Beaver-related restoration (BRR) has gained popularity as a means of improving stream ecosystems, but the effects are not fully understood. Studies of dissolved oxygen (DO) and water temperature, key water quality metrics for salmonids, have demonstrated improved conditions in some cases, but warming and decreased DO have been more commonly reported in meta-analyses. These results point to the contingencies that can influence outcomes from BRR. We examined water quality related to beaver ponds in a diverse coastal watershed (Umpqua River Basin, OR, USA). We monitored water temperature 0–400 m above and below beaver ponds and at pond surfaces and bottoms across seven study sites from June through September of 2019. DO was also recorded at two sites at pond surfaces and pond bottoms. Downstream monthly mean daily maximum temperatures were warmer than upstream reference locations by up to 1.9°C at beaver dam outlets but this heating signal attenuated with downstream distance. Downstream warming was greatest in June and July and best predicted by pond bottom temperatures. DO at pond surfaces and bottoms were hypoxic (≤5 mg/L) for more than half of the 32-day monitoring period. Water temperatures increased for short distances below monitored beaver ponds and observed oxygen conditions within ponds were largely unsuitable for salmonid fishes. These findings contrast with some commonly stated expectations of BRR, and we recommend that managers consider these expectations prior to implementation. In some cases, project goals may override water quality concerns but in streams where temperature or DO restoration are objectives, managers may consider using BRR techniques with caution.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.2430","usgsCitation":"Stevenson, J.R., Dunham, J.B., Wondzell, S., and Taylor, J.D., 2022, Dammed water quality — Longitudinal stream responses below beaver ponds in the Umpqua River Basin, Oregon: Ecohydrology, v. 15, no. 4, e2430, 16 p., https://doi.org/10.1002/eco.2430.","productDescription":"e2430, 16 p.","ipdsId":"IP-134551","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":402093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Umpqua River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.21005249023438,\n 43.6599240747891\n ],\n [\n -124.09469604492186,\n 43.60923380393403\n ],\n [\n -123.92715454101562,\n 43.520671902437606\n ],\n [\n -123.81866455078125,\n 43.38608793041562\n ],\n [\n -123.90106201171875,\n 43.24420236973\n ],\n [\n -123.59619140625001,\n 43.111009147075116\n ],\n [\n -122.72003173828124,\n 43.159112387154174\n ],\n [\n -122.61154174804686,\n 43.206176810164784\n ],\n [\n -122.34374999999999,\n 43.345154990451135\n ],\n [\n -122.32177734375,\n 43.560491112629286\n ],\n [\n -122.36572265625,\n 43.82660134505382\n ],\n [\n -122.72277832031251,\n 44.134913443750726\n ],\n [\n -123.0743408203125,\n 44.25306865928177\n ],\n [\n -123.43688964843749,\n 44.26093725039923\n ],\n [\n -123.77197265625,\n 44.166444664458595\n ],\n [\n -124.09057617187499,\n 44.044167353572185\n ],\n [\n -124.21005249023438,\n 43.6599240747891\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-06-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Stevenson, John R.","contributorId":147936,"corporation":false,"usgs":false,"family":"Stevenson","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":844547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":844548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wondzell, Steve M.","contributorId":236920,"corporation":false,"usgs":false,"family":"Wondzell","given":"Steve M.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":844549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, Jimmy D.","contributorId":140178,"corporation":false,"usgs":false,"family":"Taylor","given":"Jimmy","email":"","middleInitial":"D.","affiliations":[{"id":13402,"text":"USDA APHIS Wildlife Services","active":true,"usgs":false}],"preferred":false,"id":844550,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232196,"text":"70232196 - 2022 - Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data","interactions":[],"lastModifiedDate":"2022-06-13T15:44:24.981546","indexId":"70232196","displayToPublicDate":"2022-06-13T10:31:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data","docAbstract":"Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll-a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon, and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella, a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.
Satellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250-m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant (p < 0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17-year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring.
This paper develops a barometer that indexes water quality in the Chesapeake Bay and summarizes quality over spatial regions and temporal periods. The barometer has a basis in risk assessment and hydrology, and is a function of three different metrics of water quality relative to numerical criteria: relative frequency of criterion attainment; magnitude of deviation from a numerical criterion; and duration of criterion attainment. Metrics associated with these features are calculated at the station level, allowing flexibility for simultaneously evaluating multiple stressors, different designated uses, and physical characteristics of the water. The barometer score is then created as a geometric mean of the three metrics. The water quality barometer (WQB) station scores may be spatially aggregated to report habitat scores across a spectrum of spatial resolutions (e.g., management segment, tidal subsystem, or the whole tidal bay). Dissolved oxygen measurements in the Chesapeake Bay collected during summer seasons of 1985 to 2020 are used to evaluate water quality. The WQB score and its bootstrapped confidence interval are reported at the station, segment, tidal subsystem and whole tidal bay levels. Notably, water quality interpreted through application of the WQB with dissolved oxygen concentration data and averaged over the 29-year period of record is good (i.e. protects aquatic living resources) in tributaries such as the James River, Rappahannock River and others; but is not as good in areas such as the Upper Tributaries and the York River. Recent summaries indicate that while the water quality is improving in much of the bay and its tidal tributaries, however, there is an indication of decline in quality in the period 20182020, especially in the upper regions of the Bay. The barometer is designed around using the time series data produced by the Chesapeake Bay Programs annual monitoring strategy; the approach has application to other large water bodies with large scale monitoring programs with extended time series or for integrating information from environmental sensor systems.
Increasing demand for river water now conflicts with an increasing desire to maintain riparian ecosystems. Efficiently managing river flows for riparian vegetation requires an understanding of the time scale of flow effects, but this information is limited by the absence of long-term studies of vegetation change in response to flow variation. To investigate the influence of short- and long-term flow variability and dam operation on riparian vegetation, we determined the occurrence of 107 plant species in 133 permanent plots of known inundating discharge along the Gunnison River in Colorado on five different occasions between 1990 and 2013. Individual species moved up and down the gradient of inundating discharge coincident with increases and decreases in mean annual flow, and the correlations between flow and species occurrence were strongest when flows were weighted by time before vegetation sampling with a median half-life of 1.5 years. Some tall, rhizomatous, perennial species, however, responded to flows on a longer time scale. Logistic regression of species occurrence showed a significant relation with inundation duration for 70 out of 107 species. Plot species richness and total vegetative cover decreased in association with desiccation at low inundation durations and with fluvial disturbance at high inundation durations. Within-plot similarity in species occurrence between years decreased strongly with increasing inundation duration. Moderate inundation durations were dominated by tall, rhizomatous, perennial herbs, including invasive Phalaris arundinacea (reed canary grass). Over the 23-year study period, species richness declined, and the proportion of rhizomatous perennials increased, consistent with the hypothesis that decreases in flow peaks and increases in low flows caused by flow regulation have decreased establishment opportunities for disturbance-dependent species. In summary, annual-scale changes in vegetation were strongly influenced by flow variation, and decadal-scale changes were influenced by decreases in fluvial disturbance from upstream flow regulation beginning decades prior to the onset of this study.
Understanding effects of climate change on nitrogen fate and transport in the environment is critical to nutrient management. We used climate projections within a previously calibrated spatially referenced regression (SPARROW) model to predict effects of expected climate change over 1995 through 2025 on total nitrogen fluxes to Chesapeake Bay and in watershed streams. Assuming nitrogen inputs and other watershed conditions remain at 2012 levels, effects of increasing temperature, runoff, streamflow, and stream velocity expected between 1995 and 2025 will include an estimated net 6.5% decline in annual nitrogen delivery to the bay from its watershed. This predicted decline is attributable to declines in the delivery of nitrogen from upland nonpoint sources to streams due to predicted warmer temperatures. Such temperature-driven declines in the delivery of nitrogen to streams more than offset predicted increased delivery to and within streams due to increased runoff and streamflow and may be attributable to increasing rates of denitrification or ammonia volatilization or to changes in plant phenology. Predicted climate-driven declines in nitrogen flux are generally similar across the watershed but vary slightly among major nonpoint source sectors and tributary watersheds. Nitrogen contributions to the bay from point sources are not affected by temperature-driven changes in delivery from uplands and are therefore predicted to increase slightly between 1995 and 2025.
","language":"English","publisher":"American Water Resources Association","doi":"10.1111/1752-1688.13017","usgsCitation":"Ator, S., Schwarz, G.E., Sekellick, A.J., and Bhatt, G., 2022, Predicting near-term effects of climate change on nitrogen transport to Chesapeake Bay: Journal of the American Water Resources Association, v. 58, no. 4, p. 578-596, https://doi.org/10.1111/1752-1688.13017.","productDescription":"19 p.","startPage":"578","endPage":"596","ipdsId":"IP-125426","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":447466,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.13017","text":"Publisher Index Page"},{"id":435808,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IYEOKW","text":"USGS data release","linkHelpText":"SPARROW model input datasets and predictions for predicting near-term effects of climate change on nitrogen transport to Chesapeake Bay"},{"id":402150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.1904296875,\n 38.41916639395372\n ],\n [\n -75.223388671875,\n 38.64261790634527\n ],\n [\n -75.35522460937499,\n 38.79690830348427\n ],\n [\n -75.498046875,\n 38.87392853923629\n ],\n [\n -75.5419921875,\n 39.0533181067413\n ],\n [\n -75.662841796875,\n 39.30029918615029\n ],\n [\n -75.750732421875,\n 39.70718665682654\n ],\n [\n -75.6298828125,\n 40.052847601823984\n ],\n [\n -75.69580078125,\n 40.07807142745009\n ],\n [\n -75.95947265625,\n 40.052847601823984\n ],\n [\n -76.0693359375,\n 40.069664523297774\n ],\n [\n -76.058349609375,\n 40.18726672309203\n ],\n [\n -75.9375,\n 40.29628651711716\n ],\n [\n -75.91552734375,\n 40.3549167507906\n ],\n [\n -75.89355468749999,\n 40.47202439692057\n ],\n [\n -76.09130859375,\n 40.56389453066509\n ],\n [\n -76.190185546875,\n 40.64730356252251\n ],\n [\n -76.0693359375,\n 40.75557964275589\n ],\n [\n -75.83862304687499,\n 40.871987756697415\n ],\n [\n -75.76171875,\n 40.91351257612758\n ],\n [\n -75.706787109375,\n 40.95501133048621\n ],\n [\n -75.7177734375,\n 41.071069130806414\n ],\n [\n -75.662841796875,\n 41.1455697310095\n ],\n [\n -75.5419921875,\n 41.13729606112276\n ],\n [\n -75.322265625,\n 41.104190944576466\n ],\n [\n -75.377197265625,\n 41.22824901518529\n ],\n [\n -75.377197265625,\n 41.28606238749825\n ],\n [\n -75.377197265625,\n 41.43449030894922\n ],\n [\n -75.399169921875,\n 41.6154423246811\n ],\n [\n -75.34423828125,\n 41.68111756290652\n ],\n [\n -75.2783203125,\n 41.91045347666418\n ],\n [\n -75.38818359375,\n 42.00848901572399\n ],\n [\n -75.377197265625,\n 42.09007006868398\n ],\n [\n -75.223388671875,\n 42.17968819665961\n ],\n [\n -74.970703125,\n 42.26917949243506\n ],\n [\n -74.8388671875,\n 42.32606244456202\n ],\n [\n -74.520263671875,\n 42.415346114253616\n ],\n [\n -74.278564453125,\n 42.54498667313236\n ],\n [\n -74.322509765625,\n 42.64204079304426\n ],\n [\n -74.410400390625,\n 42.80346172417078\n ],\n [\n -74.68505859374999,\n 42.924251753870685\n ],\n [\n -75.069580078125,\n 42.98053954751642\n ],\n [\n -75.38818359375,\n 42.96446257387128\n ],\n [\n -75.684814453125,\n 42.93229601903058\n ],\n [\n -75.9375,\n 42.87596410238256\n ],\n [\n -76.201171875,\n 42.827638636242284\n ],\n [\n -76.26708984375,\n 42.72280375732727\n ],\n [\n -76.2890625,\n 42.601619944327965\n ],\n [\n -76.2890625,\n 42.52069952914966\n ],\n [\n -76.343994140625,\n 42.415346114253616\n ],\n [\n -76.46484375,\n 42.382894009614034\n ],\n [\n -76.640625,\n 42.431565872579185\n ],\n [\n -76.7724609375,\n 42.39912215986002\n ],\n [\n -76.80541992187499,\n 42.24478535602799\n ],\n [\n -76.88232421875,\n 42.285437007491545\n ],\n [\n -76.9482421875,\n 42.415346114253616\n ],\n [\n -77.04711914062499,\n 42.44778143462245\n ],\n [\n -77.14599609375,\n 42.415346114253616\n ],\n [\n -77.2998046875,\n 42.382894009614034\n ],\n [\n -77.222900390625,\n 42.54498667313236\n ],\n [\n -77.442626953125,\n 42.69858589169842\n ],\n [\n -77.574462890625,\n 42.60970621339408\n ],\n [\n -77.640380859375,\n 42.48830197960227\n ],\n [\n -77.728271484375,\n 42.439674178149424\n ],\n [\n -77.6513671875,\n 42.31793945446847\n ],\n [\n -77.596435546875,\n 42.22851735620852\n ],\n [\n -77.5634765625,\n 42.09007006868398\n ],\n [\n -77.6953125,\n 41.92680320648791\n ],\n [\n -77.9150390625,\n 41.83682786072714\n ],\n [\n -78.0908203125,\n 41.795888098191426\n ],\n [\n -78.453369140625,\n 41.599013054830216\n ],\n [\n -78.453369140625,\n 41.50857729743935\n ],\n [\n -78.42041015625,\n 41.376808565702355\n ],\n [\n -78.3984375,\n 41.21172151054787\n ],\n [\n -78.519287109375,\n 41.054501963290505\n ],\n [\n -78.541259765625,\n 40.9218144123785\n ],\n [\n -78.409423828125,\n 40.713955826286046\n ],\n [\n -78.299560546875,\n 40.55554790286311\n ],\n [\n -78.343505859375,\n 40.48873742102282\n ],\n [\n -78.475341796875,\n 40.30466538259176\n ],\n [\n -78.64013671875,\n 40.06125658140474\n ],\n [\n -78.826904296875,\n 39.9434364619742\n ],\n [\n -78.848876953125,\n 39.80853604144591\n ],\n [\n -78.85986328125,\n 39.715638134796336\n ],\n [\n -78.99169921875,\n 39.69873414348139\n ],\n [\n -79.046630859375,\n 39.64799732373418\n ],\n [\n -79.266357421875,\n 39.436192999314095\n ],\n [\n -79.420166015625,\n 39.2832938689385\n ],\n [\n -79.354248046875,\n 39.26628442213066\n ],\n [\n -79.266357421875,\n 39.232253141714885\n ],\n [\n -79.2333984375,\n 39.155622393423215\n ],\n [\n -79.244384765625,\n 39.01918369029134\n ],\n [\n -79.27734374999999,\n 38.89103282648846\n ],\n [\n -79.398193359375,\n 38.74551518488265\n ],\n [\n -79.661865234375,\n 38.54816542304656\n ],\n [\n -79.683837890625,\n 38.47079371120379\n ],\n [\n -79.727783203125,\n 38.34165619279595\n ],\n [\n -79.815673828125,\n 38.20365531807149\n ],\n [\n -80.04638671875,\n 38.013476231041935\n ],\n [\n -80.17822265625,\n 37.779398571318765\n ],\n [\n -80.2880859375,\n 37.59682400108367\n ],\n [\n -80.4638671875,\n 37.47485808497102\n ],\n [\n -80.694580078125,\n 37.38761749978395\n ],\n [\n -80.771484375,\n 37.23032838760387\n ],\n [\n -80.57373046875,\n 37.26530995561875\n ],\n [\n -80.44189453125,\n 37.309014074275915\n ],\n [\n -80.255126953125,\n 37.31775185163688\n ],\n [\n -80.013427734375,\n 37.3002752813443\n ],\n [\n -79.8486328125,\n 37.23907530202184\n ],\n [\n -79.771728515625,\n 37.18657859524883\n ],\n [\n -79.6728515625,\n 37.07271048132943\n ],\n [\n -79.541015625,\n 37.09900294387622\n ],\n [\n -79.354248046875,\n 37.142803443716836\n ],\n [\n -79.1455078125,\n 37.10776507118514\n ],\n [\n -79.112548828125,\n 37.055177106660814\n ],\n [\n -78.936767578125,\n 36.932330061503144\n ],\n [\n -78.837890625,\n 36.94111143010769\n ],\n [\n -78.662109375,\n 37.055177106660814\n ],\n [\n -78.486328125,\n 37.03763967977139\n ],\n [\n -78.42041015625,\n 36.94111143010769\n ],\n [\n -78.20068359374999,\n 36.96744946416934\n ],\n [\n -77.904052734375,\n 37.03763967977139\n ],\n [\n -77.750244140625,\n 37.081475648860525\n ],\n [\n -77.53051757812499,\n 37.081475648860525\n ],\n [\n -77.354736328125,\n 37.07271048132943\n ],\n [\n -77.069091796875,\n 37.081475648860525\n ],\n [\n -76.959228515625,\n 37.01132594307015\n ],\n [\n -76.893310546875,\n 36.932330061503144\n ],\n [\n -76.871337890625,\n 36.83566824724438\n ],\n [\n -76.849365234375,\n 36.677230602346214\n ],\n [\n -76.7724609375,\n 36.527294814546245\n ],\n [\n -76.629638671875,\n 36.55377524336089\n ],\n [\n -76.46484375,\n 36.589068371399115\n ],\n [\n -76.35498046875,\n 36.48314061639213\n ],\n [\n -76.256103515625,\n 36.57142382346277\n ],\n [\n -76.190185546875,\n 36.66841891894786\n ],\n [\n -76.0693359375,\n 36.65079252503471\n ],\n [\n -75.9375,\n 36.66841891894786\n ],\n [\n -75.948486328125,\n 36.76529191711624\n ],\n [\n -75.904541015625,\n 37.01132594307015\n ],\n [\n -75.926513671875,\n 37.17782559332976\n ],\n [\n -75.882568359375,\n 37.42252593456307\n ],\n [\n -75.618896484375,\n 37.640334898059486\n ],\n [\n -75.509033203125,\n 37.82280243352756\n ],\n [\n -75.38818359375,\n 38.013476231041935\n ],\n [\n -75.16845703124999,\n 38.272688535980976\n ],\n [\n -75.1904296875,\n 38.41916639395372\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ator, Scott 0000-0002-9186-4837","orcid":"https://orcid.org/0000-0002-9186-4837","contributorId":215458,"corporation":false,"usgs":true,"family":"Ator","given":"Scott","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":844662,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sekellick, Andrew J. 0000-0002-0440-7655","orcid":"https://orcid.org/0000-0002-0440-7655","contributorId":215462,"corporation":false,"usgs":true,"family":"Sekellick","given":"Andrew","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844663,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bhatt, Gopal 0000-0002-6627-793X","orcid":"https://orcid.org/0000-0002-6627-793X","contributorId":252963,"corporation":false,"usgs":false,"family":"Bhatt","given":"Gopal","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":844664,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70246519,"text":"70246519 - 2022 - A novel method for conducting a geoenvironmental assessment of undiscovered ISR-amenable uranium Resources: Proof-of-concept in the Texas Coastal Plain","interactions":[],"lastModifiedDate":"2023-07-07T11:52:02.950211","indexId":"70246519","displayToPublicDate":"2022-06-12T06:46:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"A novel method for conducting a geoenvironmental assessment of undiscovered ISR-amenable uranium Resources: Proof-of-concept in the Texas Coastal Plain","docAbstract":"Observations of woody plant mortality in coastal ecosystems are globally widespread, but the overarching processes and underlying mechanisms are poorly understood. This knowledge deficiency, combined with rapidly changing water levels, storm surges, atmospheric CO2, and vapor pressure deficit, creates large predictive uncertainty regarding how coastal ecosystems will respond to global change. Here we synthesize the literature on the mechanisms that underlie coastal woody-plant mortality, with the goal of producing a testable hypothesis framework. The key emergent mechanisms underlying mortality include hypoxic, osmotic, and ionic-driven reductions in whole-plant hydraulic conductance and photosynthesis that ultimately drive the coupled processes of hydraulic failure and carbon starvation. The relative importance of these processes in driving mortality, their order of progression, and their degree of coupling depends on the characteristics of the anomalous water exposure, on topographic effects, and on taxa-specific variation in traits and trait acclimation. Greater inundation exposure could accelerate mortality globally; however, the interaction of changing inundation exposure with elevated CO2, drought, and rising vapor pressure deficit could influence mortality likelihood. Models of coastal forests that incorporate the frequency and duration of inundation, the role of climatic drivers, and the processes of hydraulic failure and carbon starvation can yield improved estimates of inundation-induced woody-plant mortality.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16297","usgsCitation":"McDowell, N.G., Ball, M., Bond-Lamberty, B., Kirwan, M.L., Krauss, K., Megonigal, J.P., Mencuccini, M., Ward, N.D., Weintraub, M., and Bailey, V., 2022, Processes and mechanisms of coastal woody-plant mortality: Global Change Biology, v. 28, no. 20, p. 5881-5900, https://doi.org/10.1111/gcb.16297.","productDescription":"20 p.","startPage":"5881","endPage":"5900","ipdsId":"IP-138484","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":447473,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gcb.16297","text":"External Repository"},{"id":404111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"20","noUsgsAuthors":false,"publicationDate":"2022-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"McDowell, Nate G.","contributorId":207743,"corporation":false,"usgs":false,"family":"McDowell","given":"Nate","email":"","middleInitial":"G.","affiliations":[{"id":37622,"text":"Earth Systems Science Division, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":847019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ball, Marilyn","contributorId":293463,"corporation":false,"usgs":false,"family":"Ball","given":"Marilyn","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":847020,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bond-Lamberty, Ben","contributorId":224752,"corporation":false,"usgs":false,"family":"Bond-Lamberty","given":"Ben","email":"","affiliations":[{"id":40935,"text":"Joint Global Research Institute, Maryland","active":true,"usgs":false}],"preferred":false,"id":847021,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kirwan, Matthew L.","contributorId":191373,"corporation":false,"usgs":false,"family":"Kirwan","given":"Matthew","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":847022,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":221936,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":847023,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Megonigal, J. Patrick","contributorId":288317,"corporation":false,"usgs":false,"family":"Megonigal","given":"J.","email":"","middleInitial":"Patrick","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":847024,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mencuccini, Maurizio","contributorId":199454,"corporation":false,"usgs":false,"family":"Mencuccini","given":"Maurizio","email":"","affiliations":[],"preferred":false,"id":847025,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ward, Nicholas D.","contributorId":293465,"corporation":false,"usgs":false,"family":"Ward","given":"Nicholas","email":"","middleInitial":"D.","affiliations":[{"id":40277,"text":"U.S. Department of Energy","active":true,"usgs":false}],"preferred":false,"id":847026,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Weintraub, Michael N.","contributorId":293467,"corporation":false,"usgs":false,"family":"Weintraub","given":"Michael N.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":847027,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bailey, Vanessa","contributorId":224753,"corporation":false,"usgs":false,"family":"Bailey","given":"Vanessa","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":847028,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236449,"text":"70236449 - 2022 - Vadose zone thickness limits pore-fluid pressures and acceleration in a large, slow-moving landslide","interactions":[],"lastModifiedDate":"2022-09-07T11:59:46.590963","indexId":"70236449","displayToPublicDate":"2022-06-10T06:56:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Vadose zone thickness limits pore-fluid pressures and acceleration in a large, slow-moving landslide","docAbstract":"The rate and timing of hydrologically forced landslides is a complex function of precipitation patterns, material properties, topography, and groundwater hydrology. In the simplest form, however, slopes fail when subsurface pore pressure grows large enough to exceed the Mohr-Coulomb failure criterion. The capacity for pore pressure rise in a landslide is determined in part by the thickness of the unsaturated zone above the water table, which itself is set by weathering patterns that should have predictable patterns across different lithologies. To investigate how this structure affects landslide behavior, we exploit a multi-year record of precipitation, pore pressure, and velocity from Oak Ridge earthflow, a slow-moving landslide set in Franciscan mélange, northern California, USA. In conjunction with electrical resistivity tomography and hydraulic conductivity measurements, these data show that Oak Ridge has a thin weathered profile that is comparable in thickness to other mélange landslides in California. We propose that due to the inherently thin vadose zone, mélange landscapes experience an unusually high water table that frequently brings them close to movement; however, the capacity to increase stress is limited by the small amount of dynamic storage available. Instead, excess pore pressure is shed via springs and saturation overland flow once the water table reaches the surface. Linkages between weathering patterns, hydrology, and deformation can explain behavior patterns exhibited by Franciscan mélange earthflows across a large precipitation gradient.
Despite the recognized role of wild waterfowl in the potential dispersal and transmission of highly pathogenic avian influenza (HPAI) virus, little is known about how infection affects these birds. This lack of information limits our ability to estimate viral spread in the event of an HPAI outbreak, thereby limiting our abilities to estimate and communicate risk. Here we present telemetry data from a wild Lesser Scaup (Aythya affinis), captured during a separate ecology study in the Chesapeake Bay, Maryland. This bird tested positive for infection with clade 2.3.4.4 HPAI virus of the A/goose/Guangdong/1/1996 (Gs/GD) H5N1 lineage (results received post-release) during the 2021–22 ongoing outbreaks in North America. While the infected bird was somewhat lighter than other adult males surgically implanted with transmitters (790g, ߂ = 868g, n = 11), it showed no clinical signs of infection at capture, during surgery, nor upon release. The bird died 3d later, pathology undetermined as the specimen was not able to be recovered. Analysis of movement data within the 3d window showed that the infected individual's maximum and average hourly movements (3894.3m, 428.8m respectively) were noticeably lower than noninfected conspecifics tagged and released the same day (߂ = 21594.5m, ߂ = 1097.9m, respectively; n = 4). We identified four instances where the infected bird had close contact (fixes located within 25m and 15 min) with another marked bird during this time. Collectively, these data suggest that the HPAI positive bird observed in this study may have been shedding virus for some period prior to death, with opportunities for direct bird to bird or environmental transmission. Although limited by low sample size and proximity to the time of tagging, we hope that these data will provide useful information as managers continue to respond to this ongoing outbreak event.
","language":"English","publisher":"Wiley","doi":"10.1111/tbed.14614","usgsCitation":"Prosser, D., Schley, H., Simmons, N., Sullivan, J.D., Homyack, J., Weegman, M.M., Olsen, G.H., Berlin, A., Poulson, R., Stallknecht, D., and Williams, C.K., 2022, A lesser scaup (Aythya affinis ) naturally infected with Eurasian 2.3.4.4 highly pathogenic H5N1 avian influenza virus – Movement ecology and host factors: Transboundary and Emerging Diseases, v. 69, no. 5, p. e2653-e2660, https://doi.org/10.1111/tbed.14614.","productDescription":"8 p.","startPage":"e2653","endPage":"e2660","ipdsId":"IP-138877","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":435810,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MJG53M","text":"USGS data release","linkHelpText":"Telemetry data of a Lesser Scaup (Aythya affinis) positive for 2.3.4.4 Highly Pathogenic H5N1"},{"id":402195,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"69","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schley, Hannah","contributorId":292145,"corporation":false,"usgs":false,"family":"Schley","given":"Hannah","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":844691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simmons, Nathan","contributorId":292146,"corporation":false,"usgs":false,"family":"Simmons","given":"Nathan","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":844692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":844693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Homyack, Josh","contributorId":292150,"corporation":false,"usgs":false,"family":"Homyack","given":"Josh","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":844694,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weegman, Matthew M.","contributorId":200610,"corporation":false,"usgs":false,"family":"Weegman","given":"Matthew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":844695,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olsen, Glenn H. 0000-0002-7188-6203","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":238130,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844696,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Berlin, Alicia 0000-0002-5275-3077","orcid":"https://orcid.org/0000-0002-5275-3077","contributorId":216023,"corporation":false,"usgs":true,"family":"Berlin","given":"Alicia","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844697,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Poulson, Rebecca L.","contributorId":198807,"corporation":false,"usgs":false,"family":"Poulson","given":"Rebecca L.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":844698,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stallknecht, David E.","contributorId":225107,"corporation":false,"usgs":false,"family":"Stallknecht","given":"David E.","affiliations":[{"id":36701,"text":"Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":844699,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Williams, Christopher K.","contributorId":202263,"corporation":false,"usgs":false,"family":"Williams","given":"Christopher","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":844700,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70232247,"text":"70232247 - 2022 - Turbidity and estimated phosphorus retention in a reconnected Lake Erie coastal wetland","interactions":[],"lastModifiedDate":"2022-06-17T14:15:00.422295","indexId":"70232247","displayToPublicDate":"2022-06-09T09:05:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Turbidity and estimated phosphorus retention in a reconnected Lake Erie coastal wetland","docAbstract":"Nearly all of the wetlands in the coastal zone of Lake Erie have been degraded or destroyed since the 1860s, and most of those that remain are separated from their watersheds by earthen dikes. Hydrologic isolation of these wetlands disrupts ecosystem benefits typical to Great Lakes coastal wetlands, particularly the ability to trap sediments and retain nutrients when inundated by runoff and lake water. High-frequency measurements of turbidity and discharge were taken in 2013 and 2014 to observe turbidity and water flow dynamics to estimate total phosphorus flux of a hydrologically reconnected diked wetland pool in the Crane Creek-Lake Erie wetland complex. Modeled estimates suggest the reconnected pool retained 8% of the total phosphorus loading in 2013 and 10% in 2014, which included short periods of phosphorus export to Lake Erie. Water flowing out of the wetland generally had lower turbidity than inflowing water, but flux in and out of the pool varied seasonally and was linked to changes in lake-levels, seiche dynamics, and weather conditions. More frequent storms, higher winds, and stronger seiches in the spring and fall created turbidity patterns that suggest more phosphorus retention than in summer or winter. Estimates suggest that phosphorus was released during the summer when higher lake levels and the absence of frequent storms, larger short-term seiche oscillations, and potentially soil oxygen availability were driving flux dynamics. This study demonstrated that reestablishing lake hydrology through reconnection of wetland pools can reduce loading and alter timing of delivery of total phosphorus to Lake Erie.
","language":"English","publisher":"MDPI","doi":"10.3390/w14121853","usgsCitation":"Carter, G., Kowalski, K., and Eggleston, M., 2022, Turbidity and estimated phosphorus retention in a reconnected Lake Erie coastal wetland: Water, v. 14, no. 2, 1853, 12 p., https://doi.org/10.3390/w14121853.","productDescription":"1853, 12 p.","ipdsId":"IP-110303","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":447485,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14121853","text":"Publisher Index Page"},{"id":435812,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71V5C3B","text":"USGS data release","linkHelpText":"Total phosphorus and water flux at a restored hydrologic connection at Ottawa National Wildlife Refuge in 2013 and 2014"},{"id":402325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Crane Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.24014663696289,\n 41.59772934193236\n ],\n [\n -83.17886352539062,\n 41.59772934193236\n ],\n [\n -83.17886352539062,\n 41.64764694964725\n ],\n [\n -83.24014663696289,\n 41.64764694964725\n ],\n [\n -83.24014663696289,\n 41.59772934193236\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Glenn 0000-0001-6630-7513","orcid":"https://orcid.org/0000-0001-6630-7513","contributorId":292490,"corporation":false,"usgs":false,"family":"Carter","given":"Glenn","email":"","affiliations":[],"preferred":false,"id":844792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":844793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eggleston, Michael 0000-0003-1068-3290","orcid":"https://orcid.org/0000-0003-1068-3290","contributorId":204833,"corporation":false,"usgs":true,"family":"Eggleston","given":"Michael","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":844794,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232149,"text":"fs20223041 - 2022 - Montana and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:06:36.631689","indexId":"fs20223041","displayToPublicDate":"2022-06-08T14:59:37","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3041","displayTitle":"Montana and Landsat","title":"Montana and Landsat","docAbstract":"The landscapes beneath Montana’s big sky are as breathtaking as the State’s nickname would suggest. Visitors to the 41st State's \"Big Sky Country\" can take in the stunning icy hues of aquamarine at Glacier National Park; explore the northern swaths of Yellowstone National Park; or hike, bike, or boat through Bighorn Canyon National Recreation Area, and those are just the National parks.
Montana is the fourth-largest State by land area, with miles upon miles of forests, rolling prairie rangelands, croplands, badlands, and mountains, from which flow a sizable part of the Nation’s water supply. The headwaters of the Missouri River, which covers 2,341 miles before merging with the Mississippi River, are located in Three Forks, Montana. On the opposite side of the Continental Divide, the Kootenai, Clark Fork, Blackfoot, Bitterroot, and Flathead Rivers flow across Montana and into the Columbia River, which ultimately empties into the Pacific Ocean.
The Treasure State’s cherished landscapes face many threats, however: fire-fueling invasive grasses, increasing temperatures caused by climate change, shifting land use patterns, water supply contractions, and more. The U.S. Geological Survey Landsat satellite program’s imagery can improve Montanans’ understanding of land change and offer valuable insight for the ranchers, farmers, land and resource managers, firefighters, and urban planners.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223041","usgsCitation":"U.S. Geological Survey, 2022, Montana and Landsat: U.S. Geological Survey Fact Sheet 2022–3041, 2 p., https://doi.org/10.3133/fs20223041.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-141519","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":401926,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223041/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":401924,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3041/images"},{"id":401923,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3041/fs20223041.XML"},{"id":401922,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3041/fs20223041.pdf","text":"Report","size":"4.46 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3041"},{"id":401921,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3041/coverthb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-105.038405,45.000345],[-105.848065,45.000396],[-105.913382,45.000941],[-105.914258,44.999986],[-105.928184,44.993647],[-106.263586,44.993788],[-107.050801,44.996424],[-107.074996,44.997004],[-107.084939,44.996599],[-107.105685,44.998734],[-107.125633,44.999388],[-107.13418,45.000109],[-107.351441,45.001407],[-107.49205,45.00148],[-107.608854,45.00086],[-107.750654,45.000778],[-107.997353,45.001565],[-108.000663,45.001223],[-108.14939,45.001062],[-108.218479,45.000541],[-108.238139,45.000206],[-108.249345,44.999458],[-108.271201,45.000251],[-108.500679,44.999691],[-108.565921,45.000578],[-109.08301,44.99961],[-109.103445,45.005904],[-109.375713,45.00461],[-109.386432,45.004887],[-109.574321,45.002631],[-109.663673,45.002536],[-109.75073,45.001605],[-109.875735,45.003275],[-109.99505,45.003174],[-110.026347,45.003665],[-110.110103,45.003905],[-110.199503,44.996188],[-110.28677,44.99685],[-110.324441,44.999156],[-110.342131,44.999053],[-110.362698,45.000593],[-110.402927,44.99381],[-110.48807,44.992361],[-110.552433,44.992237],[-110.705272,44.992324],[-110.750767,44.997948],[-110.761554,44.999934],[-110.785008,45.002952],[-111.055199,45.001321],[-111.056207,44.935901],[-111.055629,44.933578],[-111.056888,44.866658],[-111.056416,44.749928],[-111.055511,44.725343],[-111.055208,44.624927],[-111.048974,44.474072],[-111.062729,44.476073],[-111.122654,44.493659],[-111.131379,44.499925],[-111.139455,44.517112],[-111.143557,44.535732],[-111.15959,44.546376],[-111.166892,44.54722],[-111.175747,44.552219],[-111.182551,44.566874],[-111.189617,44.571062],[-111.201459,44.575696],[-111.225208,44.581006],[-111.23018,44.587025],[-111.231227,44.606915],[-111.224161,44.623402],[-111.25268,44.651092],[-111.262839,44.649658],[-111.276956,44.655626],[-111.26875,44.668279],[-111.29626,44.702271],[-111.323669,44.724474],[-111.341351,44.7293],[-111.348184,44.725459],[-111.355768,44.727602],[-111.366723,44.738361],[-111.36627,44.742234],[-111.37476,44.750295],[-111.385005,44.755128],[-111.393854,44.752549],[-111.397805,44.746738],[-111.394459,44.744578],[-111.398575,44.723343],[-111.414271,44.710741],[-111.424214,44.714024],[-111.429604,44.720149],[-111.438793,44.720546],[-111.486019,44.707654],[-111.489339,44.704946],[-111.490228,44.700221],[-111.484898,44.687578],[-111.47798,44.682393],[-111.468833,44.679335],[-111.473178,44.665479],[-111.50494,44.635746],[-111.521688,44.613371],[-111.525764,44.604883],[-111.524213,44.595585],[-111.519126,44.582916],[-111.492024,44.56081],[-111.469185,44.552044],[-111.467736,44.544521],[-111.471682,44.540824],[-111.500792,44.540062],[-111.518095,44.544177],[-111.524006,44.548385],[-111.546637,44.557099],[-111.556577,44.554495],[-111.562814,44.555209],[-111.585763,44.562843],[-111.591768,44.561502],[-111.601249,44.55421],[-111.614405,44.548991],[-111.681571,44.559864],[-111.704218,44.560205],[-111.709553,44.550206],[-111.715474,44.543543],[-111.737191,44.54306],[-111.746401,44.540766],[-111.758966,44.533766],[-111.761904,44.529841],[-111.806512,44.516264],[-111.807914,44.511716],[-111.821488,44.509286],[-111.842542,44.526069],[-111.849293,44.539837],[-111.870504,44.564033],[-111.887852,44.563413],[-111.903566,44.55723],[-111.947941,44.556776],[-111.951522,44.550062],[-111.980833,44.536682],[-111.995231,44.535444],[-112.01385,44.542348],[-112.032707,44.546642],[-112.035025,44.542691],[-112.034133,44.537716],[-112.036943,44.530323],[-112.053434,44.535089],[-112.069011,44.537104],[-112.093304,44.530002],[-112.096299,44.523212],[-112.101564,44.520847],[-112.106755,44.520829],[-112.125101,44.528527],[-112.129078,44.5363],[-112.136454,44.539911],[-112.164597,44.541666],[-112.179703,44.533021],[-112.183937,44.533067],[-112.221698,44.543519],[-112.229477,44.549494],[-112.226841,44.555239],[-112.230117,44.562759],[-112.242785,44.568091],[-112.258665,44.569516],[-112.286187,44.568472],[-112.307642,44.557651],[-112.312899,44.553536],[-112.315047,44.550049],[-112.315008,44.5419],[-112.319198,44.53911],[-112.348794,44.538691],[-112.35421,44.535638],[-112.358917,44.528847],[-112.3566,44.493127],[-112.358926,44.48628],[-112.368764,44.467153],[-112.387389,44.448058],[-112.435342,44.462216],[-112.460347,44.47571],[-112.473207,44.480027],[-112.50031,44.463051],[-112.511713,44.466445],[-112.512036,44.47042],[-112.518871,44.475784],[-112.541989,44.483971],[-112.550557,44.484928],[-112.573513,44.480983],[-112.584197,44.481368],[-112.601863,44.491015],[-112.660696,44.485756],[-112.664485,44.48645],[-112.671169,44.491265],[-112.707815,44.503023],[-112.71911,44.504344],[-112.735084,44.499159],[-112.749011,44.491233],[-112.781294,44.484888],[-112.797863,44.466112],[-112.828191,44.442472],[-112.836034,44.422653],[-112.821896,44.407436],[-112.812608,44.392275],[-112.81324,44.378103],[-112.820489,44.370946],[-112.844859,44.358221],[-112.855395,44.359975],[-112.881769,44.380315],[-112.886041,44.395874],[-112.915602,44.402699],[-112.951146,44.416699],[-112.981682,44.434279],[-113.003544,44.450814],[-113.020917,44.493827],[-113.018636,44.520064],[-113.019777,44.528505],[-113.032722,44.537137],[-113.04282,44.546757],[-113.042363,44.565237],[-113.061071,44.577329],[-113.083819,44.60222],[-113.07376,44.613928],[-113.065593,44.617391],[-113.05677,44.618657],[-113.053529,44.621187],[-113.049349,44.62938],[-113.051504,44.63695],[-113.065589,44.649371],[-113.068306,44.656374],[-113.07042,44.667844],[-113.067756,44.672807],[-113.06776,44.679474],[-113.081906,44.691392],[-113.098064,44.697477],[-113.101154,44.708578],[-113.102138,44.729027],[-113.116874,44.738104],[-113.134824,44.752763],[-113.137704,44.760109],[-113.131387,44.764738],[-113.131453,44.772837],[-113.140618,44.776698],[-113.158206,44.780847],[-113.163806,44.778921],[-113.179366,44.787142],[-113.183395,44.793565],[-113.19436,44.802151],[-113.209624,44.80907],[-113.238729,44.814144],[-113.247166,44.82295],[-113.278382,44.812706],[-113.29683,44.803358],[-113.301508,44.798985],[-113.341704,44.784853],[-113.354034,44.791745],[-113.354763,44.795468],[-113.346692,44.798898],[-113.3461,44.800611],[-113.356062,44.819798],[-113.377153,44.834858],[-113.383984,44.8374],[-113.422376,44.842595],[-113.455071,44.865424],[-113.474573,44.910846],[-113.491121,44.927548],[-113.498745,44.942314],[-113.494446,44.948597],[-113.480836,44.95031],[-113.474781,44.948795],[-113.467467,44.948061],[-113.448958,44.953544],[-113.443782,44.95989],[-113.444862,44.976141],[-113.447013,44.984637],[-113.446884,44.998545],[-113.437726,45.006967],[-113.449909,45.035167],[-113.44912,45.046098],[-113.45197,45.059247],[-113.460578,45.064879],[-113.465073,45.062755],[-113.47377,45.0617],[-113.485278,45.063519],[-113.520134,45.093033],[-113.510819,45.099902],[-113.506638,45.107288],[-113.513342,45.115225],[-113.538037,45.11503],[-113.546488,45.112285],[-113.554744,45.112901],[-113.57467,45.128411],[-113.594632,45.166034],[-113.589891,45.176986],[-113.599506,45.191114],[-113.636889,45.212983],[-113.647399,45.228282],[-113.650064,45.23471],[-113.657027,45.241436],[-113.665633,45.246265],[-113.674409,45.249411],[-113.678749,45.24927],[-113.684946,45.253706],[-113.692039,45.265191],[-113.691557,45.270912],[-113.688077,45.276407],[-113.689359,45.28355],[-113.735601,45.325265],[-113.738729,45.329741],[-113.7402,45.34559],[-113.73553,45.364738],[-113.73239,45.385058],[-113.733092,45.390173],[-113.734402,45.392353],[-113.750546,45.40272],[-113.760924,45.406501],[-113.765203,45.410601],[-113.768058,45.418147],[-113.763368,45.427732],[-113.764591,45.431403],[-113.783272,45.451839],[-113.78416,45.454946],[-113.759986,45.480735],[-113.766022,45.520621],[-113.778361,45.523415],[-113.796579,45.523462],[-113.802849,45.523159],[-113.809144,45.519908],[-113.834555,45.520729],[-113.819868,45.566326],[-113.804796,45.580358],[-113.803261,45.584193],[-113.802955,45.592631],[-113.806729,45.602146],[-113.823068,45.612486],[-113.861404,45.62366],[-113.886006,45.61702],[-113.904691,45.622007],[-113.902539,45.636945],[-113.898883,45.644167],[-113.900588,45.648259],[-113.903582,45.651165],[-113.919752,45.658536],[-113.930403,45.671878],[-113.93422,45.682232],[-113.971565,45.700636],[-113.986656,45.704564],[-114.015633,45.696127],[-114.019315,45.692937],[-114.020533,45.681223],[-114.02007,45.670332],[-114.013786,45.658238],[-114.014973,45.654008],[-114.018731,45.648616],[-114.033456,45.648629],[-114.067619,45.627706],[-114.08179,45.611329],[-114.083149,45.603996],[-114.0821,45.596958],[-114.086584,45.59118],[-114.100308,45.586354],[-114.122322,45.58426],[-114.131469,45.574444],[-114.132359,45.572531],[-114.128601,45.568996],[-114.129099,45.565491],[-114.135249,45.557465],[-114.154837,45.552916],[-114.180043,45.551432],[-114.18647,45.545539],[-114.192802,45.536596],[-114.203665,45.53557],[-114.227942,45.546423],[-114.248121,45.545877],[-114.251836,45.537812],[-114.248183,45.533226],[-114.247824,45.524283],[-114.261616,45.495816],[-114.270717,45.486116],[-114.279217,45.480616],[-114.333218,45.459316],[-114.345019,45.459916],[-114.360719,45.474116],[-114.36562,45.490416],[-114.36852,45.492716],[-114.388618,45.502903],[-114.415804,45.509753],[-114.438991,45.536076],[-114.450863,45.54253],[-114.456764,45.543983],[-114.460542,45.561283],[-114.473759,45.563278],[-114.498176,45.555473],[-114.506341,45.559216],[-114.517761,45.568129],[-114.526075,45.570771],[-114.549508,45.56059],[-114.559038,45.565706],[-114.558253,45.585104],[-114.553999,45.591279],[-114.538132,45.606834],[-114.544905,45.616673],[-114.553937,45.619299],[-114.563305,45.631612],[-114.563652,45.637412],[-114.561046,45.639906],[-114.53577,45.650613],[-114.529678,45.65232],[-114.522142,45.64934],[-114.507645,45.658949],[-114.499637,45.669035],[-114.495421,45.703321],[-114.497553,45.710677],[-114.504869,45.722176],[-114.535634,45.739095],[-114.566172,45.773864],[-114.562509,45.779927],[-114.555487,45.786249],[-114.544692,45.791447],[-114.512973,45.828825],[-114.51704,45.833148],[-114.517143,45.835993],[-114.514596,45.840785],[-114.509303,45.845531],[-114.498809,45.850676],[-114.470296,45.851343],[-114.455532,45.855012],[-114.44868,45.858891],[-114.422963,45.855381],[-114.409477,45.85164],[-114.388243,45.88234],[-114.387166,45.889164],[-114.395059,45.901458],[-114.404314,45.903497],[-114.413168,45.911479],[-114.431159,45.935737],[-114.431328,45.938023],[-114.427717,45.939625],[-114.423681,45.9441],[-114.415977,45.947891],[-114.411933,45.952358],[-114.404708,45.9559],[-114.402261,45.961489],[-114.403712,45.967049],[-114.409353,45.97141],[-114.411892,45.977883],[-114.419899,45.981106],[-114.425843,45.984984],[-114.470965,45.995742],[-114.47729,46.000802],[-114.477922,46.009025],[-114.473811,46.016614],[-114.480241,46.030325],[-114.485793,46.030022],[-114.490572,46.032427],[-114.493418,46.03717],[-114.494683,46.042546],[-114.492153,46.04729],[-114.468529,46.062484],[-114.461864,46.078571],[-114.460049,46.097104],[-114.474415,46.112515],[-114.488303,46.113106],[-114.5213,46.125287],[-114.527096,46.146218],[-114.514706,46.167726],[-114.489254,46.167684],[-114.478333,46.160876],[-114.472643,46.162202],[-114.457549,46.170231],[-114.445928,46.173933],[-114.443215,46.202943],[-114.445497,46.220227],[-114.449819,46.237119],[-114.451912,46.241253],[-114.468254,46.248796],[-114.470479,46.26732],[-114.465024,46.273127],[-114.453257,46.270939],[-114.441326,46.2738],[-114.43544,46.27661],[-114.427309,46.283624],[-114.425587,46.287899],[-114.433478,46.305502],[-114.431708,46.310744],[-114.413758,46.335945],[-114.410682,46.360673],[-114.411592,46.366688],[-114.422458,46.387097],[-114.408974,46.400438],[-114.384756,46.411784],[-114.376413,46.442983],[-114.379338,46.460166],[-114.383051,46.466402],[-114.394447,46.469549],[-114.400068,46.47718],[-114.403019,46.498675],[-114.400257,46.502143],[-114.395204,46.503148],[-114.385871,46.50437],[-114.375348,46.501855],[-114.35874,46.505306],[-114.351655,46.508119],[-114.342072,46.519679],[-114.349208,46.529514],[-114.348733,46.533792],[-114.34534,46.548444],[-114.339533,46.564039],[-114.33175,46.571914],[-114.331338,46.577781],[-114.333931,46.582732],[-114.334992,46.588154],[-114.333931,46.592162],[-114.322519,46.611066],[-114.322912,46.642938],[-114.320665,46.646963],[-114.32456,46.653579],[-114.332887,46.660756],[-114.360709,46.669059],[-114.394514,46.664846],[-114.403383,46.659633],[-114.410907,46.657466],[-114.424424,46.660648],[-114.453239,46.649266],[-114.45425,46.640974],[-114.466902,46.631695],[-114.486218,46.632829],[-114.498007,46.637655],[-114.547321,46.644485],[-114.561582,46.642043],[-114.583385,46.633227],[-114.593292,46.632848],[-114.615036,46.639733],[-114.616354,46.643646],[-114.611676,46.647704],[-114.614716,46.655256],[-114.621483,46.658143],[-114.635713,46.659375],[-114.642713,46.673145],[-114.641745,46.679286],[-114.631898,46.68397],[-114.623198,46.691511],[-114.620859,46.707415],[-114.626695,46.712889],[-114.632954,46.715495],[-114.649388,46.73289],[-114.696656,46.740572],[-114.699008,46.740223],[-114.710425,46.717704],[-114.713516,46.715138],[-114.727445,46.714604],[-114.740115,46.711771],[-114.747758,46.702649],[-114.749257,46.699688],[-114.751921,46.697207],[-114.76689,46.696901],[-114.787065,46.711255],[-114.788656,46.714033],[-114.779668,46.730411],[-114.773765,46.731805],[-114.76718,46.738828],[-114.765127,46.745383],[-114.765106,46.758153],[-114.79004,46.778729],[-114.808587,46.78235],[-114.818161,46.781139],[-114.829117,46.782503],[-114.835917,46.791111],[-114.844794,46.794305],[-114.856874,46.801633],[-114.860067,46.804988],[-114.861376,46.81196],[-114.864342,46.813858],[-114.880588,46.811791],[-114.888146,46.808573],[-114.897857,46.813184],[-114.904505,46.822851],[-114.920459,46.827697],[-114.927837,46.83599],[-114.92845,46.843242],[-114.92349,46.847594],[-114.928615,46.854815],[-114.940398,46.85605],[-114.947413,46.859324],[-114.943281,46.867971],[-114.938713,46.869021],[-114.931608,46.876799],[-114.931058,46.882108],[-114.936805,46.897378],[-114.935035,46.901749],[-114.927948,46.909948],[-114.927432,46.914185],[-114.929997,46.919625],[-114.960597,46.93001],[-114.986539,46.952099],[-115.00091,46.967703],[-115.001274,46.971901],[-115.028386,46.975659],[-115.028994,46.973159],[-115.031651,46.971548],[-115.047857,46.969533],[-115.049538,46.970774],[-115.057098,46.986758],[-115.066223,46.996375],[-115.071254,47.022083],[-115.087806,47.045519],[-115.098136,47.048897],[-115.102681,47.047239],[-115.107132,47.049041],[-115.120917,47.061237],[-115.136671,47.078276],[-115.139515,47.08456],[-115.140375,47.093013],[-115.170436,47.106265],[-115.172938,47.112881],[-115.189451,47.131032],[-115.200547,47.139154],[-115.223246,47.148974],[-115.243707,47.150347],[-115.255146,47.162876],[-115.255786,47.174725],[-115.261885,47.181742],[-115.286353,47.18327],[-115.300504,47.188139],[-115.300805,47.19393],[-115.295986,47.205658],[-115.29211,47.209861],[-115.294785,47.220914],[-115.298794,47.225245],[-115.307239,47.229892],[-115.311875,47.229673],[-115.317124,47.233305],[-115.324832,47.244841],[-115.326903,47.255912],[-115.339201,47.261623],[-115.3593,47.259461],[-115.36628,47.261485],[-115.371825,47.265213],[-115.410685,47.264228],[-115.421645,47.271736],[-115.423173,47.276222],[-115.428359,47.278722],[-115.443566,47.277309],[-115.457077,47.277794],[-115.470959,47.284873],[-115.487314,47.286518],[-115.51186,47.295219],[-115.526751,47.303219],[-115.531971,47.314121],[-115.548658,47.332213],[-115.551079,47.349856],[-115.556318,47.353076],[-115.564766,47.353476],[-115.570887,47.356375],[-115.578619,47.367007],[-115.609492,47.380799],[-115.617247,47.382521],[-115.639186,47.378605],[-115.644341,47.381826],[-115.648479,47.390293],[-115.657681,47.400651],[-115.69057,47.415059],[-115.71034,47.417784],[-115.718934,47.420967],[-115.72148,47.424469],[-115.728801,47.428925],[-115.731348,47.433381],[-115.728801,47.445159],[-115.718247,47.45316],[-115.69293,47.457237],[-115.671188,47.45439],[-115.663867,47.456936],[-115.654318,47.468077],[-115.653044,47.476035],[-115.655272,47.477944],[-115.686704,47.485596],[-115.694106,47.498634],[-115.708748,47.51264],[-115.71034,47.52951],[-115.717024,47.532693],[-115.741371,47.538645],[-115.747263,47.543197],[-115.748536,47.549245],[-115.746945,47.555293],[-115.734674,47.567401],[-115.721207,47.576323],[-115.706473,47.577299],[-115.689404,47.595402],[-115.694284,47.62346],[-115.708537,47.635356],[-115.715193,47.63634],[-115.72993,47.642442],[-115.73627,47.654762],[-115.726613,47.672093],[-115.72377,47.696671],[-115.730764,47.704426],[-115.752349,47.716743],[-115.758623,47.719041],[-115.763424,47.717313],[-115.77177,47.717412],[-115.776219,47.719818],[-115.783504,47.729305],[-115.780441,47.743447],[-115.797299,47.75752],[-115.803917,47.75848],[-115.824597,47.752154],[-115.831755,47.755785],[-115.835365,47.760957],[-115.835069,47.77006],[-115.837438,47.774846],[-115.84044,47.780172],[-115.847487,47.785227],[-115.848509,47.809331],[-115.845474,47.814967],[-115.852291,47.827991],[-115.870861,47.834939],[-115.875262,47.843272],[-115.881522,47.849672],[-115.900934,47.843064],[-115.906409,47.846261],[-115.919291,47.857406],[-115.939993,47.883153],[-115.959946,47.898142],[-115.965153,47.910131],[-115.969076,47.914256],[-115.982791,47.915994],[-115.993678,47.926183],[-115.995121,47.933827],[-115.998236,47.938779],[-116.007246,47.950087],[-116.030751,47.973349],[-116.03834,47.971318],[-116.04882,47.97716],[-116.049153,47.999923],[-116.048739,48.060093],[-116.04932,48.066644],[-116.049415,48.07722],[-116.048911,48.12493],[-116.049977,48.237604],[-116.049353,48.249936],[-116.049735,48.274668],[-116.048948,48.309847],[-116.050003,48.413492],[-116.049155,48.481247],[-116.049193,49.000912],[-116.00103,49.00127],[-115.990685,49.000909],[-115.501016,49.000694],[-115.207912,48.999228],[-114.375977,49.00139],[-114.250971,49.000905],[-114.224912,48.999687],[-114.201107,48.999249],[-114.180211,48.999703],[-114.059188,48.998856],[-113.907487,48.998858],[-113.864127,48.998276],[-113.692982,48.997632],[-113.576118,48.998478],[-113.375925,48.998562],[-113.356874,48.998224],[-113.19474,48.998909],[-113.116356,48.998462],[-112.143769,48.998917],[-112.003866,48.99857],[-112.000878,48.998921],[-111.761679,48.997614],[-111.500812,48.996963],[-111.003916,48.997537],[-110.592465,48.999012],[-110.531615,48.99839],[-110.438151,48.999188],[-109.500737,49.00044],[-109.454023,49.001132],[-109.437397,49.000631],[-109.384068,49.000374],[-109.285975,49.000479],[-109.250722,49.000011],[-109.000708,48.999234],[-107.704696,48.999872],[-107.441017,48.999363],[-107.363582,49.000019],[-106.050543,48.999207],[-105.612577,48.999703],[-104.875527,48.998991],[-104.048736,48.999877],[-104.0489,48.847387],[-104.047582,48.633984],[-104.048212,48.599055],[-104.047876,48.530798],[-104.047513,48.525913],[-104.048054,48.500025],[-104.047555,48.49414],[-104.046969,48.390675],[-104.046371,48.374154],[-104.046039,48.256761],[-104.045645,48.246179],[-104.044162,47.992836],[-104.041662,47.862282],[-104.043242,47.747106],[-104.043742,47.625016],[-104.044241,47.612288],[-104.043912,47.603229],[-104.044109,47.523595],[-104.045333,47.343452],[-104.045057,47.266868],[-104.045517,47.215666],[-104.044788,47.12743],[-104.045018,47.081202],[-104.045354,47.078574],[-104.045052,47.040863],[-104.045901,46.83079],[-104.045045,46.509788],[-104.046103,46.383916],[-104.045481,46.366871],[-104.045237,46.125002],[-104.045759,46.123946],[-104.046822,46.000199],[-104.04403,45.881975],[-104.042597,45.749998],[-104.041937,45.557915],[-104.041145,45.503367],[-104.041764,45.490789],[-104.040816,45.462708],[-104.040114,45.374214],[-104.039977,45.124988],[-104.039563,45.124039],[-104.040128,44.999987],[-104.039681,44.998041],[-104.057698,44.997431],[-104.250145,44.99822],[-104.665171,44.998618],[-104.72637,44.999518],[-104.759855,44.999066],[-105.038405,45.000345]]]},\"properties\":{\"name\":\"Montana\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Coral reefs and their ecosystems are threatened by both global stressors, including increasing sea-surface temperatures and ocean acidification (OA), and local stressors such as land-based sources of pollution that can magnify the effects of OA. Corals can physiologically control the chemistry of their internal calcifying fluids (CF) and can thereby regulate their calcification process. Specifically, increasing aragonite saturation state in the CF (ΩCF) may allow corals to calcify even under external low saturation conditions. Questions remain regarding the physiological processes that govern the CF chemistry and how they change in response to multiple stressors. To address this knowledge gap, the boron δ11B and B/Ca were analyzed in tropical corals, Porites lobata, collected at submarine groundwater seeps impacted by the release of treated wastewater in west Maui, Hawai'i, to document the interactions between high nutrient / low pH seep water on CF carbonate chemistry. Results show substantial up-regulation of pH and dissolved inorganic carbon (DIC) with respect to seawater in P. lobata corals collected from within the wastewater impacted area at Kahekili Beach Park compared to the control site at Olowalu Beach. The ΩCF was 9 to 10 times higher than ambient seawater Ω, and 13 to 26% higher than in corals from the control site and from values previously observed in tropical Porites spp. corals. Such elevated up-regulation suggests that corals exposed to nutrient-enriched, low pH effluent sustain CF supersaturated with respect to aragonite, possibly as an internal coping mechanism to combat multiple stressors from land-based sources of pollution. This elevated up-regulation has implications to coral vulnerability to future climate- and ocean-change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marchem.2022.104134","usgsCitation":"Prouty, N.G., Wall, M., Fietzke, J., Cheriton, O.M., Anagnostou, E., Phillip, B., and Paytan, A., 2022, The role of pH up-regulation in response to nutrient-enriched, low-pH groundwater discharge: Marine Chemistry, v. 243, 104134, 11 p., https://doi.org/10.1016/j.marchem.2022.104134.","productDescription":"104134, 11 p.","ipdsId":"IP-136166","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":447491,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marchem.2022.104134","text":"Publisher Index Page"},{"id":402149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kahekili Beach Park, Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.7,\n 20.929\n ],\n [\n -156.68,\n 20.929\n ],\n [\n -156.68,\n 20.95\n ],\n [\n -156.7,\n 20.95\n ],\n [\n -156.7,\n 20.929\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"243","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":844679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wall, Marlene","contributorId":292468,"corporation":false,"usgs":false,"family":"Wall","given":"Marlene","email":"","affiliations":[{"id":62913,"text":"2GEOMAR Helmholtz Centre for Ocean Research","active":true,"usgs":false}],"preferred":false,"id":844680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fietzke, J.","contributorId":41656,"corporation":false,"usgs":true,"family":"Fietzke","given":"J.","affiliations":[],"preferred":false,"id":844681,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheriton, Olivia M. 0000-0003-3011-9136","orcid":"https://orcid.org/0000-0003-3011-9136","contributorId":204459,"corporation":false,"usgs":true,"family":"Cheriton","given":"Olivia","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":844682,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anagnostou, Eleni 0000-0002-7200-4794","orcid":"https://orcid.org/0000-0002-7200-4794","contributorId":292469,"corporation":false,"usgs":false,"family":"Anagnostou","given":"Eleni","email":"","affiliations":[{"id":13697,"text":"GEOMAR Helmholtz Centre for Ocean Research","active":true,"usgs":false}],"preferred":false,"id":844683,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Phillip, Brian","contributorId":292470,"corporation":false,"usgs":false,"family":"Phillip","given":"Brian","email":"","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":844684,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Paytan, Adina 0000-0001-8360-4712","orcid":"https://orcid.org/0000-0001-8360-4712","contributorId":193046,"corporation":false,"usgs":false,"family":"Paytan","given":"Adina","email":"","affiliations":[],"preferred":false,"id":844685,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70231446,"text":"ofr20221031 - 2022 - Dynamic rating method for computing discharge from time-series stage data","interactions":[],"lastModifiedDate":"2026-03-27T20:08:34.586583","indexId":"ofr20221031","displayToPublicDate":"2022-06-08T08:55:54","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1031","displayTitle":"Dynamic Rating Method for Computing Discharge from Time-Series Stage Data","title":"Dynamic rating method for computing discharge from time-series stage data","docAbstract":"Ratings are used for a variety of reasons in water-resources investigations. The simplest rating relates discharge to the stage of the river. From a pure hydrodynamics perspective, all rivers and streams have some form of hysteresis in the relation between stage and discharge because of unsteady flow as a flood wave passes. Simple ratings are unable to represent hysteresis in a stage/discharge relation. A dynamic rating method is capable of capturing hysteresis owing to the variable energy slope caused by unsteady momentum and pressure.
A dynamic rating method developed to compute discharge from stage for compact channel geometry, referred to as DYNMOD, previously has been developed through a simplification of the one-dimensional Saint-Venant equations. A dynamic rating method, which accommodates compound and compact channel geometry, referred to as DYNPOUND, has been developed through a similar simplification as a part of this study. The DYNMOD and DYNPOUND methods were implemented in the Python programming language. Discharge time series computed with the dynamic rating method implementations were then compared to simulated discharge time series and discrete discharge measurements made at U.S. Geological Survey streamgage sites.
Four sets of stage and discharge time series were created using one-dimensional unsteady simulation software with compound channel geometry to compare the results of both dynamic rating methods to results from the full one-dimensional shallow water equations. Discharge time series were computed from stage time series using DYNMOD and DYNPOUND. DYNPOUND outperformed DYNMOD in all four scenarios. The minimum and maximum mean squared logarithmic error (MSLE) for the DYNMOD results were 2.75×10−2 and 3.40×10−2, respectively. The minimum and maximum MSLE for the DYNPOUND results were 2.51×10−7 and 1.91×10−4, respectively.
The dynamic rating methods were calibrated for six U.S. Geological Survey streamgage sites using observed discharge data collected at the sites. The calibration objective for each site was to minimize the MSLE of the discharge computed with the rating method with respect to observed discharge. For each site, the calibration included all field measurements within a selected water year. The DYNMOD method failed to compute discharge for the full calibration time series for three sites. A method fails to compute when the implementation returns a nonfinite value at a time step. Because the values computed for following time steps are dependent on the previous time step, a nonfinite value results in nonfinite values that follow. For the three sites for which DYNMOD computed the complete discharge time series, the minimum MSLE for calibration was 2.19×10−3 and the maximum was 9.77×10−3. The MSLE of the DYNPOUND computed discharge calibration time series for the six sites ranged from 3.70×10−3 to 1.25. For each site, an event-based time period was selected to compare the discharge time series computed with the dynamic rating methods to discrete discharge field measurements made at the streamgage sites. The DYNMOD-computed discharge time series for the three sites had an MSLE range of 2.76×10−3 to 3.14×10−2. The range of MSLE for the six DYNPOUND sites was 3.64×10−3 to 7.23×10−2. Although the DYNMOD method outperforms the DYNPOUND method when calibrated streamgage sites are under consideration, the DYNMOD method failed to compute a discharge time series at three of the six sites. The DYNPOUND method, therefore, was more robust than the DYNMOD method. Improvements to the implementation of the DYNPOUND method may improve the accuracy of the method.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221031","programNote":"Groundwater and Streamflow Information Program","usgsCitation":"Domanski, M., Holmes, R.R., Jr., and Heal, E.N., 2022, Dynamic rating method for computing discharge from time-series stage data: U.S. Geological Survey Open-File Report 2022–1031, 48 p., https://doi.org/10.3133/ofr20221031.","productDescription":"Report: vii, 48 p.; 2 Data Releases; Dataset","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-128037","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501770,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113161.htm","linkFileType":{"id":5,"text":"html"}},{"id":400457,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":400459,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YUV9DG","text":"USGS data release","linkHelpText":"Dynamic stage to discharge rating model archive"},{"id":400458,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P955QRPQ","text":"USGS data release","linkHelpText":"Dynamic rating method for computing discharge from time series stage data—Site datasets"},{"id":400454,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1031/ofr20221031.XML"},{"id":400455,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1031/images"},{"id":400453,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1031/ofr20221031.pdf","text":"Report","size":"2.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1031"},{"id":400452,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1031/coverthb.jpg"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Drought has impacted the Colorado River basin for the past 20 years and is predicted to continue. In response, decisions about how much water should be stored in large reservoirs and how much water can be consumptively used will be necessary. These decisions have the potential to limit riverine ecosystem management options through the effect water-supply decisions have on reservoir elevations. We used projected hydrology and river temperatures to compare the outcome of combinations of water storage scenarios and consumptive use limits on metrics associated with ecosystem management of the Colorado River in Grand Canyon. Ecosystem management metrics included the ability to implement designer flows, temperature suitability for fishes, and fragmentation. We compared current water management operations to prioritizing storage in either Lake Mead or Lake Powell combined with three levels of consumptive use. Projected reservoir levels limited environmental flow delivery and increased fragmentation regardless of where water was stored if consumptive use was not limited. Warmer river temperatures associated with low reservoir levels are likely, creating suitable conditions for non-native species of concern, such as smallmouth bass. Water storage decisions provided variability and management flexibility, but water storage was less important when less water was available, highlighting the importance of keeping water in the system to provide flexibility for achieving ecosystem goals.
Wastewater generated during petroleum extraction (produced water) may contain high concentrations of dissolved organics due to their intimate association with organic-rich source rocks, expelled petroleum, and organic additives to fluids used for hydraulic fracturing of unconventional (e.g., shale) reservoirs. Dissolved organic matter (DOM) within produced water represents a challenge for treatment prior to beneficial reuse. High salinities characteristic of produced water, often 10× greater than seawater, coupled to the complex DOM ensemble create analytical obstacles with typical methods. Excitation-emission matrix spectroscopy (EEMS) can rapidly characterize the fluorescent component of DOM with little impact from matrix effects. We applied EEMS to evaluate DOM composition in 18 produced water samples from six North American unconventional petroleum plays. Represented reservoirs include the Eagle Ford Shale (Gulf Coast Basin), Wolfcamp/Cline Shales (Permian Basin), Marcellus Shale and Utica/Point Pleasant (Appalachian Basin), Niobrara Chalk (Denver-Julesburg Basin), and the Bakken Formation (Williston Basin). Results indicate that the relative chromophoric DOM composition in unconventional produced water may distinguish different lithologies, thermal maturity of resource types (e.g., heavy oil vs. dry gas), and fracturing fluid compositions, but is generally insensitive to salinity and DOM concentration. These results are discussed with perspective toward DOM influence on geochemical processes and the potential for targeted organic compound treatment for the reuse of produced water.
Mercury is a global contaminant that can be detrimental to wildlife and human health. Anthropogenic emissions and point sources are primarily responsible for elevated mercury concentrations in sediments and waters. Mercury can physically move and chemically transform in the environment, resulting in biomagnification of mercury, in the form of methylmercury, in the food web and causing elevated mercury concentrations in upper trophic levels. The ability to measure total mercury concentrations in the environment has existed for several decades and makes it possible to detect hotspots that might exist because of ongoing or previous anthropogenic activity. However, recent (within the past 15 years) developments in mass spectrometry have made it possible to complete low level stable isotope analysis allowing for the determination of mercury sources—natural and anthropogenic—in the environment through “fingerprinting.” Grubers Grove Bay in Lake Wisconsin, the focus area of this study, was determined to have elevated mercury levels even after multiple remediation efforts, resulting in its listing on the Federal list of impaired waters pursuant to the Clean Water Act. Adjacent to the bay is the former Badger Army Ammunition Plant, which manufactured ammunition for the U.S. Army during the early and middle 20th century, after which it was put on standby before being fully decommissioned. This study assesses mercury concentrations in the sediments and suspended particulate matter of Grubers Grove Bay, Wiegands Bay, and upstream sites, and in adjacent soils on the former Badger Army Ammunition Plant site. This study confirmed that mercury contamination exists in the sediments of Grubers Grove Bay even after dredging attempts by the U.S. Army. Additionally, using isotope ratios and a two-endmember mixing model, it was determined that soil from within Badger Army Ammunition Plant’s former site contributed a substantial amount of mercury to the bay. This result was supported by an observed gradient of high to low mercury concentrations from the innermost (nearest Badger Army Ammunition Plant) to the outermost (farthest from Badger Army Ammunition Plant) part of the bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221051","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Routhier, E.J., Janssen, S.E., Tate, M.T., Ogorek, J.M., DeWild, J.F., and Krabbenhoft, D.P., 2022, Assessment of mercury in sediments and waters of Grubers Grove Bay, Wisconsin: U.S. Geological Survey Open-File Report 2022–1051, 20 p., https://doi.org/10.3133/ofr20221051.","productDescription":"Report: vii, 20 p.; Data release","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-133343","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501779,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113162.htm","linkFileType":{"id":5,"text":"html"}},{"id":401822,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990MFHU","text":"USGS data release","linkHelpText":"Gruber's Grove Bay mercury site assessment"},{"id":401821,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1051/images"},{"id":401819,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1051/ofr20221051.pdf","text":"Report","size":"2.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1051"},{"id":401818,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1051/coverthb.jpg"},{"id":401820,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1051/ofr20221051.XML"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Grubers Grove Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.74800109863281,\n 43.32467816302811\n ],\n [\n -89.64569091796874,\n 43.32467816302811\n ],\n [\n -89.64569091796874,\n 43.393572674883146\n ],\n [\n -89.74800109863281,\n 43.393572674883146\n ],\n [\n -89.74800109863281,\n 43.32467816302811\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
Denver Federal Center, Bldg. 67
P.O. Box 25046, MS 911
Denver, CO 80225–0046
Under-representations of headwater channels in digital stream networks can result in uncertainty in the magnitude of headwater habitat loss, stream burial, and watershed function. Increased availability of high-resolution (<2 m) elevation data makes the delineation of headwater channels more attainable. In this study, elevation data derived from light detection and ranging was used to predict ephemeral stream networks across a forested and urban watershed in the Maryland Piedmont USA. A method was developed using topographic openness (TO) and wetness index to remotely predict the extent of stream networks. Predicted networks were compared against a comprehensive field survey of the ephemeral network in each watershed to evaluate performance. Comparisons were also made to the U.S. Geological Survey National Hydrography Dataset (NHD) and a flow accumulation approach where a single drainage area threshold defined channel initiation. Although the NHD and flow accumulation methods resulted in low commission errors, omission errors were highest in these networks. The TO-based networks detected a larger number of ephemeral channels, but with higher commission error. Small ephemeral channels with less defined banks or originating at groundwater seeps were difficult to detect in all methods. Comparisons between forested and urban watersheds also highlight the difficulty of identifying headwater channels using topographic attributes in human-modified landscapes.
Ecological transitions across salinity boundaries have led to some of the most important diversification events in the animal kingdom, especially among fishes. Adaptations accompanying such transitions include changes in morphology, diet, whole-organism performance, and osmoregulatory function, which may be particularly prominent since divergent salinity regimes make opposing demands on systems that maintain ion and water balance. Research in the last decade has focused on the genetic targets underlying such adaptations, most notably by comparing populations of species that are distributed across salinity boundaries. Here, we synthesize research on the targets of natural selection using whole-genome approaches, with a particular emphasis on the osmoregulatory system. Given the complex, integrated and polygenic nature of this system, we expected that signatures of natural selection would span numerous genes across functional levels of osmoregulation, especially salinity sensing, hormonal control, and cellular ion exchange mechanisms. We find support for this prediction: genes coding for V-type, Ca2+, and Na+/K+-ATPases, which are key cellular ion exchange enzymes, are especially common targets of selection in species from six orders of fishes. This indicates that while polygenic selection contributes to adaptation across salinity boundaries, changes in ATPase enzymes may be of particular importance in supporting such transitions.
","language":"English","publisher":"Society for Integrative and Comparative Biology.","doi":"10.1093/icb/icac072","usgsCitation":"Velotta, J., McCormick, S.D., Whitehead, A., Durso, C.S., and Schultz, E., 2022, Repeated genetic targets of natural selection underlying adaptation of euryhaline fishes to changing salinity: Integrative and Comparative Biology, v. 62, no. 2, p. 357-375, https://doi.org/10.1093/icb/icac072.","productDescription":"19 p.","startPage":"357","endPage":"375","ipdsId":"IP-139776","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447518,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icb/icac072","text":"Publisher Index Page"},{"id":402451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Velotta, Jonathan P","contributorId":192317,"corporation":false,"usgs":false,"family":"Velotta","given":"Jonathan P","affiliations":[],"preferred":false,"id":844957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":844958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitehead, Andrew","contributorId":221105,"corporation":false,"usgs":false,"family":"Whitehead","given":"Andrew","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":844959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Durso, Catherine S","contributorId":292523,"corporation":false,"usgs":false,"family":"Durso","given":"Catherine","email":"","middleInitial":"S","affiliations":[{"id":12651,"text":"University of Denver","active":true,"usgs":false}],"preferred":false,"id":844960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schultz, Eric T.","contributorId":260102,"corporation":false,"usgs":false,"family":"Schultz","given":"Eric T.","affiliations":[],"preferred":false,"id":844961,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232109,"text":"sir20225006 - 2022 - Tracking heat in the Willamette River system, Oregon","interactions":[],"lastModifiedDate":"2026-04-08T17:12:43.533209","indexId":"sir20225006","displayToPublicDate":"2022-06-06T14:10:06","publicationYear":"2022","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":"2022-5006","displayTitle":"Tracking Heat in the Willamette River System, Oregon","title":"Tracking heat in the Willamette River system, Oregon","docAbstract":"The Willamette River Basin in northwestern Oregon is home to several cold-water fish species whose habitat has been altered by the Willamette Valley Project, a system of 13 dams and reservoirs operated by the U.S. Army Corps of Engineers. Water-resource managers use a variety of flow- and temperature-management strategies to ameliorate the effects of upstream Willamette Valley Project dams on the habitat and viability of these anadromous and native fish. In this study, new capabilities were added to the CE-QUAL-W2 two-dimensional flow and water-quality model to inform those flow- and temperature-management strategies by tracking the quantities and ages of water and heat from individual upstream sources to downstream locations in the Willamette River system. Specifically, the fraction of water and heat attributable to upstream dam releases or other water inputs, and the fraction of heat sourced from environmental heat fluxes across the water and sediment surfaces, were tracked and quantified in the river at all locations and times simulated by the model. Applying the updated CE-QUAL-W2 models to the Willamette River system for the months of March through October in the years 2011 (cool and wet), 2015 (hot and dry), and 2016 (warm and somewhat dry) demonstrated that the influence of upstream dam releases on downstream water temperature diminished within a few days as water moved downstream. At sites that are roughly two or more days of travel from upstream dams (Albany and downstream), the July–August fraction of riverine heat content that could be tracked back to upstream dam releases typically diminished to less than 20 percent, despite the fact that roughly 50 percent of July–August streamflow could be attributed to upstream dam releases at the same sites. In contrast, the fraction of riverine heat content that could be attributed to environmental energy fluxes continued to increase with downstream distance, from about 59 to 67 percent at Albany during July–August to 62 to 73 percent at Keizer and 68 to 79 percent at Newberg.
At locations sufficiently far downstream, upstream dam releases affect water temperature mainly through a decrease in travel time (less time for environmental heat fluxes to warm the river during summer) and an increase in thermal mass (more water to dilute and buffer incoming heat fluxes) rather than through the simple transport of heat content (water temperature) released from the dams. This concept was explored not only for the baseline conditions that occurred in March–October of 2011, 2015, and 2016, but also for a hypothetical high-flow release during August 2016 and an actual high-flow release during August 2017. In these high-flow releases, an extra 2,500 cubic feet per second (roughly) was released from Dexter Dam on the Middle Fork Willamette River, and downstream effects were measured (2017, actual) and simulated (2016, hypothetical). Results of the simulations were consistent with insights gained from the baseline conditions, such that temperature changes caused by flow augmentation were substantial in upstream reaches (measured cooling of about 1.5 °C near Harrisburg [43 miles downstream] and Albany [84 miles downstream] in 2017, and cooling of about 0.5 °C near Albany in 2016) and diminished farther downstream, but still measurable (more than a few tenths of a degree Celsius) even at Newberg, which is about 154 miles downstream. The direct downstream effects of dam releases on the river heat content attributable to those releases were increased by the hypothetical flow augmentation, with increases of 20 percent at Harrisburg and 12 percent at Keizer. Even with a decreased influence of environmental energy fluxes on river heat content, however, the fraction of heat content attributable to such fluxes was still more than 50 percent at and downstream of Albany and more than 70 percent at Newberg, where the river temperature was less affected by upstream dam-release temperatures and instead was affected primarily by a decreased travel time and increased thermal mass.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225006","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Rounds, S.A., and Stratton Garvin, L.E., 2022, Tracking heat in the Willamette River system, Oregon: U.S. Geological Survey Scientific Investigations Report 2022–5006, 47 p., https://doi.org/10.3133/sir20225006.","productDescription":"Report: vii, 47 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119740","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":401781,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P908DXKH","text":"USGS data release","description":"USGS data release","linkHelpText":"CE-QUAL-W2 models for the Willamette River and major tributaries below U.S. Army Corps of Engineers dams—2011, 2015, and 2016: U.S. Geological Survey data release, https://doi.org/10.5066/P908DXKH."},{"id":401780,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5006/sir20225006.pdf","text":"Report","size":"5.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5006"},{"id":401779,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5006/coverthb.jpg"},{"id":401783,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225035","text":"SIR 2022-5035 —","description":"SIR 2022-5035","linkHelpText":"The thermal landscape of the Willamette River—Patterns and controls on stream temperature and implications for flow management and cold-water salmonids"},{"id":401782,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221017","text":"OFR 2022-1017 —","description":"OFR 2022-1017","linkHelpText":"Updates to models of streamflow and water temperature for 2011, 2015, and 2016 in rivers of the Willamette River Basin, Oregon"},{"id":401784,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225034","text":"SIR 2022-5034 —","description":"SIR 2022-5034","linkHelpText":"Assessment of habitat availability for juvenile Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss) in the Willamette River, Oregon"},{"id":401870,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5006/sir20225006.XML"},{"id":401869,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5006/images"},{"id":502294,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113159.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.67858886718747,\n 43.572431747409695\n ],\n [\n -121.44836425781247,\n 43.572431747409695\n ],\n [\n -121.44836425781247,\n 45.7905094675247\n ],\n [\n -123.67858886718747,\n 45.7905094675247\n ],\n [\n -123.67858886718747,\n 43.572431747409695\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Water temperature is a primary control on the health, diversity, abundance, and distribution of aquatic species, but thermal degradation resulting from anthropogenic influences on rivers is a challenge to threatened species worldwide. In the Willamette River Basin, northwestern Oregon, spring-run Chinook salmon (Oncorhynchus tshawytscha) and winter-run steelhead (O. mykiss) are formerly abundant cold-water-adapted species that are now protected under the Endangered Species Act. Among the challenges to the health of cold-water salmonids in the Willamette River Basin, disruptions in the seasonal patterns of stream temperature imposed by 13 large, multipurpose dams on tributaries to the Willamette River, as well as temperatures routinely in excess of regulatory limits in the Willamette River Basin, are contributing factors. To better understand controls on stream temperature, the sensitivity of stream temperature to flow augmentation as a management tool for suppressing high temperatures, and the implications for threatened salmonids, this study used a two-dimensional hydrodynamic and water-quality model (CE-QUAL-W2) to investigate spatial and temporal patterns of stream temperature in the Willamette River Basin. This study focused on the upper 160.4 river miles of the Willamette River from the confluence of the Middle Fork and Coast Fork Willamette Rivers (river mile 187.2) to Willamette Falls (river mile 26.8), three representative climate years (2011, a cool and wet year; 2015, an extremely hot and dry year; and 2016, a moderately hot and dry year), and a series of flow-augmentation scenarios. Model results show that the Willamette River upstream from Willamette Falls is divisible into four characteristic “thermal reaches” with similar thermal patterns, depending on tributary input, warming rate, and the timing of thermal response. In general, the Willamette River warms downstream during spring and summer, but patterns are complex, influenced by tributary inflows, and seasonally variable. Except in cool wet years (as illustrated by 2011), modeling suggests that adversely warm conditions for spring-run Chinook salmon are extensive from June or July through August. The thermal influence of flow augmentation from dam storage on four tributaries with U.S. Army Corps of Engineers dams varies spatially along the Willamette River, seasonally, and in magnitude, depending on a range of factors like distance from the Willamette River, the temperature of dam outflow, and the thermal template of tributary reaches controlling stream temperature adjustment to environmental heat fluxes. Modeling suggests that targeted flow management (via augmentation from dam storage) can reduce the extent and duration of thermally stressful conditions for Chinook salmon for short periods, but modeling suggests that flow augmentation is limited in its ability to fundamentally alter the “thermal landscape” (the entire range of temperature variation in a river system over space and time) of the Willamette River. While this research provides general insights into the thermal landscape of the Willamette River and its sensitivity to flow management, additional investigation into the thermal landscape of tributaries downstream from U.S. Army Corps of Engineers dams, as well as the thermal management of reservoirs, storage availability, and dam outflows, would be necessary to target specific management actions for supporting specified rearing or migration conditions for spring-run Chinook salmon and other cold-water-adapted species in the Willamette River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225035","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Stratton Garvin, L.E., and Rounds, S.A., 2022, The thermal landscape of the Willamette River—Patterns and controls on stream temperature and implications for flow management and cold-water salmonids: U.S. Geological Survey Scientific Investigations Report 2022–5035, 43 p., https://doi.org/10.3133/sir20225035.","productDescription":"Report: vi, 43 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-126305","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":401764,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5035/sir20225035.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5035"},{"id":401763,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5035/coverthb.jpg"},{"id":401765,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P908DXKH","text":"USGS data release","description":"USGS data release","linkHelpText":"CE-QUAL-W2 models for the Willamette River and major tributaries below U.S. Army Corps of Engineers dams—2011, 2015, and 2016"},{"id":401768,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221017","text":"OFR 2022-1017 —","description":"OFR 2022-1017","linkHelpText":"Updates to models of streamflow and water temperature for 2011, 2015, and 2016 in rivers of the Willamette River Basin, Oregon"},{"id":401767,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225006","text":"SIR 2022-5006 —","description":"SIR 2022-5006","linkHelpText":"Tracking heat in the Willamette River system, Oregon"},{"id":401769,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225034","text":"SIR 2022-5034 —","description":"SIR 2022-5034","linkHelpText":"Assessment of habitat availability for juvenile Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss) in the Willamette River, Oregon"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.67858886718747,\n 43.572431747409695\n ],\n [\n -121.44836425781247,\n 43.572431747409695\n ],\n [\n -121.44836425781247,\n 45.7905094675247\n ],\n [\n -123.67858886718747,\n 45.7905094675247\n ],\n [\n -123.67858886718747,\n 43.572431747409695\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 Willamette River, Oregon, is home to two salmonid species listed as threatened under the Endangered Species Act, Upper WIllamette River spring Chinook salmon (Oncorhynchus tshawytscha) and Upper Willamette River winter steelhead (O. mykiss). Streamflow in the Willamette River is regulated by upstream dams, 13 of which are operated by the U.S. Army Corps of Engineers (USACE) as part of the Willamette Valley Project. In 2008, these dams were determined to have a deleterious effect on Endangered Species Act-listed salmonids, resulting in USACE taking actions to mitigate those effects. Mitigation actions included setting seasonal streamflow targets at various locations along the river to improve survival and migration of juvenile salmonids. Although these targets were established with the best available information at the time, recent data and models have advanced understanding of Willamette River bathymetric, hydraulic, and thermal conditions, allowing for a more robust analysis of the effect of streamflow on downstream habitat. This study integrates those recent advances to build high-resolution models of usable habitat for juvenile Chinook salmon and steelhead to assess variation in spatial and seasonal patterns of habitat availability. Specifically, this study develops detailed maps of habitat availability for juvenile Chinook salmon and steelhead for two size classes (fry and pre-smolt). Habitat availability is modeled in a three-step process whereby (1) two-dimensional hydraulic models are paired with literature-supplied data on habitat preferences to create spatially explicit maps of rearing habitats for a wide range of streamflows; (2) reach-specific relations between streamflow and habitat area are developed and paired with streamgage records to create habitat time series for 2011, 2015, and 2016, which reflect “cool and wet,” “hot and dry,” and “warm but average precipitation” conditions, respectively; (3) temperature models are coupled with literature-based thermal thresholds to determine time periods and locations along the river corridor when rearing habitat has optimal, harmful, or lethal temperature conditions; (4) finally, habitat availability is summarized at several spatial scales to characterize longitudinal and seasonal patterns.
Findings show that modeled area of rearing habitat for Chinook salmon and steelhead responds non-uniformly to streamflow, where habitat in some reaches of the Willamette River consistently increase with additional streamflow, while in other reaches, habitat area decreases when streamflows increase from low to moderate flows. Modeled differences in flow-habitat relations are primarily explained by local geomorphology in each reach and resulting hydraulic conditions that arise with different streamflows. These are most pronounced when comparing laterally active, multi-channel reaches upstream from Corvallis with downstream reaches that are laterally stable with single-channel planforms. The reaches upstream from Corvallis generally have more habitat available per unit stream distance than downstream reaches, but all reaches display greatest amounts of habitat at the highest streamflows. Finally, results show that warm water temperature in summer greatly decreases the utility of habitat available to the focal species, particularly downstream from Corvallis. Together, these findings serve to inform flow management by characterizing spatial and seasonal patterns of habitat availability for juvenile spring Chinook salmon and winter steelhead and provide a quantitative assessment of the effects of streamflow on rearing habitat.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225034","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"White, J.S., Peterson, J.T., Stratton Garvin, L.E., Kock, T.J., and Wallick, J.R., 2022, Assessment of habitat availability for juvenile Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss) in the Willamette River, Oregon: U.S. Geological Survey Scientific Investigations Report 2022–5034, 44 p., https://doi.org/10.3133/sir20225034.","productDescription":"viii, 44 p.","onlineOnly":"Y","ipdsId":"IP-130018","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":401758,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5034/coverthb.jpg"},{"id":401759,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5034/sir20225034.pdf","text":"Report","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5034"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.40942382812501,\n 44.05995928349327\n ],\n [\n -122.2943115234375,\n 44.05995928349327\n ],\n [\n -122.2943115234375,\n 45.66780526567164\n ],\n [\n -123.40942382812501,\n 45.66780526567164\n ],\n [\n -123.40942382812501,\n 44.05995928349327\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Mechanistic river models capable of simulating hydrodynamics and stream temperature are valuable tools for investigating thermal conditions and their relation to streamflow in river basins where upstream water storage and management decisions have an important influence on river reaches with threatened fish populations. In the Willamette River Basin in northwestern Oregon, a two-dimensional, hydrodynamic water-quality model (CE‑QUAL‑W2) has been used to investigate the downstream effects of dam operations and other anthropogenic influences on stream temperature. By simulating the managed releases of water and various temperatures from the large Willamette Valley Project dams upstream of the modeling domain, these models can be used to investigate riverine temperature conditions and their relation to streamflow to determine where and when conditions are most challenging for threatened fish populations and how dam operations and flow management can affect and optimize thermal conditions in the river.
The original models were initially developed to simulate conditions in spring–autumn of 2001 and 2002. This report documents (1) the upgrade of the river models to CE‑QUAL‑W2 version 4.2 and (2) the update of those models to simulate conditions that occurred from March through October of 2011, 2015, and 2016. These years were selected to represent a range of climatic and hydrologic conditions in the Willamette River Basin, including a “cool, wet” year (2011), a “hot, dry” year (2015), and a “normal” year (2016). Six submodels comprise the modeling system updated in this report; each submodel can be run independently or run with the others as a system. These models include the Coast Fork and Middle Fork Willamette River submodel, which includes the Coast Fork and Middle Fork Willamette Rivers, the Row River, and Fall Creek; the McKenzie River submodel, which includes the South Fork McKenzie River downstream of Cougar Dam and the McKenzie River from its confluence with the South Fork McKenzie River to its mouth; the South Santiam River submodel, which comprises the South Santiam River from Foster Dam to the Santiam River; the North Santiam and Santiam River submodel, which includes the Santiam River and the North Santiam River downstream of Big Cliff Dam; the Upper Willamette River submodel, which includes the Willamette River from Eugene to Salem; and the Middle Willamette River submodel, which includes the Willamette River from Salem to Willamette Falls near Oregon City.
The models included in this report were originally developed, calibrated, and documented by other researchers. As part of the model updates described here, some model parameters were adjusted to improve stability and decrease runtime. Boundary conditions including meteorological, hydrologic, and thermal parameters were developed and updated for model years 2011, 2015, and 2016. In many cases, the data sources used to drive the 2001 and 2002 models were no longer available, which required the use of new data sources, the determination of a proxy record, or the development of appropriate estimation techniques. Goodness-of-fit statistics for the updated models show a good model fit, with the models simulating subdaily water temperatures at most comparable locations with a mean absolute error of generally less than 1 °C and often nearing 0.5 °C, depending on the individual submodel, and a reasonably low bias. The subdaily mean error for the South Santiam River submodel produced the highest bias of any of the submodels. Goodness-of-fit statistics indicate that the results may be biased cool (ranging from -0.43 °C in 2016 to -0.80 °C in 2011 for subdaily results), but the only water temperature data available for comparison on the South Santiam River is itself estimated, and those estimates are known to be too high in summer. Depending on future modeling needs, that submodel may warrant further refinement, along with additional data collection to properly define and minimize any model bias.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221017","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Stratton Garvin, L.E., Rounds, S.A., and Buccola, N.L., 2022, Updates to models of streamflow and water temperature for 2011, 2015, and 2016 in rivers of the Willamette River Basin, Oregon: U.S. Geological Survey Open-File Report 2022–1017, 73 p., https://doi.org/10.3133/ofr20221017.","productDescription":"Report: x, 73 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119723","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":401872,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1017/ofr20221017.XML"},{"id":401871,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1017/images"},{"id":401815,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225035","text":"SIR 2022-5035 —","linkHelpText":"The thermal landscape of the Willamette River—Patterns and controls on stream temperature and implications for flow management and cold-water salmonids"},{"id":401814,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225034","text":"SIR 2022-5034 —","linkHelpText":"Assessment of habitat availability for juvenile Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss) in the Willamette River, Oregon"},{"id":501762,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113157.htm","linkFileType":{"id":5,"text":"html"}},{"id":401754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1017/coverthb.jpg"},{"id":401755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1017/ofr20221017.pdf","text":"Report","size":"10.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1017"},{"id":401756,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P908DXKH","text":"USGS data release","description":"USGS data release","linkHelpText":"CE-QUAL-W2 models for the Willamette River and major tributaries below U.S. Army Corps of Engineers dams—2011, 2015, and 2016"},{"id":401813,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225006","text":"SIR 2022-5006 —","linkHelpText":"Tracking heat in the Willamette River system, Oregon"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.134765625,\n 42.779275360241904\n ],\n [\n -120.673828125,\n 42.779275360241904\n ],\n [\n -120.673828125,\n 45.9511496866914\n ],\n [\n -123.134765625,\n 45.9511496866914\n ],\n [\n -123.134765625,\n 42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Human activities (e.g., shipping, tourism, oil, gas development) have increased in the Chukchi Sea because of declining sea ice. The declining sea ice itself and these activities may affect Pacific walrus (Odobenus rosmarus divergens) abundance; however, previous walrus abundance estimates have been notably imprecise. When sea ice is absent from the eastern Chukchi Sea, walruses in waters of the United States usually rest together onshore at a single Alaska coastal haulout, where they can be surveyed more easily than when they rest on dispersed offshore ice floes. We estimated the number of walruses on land (herd size) at this haulout from 13 unoccupied aircraft system (UAS) surveys flown within a 10-day period in each of 2018 and 2019. We estimated population size of walruses using the haulout over the course of the surveys by combining herd size data with data from satellite-linked transmitters that indicated whether tagged walruses were in or out of water during each survey. Our estimates of the population size of walruses using the haulout during each year's survey period were similar to each other and more precise than historical walrus abundance estimates: posterior means (95% credibility intervals) were 166,000 (133,000–201,000) for 2018 and 189,000 (135,000–251,000) for 2019. Auxiliary observations support using these estimates to represent the size of the population using the eastern Chukchi Sea in autumn during the surveyed years. Our study site was the only substantial Chukchi Sea coastal haulout in the United States during the survey periods and study-specific tracking data (consistent with known distribution and movement patterns) indicated tagged walruses remained in eastern Chukchi waters during the survey periods. In addition, the imagery, telemetry, and analytical methods developed for this study advance the prospect for precise range-wide walrus population size estimates.