Embedded within the North American Prairie Pothole Region (PPR) are millions of small, depressional wetlands that annually support 50–80% of the continent’s waterfowl production. We recently assembled evidence that demonstrates a change towards a wetter climate that is driving a shift in the state of the region’s wetland ecosystems. This ecological state-shift has been primarily the result of a sustained wet climate that has influenced timing and magnitude of surface-water inputs to wetlands, connections to groundwater, and inputs of dissolved salts. As climate influences continue to change in the PPR, it is important to understand the potential of these changes to impact wetland habitats important for waterfowl production. Previous model simulations of prairie-pothole wetlands under future climate scenarios projected decreases in the ability of wetlands to facilitate waterfowl production throughout the majority of what is currently the most productive portion of the region. Results from these modeling efforts also suggested that suitable waterfowl breeding-habitat would be limited mostly to the southeastern portion of the PPR, a portion of the region in which most depressional wetlands (> 90%) have been drained. Thus, if these modeled outcomes materialize, a significant restoration effort would be needed in the southeastern PPR to support waterfowl production. However, the models used in earlier efforts were developed from a small number of wetlands using data from a relatively dry period and did not allow for changing mechanisms influencing surface-water, groundwater and dissolved salt inputs to prairie-pothole wetlands.
The primary objective of our research is to improve our understanding of future climate change on impacts to wetland ecosystems and breeding waterfowl habitat in the PPR. We used a newly developed Pothole Hydrology Linked Systems Simulator (PHyLiSS) model to estimate wetland ecosystem responses to 32 distinct climate models under 2 different emissions scenarios. Unlike previous wetland hydrology models, the PHyLiSS model allows for shifting hydrological and geochemical mechanisms influencing wetland ecosystems. We modeled one average-sized seasonal wetland basin at 18 different geographic locations (hereafter “sites”) across the PPR with 3 sites represented for each of 6 ecoregions coincident to early research. We applied the PHyLiSS model using historical daily precipitation and temperature data from 1982–2015 and developed linear models relative to ponded water depth in the simulated wetlands and the observed regional WBPHS May Pond count number for 16 of the 18 sites. Based on the output of 32 climate models and 2 emission scenarios we found a projected change in May pond numbers from -23% to +.02% when comparing the most recent climate period (1989–2018) to the end of the 21st century (2070–2099). We also found no evidence that the distribution of May ponds will shift in the future. These results suggest that management and conservation strategies for wetlands in the PPR should continue to focus on areas where high densities of intact wetland basins support large numbers of breeding duck pairs.
","language":"English","publisher":"Climate Adaptation Science Centers","usgsCitation":"Mushet, D.M., and McKenna, O.P., 2019, Climate-driven state shifts in the Prairie Pothole Region: Assessing future impacts relevant to the management of wetland habitats critical to waterfowl: Final Report, 15 p.","productDescription":"15 p.","ipdsId":"IP-117473","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":396800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381010,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f83509de4b0e84f60868124/5b33be6fe4b040769c172fad"}],"country":"Canada, United States","state":"Alberta, Iowa, Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota","otherGeospatial":"Prairie Potholes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.4248046875,\n 53.25206880589411\n ],\n [\n -110.5224609375,\n 54.265224078605684\n ],\n [\n -115.6640625,\n 55.65279803318956\n ],\n [\n -116.01562499999999,\n 51.645294049305406\n ],\n [\n -113.99414062499999,\n 50.680797145321655\n ],\n [\n -113.115234375,\n 49.296471602658066\n ],\n [\n -113.37890625,\n 48.3416461723746\n ],\n [\n -111.884765625,\n 47.42808726171425\n ],\n [\n -106.4794921875,\n 47.45780853075031\n ],\n [\n -105.908203125,\n 47.87214396888731\n ],\n [\n -104.94140625,\n 47.931066347509784\n ],\n [\n -103.0078125,\n 47.84265762816538\n ],\n [\n -101.689453125,\n 47.42808726171425\n ],\n [\n -100.546875,\n 46.558860303117164\n ],\n [\n -100.5908203125,\n 45.058001435398275\n ],\n [\n -100.94238281249999,\n 44.59046718130883\n ],\n [\n -99.7119140625,\n 43.99281450048989\n ],\n [\n -98.8330078125,\n 42.84375132629021\n ],\n [\n -98.3056640625,\n 41.0130657870063\n ],\n [\n -96.8115234375,\n 40.91351257612758\n ],\n [\n -96.6357421875,\n 42.48830197960227\n ],\n [\n -97.0751953125,\n 43.26120612479979\n ],\n [\n -96.6357421875,\n 43.99281450048989\n ],\n [\n -94.9658203125,\n 42.90816007196054\n ],\n [\n -94.1748046875,\n 41.64007838467894\n ],\n [\n -93.42773437499999,\n 40.91351257612758\n ],\n [\n -91.7138671875,\n 41.21172151054787\n ],\n [\n -91.8896484375,\n 42.391008609205045\n ],\n [\n -93.33984375,\n 44.49650533109348\n ],\n [\n -94.1748046875,\n 44.33956524809713\n ],\n [\n -94.306640625,\n 45.089035564831036\n ],\n [\n -95.00976562499999,\n 45.55252525134013\n ],\n [\n -95.8447265625,\n 46.164614496897094\n ],\n [\n -96.1083984375,\n 47.54687159892238\n ],\n [\n -95.09765625,\n 47.87214396888731\n ],\n [\n -95.09765625,\n 48.3416461723746\n ],\n [\n -95.5810546875,\n 51.26191485308451\n ],\n [\n -96.416015625,\n 51.31688050404585\n ],\n [\n -96.7236328125,\n 50.20503326494332\n ],\n [\n -96.9873046875,\n 50.3734961443035\n ],\n [\n -96.8115234375,\n 51.590722643120145\n ],\n [\n -98.5693359375,\n 52.669720383688166\n ],\n [\n -99.7119140625,\n 52.348763181988105\n ],\n [\n -101.29394531249999,\n 52.802761415419674\n ],\n [\n -101.6015625,\n 52.16045455774706\n ],\n [\n -100.5029296875,\n 51.6180165487737\n ],\n [\n -99.7119140625,\n 51.04139389812637\n ],\n [\n -99.6240234375,\n 50.62507306341435\n ],\n [\n -100.634765625,\n 50.54136296522161\n ],\n [\n -101.42578124999999,\n 50.98609893339354\n ],\n [\n -102.0849609375,\n 52.05249047600099\n ],\n [\n -103.7548828125,\n 52.72298552457069\n ],\n [\n -105.4248046875,\n 53.25206880589411\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":806183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":806182,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229332,"text":"70229332 - 2019 - Nest survival of Black-necked Stilts (Himantopus mexicanus) on the upper Texas coast, USA","interactions":[],"lastModifiedDate":"2022-03-04T14:50:30.412736","indexId":"70229332","displayToPublicDate":"2019-10-16T08:37:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Nest survival of Black-necked Stilts (Himantopus mexicanus) on the upper Texas coast, USA","title":"Nest survival of Black-necked Stilts (Himantopus mexicanus) on the upper Texas coast, USA","docAbstract":"The Black-necked Stilt (Himantopus mexicanus) is a migratory shorebird of temperate and tropical America. Declining wetland quality and associated declines in hydrological integrity may contribute to widespread habitat loss for stilts nesting on the upper Texas Gulf of Mexico coast of the USA, as both fresh and brackish marshes are converting to open water and saline marsh. Nests (n = 356) were monitored in three wetland types on the upper Texas coast from 21 April-30 June 2011-2012. Of these 356 nests, 151 were located in managed freshwater wetlands (16 in 2011 and 135 in 2012), 128 were located in managed intermediate wetlands (75 in 2011 and 53 in 2012), and 77 were located in rice fields (all in 2012). Collectively, nest success was 0.2% (0 in rice fields and as high as 4.3% in freshwater wetlands in 2012), among the lowest ever reported for the species. The most frequent cause of nest failure was predation by mammalian and avian predators (∼50%). Daily nest survival rate was positively related to mudflat nesting substrates and negatively related to colony size, rice field, and brackish coastal wetland habitats. Future efforts to minimize edge effects in managed wetlands may prove valuable to improve nest success of stilts and other species that nest in similar wetland types.
","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.042.0302","usgsCitation":"Riecke, T., Conway, W.C., Haukos, D.A., Moon, J.A., and Comer, C.E., 2019, Nest survival of Black-necked Stilts (Himantopus mexicanus) on the upper Texas coast, USA: Waterbirds, v. 42, no. 3, p. 261-271, https://doi.org/10.1675/063.042.0302.","productDescription":"11 p.","startPage":"261","endPage":"271","ipdsId":"IP-094016","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Chambers County","otherGeospatial":"Anahuac National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.59709167480467,\n 29.561512529746743\n ],\n [\n -94.5703125,\n 29.570471069643638\n ],\n [\n -94.559326171875,\n 29.574054263095935\n ],\n [\n -94.54765319824219,\n 29.57106827738255\n ],\n [\n -94.5428466796875,\n 29.562707047643396\n ],\n [\n -94.53254699707031,\n 29.554942428835226\n ],\n [\n -94.52774047851562,\n 29.547774558769333\n ],\n [\n -94.52430725097656,\n 29.538216607905866\n ],\n [\n -94.49821472167969,\n 29.54419043309506\n ],\n [\n -94.49272155761719,\n 29.55076123308153\n ],\n [\n -94.47555541992188,\n 29.554942428835226\n ],\n [\n -94.46731567382812,\n 29.552553195302863\n ],\n [\n -94.45289611816406,\n 29.563304301294636\n ],\n [\n -94.42405700683594,\n 29.566290516578164\n ],\n [\n -94.41444396972656,\n 29.575248632655892\n ],\n [\n -94.41444396972656,\n 29.59375955382993\n ],\n [\n -94.39933776855469,\n 29.596147812456916\n ],\n [\n -94.38972473144531,\n 29.59435662378732\n ],\n [\n -94.37255859375,\n 29.6361427369564\n ],\n [\n -94.37461853027344,\n 29.6773149449586\n ],\n [\n -94.46868896484375,\n 29.67850809103362\n ],\n [\n -94.54421997070312,\n 29.660012735895606\n ],\n [\n -94.59846496582031,\n 29.676121784724305\n ],\n [\n -94.59709167480467,\n 29.561512529746743\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Riecke, Thomas V.","contributorId":171482,"corporation":false,"usgs":false,"family":"Riecke","given":"Thomas V.","affiliations":[],"preferred":false,"id":837061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Warren C.","contributorId":51550,"corporation":false,"usgs":true,"family":"Conway","given":"Warren","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":837060,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moon, Jena A.","contributorId":171483,"corporation":false,"usgs":false,"family":"Moon","given":"Jena","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":837059,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Comer, Christopher E.","contributorId":166690,"corporation":false,"usgs":false,"family":"Comer","given":"Christopher","email":"","middleInitial":"E.","affiliations":[{"id":32360,"text":"Stephen F. Austin State University, Nacogdoches, TX","active":true,"usgs":false}],"preferred":false,"id":837058,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205944,"text":"sir20195108 - 2019 - Spatial and temporal distribution of bacterial indicators and microbial-source tracking within Tumacácori National Historical Park and the upper Santa Cruz River, southern Arizona and northern Mexico, 2015–2016","interactions":[],"lastModifiedDate":"2019-10-15T14:56:51","indexId":"sir20195108","displayToPublicDate":"2019-10-15T09:30:42","publicationYear":"2019","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":"2019-5108","displayTitle":"Spatial and Temporal Distribution of Bacterial Indicators and Microbial-Source Tracking within Tumacácori National Historical Park and the Upper Santa Cruz River, Southern Arizona and Northern Mexico, 2015–2016","title":"Spatial and temporal distribution of bacterial indicators and microbial-source tracking within Tumacácori National Historical Park and the upper Santa Cruz River, southern Arizona and northern Mexico, 2015–2016","docAbstract":"Tumacácori National Historical Park (TUMA) in southern Arizona protects the culturally important Mission San José de Tumacácori, while also managing a part of the ecologically diverse riparian corridor of the Santa Cruz River. The quality of the water flowing through depends solely on upstream watershed activities, and among the water-quality issues concerning TUMA is the microbiological pathogens in the river introduced by human and animal sources that pose a significant human health risk to employees and visitors. The U.S. Geological Survey (USGS) conducted a 3-year study to understand the sources, timing, and distribution of the fecal-indicator bacteria Escherichia coli (E. coli) within TUMA and the upstream watershed.
The information provided in this investigation is a result of a comprehensive approach to quantify the spatial and temporal variability of E. coli and suspended sediment in the Upper Santa Cruz River Watershed. Several types of flow were sampled from base flow to flood flow and at high frequency intervals (rise, peak, and recession) to determine daily variability, as well as seasonal variability. Hydrologic data collection and estimation techniques were used to establish a hydrologic relation with E. coli and suspended sediment. Furthermore, source tracking was used to describe the potential sources of E. coli. Models were developed that are expected to be useful for predicting E. coli concentrations to help TUMA managers understand instantaneous conditions to keep the public and staff informed about potentially harmful water-quality conditions. In addition, the concentration, flux, and source information will provide more accurate data for other surface-water modeling and can be useful in the development of total maximum daily load standards. This will help TUMA describe the water-quality conditions at the park and waters flowing through the park, as well as prioritize and help carry out future best-management actions to address these issues.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Floodplain systems in the Driftless Area have experienced widespread historical transformations in hydrologic and sediment characteristics as well as rates of hydrogeomorphic processes. These changes exceed natural variability experienced during the Holocene and are driven by nearly two centuries of major land-cover alterations coupled with shifting precipitation patterns. On the pre–Euro-American landscape, tributaries to the Upper Mississippi River had clear, constant base flow and low sedimentation rates due to a protective cover of prairie, oak savanna, and woodland. The Upper Mississippi River was sandy and braided, with geomorphologically diverse backwaters, side channels, and vegetated islands. Soil erosion and gullying caused by agriculture-related land clearance have had the largest historical effects on Upper Mississippi River tributary stream morphology and floodplain sedimentation. Floodplain sedimentation rates for tributaries and the Upper Mississippi River were 0.2 and 0.9 mm/yr, respectively, before Euro-American settlement, compared to 2–20 and 5–20 mm/yr after Euro-American settlement, respectively. The soil conservation movement had its birthplace in the Driftless Area in the 1920s because of the region’s widespread landscape degradation. As soil erosion decreased and gullies were stabilized in the middle to late twentieth century, land management efforts turned toward the lingering problem of fine-grained, phosphorus-rich sediment stored in tributary floodplains and channels. This trend has been complicated by a climatic shift in the late twentieth century toward increased annual precipitation, increased flood variability, and more floods in late fall and winter months, when bare fields are vulnerable to runoff. Floods are major contributors to channel erosion and deposition, and variability in magnitudes and frequency will likely continue in the early twenty-first century. Restoration efforts in tributaries have included reducing bank erosion, reconnecting floodplains, and adding trout habitat features. Lock and dam structures have altered sediment transport and erosion processes within the Upper Mississippi River, and restoration efforts there have focused on creation and rehabilitation of islands and protection of remnant off-channel backwater habitats.
Digital flood-inundation maps for a 4-mile reach of Nimishillen Creek near North Industry, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Muskingum Watershed Conservancy District, Ohio, and the Stark County Board of Commissioners. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping (FIM) Program website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on Nimishillen Creek at North Industry, Ohio (station number 03118500). Near-real-time stages at this streamgage can be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site.
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated to the current stage-discharge relation at the streamgage on Nimishillen Creek at North Industry and documented high-water marks from the flood of January 12, 2017.
The hydraulic model was then used to compute seven water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 8 to 14 ft, which is from “action stage” to above “major flood stage” as reported by the National Weather Service. The simulated water-surface profiles were then used in combination with a geographic information system (GIS) digital elevation model derived from light detection and ranging data to delineate the areas flooded at each water level.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts. Forecasts for the USGS streamgage on Nimishillen Creek at North Industry, Ohio are issued as needed during times of high water, but are not routinely available (National Weather Service, 2017).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195083","collaboration":"Prepared in cooperation with the Muskingum Watershed Conservancy District, Ohio, and the Stark County Board of Commissioners","usgsCitation":"Whitehead, M.T., 2019, Flood-inundation maps for Nimishillen Creek near North Industry, Ohio, 2019: U.S. Geological Survey Scientific Investigations Report 2019–5083, 11 p., https://doi.org/10.3133/sir20195083.\n","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-104812","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368076,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WFOVN2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and hydraulic model for flood-inundation maps of Nimishillen Creek near North Industry, Ohio:"},{"id":368075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5083/sir20195083.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5083"},{"id":368074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5083/coverthb.jpg"}],"country":"United States","state":"Ohio","county":"Stark County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.0864,40.9879],[-81.0865,40.9839],[-81.0866,40.978],[-81.0869,40.9013],[-81.0873,40.728],[-81.0922,40.7285],[-81.1001,40.7281],[-81.1989,40.7292],[-81.1991,40.7224],[-81.2373,40.7237],[-81.241,40.6507],[-81.2755,40.651],[-81.2791,40.6511],[-81.304,40.6518],[-81.3173,40.6519],[-81.4372,40.6529],[-81.4365,40.6584],[-81.4395,40.6625],[-81.4467,40.6657],[-81.4589,40.6654],[-81.4675,40.6555],[-81.6489,40.6346],[-81.6491,40.6681],[-81.6483,40.7371],[-81.648,40.9145],[-81.4201,40.9064],[-81.4164,40.9889],[-81.3932,40.9887],[-81.1059,40.9882],[-81.0925,40.988],[-81.0864,40.9879]]]},\"properties\":{\"name\":\"Stark\",\"state\":\"OH\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard
Columbus OH 43229–1753
Dryland grasslands are vast and globally important and, as in all terrestrial ecosystems, soil microbial communities play fundamental roles in regulating dryland ecosystem function. A typical characteristic of drylands is the spatial mosaic of vascular plant cover surrounded by interspace soils, where biological soil crusts (biocrusts)—a complex community of organisms including bacteria, fungi, algae, mosses, and lichens—are common. The implications of this heterogeneity, where plants and biocrust cover co-occur, are often explored in the context of soil fertility and hydrology, but rarely has the impact of these multiple microhabitat types been simultaneously explored to determine the influence on bacterial and fungal communities, key biological players in these ecosystems. Further, our understanding of the temporal dynamics of bacterial and fungal communities in grasslands, and of how these dynamics depend on the microhabitat within the ecosystem, is notably poor. Here we used a temporally and spatially explicit approach to assess bacterial and fungal communities in a grassland on the Colorado Plateau, and to link variation in these communities to edaphic characteristics. We found that microhabitat (e.g., vascular plant rhizosphere, biocrust, and below biocrust) was the strongest driver of differences in bacterial and fungal community richness, diversity, and composition. Microhabitat type also significantly mediated the impact of temporal change in shaping community composition. Taken together, 29% of the variation in bacterial community composition could be explained by microhabitat, date, and microhabitat-by-date interactions, while only 11% of the variation in fungal community composition could be explained by the same factors, suggesting important differences in community assembly processes. Soil microbial communities dictate myriad critical ecosystem functions, thus understanding the factors that control their compostition is crucial to considering and forecasting how terrestrial ecosystems work. Overall, this case study provides insights for future studies on the spatial and temporal dynamics of bacterial and fungal communities in dryland grasslands.
The U.S. Geological Survey, in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring stations in Kansas. In water year 2018, this network included 219 real-time streamgages. A water year is the 12-month period from October 1 through September 30 and is designated by the calendar year in which it ends. Real-time data are calibrated and validated by U.S. Geological Survey personnel throughout the year with regular measurements of streamflow, streamgage height, and lake levels. These data and accompanying analyses provide an overview of hydrologic conditions in Kansas and help advance the understanding of water resources in the State. Annual assessments of hydrologic conditions are made by comparing statistical analyses of current and past water year data for the period of record. Long-term monitoring of hydrologic conditions in Kansas provides imperative information for protecting human life and property, managing water supplies, forecasting floods, operating reservoirs, designing bridges and culverts, processing interstate and intrastate water rights claims, forecasting ecological conditions, and many other uses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193042","usgsCitation":"Unrein, A.H., 2019, Hydrologic conditions in Kansas, water year 2018: U.S. Geological Survey Fact Sheet 2019–3042, 4 p., https://doi.org/10.3133/fs20193042.\n","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-106493","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":368023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3042/fs20193042.pdf","text":"Report","size":"4.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3042"},{"id":368022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3042/coverthb.jpg"}],"country":"United 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U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The U.S. Geological Survey collaborated with the U.S. Army Corps of Engineers, Louisville District, on a synoptic study of water quality at Nolin River Lake during August 2016. The purpose of the study was to develop a better understanding of the potential for interaction between groundwater and surface water at Nolin River Lake, Kentucky. Groundwater can have properties that are measurably different from those in adjacent surface water, and inflows and outflows can be an important component of water quality and quantity. An improved understanding of potential interaction of groundwater and surface water at Nolin River Lake may be used to refine lake-management strategies. This study (1) compiled and interpreted existing information to characterize the hydrogeological setting and implications for potential interaction of groundwater and surface water in the Nolin River Lake watershed; (2) collected transects of onsite water-quality parameters using an autonomous underwater vehicle (AUV) in areas with potential for interaction of groundwater and surface water, including five sites on Nolin River Lake and one site on the Nolin River; and (3) collected discrete water-quality and phytoplankton community data at the same six sites.
A review of existing hydrogeologic information did not indicate the presence of karst features adjacent to or beneath Nolin River Lake that would facilitate groundwater interaction with the reservoir. Observations leading to this conclusion include (1) limestone that is adjacent to the shoreline and perhaps beneath the lake, is overlain with siliciclastic rocks and fine-grained sediment that inhibits infiltration and development of karst features that encourage rapid groundwater flow; (2) the geologic deposits surrounding the reservoir are described as having limited or no potential for development of karst features, some exceptions may exist in tributary valleys; (3) very few karst features were mapped within 1 mile of the reservoir or in the area currently occupied by the reservoir; and (4) faults that intersect the reservoir but may not possess hydraulic properties that cause the faults to be conduits for groundwater flow. Groundwater interaction with reservoir tributaries is likely more common in areas of the watershed upstream from Nolin River Lake where karst hydrogeology is prevalent.
Results of water-quality surveys using an AUV from August 15 to 19, 2016, did not identify areas of anomalous values that might indicate groundwater inflows through preferential flow zones. Spatial distributions of water-quality parameters were generally uniform within each constant-depth layer. The constant-depth layers were selected to be above, within, and below the thermocline and ranged from the water surface to 25 feet. Surveys near the bottom of the reservoir that might have been more sensitive to groundwater inflows were not done because presurvey data were not available to indicate locations of obstacles that could ensnare the AUV. Water-quality data collected with the AUV did identify water-quality anomalies where stream tributaries were discharging to the reservoir.
The discrete water-quality samples indicated uniformity among the five reservoir sites. The riverine site that is immediately upstream from Nolin River Lake, however, had some unique water-quality characteristics relative to sites on the reservoir. The highest concentrations of nitrate plus nitrite as nitrogen (0.145 milligrams per liter [mg/L]), total phosphorous (0.07 mg/L), chlorophyll a (36.1 micrograms per liter), and pheophytin a (10.2 micrograms per liter) were measured at the Nolin River Lake riverine site (site 2NRR20034). The concentrations of nutrients and chlorophyll a at the riverine site did exceed the 25th percentile of median concentrations measured by the U.S. Environmental Protection Agency (EPA) at other lakes and reservoirs in EPA level IV ecoregion 71a. Concentrations of most nutrients and chlorophyll a at the five reservoir sites also exceeded the 25th percentile of median concentrations in EPA level IV ecoregion 72h. The exception was the concentrations of total phosphorus as phosphorus at the reservoir sites that were at or below the 25th percentile of median concentrations measured by EPA (0.03 mg/L). Concentrations of orthophosphate as phosphorus were less than the method detection limit of 0.004 mg/L at all sites. The phytoplankton community in Nolin River Lake was almost exclusively (greater than 90 percent of total phytoplankton abundance) cyanobacteria, also known as blue-green algae. A species of Cylindrospermopsis dominated the cyanobacterial community at the five reservoir sites, while Chroococcus microscopicus was most abundant at the riverine site. Cyanobacterial cell densities ranged from 10,000 to 198,067,460 cells per liter in five areas in the reservoir and from 4,800 to 73,751,253 cells per liter at the riverine site.
Multiple potential sources of water to Nolin River Lake include direct precipitation, overland flow, interflow, groundwater, and surface water. Understanding the exact contribution of each of these components to the water budget at Nolin River Lake may help the U.S. Army Corps of Engineers manage the water quality, water quantity, and biological communities in the reservoir. Additional hydrogeologic and water-quality data that builds on the results of this study may refine the inferences of this study; for example, deeper AUV surveys that target the largest fault zones might further the understanding of the potential for groundwater flow through those features. A complete understanding of the reservoir hydrology, however, may require the use of scientific methods intended for water bodies as large as Nolin River Lake, such as aerial infrared photography and imagery; water mass, chemical, and isotopic balance studies; geophysical measurements; and numerical simulations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195075","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Louisville District","usgsCitation":"Crain, A.S., Boldt, J.A., Bayless, E.R., Bunch, A.R., Young, J.L., Thomason, J.C., and Wolf, Z.L., 2019, Potential interaction of groundwater and surface water including autonomous underwater vehicle reconnaissance at Nolin River Lake, Kentucky, 2016: U.S. Geological Survey Scientific Investigations Report 2019–5075, 36 p., https://doi.org/10.3133/sir20195075.\n","productDescription":"Report: vi, 36 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-085091","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":367882,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5075/sir20195075.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5075"},{"id":367881,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5075/coverthb.jpg"},{"id":367883,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F798857D","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-Quality Datasets from Synoptic Surveys in Nolin River Lake, Kentucky, using an Autonomous Underwater Vehicle, Discrete Sampling, and Depth Profiles, August 2016"}],"country":"United States","state":"Kentucky","otherGeospatial":"Nolin River Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.28387451171875,\n 37.25929865437848\n ],\n [\n -86.0504150390625,\n 37.25929865437848\n ],\n [\n -86.0504150390625,\n 37.40780092202727\n ],\n [\n -86.28387451171875,\n 37.40780092202727\n ],\n [\n -86.28387451171875,\n 37.25929865437848\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299–1906
Coastal droughts have a different dynamic than upland droughts, which are typically characterized by agricultural, hydrologic, meteorological, and (or) socioeconomic effects. Drought uniquely affects coastal ecosystems because of changes in the salinity conditions of estuarine creeks and rivers. The location of the freshwater-saltwater interface in surface-water bodies is an important factor in the ecological and socioeconomic dynamics of coastal communities. To address the data and information gap for characterizing coastal drought, the Coastal Salinity Index (CSI) was developed by using salinity data. The CSI uses a computational approach similar to the Standardized Precipitation Index. The CSI can be computed for unique time intervals (for example 1-, 6-, 12-, and 24-month intervals) to characterize short- and long-term drought (saline) conditions, as well as wet (high freshwater inflow) conditions.
To encourage the use of the CSI in current and future research endeavors, this investigation addressed three activities to enhance the use and application of the CSI. First, a software package was developed for the consistent computation of the CSI that includes preprocessing of salinity data, filling missing data, computing the CSI, post-processing, and generating the supporting metadata. This software package is available for download from the U.S. Geological Survey GitLab repository. Second, the CSI has been computed at sites along the southeastern Atlantic coast (Florida to North Carolina) and the Gulf of Mexico (Texas to Florida) to increase the opportunity for linking the CSI to ecological response data. Third, using telemetered salinity data, the real-time computation of the CSI has been prototyped and disseminated on the web.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191090","collaboration":"Prepared in cooperation with the National Integrated Drought Information System","usgsCitation":"Petkewich, M.D., Lackstrom, K., McCloskey, B.J., Rouen, L.F, and Conrads, P.A., 2019, Coastal Salinity Index along the southeastern Atlantic coast and the Gulf of Mexico, 1983 to 2018 (ver. 1.1, April 2023): U.S. Geological Survey\nOpen-File Report 2019–1090, 26 p., https://doi.org/10.3133/ofr20191090.","productDescription":"Report: vi, 26 p.; Appendix; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105920","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":499716,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109078.htm","linkFileType":{"id":5,"text":"html"}},{"id":415336,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1090/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":415335,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1090/ofr20191090_appendix1.pdf","text":"Appendix 1","size":"1.68 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1090 Appendix 1","linkHelpText":"—Coastal Salinity Index User Guide"},{"id":415334,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1090/ofr20191090.pdf","text":"Report","size":"3.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1090"},{"id":367860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1090/coverthb2.jpg"},{"id":367858,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MQLNL2","text":"USGS data release","linkHelpText":"Coastal Salinity Index for Monitoring Drought"}],"country":"United States","state":"Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, Puerto Rico, South Carolina, Texas","otherGeospatial":"Gulf of Mexico Coast, South Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.5439453125,\n 17.45547257997284\n ],\n [\n -65.21484375,\n 17.45547257997284\n ],\n [\n -65.21484375,\n 18.95824648598139\n ],\n [\n -67.5439453125,\n 18.95824648598139\n ],\n [\n -67.5439453125,\n 17.45547257997284\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 24.487148563173424\n ],\n [\n -75.0146484375,\n 24.487148563173424\n ],\n [\n -75.0146484375,\n 36.38591277287651\n ],\n [\n -98.61328125,\n 36.38591277287651\n ],\n [\n -98.61328125,\n 24.487148563173424\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: October 1, 2019; Version 1.1: April 6, 2023","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
The Anderson Ranch property (study area), located in Taos County, north-central New Mexico, was transferred from Chevron Mining, Inc. (CMI) to the Bureau of Land Management (BLM) as part of a Natural Resource Damage Assessment and Restoration (NRDAR) court-ordered settlement. The study area supports freshwater emergent wetlands and freshwater ponds. The settlement states that CMI will provide the land and a monetary settlement to support the restoration of the wetlands on the property. To best manage the study area, the BLM requires an understanding of potential effects of climate variability and groundwater withdrawals on the wetland function. This study, completed by the U.S. Geological Survey in cooperation with the BLM, provides an initial hydrologic characterization of the study area, which included literature review, collection of groundwater-level and aqueous-chemistry data, completion of a vegetation survey, and preliminary data analysis. The data compiled, collected, and analyzed as part of this study indicate that the wetlands within the study area are groundwater fed and that the water maintaining the wetlands is modern. Surface-water levels in the pond and groundwater levels in the surrounding wetland fluctuate seasonally. The hydraulic gradient in the study area is from northeast to southwest. Evapotranspiration is a main driver of water demand within the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191100","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Galanter, A.E., Shephard, Z.M., and Herrera-Olivas, P., 2019, Anderson Ranch wetlands hydrologic characterization in Taos County, New Mexico: U.S. Geological Survey Open-File Report 2019–1100, 42 p., https://doi.org/10.3133/ofr20191100. ","productDescription":"iii, 42 p. 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U.S. Geological Survey
6700 Edith Blvd. NE, Suite B
Albuquerque, NM 87113