characterized within the context of the environmental conditions of the park setting.
Of the 48 water samples collected and analyzed for glyphosate and AMPA, 10.4 percent and 14.6 percent were detected above the laboratory minimum detection limit, respectively. Mean water concentrations, calculated using specific statistical methods for non-detects, were equal to the laboratory minimum detection limit of 0.02 microgram per liter for samples collected in all the watersheds. In aquatic sediments, glyphosate and AMPA were detected in 10.7 and 25.0 percent of the samples, whereas 89.5 and 100 percent of the terrestrial soil samples had detections for glyphosate and AMPA, respectively. Mean aquatic sediment concentrations were 1.13 and 4.42 micrograms per kilogram (μg/kg) for glyphosate and AMPA, respectively. Mean terrestrial soil concentrations were orders of magnitude greater than water and aquatic sediment with concentrations of 678 μg/kg for AMPA and 1,240 μg/kg for glyphosate. Hours after glyphosate-based herbicide was applied, the concentrations of glyphosate and AMPA were present in terrestrial soil samples near or above the laboratory maximum detection limit of 5,000 μg/kg. The Box Canyon watershed was the most intensively treated watershed in terms of total land area treated, total amount of GBH applied, and number of years treated. The frequent and large volume of treatment resulted in the highest number of detections of glyphosate and AMPA in water (3 and 7 detections, respectively) and in aquatic sediment (2 and 6 detections, respectively) samples. In comparison, the other two watersheds had two or fewer detections for glyphosate and AMPA in water and aquatic sediment.
Glyphosate detected in pools was associated with increased rainfall closer in time to the last herbicide treatment. Glyphosate and AMPA concentration ratios above one, along with stable-isotope and tritium results, indicated that runoff processes were the primary transport mechanism for the two compounds when found in streams and pools rather than subsurface recharge or deeper flow paths. One pool in a small tributary of Box Canyon consistently had detections of glyphosate and AMPA in aquatic sediments, but these frequent concentrations were likely related to the intensive application upstream, near the steep terrain above the head of the channel that supplies the downstream pool. Intense flows during summer rainfall events move treated sediments into this channel where vegetation and the incised bedrock banks of the pool retained those sediments and ultimately led to frequent detections of both compounds. Isotope results in most of the pools and tinajas indicated that the water source had residence time representative of recently recharged waters, on the order of years.
No water concentrations exceeded published criteria for human health or aquatic life. Median and maximum glyphosate and AMPA water concentrations were lower than those reported in other national assessments, but maximum concentrations observed in individual runoff samples were higher than median concentrations measured in the national assessments. A similar finding was observed with aquatic sediment concentrations measured in the Rincon Mountain District. Results from the study were compared and assessed in the context of other studies examining GBHs and their effects on amphibians, fish, and macroinvertebrates. This comparison was used to generalize the potential risk to aquatic species similar to those species in the Rincon Mountain District. Concentrations of published effect levels were several orders of magnitude greater than the highest concentration detected in water at the Rincon Mountain District. Most published studies evaluate acute and chronic toxicity for glyphosate and GBHs, and these criteria may not be representative of environmental conditions in the Rincon Mountain District. The classic lethal dose studies conducted in a controlled laboratory setting may not be suitable for comparison to the longer, variable, low-dose exposure conditions in the pools and tinajas in the Rincon Mountain District. However, this study determined that the fate of GBHs transported from treated areas to potential aquatic habitat was highly variable in occurrence, timing, and concentrations. This variability in glyphosate concentrations was too high, and the potential exposure was determined to be far too complex to directly compare with the results from controlled studies.
This study provides the first information collected on GBHs used to control invasive buffelgrass in a remote, mountainous, and semiarid setting. The information about the transport and fate of herbicide application near aquatic habitat will help to inform managers about the broader ecosystem implications and provide useful information to other agencies implementing buffelgrass remediation strategies near aquatic habitat.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215039","collaboration":"Prepared in cooperation with the National Park Service and Saguaro National Park","usgsCitation":"Paretti, N.V., Beisner, K.R., Gungle, B., Meyer, M.T., Kunz, B.K., Hermosillo, E., Cederberg, J.R., and Mayo, J.P., 2021, Occurrence, fate, and transport of aerially applied herbicides to control invasive buffelgrass within Saguaro National Park Rincon Mountain District, Arizona, 2015–18: U.S. Geological Survey Scientific Investigations Report 2021–5039, 65 p., https://doi.org/10.3133/sir20215039.","productDescription":"Report: ix, 65 p.; Dataset","numberOfPages":"65","onlineOnly":"Y","ipdsId":"IP-099223","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":404268,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215039/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Scientific Investigations Report 2021–5039"},{"id":388749,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5039/images"},{"id":388746,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5039/covrthb.jpg"},{"id":388747,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5039/sir20215039.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388748,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5039/sir20215039.xml"},{"id":388760,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":404267,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20223029","text":"Fact Sheet 2022-3029","description":"Paretti, N.V., and Gungle, B., 2022, Occurrence and transport of aerially applied herbicides to control invasive buffelgrass in Rincon Mountain District, Saguaro National Park, Arizona: U.S. Geological Survey Fact Sheet 2022-3029, 6 p., https://doi.org/10.3133/fs20223029.","linkHelpText":"- Occurrence and Transport of Aerially Applied Herbicides to Control Invasive Buffelgrass in Rincon Mountain District, Saguaro National Park, Arizona"}],"country":"United States","state":"Arizona","otherGeospatial":"Saguaro National Park Rincon Mountain District","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.75248718261719,\n 32.027870563435584\n ],\n [\n -110.37483215332031,\n 32.027870563435584\n ],\n [\n -110.37483215332031,\n 32.27320009948135\n ],\n [\n -110.75248718261719,\n 32.27320009948135\n ],\n [\n -110.75248718261719,\n 32.027870563435584\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
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Landslides are common geohazards associated with natural drivers such as precipitation, land degradation, toe erosion by rivers and wave attack, and ground shaking. On the other hand, human alterations such as inundation by water impoundment or rapid drawdown may also destabilize the surrounding slopes. The Guobu slope is an ancient rockslide on the banks of the Laxiwa hydropower station reservoir (China), which reactivated during the reservoir impoundment in 2009. We extracted three-dimensional surface displacements with azimuth and range radar interferometry using European Space Agency's Copernicus Sentinel-1 and German Aerospace Center's TerraSAR-X data during 20152019. The upper part of the Guobu rockslide is characterized by toppling and is mostly subsiding with maximum rates over 0.4 m/yr and 0.7 m/yr in the vertical and horizontal directions, respectively. During filling of the reservoir prior to 2014, there was a long-wavelength in-phase response between rising reservoir level and GPS-observed increased slope movements. After the reservoir water level stabilized from 2015 to 2019, the slide movement became seasonal and we see a correlation between rainfall and landslide movement. These observations suggest that the slide motion is now primarily controlled by rainfall. The spatiotemporal landslide displacements allow us to estimate the hydraulic diffusivity of the rock mass, to be on the order (~1.05 × 10‐7 m2/s) and the thickness of the moving rock mass (~200 m). Our results demonstrate that InSAR is a useful tool for monitoring the rockslide movement as a function of seasonal precipitation.
Two datasets containing the first complete estimates of reach-scale nutrient, water use, dissolved oxygen, and pH conditions for the Pacific drainages of the United States were created to help inform water-quality management decisions in that region. The datasets were developed using easily obtainable watershed data, most of which have not been available until recently, and the techniques that were used provide a framework for integrating watershed data to assess water-quality impairment across other large hydrologic regions in the United States. These datasets were used to summarize regional nutrient and water-use conditions within impaired water bodies and to summarize regional dissolved oxygen concentrations and pH conditions for free-flowing stream reaches. Two examples are also presented that show how the datasets can be applied to specific water-quality management issues: (1) nutrient conditions in water bodies that have recently experienced problems with harmful algal blooms; and (2) dissolved oxygen and pH conditions in stream reaches likely to be populated by steelhead trout (Oncorhynchus mykiss irideus) during their summer run. The nutrient and water-use estimates could help inform actions aimed at managing water-quality conditions in impaired water bodies while the dissolved oxygen and pH predictions could be useful as screening tools to identify water bodies experiencing potential impairment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215087","programNote":"National Water Quality Program","usgsCitation":"Wise, D.R., 2021, Using regional watershed data to assess water-quality impairment in the Pacific Drainages of the United States: U.S. Geological Survey Scientific Investigations Report 2021–5087, 29 p., https://doi.org/10.3133/sir20215087.","productDescription":"vii, 29 p.","onlineOnly":"Y","ipdsId":"IP-123766","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":436221,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B3BQOW","text":"USGS data release","linkHelpText":"Reach-scale estimates of nutrient, water use, dissolved oxygen, and pH conditions in the Pacific drainages of the United States"},{"id":388699,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5087/coverthb.jpg"},{"id":388700,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5087/sir20215087.pdf","text":"Report","size":"6.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5087"}],"country":"United States","state":"California, Idaho, Montana, Nevada, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.958984375,\n 49.03786794532644\n ],\n [\n -123.04687499999999,\n 48.545705491847464\n ],\n [\n -124.541015625,\n 48.48748647988415\n ],\n [\n -124.93652343749999,\n 48.28319289548349\n ],\n [\n -124.23339843749999,\n 46.76996843356982\n ],\n [\n -124.18945312500001,\n 45.42929873257377\n ],\n [\n -124.71679687499999,\n 43.83452678223682\n ],\n [\n -124.76074218749999,\n 42.5530802889558\n ],\n [\n -124.49707031249999,\n 41.47566020027821\n ],\n [\n -124.71679687499999,\n 40.34654412118006\n ],\n [\n -124.18945312500001,\n 39.639537564366684\n ],\n [\n -123.96972656249999,\n 38.993572058209466\n ],\n [\n -123.00292968749999,\n 38.03078569382294\n ],\n [\n -122.03613281249999,\n 36.27970720524017\n ],\n [\n -121.025390625,\n 35.02999636902566\n ],\n [\n -120.4541015625,\n 34.34343606848294\n ],\n [\n -118.91601562499999,\n 34.05265942137599\n ],\n [\n -117.68554687499999,\n 33.46810795527896\n ],\n [\n -117.20214843749999,\n 32.54681317351514\n ],\n [\n -116.4111328125,\n 32.62087018318113\n ],\n [\n -116.54296874999999,\n 33.211116472416855\n ],\n [\n -117.6416015625,\n 34.08906131584994\n ],\n [\n -118.0810546875,\n 34.23451236236987\n ],\n [\n -118.21289062499999,\n 35.96022296929667\n ],\n [\n -118.91601562499999,\n 36.70365959719456\n ],\n [\n -119.970703125,\n 37.92686760148135\n ],\n [\n -120.41015624999999,\n 38.61687046392973\n ],\n [\n -120.58593749999999,\n 39.470125122358176\n ],\n [\n -120.58593749999999,\n 40.27952566881291\n ],\n [\n -121.11328124999999,\n 41.07935114946899\n ],\n [\n -121.6845703125,\n 42.35854391749705\n ],\n [\n -121.59667968749999,\n 42.94033923363181\n ],\n [\n -121.46484375,\n 43.96119063892024\n ],\n [\n -121.11328124999999,\n 44.77793589631623\n ],\n [\n -120.2783203125,\n 44.33956524809713\n ],\n [\n -119.4873046875,\n 43.8028187190472\n ],\n [\n -118.69628906249999,\n 42.5530802889558\n ],\n [\n -118.0810546875,\n 41.83682786072714\n ],\n [\n -117.158203125,\n 41.07935114946899\n ],\n [\n -116.1474609375,\n 40.78054143186033\n ],\n [\n -114.2138671875,\n 41.343824581185686\n ],\n [\n -113.466796875,\n 42.00032514831621\n ],\n [\n -111.884765625,\n 42.35854391749705\n ],\n [\n -111.005859375,\n 42.4234565179383\n ],\n [\n -109.6875,\n 42.5530802889558\n ],\n [\n -109.4677734375,\n 43.26120612479979\n ],\n [\n -109.951171875,\n 44.05601169578525\n ],\n [\n -110.7861328125,\n 44.55916341529182\n ],\n [\n -112.06054687499999,\n 44.59046718130883\n ],\n [\n -112.8515625,\n 44.43377984606822\n ],\n [\n -113.73046875,\n 45.24395342262324\n ],\n [\n -114.0380859375,\n 45.460130637921004\n ],\n [\n -113.203125,\n 45.73685954736049\n ],\n [\n -112.236328125,\n 46.40756396630067\n ],\n [\n -112.19238281249999,\n 47.39834920035926\n ],\n [\n -113.64257812499999,\n 48.980216985374994\n ],\n [\n -122.958984375,\n 49.03786794532644\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The Big Bend region is located within the heart of the Chihuahan Desert of North America. Within this region, the Rio Grande, referred to as the Rio Bravo in Mexico, is the international border between the United States and Mexico. The area known as the Big Bend is named after the large northerly bend that the river makes before flowing southeast to the Gulf of Mexico. This region is environmentally protected by both countries. Although large binational conservation efforts exist, the physical and ecological characteristics of the river have been substantially altered. Changes in Rio Grande hydrology (the seasonality, magnitude, duration, and variability in streamflow) have resulted in the widespread physical transformation of the river, resulting in the loss of important habitat for native and endangered fish and increased flood risk. U.S. Geological Survey (USGS) scientists, in cooperation with many other government agencies, universities, and non-governmental organizations (NGOs), are working to better understand these changes to inform management of the Rio Grande.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213036","collaboration":"Prepared in cooperation with the National Park Service, Utah State University, Sul Ross State University, World Wildlife Fund, Alpine Test Services, Rio Grande Scientific Support Services, and RiversEdge West","usgsCitation":"Dean, D.J., 2021, A river of change—The Rio Grande in the Big Bend region: U.S. Geological Survey Fact Sheet 2021-3036, 4 p., https://doi.org/10.3133/fs20213036.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-119891","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388703,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3036/fs20213036_spanish.pdf","text":"Report (Spanish version)","size":"4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388702,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3036/fs20213036.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388701,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3036/covrthb.jpg"}],"country":"Mexico, United States","state":"Texas","otherGeospatial":"Rio Grande, Big Bend Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.51293945312499,\n 28.285033294640684\n ],\n [\n -102.3486328125,\n 28.285033294640684\n ],\n [\n -102.3486328125,\n 29.888280933159265\n ],\n [\n -104.51293945312499,\n 29.888280933159265\n ],\n [\n -104.51293945312499,\n 28.285033294640684\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"A common impact on riparian ecosystem function following river regulation is the expansion and encroachment of riparian plant species in the active river channels and floodplain, which reduces flow of water and suspended sediment between the river, riparian area, and upland ecosystems. We characterized riparian plant species occurrence and quantified encroachment within the dam-regulated Colorado River in Grand Canyon, Arizona, USA. We mapped 10 riparian species with high-resolution multispectral imagery and examined effects of river hydrology and geomorphology on the spatial distribution of plant species and open sand. Analysis spanned an image time-series from 2002-2009-2013; a period when plant species and sand were spatially dynamic, and operations of Glen Canyon Dam included daily hydro-peaking and small episodic controlled flood releases. Plant species occurrence and encroachment rates varied with hydrology, geomorphology, and local species pool. Encroachment was greatest on surfaces frequently inundated by hydro-peaking. Seep willow (Baccharis spp.), tamarisk (Tamarix spp.) and arrowweed (Pluchea sericea) were the primary encroaching woody species. Common reed (Phragmites australis) and horsetail (Equisetum xferrissii) were the primary encroaching herbaceous species. Encroachment composition from 2002 to 2009 was similar to the entire riparian landscape, whereas encroachment from 2009 to 2013 primarily consisted of seep willow and early-colonizing herbaceous species. Emergence of seep willow and arrowweed after burial by sand deposited by controlled floods indicated that those species were resilient to this form of disturbance. Describing patterns of species encroachment is an important step towards designing flow regimes that favor riparian species and ecosystem functions valued by stakeholders.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.2344","usgsCitation":"Durning, L., Sankey, J., Yackulic, C., Grams, P.E., Butterfield, B.J., and Sankey, T.T., 2021, Hydrologic and geomorphic effects on riparian plant species occurrence and encroachment: Remote sensing of 360 km of the Colorado River in Grand Canyon: Ecohydrology, v. 14, no. 8, e2344, 21 p., https://doi.org/10.1002/eco.2344.","productDescription":"e2344, 21 p.","ipdsId":"IP-126711","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":389814,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.93920898437499,\n 35.639441068973944\n ],\n [\n -111.33544921874999,\n 35.639441068973944\n ],\n [\n -111.33544921874999,\n 36.94111143010769\n ],\n [\n -113.93920898437499,\n 36.94111143010769\n ],\n [\n -113.93920898437499,\n 35.639441068973944\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Durning, Laura E. 0000-0003-3282-2458","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":177023,"corporation":false,"usgs":false,"family":"Durning","given":"Laura E.","affiliations":[],"preferred":false,"id":823991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":823992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":823993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grams, Paul E. 0000-0002-0873-0708","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":216115,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":823994,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":823995,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":823996,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223485,"text":"sir20215067 - 2021 - Historical hydrologic and geomorphic conditions on the Black River and selected tributaries, Arkansas and Missouri","interactions":[],"lastModifiedDate":"2021-08-31T11:50:23.918631","indexId":"sir20215067","displayToPublicDate":"2021-08-30T13:01:52","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5067","displayTitle":"Historical Hydrologic and Geomorphic Conditions on the Black River and Selected Tributaries, Arkansas and Missouri","title":"Historical hydrologic and geomorphic conditions on the Black River and selected tributaries, Arkansas and Missouri","docAbstract":"The Black River flows through southeast Missouri and northeast Arkansas to its confluence with the White River in Arkansas. The U.S. Army Corps of Engineers operates Clearwater Dam on the Black River and a series of dams in the White River Basin primarily for flood control. In this study, the hydrology and geomorphology of the Black River are examined through an analysis of annual mean and peak discharges at streamgages, a specific stage analysis of stage and discharge at streamgages, and an examination of bathymetric data and aerial imagery. Five streamgages on the Black River were analyzed, in addition to four streamgages on Black River tributaries and one streamgage on the White River, located just downstream from the Black River confluence. The analyses indicated that regulation of discharges at the flood-control dams caused a decrease in the magnitude and variability of the peak discharges at several of the analyzed gages on the Black and White Rivers. Conversely, peak discharges on the Black River have been increasing since water year 2000, though this is not matched by an increase in peak discharges on the White River for the same time period. The specific stage analyses and the available morphologic data generally did not indicate pronounced changes in stage-discharge relations at streamgages on the Black River, with the exception of the gages nearest to Clearwater Dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215067","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"LeRoy, J.Z., Huizinga, R.J., Heimann, D.C., Lindroth, E.M., and Doyle, H.F., 2021, Historical hydrologic and geomorphic conditions on the Black River and selected tributaries, Arkansas and Missouri: U.S. Geological Survey Scientific Investigations Report 2021–5067, 72 p., https://doi.org/10.3133/sir20215067.","productDescription":"Report: ix, 72 p.; Appendix; Dataset","numberOfPages":"86","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114034","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":388646,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5067/images","description":"SIR 2021–5067 Images"},{"id":388645,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5067/sir20215067.xml","text":"Report","size":"298 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5067"},{"id":388644,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388641,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5067/coverthb.jpg"},{"id":388642,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5067/sir20215067.pdf","text":"Report","size":"7.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5067"},{"id":388643,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5067/downloads","text":"Appendix Tables 1.0 through 1.10 (.csv and .xlsx formats)"}],"country":"United States","state":"Arkansas, Missouri","otherGeospatial":"Black River and selected tributaries","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.51611328125,\n 35.585851593232384\n ],\n [\n -89.95605468749999,\n 35.585851593232384\n ],\n [\n -89.95605468749999,\n 37.317751851636906\n ],\n [\n -91.51611328125,\n 37.317751851636906\n ],\n [\n -91.51611328125,\n 35.585851593232384\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Hydrologic processes are often important determinants of successful recruitment of native fishes. However, water management practices can result in abnormal changes in daily and seasonal hydrology patterns. Rarely has fish recruitment across river–reservoir landscapes been considered in relation to flow management, despite the direct relationship between reservoir water management and the resulting upstream and downstream hydrology. We evaluated the relationships between lotic and lentic hydrology and recruitment of two native broadcast-spawning fishes, Freshwater Drum Aplodinotus grunniens and Gizzard Shad Dorosoma cepedianum. Four seasonal periods for each species were identified that related to the species’ spawning biology, from which we derived our remaining hydrology variables. Annual hydrology variables were also considered in our analysis. We developed regression models in conjunction with a model-selection procedure for each species and habitat type based on the catch-curve residuals from fish populations in hydrologically connected river–reservoir systems in the Ozark Highland and Ouachita Mountain ecoregions, USA. Our results indicated that recruitment of reservoir Freshwater Drum was negatively correlated to annual reservoir retention time. In lotic habitats, Freshwater Drum recruitment was positively correlated with prespawn discharge conditions and negatively correlated with annual flow variability. Similarly, riverine Gizzard Shad recruitment was positively correlated to the frequency of high-flow pulses during the spawning period. Our results indicate that releasing reservoir water to best mimic relatively natural flow patterns may benefit some broadcast-spawning species that occupy both lentic and downstream lotic environments, especially during the spring. This information, combined with future efforts on additional spawning guilds, will provide a foundation for developing holistic river–reservoir water-allocation plans.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10692","usgsCitation":"Dattilo, J., Brewer, S.K., and Shoup, D., 2021, Flow dynamics influence fish recruitment in hydrologically connected river-reservoir landscapes: North American Journal of Fisheries Management, v. 41, no. 6, p. 1752-1763, https://doi.org/10.1002/nafm.10692.","productDescription":"12 p.","startPage":"1752","endPage":"1763","ipdsId":"IP-096322","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Oklahoma","otherGeospatial":"Elk River, Grand Lake O’ the Cherokee, Kiamichi River, Sardis Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.372314453125,\n 36.43012234551576\n ],\n [\n -93.80126953124999,\n 36.43012234551576\n ],\n [\n -93.80126953124999,\n 37.142803443716836\n ],\n [\n -95.372314453125,\n 37.142803443716836\n ],\n [\n -95.372314453125,\n 36.43012234551576\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.95458984375,\n 33.779147331286474\n ],\n [\n -94.493408203125,\n 33.779147331286474\n ],\n [\n -94.493408203125,\n 34.488447837809304\n ],\n [\n -95.95458984375,\n 34.488447837809304\n ],\n [\n -95.95458984375,\n 33.779147331286474\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Dattilo, J.","contributorId":274267,"corporation":false,"usgs":false,"family":"Dattilo","given":"J.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":832863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":832865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoup, D. E.","contributorId":242905,"corporation":false,"usgs":false,"family":"Shoup","given":"D. E.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":832864,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224565,"text":"70224565 - 2021 - Groundwater, biodiversity, and the role of flow system scale","interactions":[],"lastModifiedDate":"2022-01-06T17:22:41.248645","indexId":"70224565","displayToPublicDate":"2021-08-28T07:33:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater, biodiversity, and the role of flow system scale","docAbstract":"Groundwater-dependent ecosystems and species (GDEs) are found throughout watersheds at locations of groundwater discharge, yet not all GDEs are the same, nor are the groundwater systems supporting them. Groundwater moves along a variety of flow paths of different lengths and with different contributing areas, ranging from shorter local flow paths with low discharge and large seasonal variability to streams, springs and wetlands to longer regional flow paths with potentially larger discharge and low seasonal variability, commonly at low basin elevations. How does this variation in physical hydrology affect the type and distribution of GDEs? Using data on hypsographic position, groundwater-dependent species distributions, groundwater pumping and streamflow from Oregon, USA, we provide a conceptual model and initial supporting evidence demonstrating that spatial variation in groundwater flow path scales, illustrated using basin hypsography, is a driver of non-random distribution of GDEs across watersheds. Further, we posit that the spatial variation in primary stressors to groundwater (e.g. pumping and climate change) will differentially affect GDEs depending on their hypsographic position. Furthermore, because of their use for irrigation and municipal water supply, regional groundwater systems and associated species are more likely to be studied and receive regulatory protection. Our initial data point to a disproportionate focus on larger discharge, lower elevation GDEs, which leads to a bias in our understanding of the full suite of biodiversity associated with groundwater discharge as well as their stressors and potential mechanisms for protection.
Floods play a critical role in geomorphic change, but whether peak magnitude, duration, volume, or frequency determines the resulting magnitude of erosion and deposition is a question often proposed in geomorphic effectiveness studies. This study investigated that question using digital elevation model differencing to compare and contrast three hydrologically distinct epochs of topographic change spanning 18 years in the 37-km gravel–cobble lower Yuba River in northern California, USA. Scour and fill were analysed by volume at segment and geomorphic reach scales. Each epoch's hydrology was characterized using 15-min and daily averaged flow to obtain distinct peak and recurrence, duration, and volume metrics. Epochs 1 (1999–2008) and 3 (2014–2017) were wetter than average with large floods reaching 3206 and 2466 m3/s, respectively, though of different flood durations. Epoch 2 (2008–2014) was a drought period with only four brief moderate floods (peak of 1245 m3/s). Total volumetric changes showed that major geomorphic response occurred primarily during large flood events; however, total scour and net export of sediment varied greatly, with 20 times more export in epoch 3 compared to epoch 1. The key finding was that greater peak discharge was not correlated with greater net and total erosion; differences were better explained by duration and volume above floodway-filling stage. This finding highlights the importance of considering flood duration and volume, along with peak, to assess flood magnitude in the context of flood management, frequency analysis, and resulting geomorphic changes.
The U.S. Geological Survey, in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring stations in Kansas. This network included 219 real-time streamgages, 12 real-time reservoir-level monitoring stations, and 20 groundwater monitoring stations in water year (WY) 2020. A WY is a 12-month period from October 1 to September 30 and is designated by the calendar year in which it ends. Real-time data are verified by U.S. Geological Survey personnel throughout the year with regular measurements of streamflow, lake levels, and groundwater levels. Hydrologic data collected in real time aid in the understanding of, and decisions made involving, water resources of Kansas. Hydrologic conditions are assessed annually by comparing statistical analyses of current and past WY data for the period of record. The monitoring of hydrologic conditions in Kansas can provide critical information to meet several needs including water-resources management, protection of life and property, reservoir operations, agricultural practices, public supply, ecological assessments, and industrial and recreational purposes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213045","usgsCitation":"Davis, C., 2021, Hydrologic conditions in Kansas, water year 2020: U.S. Geological Survey Fact Sheet 2021–3045, 6 p., https://doi.org/10.3133/fs20213045.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-125662","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":388515,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS Water Data for the Nation"},{"id":388514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3045/fs20213045.pdf","text":"Report","size":"4.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 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U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Evapotranspiration (ET) is the water-vapor flux transported from the surface of the Earth into the atmosphere and is the sum of surface water directly evaporated and subsurface water transpired by plants. ET rates are commonly estimated by using potential or reference ET, which might differ from actual ET rates. Actual evapotranspiration (ETa) rates can be estimated by using the Operational Simplified Surface Energy Balance (SSEBop) model. This report evaluates SSEBop ETa rates at the point and basin scales in Florida and parts of Alabama and Georgia for 2000–17. ETa rates computed by using data from 24 micrometeorological stations in Florida are referred to as mETa rates and were used to quantify biases in the SSEBop ETa rates, stratified by generalized land-use type. Bias was computed as mETa minus SSEBop ETa rates for given generalized land-use types, and bias-correction equations were computed by using least-squares regressions. In addition to mETa rates at station locations, annual average ETa rates calculated from the application of a water-balance method to 55 basins in Florida and parts of Alabama and Georgia were used to assess the accuracy of the annual SSEBop ETa rates at the basin scale. Another independent model used to simulate ETa rates was based on monthly reference ET from the statewide daily reference evapotranspiration (ETo) gridded dataset for Florida computed by using Geostationary Operational Environmental Satellite estimates of solar radiation (GOES ETo). ETa at grid points was computed as monthly GOES ETo multiplied by ratios of monthly mETa to GOES ETo, computed at micrometeorological stations and stratified by each generalized land-use type.
The coefficient of determination (R2) between monthly mETa and SSEBop ETa rates for all stations combined improved from 0.37 before bias correction of SSEBop ETa rates to 0.79 after the bias correction stratified by land-use type. For individual land-uses types, R2 varied from 0.59 for the monthly mETa at a station in the land-use type forest to 0.82 for the monthly mETa at stations in the land-use type shallow-water-table pasture. Root-mean-square error (RMSE) was computed as a function of the difference between SSEBop ETa rates and mETa rates. RMSE of monthly SSEBop ETa rates was 1.27 inches per month before the bias corrections improved to 0.73 inch per month after the bias corrections. RMSE for bias-corrected annual SSEBop ETa rates based on micrometeorological stations with complete years of records ranged from 2.01 inches per year (in/yr) for the land-use type of agriculture to 5.73 in/yr for the land-use type of deep water-table pasture, or 4.96 and 21.21 percent errors relative to annual mETa rates, respectively. Bias-corrected annual SSEBop ETa rates were also compared to annual ETa rates computed by using a water-balance method (wbETa) for 55 basins in Florida. Differences in bias-corrected average annual SSEBop ETa rates and average annual wbETa rates for the 55 basins ranged from −3.67 to 5.29 in/yr (−9.24 to 17.36 percent). RMSE when computed as a function of the differences between annual SSEBop ETa rates and wbETa rates decreased, on average, from 4.13 in/yr for the uncorrected bias SSEBop ETa rates to 1.95 in/yr for the bias-corrected SSEBop rates. The average annual bias-corrected SSEBop ETa rates, from all basins, was 36.46 in/yr or 3.41 percent lower than the average annual wbETa rate of 37.79 inches.
Bias in SSEBop ETa rates varies based on time step (monthly versus annual), scale (point, basin, statewide), and land-use type. Applications to hydrologic models should consider bias relative to the inherent error in models. Bias-corrected SSEBop ETa rates could be used as calibration targets in models of hydrologic processes, such as groundwater models. Annual bias in SSEBop ETa introduced to the model calibration is typically below the margin of error associated with typical residuals in model simulations, depending on scale. Surface-water and groundwater-flow models with RMSEs on the order of a few feet could benefit from bias-corrected SSEBop values of ETa.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215072","collaboration":"Prepared in cooperation with Northwest Florida Water Management District, Suwannee River Water Management District, St. Johns River Water Management District, South Florida Water Management District, Southwest Florida Water Management District, and Tampa Bay Water","usgsCitation":"Sepúlveda, N., 2021, Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17: U.S. Geological Survey Scientific Investigations Report 2021–5072, 66 p., https://doi.org/10.3133/sir20215072.","productDescription":"Report: x, 66 p.; Data Release","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-112971","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":388346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5072/coverthb.jpg"},{"id":388349,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5072/images"},{"id":388347,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5072/sir20215072.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5072"},{"id":388348,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99AB3X4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets of actual evapotranspiration rates from 2000 to 2017 for basins in Florida and parts of Alabama and Georgia, calculated using the water-balance method, the bias-corrected Operational Simplified Surface Energy Balance (SSEBop) model, and the land-use crop coefficients model"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.71484375,\n 25.005972656239187\n ],\n [\n -79.98046875,\n 25.005972656239187\n ],\n [\n -79.98046875,\n 31.98944183792288\n ],\n [\n -87.71484375,\n 31.98944183792288\n ],\n [\n -87.71484375,\n 25.005972656239187\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Wetlands provide ecological functionality by maintaining and promoting regional biodiversity supporting quality habitat for aquatic organisms. Globally, habitat loss, fragmentation and degradation due to increases in agricultural activities and urban development have reduced or altered geographically isolated wetlands, thus reducing biodiversity. The objective of this study was to assess the relative ecological function and integrity of natural ponds, excavated ponds and stormwater basins in the New Jersey Pinelands, located in the northeastern United States by comparing hydrologic conditions, water quality, pesticide concentrations (water, sediment and tissue) and wetland assemblages including amphibians. Twenty-four wetlands were selected based on surrounding land-use and sampled for a variety of abiotic and biotic variables. Abiotic and biotic wetland variables were similar between natural and excavated ponds, with notable differences between the ponds and stormwater basins. Natural and excavated ponds displayed characteristic Pinelands water quality (low pH, high organic carbon, and low pesticide concentrations), exhibited high ecological integrity and supported native ampbibians. Stormwater basins and degraded ponds surrounded by altered land-use exhibited degraded water quality (high pH, high pesticide concentrations) and were dominated by non-native and introduced plants and amphibians. Results from this study can broadly inform resource conservation strategies for amphibians and other communities with a diverse range of habitat requirements, particularly in areas where conservation and development are competing priorities. To conserve biodiversity in changing landscapes, wetlands with similar functionality and land-use characteristics need to be identified and managed to preserve water quality for species of conservation concern.