The predictive accuracy of regional hydrologic models often varies across both time and space. Interpreting relationships between watershed characteristics, hydrologic regimes, and model performance can reveal potential areas for model improvement. In this study, we use machine learning to assess model performance of a regional hydrologic model to forecast the occurrence of streamflow drought. We demonstrate our methodology using a regional long short-term memory (LSTM) deep learning model developed by the U.S. Geological Survey (USGS) and data from 384 streamgages across the Colorado River Basin region. Performance was assessed by clustering catchments using: (a) physical and climatological catchment attributes, and (b) streamflow drought signatures time series. We examined the association of USGS LSTM model error measures with clusters generated by both approaches to interpret meaningful spatial and temporal information about LSTM model performance. Clustering static catchment attributes identified elevation, degree of streamflow regulation, baseflow contribution, catchment aridity, and drainage area as the most influential attributes to model performance. Clustering gages by their drought signatures revealed that catchments with significant seasonal peak runoff between January and June generally exhibited better model performance. Additionally, a Random Forest classifier was trained to successfully predict LSTM model performance (F1 score of 0.72) based on physical and climatological catchment attributes. Low degree of flow regulation was identified as a key indicator of better LSTM model performance. These findings point to the opportunities for improving the USGS LSTM model performance in future hydrologic drought prediction efforts across regional and CONUS scales.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR039077","usgsCitation":"Dadkhah, A., Hamshaw, S.D., van der Heijden, R., and Rizzo, D.M., 2025, A spatiotemporal interrogation of hydrologic drought model performance for machine learning model interpretability: Water Resources Research, v. 61, no. 11, e2024WR039077, 20 p., https://doi.org/10.1029/2024WR039077.","productDescription":"e2024WR039077, 20 p.","ipdsId":"IP-171117","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":496726,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr039077","text":"Publisher Index Page"},{"id":496550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, South Dakota, Texas, Utah, Wyoming","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.82376678919411,\n 36.26233085914717\n ],\n [\n -115.85085818096155,\n 32.567720561211985\n ],\n [\n -110.51032589138043,\n 31.22468250332072\n ],\n [\n -108.12617557977711,\n 31.516111303152087\n ],\n [\n -107.07123697669522,\n 32.48455428990293\n ],\n [\n -102.78172590159346,\n 34.79341128012621\n ],\n [\n -102.32122175185708,\n 37.822265600751294\n ],\n [\n -104.54120424685271,\n 39.31008340398529\n ],\n [\n -105.01672465698343,\n 42.43285898166596\n ],\n [\n -102.61536956018404,\n 44.597711605623914\n ],\n [\n -103.68883267765926,\n 45.86117991057367\n ],\n [\n -104.94111657475693,\n 46.52791159365242\n ],\n [\n -111.34981535286528,\n 46.65300676975278\n ],\n [\n -113.2114048549673,\n 44.05547092656684\n ],\n [\n -115.54731167526421,\n 41.97871186707138\n ],\n [\n -119.35050975086381,\n 42.039351853495674\n ],\n [\n -119.8152614776348,\n 40.77295494694408\n ],\n [\n -119.24558792818462,\n 38.8937217445025\n ],\n [\n -116.82376678919411,\n 36.26233085914717\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"61","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Dadkhah, Ali 0000-0002-0861-4926","orcid":"https://orcid.org/0000-0002-0861-4926","contributorId":362194,"corporation":false,"usgs":false,"family":"Dadkhah","given":"Ali","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":950171,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hamshaw, Scott Douglas 0000-0002-0583-4237","orcid":"https://orcid.org/0000-0002-0583-4237","contributorId":305601,"corporation":false,"usgs":true,"family":"Hamshaw","given":"Scott","email":"","middleInitial":"Douglas","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":950172,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van der Heijden, Ryan 0000-0003-1320-9500","orcid":"https://orcid.org/0000-0003-1320-9500","contributorId":362195,"corporation":false,"usgs":false,"family":"van der Heijden","given":"Ryan","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":950173,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rizzo, Donna M.","contributorId":362196,"corporation":false,"usgs":false,"family":"Rizzo","given":"Donna","middleInitial":"M.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":950174,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272690,"text":"70272690 - 2025 - Does tidal marsh restoration lead to the recovery of trophic pathways that support estuarine fishes?","interactions":[],"lastModifiedDate":"2025-12-04T16:09:41.080409","indexId":"70272690","displayToPublicDate":"2025-10-11T10:05:12","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Does tidal marsh restoration lead to the recovery of trophic pathways that support estuarine fishes?","docAbstract":"Evaluation of tidal marsh restoration success is typically based on the recovery of habitat size and target species. However, food-web structure may provide valuable insight into ecosystem functioning trajectories. Here, we studied restored tidal marshes of different ages (new, young, old; spanning 1–150 years) in comparison with nearby reference sites along the San Francisco Estuary. We asked: (1) How does restoration help recover energy pathways that support fishes? (2) Do fishes rely more on algal versus detrital pathways in restored sites?; and (3) How does food-web structure vary as a function of species origin and life history? To answer these questions, we sampled fish (n = 806) and basal resources (emergent vegetation and phytoplankton; n = 109) seasonally over two hydrologically contrasting years. Using stable isotopes (δ13C, δ15N, and δ34S), we calculated fish isotopic niche volumes, food chain lengths, and the relative importance of algal versus detrital energy pathways. We found that food chains in restored sites were 8% shorter than in their paired reference sites. Additionally, the young and old restored sites had 37% smaller niche volumes than their references, but the opposite was true for the new restored site (11% larger), illustrating the characteristic trophic surge of early succession. Fishes found in restored sites relied significantly less on detrital energy (7% less) than fishes found in reference sites, and resident fishes showed 12% higher reliance on the detrital pathway than transient species. Finally, most of the native niche volume overlapped with that of introduced fish, which was in turn 38% larger, and a similar pattern was observed when comparing resident to transient fish. Our findings demonstrate that food-web structure does not immediately recover with tidal marsh restoration, even if fish assemblages are species-rich; and show that transient trophic surges may complicate restoration success assessments of newly restored marshes. We contend that incorporating recovery of energy pathways as an indicator of performance may help strengthen monitoring and design of wetland ecosystem restoration projects.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.70110","usgsCitation":"Pagliaro, M.D., De La Cruz, S.E., Woo, I., Sousa, J., Rich, N., Grimaldo, L., Colombano, D., and Ruhí, A., 2025, Does tidal marsh restoration lead to the recovery of trophic pathways that support estuarine fishes?: Ecological Applications, v. 35, no. 7, e70110, 19 p., https://doi.org/10.1002/eap.70110.","productDescription":"e70110, 19 p.","ipdsId":"IP-172413","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":497112,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.70110","text":"Publisher Index Page"},{"id":497058,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay-Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.13450674043398,\n 38.15156417948114\n ],\n [\n -122.13450674043398,\n 37.99624365988022\n ],\n [\n -121.70778013191887,\n 37.99624365988022\n ],\n [\n -121.70778013191887,\n 38.15156417948114\n ],\n [\n -122.13450674043398,\n 38.15156417948114\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"35","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Pagliaro, Megan D.","contributorId":363231,"corporation":false,"usgs":false,"family":"Pagliaro","given":"Megan","middleInitial":"D.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":951339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":951340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woo, Isa","contributorId":363234,"corporation":false,"usgs":false,"family":"Woo","given":"Isa","affiliations":[{"id":37814,"text":"Former USGS","active":true,"usgs":false}],"preferred":false,"id":951341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sousa, Jake","contributorId":363235,"corporation":false,"usgs":false,"family":"Sousa","given":"Jake","affiliations":[{"id":80078,"text":"ICF","active":true,"usgs":false}],"preferred":false,"id":951342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rich, Natalie","contributorId":363238,"corporation":false,"usgs":false,"family":"Rich","given":"Natalie","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":951343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grimaldo, Lenny","contributorId":10728,"corporation":false,"usgs":false,"family":"Grimaldo","given":"Lenny","email":"","affiliations":[{"id":35724,"text":"ICF, San Francisco, USA","active":true,"usgs":false}],"preferred":false,"id":951344,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Colombano, Denise","contributorId":297365,"corporation":false,"usgs":false,"family":"Colombano","given":"Denise","affiliations":[],"preferred":false,"id":951345,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ruhí, Albert","contributorId":363243,"corporation":false,"usgs":false,"family":"Ruhí","given":"Albert","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":951346,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70274600,"text":"70274600 - 2025 - Ambient field seismology in critical zone hydrological sciences","interactions":[],"lastModifiedDate":"2026-04-01T15:12:52.682494","indexId":"70274600","displayToPublicDate":"2025-10-06T10:07:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23777,"text":"Comptes Rendus. Géoscience","active":true,"publicationSubtype":{"id":10}},"title":"Ambient field seismology in critical zone hydrological sciences","docAbstract":"Passive ambient noise monitoring is an emerging tool in environmental seismology, leveraging the ambient seismic field to assess temporal variations in shallow subsurface properties. This review focuses on the potential and challenges of using scattered coda waves from noise correlation functions to monitor critical zone dynamics. The sensitivity of seismic velocities to various environmental factors, including precipitation, snowmelt, atmospheric pressure, and groundwater fluctuations, underscores the method’s versatility. While coda waves excel in detecting subtle changes due to their scattered nature, ballistic waves provide higher spatial resolution, albeit with challenges in source stability. Advances in seismic sensing, including distributed acoustic sensing and low-cost geophone networks, have enabled high-resolution monitoring of hydrological processes, subsurface deformation, and seismic hazards. Integrating seismic data with hydrological models provides insights into water storage, pore pressure changes, and soil moisture dynamics. However, limitations in spatial resolution, calibration with ground truth data, and coupled effects between environmental factors remain key challenges. This review emphasizes the importance of interdisciplinary approaches in refining methodologies, enhancing sensor deployments, and addressing data gaps. Passive seismic monitoring offers opportunities to understand critical zone processes and their broader impacts on seismic hazards and environmental sustainability.
","language":"English","publisher":"Academie des Sciences, Institut de France","doi":"10.5802/crgeos.310","usgsCitation":"Denolle, M.A., Shi, Q., Clements, T., Viens, L., Rodriguez-Tribaldos, V., and Cotton, F., 2025, Ambient field seismology in critical zone hydrological sciences: Comptes Rendus. Géoscience, v. 357, p. 425-451, https://doi.org/10.5802/crgeos.310.","productDescription":"27 p.","startPage":"425","endPage":"451","ipdsId":"IP-181097","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":502104,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5802/crgeos.310","text":"Publisher Index Page"},{"id":501930,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"357","noUsgsAuthors":false,"publicationDate":"2025-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Denolle, Marine A.","contributorId":345689,"corporation":false,"usgs":false,"family":"Denolle","given":"Marine","email":"","middleInitial":"A.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":958469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Qibin","contributorId":369115,"corporation":false,"usgs":false,"family":"Shi","given":"Qibin","affiliations":[{"id":49969,"text":"Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA","active":true,"usgs":false}],"preferred":false,"id":958470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clements, Timothy Hugh 0000-0001-6632-1796","orcid":"https://orcid.org/0000-0001-6632-1796","contributorId":350753,"corporation":false,"usgs":true,"family":"Clements","given":"Timothy Hugh","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":958471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Viens, Loic","contributorId":362345,"corporation":false,"usgs":false,"family":"Viens","given":"Loic","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":958472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rodriguez-Tribaldos, Veronica","contributorId":369117,"corporation":false,"usgs":false,"family":"Rodriguez-Tribaldos","given":"Veronica","affiliations":[{"id":87725,"text":"GFZ Helmholtz Centre for Geosciences, Telegrafenberg 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":958473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cotton, Fabrice","contributorId":264167,"corporation":false,"usgs":false,"family":"Cotton","given":"Fabrice","email":"","affiliations":[],"preferred":false,"id":958474,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274252,"text":"70274252 - 2025 - Thirty years of the U.S. National Land Cover Database: Impacts and future direction","interactions":[],"lastModifiedDate":"2026-03-19T20:00:20.373012","indexId":"70274252","displayToPublicDate":"2025-10-01T14:33:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5987,"text":"Photogrammetric Engineering & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Thirty years of the U.S. National Land Cover Database: Impacts and future direction","docAbstract":"The National Land Cover Database (NLCD), developed through the Multi-Resolution Land Characteristics Consortium, was initiated 30 years ago and has continually provided critical, Landsat-based landcover and land-change information for the United States. Originally launched to address the lack of national-scale, moderate-resolution land-cover data, NLCD has evolved from the pioneering 1992 dataset into a comprehensive, annually updated product suite. Key innovations include the introduction of impervious surface mapping, forest canopy mapping, standardized Landsat mosaics, national-scale accuracy assessments, continual evolution of deep learning and artificial intelligence methodologies, and a transition toward operational, change-focused monitoring. The NLCD has become an essential resource for scientific research, land management, and policy development, with extensive adoption across federal, state, and local agencies; academia; and the private sector. The NLCD data underpin a wide array of applications, including biodiversity conservation, urban planning, hydrology, human health studies, and natural hazard assessment. As new global and high-resolution commercial land-cover products emerge, the NLCD continues to distinguish itself through its temporal depth, federal backing, and thematic consistency. Moving forward, the NLCD will maintain its niche as the leading, moderate-resolution, long-term land-cover and land-change dataset for the United States, ensuring continued support for broad national applications while complementing higher-resolution and global-mapping efforts.
","language":"English","publisher":"Ingenta","doi":"10.14358/PERS.25-00121R2","usgsCitation":"Sohl, T.L., Jin, S., Dewitz, J., Wickham, J., Brown, J.F., Stehman, S., Herold, N., Schleeweis, K., Tollerud, H.J., and Deering, C., 2025, Thirty years of the U.S. National Land Cover Database: Impacts and future direction: Photogrammetric Engineering & Remote Sensing, v. 91, no. 10, p. 647-659, https://doi.org/10.14358/PERS.25-00121R2.","productDescription":"13 p.","startPage":"647","endPage":"659","ipdsId":"IP-180474","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":501337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"91","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":957183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":957184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dewitz, Jon 0000-0002-0458-212X","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":222454,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":957185,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wickham, James","contributorId":140259,"corporation":false,"usgs":false,"family":"Wickham","given":"James","affiliations":[{"id":12657,"text":"EPA NEIC","active":true,"usgs":false}],"preferred":false,"id":957186,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":957187,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stehman, Stephen","contributorId":39747,"corporation":false,"usgs":true,"family":"Stehman","given":"Stephen","affiliations":[],"preferred":false,"id":957188,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Herold, Nathaniel","contributorId":140258,"corporation":false,"usgs":false,"family":"Herold","given":"Nathaniel","email":"","affiliations":[{"id":12641,"text":"NOAA NMFS","active":true,"usgs":false}],"preferred":false,"id":957189,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schleeweis, Karen","contributorId":169308,"corporation":false,"usgs":false,"family":"Schleeweis","given":"Karen","email":"","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":957190,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tollerud, Heather J. 0000-0001-9507-4456","orcid":"https://orcid.org/0000-0001-9507-4456","contributorId":210820,"corporation":false,"usgs":true,"family":"Tollerud","given":"Heather","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":957191,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Deering, Carol 0000-0003-3565-6264 cdeering@usgs.gov","orcid":"https://orcid.org/0000-0003-3565-6264","contributorId":3001,"corporation":false,"usgs":true,"family":"Deering","given":"Carol","email":"cdeering@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":957192,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70272235,"text":"70272235 - 2025 - A systematic literature review of forecasting and predictive models of harmful algal blooms in flowing waters","interactions":[],"lastModifiedDate":"2025-11-19T15:10:27.951072","indexId":"70272235","displayToPublicDate":"2025-10-01T09:02:45","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":19846,"text":"BioRxiv","active":true,"publicationSubtype":{"id":32}},"title":"A systematic literature review of forecasting and predictive models of harmful algal blooms in flowing waters","docAbstract":"Occurrences of harmful algal blooms (HABs) in rivers challenge the belief that rivers are not susceptible to HABs because of their short residence times and fluctuating hydrology. Here we present a systematic literature review of predictive and forecasting models for HABs in flowing waters, including rivers, flowing in-stream reservoirs (e.g., run-of-river reservoirs and lock-and-dam systems) and tidal or estuarine systems with riverine processes. The review aimed to understand current and historical modeling approaches for predicting and forecasting river HABs, without restricting to specific taxa, such as cyanobacteria, or modeling endpoints. The review included 162 articles published over nearly 50 years, covering more than 80 rivers worldwide. Eutrophic, non-wadable rivers with in-stream obstruction were commonly modeled, though diverse environmental characteristics were reported. Most articles used algal biomass or chlorophyll as modeling endpoints, with a quarter using novel or unique endpoints. Algal toxins motivated model development in 23% of the articles, however just 5% used algal toxins as an endpoint. Only 6% of the articles modeled benthic HABs; the rest focused on pelagic HABs. There was no standard model used for modeling river HABs. Process-based models were more common (59%) than data-driven approaches (37%), with model formulations ranging from simple to complex, which contrasts with a lake-focused literature review of HAB models that found data-driven models were more common. Models in river settings shared similar input variables as those previously identified for lakes, such as water temperature, nutrients, and light availability. However, streamflow and other transport metrics took prominence in river models compared to lake models. Algal cell physiology (such as growth, predation, and motility) was routinely included as input data or as mathematical formulations in process-based models and these processes were frequently identified as an important predictor by the articles’ authors. Conversely, data-driven models rarely included these processes, instead using predictors related to environmental conditions, such as nutrients, water quality, water temperature, and streamflow. These important proxy predictors have apparent success with modeling overall algal biomass (irrespective of taxa) whereas other factors, such as those related to algal physiology and other biological processes, are likely responsible for more subtle shifts in community composition. These differences highlight the influence of data availability, especially for processes that are difficult, time-consuming, or expensive to measure, on model development and model outcomes, raising questions about the selection of modeling inputs and endpoints. Challenges to advancing river HAB modeling include the lack of site-specific model inputs representing key processes (e.g., photosynthetic parameters and predation rates), overlooked riverine environments like the benthos and side/back-channel areas, lack of information on environmental settings, and poorly reported model performance metrics. This review emphasizes opportunities for advancing river HAB modeling by learning from well-honed estuarine models, supporting current forecasting and operationalization efforts, and developing common datasets for river HAB model development and evaluation.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2025.09.29.679270","usgsCitation":"Murphy, J.C., Gorney, R.M., Lucas, L., Zwart, J.A., and Graham, J.L., 2025, A systematic literature review of forecasting and predictive models of harmful algal blooms in flowing waters: BioRxiv, https://doi.org/10.1101/2025.09.29.679270.","productDescription":"52 p.","ipdsId":"IP-179513","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":496741,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2025.09.29.679270","text":"External Repository"},{"id":496628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":4281,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950534,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gorney, Rebecca Michelle 0000-0003-4406-261X","orcid":"https://orcid.org/0000-0003-4406-261X","contributorId":317259,"corporation":false,"usgs":true,"family":"Gorney","given":"Rebecca","email":"","middleInitial":"Michelle","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950535,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":950536,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":950537,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":202923,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":950538,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271982,"text":"sir20255031 - 2025 - User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)","interactions":[{"subject":{"id":70206120,"text":"ofr20191096 - 2019 - User's guide for the national hydrography dataset plus (NHDPlus) high resolution","indexId":"ofr20191096","publicationYear":"2019","noYear":false,"displayTitle":"User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution","title":"User's guide for the national hydrography dataset plus (NHDPlus) high resolution"},"predicate":"SUPERSEDED_BY","object":{"id":70271982,"text":"sir20255031 - 2025 - User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)","indexId":"sir20255031","publicationYear":"2025","noYear":false,"title":"User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)"},"id":1}],"lastModifiedDate":"2026-02-03T16:23:33.096091","indexId":"sir20255031","displayToPublicDate":"2025-09-30T13:20:00","publicationYear":"2025","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":"2025-5031","displayTitle":"User’s Guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)","title":"User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)","docAbstract":"The National Hydrography Dataset Plus High Resolution (NHDPlus HR) is a scalable hydrologic geospatial fabric or framework, built from (1) the High Resolution (1:24,000-scale or better) National Hydrography Dataset (NHD), (2) nationally complete Watershed Boundary Dataset (WBD), and (3) 1/3-arc-second 3D Elevation Program (3DEP) digital elevation model (DEM) data (at a 10-meter ground spacing; or 5-meter 3DEP DEM in Alaska only). The NHDPlus HR provides a modeling and assessment framework at a local 1:24,000 scale, while nesting seamlessly into the national context.
NHDPlus HR is modeled after the highly successful NHDPlus version 2 (NHDPlusV2). Like NHDPlusV2, the NHDPlus HR includes data for a nationally seamless network of stream reaches, elevation-based catchment areas, flow surfaces, and value-added attributes that enhance stream-network navigation, analysis, and data display. However, NHDPlus HR provides much greater spatial detail than NHDPlusV2, while NHDPlusV2 is, at present, more complete in its attribution of additions, removals, and diversions, as well as stream connectivity. This user’s guide is intended to provide necessary information and guidance in the use of NHDPlus HR data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255031","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","programNote":"National Geospatial Program","usgsCitation":"Moore, R.B., McKay, L.D., Rea, A.H., Bondelid, T.R., Price, C.V., Dewald, T.G., and Hayes, L., 2025, User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR): U.S. Geological Survey Scientific Investigations Report 2025–5031, 78 p., https://doi.org/10.3133/sir20255031. [Supersedes USGS Open-File Report 2019–1096.]","productDescription":"Report: xiii, 78 p.; 2 Data Releases; Project Site","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150034","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":496237,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5031/sir20255031.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5031 XML"},{"id":496238,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5031/images"},{"id":496239,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WFOBQI","text":"USGS data release","linkHelpText":"USGS National Hydrography Dataset Plus High Resolution National Release 1 FileGDB"},{"id":496240,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://apps.nationalmap.gov/downloader/#/","text":"USGS data release","linkHelpText":"The National Map downloader (ver. 2.0)"},{"id":496273,"rank":8,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/national-hydrography/nhdplus-high-resolution","text":"NHDPlus High Resolution"},{"id":496236,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255031/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5031 HTML"},{"id":496235,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5031/sir20255031.pdf","text":"Report","size":"9.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5031 PDF"},{"id":496234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5031/coverthb.jpg"}],"contact":"Director, National Geospatial Program
Core Science Systems
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Fostering a sense of classroom community in earth science classes supports students’ sense of belonging within the classroom and the broader scientific community, helping them build a sense of identity as a geoscientist. This study examines the effects of incorporating a 2-week, collaborative role-playing activity on sense of classroom community and science identity in an introductory hydrology class. Students assumed roles of residents, medical center representatives, government employees, and environmental activists to learn about flooding through a community-centered lens, focusing on a flood event in Harris County, Texas during Tropical Storm Allison. Pre-post-surveys were given immediately before and after the learning module to evaluate classroom community, science identity using Likert scales, and hydrologist identity using a pictorial scale. Qualitative analysis of a short-answer question in which students defined “hydrologist” provided context for quantitative identity data. Post-survey data on classroom community shows an increase in mean agreement as compared to the pre-survey. This increase was statistically significant for four classroom community statements. Paired science identity data show small effect size and no significant change, but pictorial identity as a hydrologist shows significant growth. Social aspects of the role-playing activity did not significantly alter student’s already high science identity but altered their conceptions of the social implications of hydrology and increased identity as hydrologists. The significant increase in classroom community has important implications for using role-playing as an active learning strategy to enhance student learning experience by creating a positive classroom climate and connecting hydrology concepts to community needs.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10899995.2025.2565990","usgsCitation":"Plenge, M., Dolan, W., Tomlinson, A., Hutson, B., and Pavelsky, T., 2025, Impact of a place-based role-playing exercise on student sense of classroom community and science identity in a hydrology class: Journal of Geoscience Education, https://doi.org/10.1080/10899995.2025.2565990.","ipdsId":"IP-171850","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":498550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Plenge, Megan","contributorId":365013,"corporation":false,"usgs":false,"family":"Plenge","given":"Megan","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":953582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dolan, Wayana 0000-0001-8405-4302","orcid":"https://orcid.org/0000-0001-8405-4302","contributorId":354442,"corporation":false,"usgs":true,"family":"Dolan","given":"Wayana","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":953583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tomlinson, Alexa","contributorId":365016,"corporation":false,"usgs":false,"family":"Tomlinson","given":"Alexa","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":953584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hutson, Bryant","contributorId":365018,"corporation":false,"usgs":false,"family":"Hutson","given":"Bryant","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":953585,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pavelsky, Tamlin","contributorId":149629,"corporation":false,"usgs":false,"family":"Pavelsky","given":"Tamlin","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":953586,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271935,"text":"fs20253022 - 2025 - Beaver dams and their effects on urban streams in the Tualatin River Basin, northwestern Oregon","interactions":[],"lastModifiedDate":"2026-02-03T16:22:51.589121","indexId":"fs20253022","displayToPublicDate":"2025-09-30T07:55:41","publicationYear":"2025","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":"2025-3022","displayTitle":"Beaver Dams and Their Effects on Urban Streams in the Tualatin River Basin, Northwestern Oregon","title":"Beaver dams and their effects on urban streams in the Tualatin River Basin, northwestern Oregon","docAbstract":"In response to growing interest in beaver-assisted restoration in the Tualatin River Basin of northwestern Oregon, the U.S. Geological Survey (USGS), in partnership with Clean Water Services, collected data from 2016–17 and completed a series of studies to: (1) inventory known locations of beaver dams and activity in the Tualatin River Basin, (2) estimate the number of beaver dams in the Tualatin River Basin as of 2017 and the potential number of beaver dams that could be supported with riparian vegetation improvements, and (3) assess the effects of beaver dams and ponds on storm hydrology, hydraulics, and floodplain inundation, suspended-sediment transport and deposition, and water quality along two urban stream reaches (Fanno Creek at Greenway Park and Bronson Creek between Kaiser and Saltzman Roads). This fact sheet summarizes the results of these studies and implications for beaver-assisted restoration in the Tualatin River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253022","usgsCitation":"Jones, K.L., Smith, C.D., White, J.S., Rounds, S.A., Doyle, M.C., and Leahy, E.K., Beaver dams and their effects on urban streams in the Tualatin River Basin, northwestern Oregon: U.S. Geological Survey Fact Sheet 2025–3022, 6 p., https://doi.org/10.3133/fs20253022","productDescription":"6 p.","onlineOnly":"Y","ipdsId":"IP-138686","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":496063,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3022/fs20253022.XML"},{"id":496061,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3022/coverthb.jpg"},{"id":496062,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3022/fs20253022.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3022"},{"id":496206,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.3133/sir20255039","text":"SIR 2025-5039","description":"SIR 2025-5039","linkHelpText":"- Beavers in the Tualatin River Basin, Northwestern Oregon"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.375\n ],\n [\n -122.5,\n 45.375\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
601 SW 2nd Ave., Suite 1950
Portland, Oregon 97204
American beaver (Castor canadensis) dams fundamentally alter stream hydraulics and hydrology by temporarily impounding water in stream channels. Water managers are interested in how this impoundment translates to changes in hydrograph dynamics, particularly regarding the magnitude and duration of high flows, the temporary storage of storm water, and the range and spatial distribution of water depths and velocities. High-resolution two-dimensional hydraulic models were developed to compare hydraulic responses to storm events in two 1-kilometer long, relatively small (less than 5-meter-wide channel), urban stream reaches in the Tualatin River Basin (northwestern Oregon) with and without beaver dams. Results from modeling unsteady storm events show that: (1) beaver dams generally attenuate (temporarily impound) more water during storm events than an undammed reach, (2) the timing and dynamics of this attenuation are complicated and thus do not always result in a reduction of peak flows, and (3) the influence of beaver dams on stream hydraulics diminishes as the magnitude of flow events increase. Local geomorphic conditions, specifically the presence of off-channel features, affect the extent to which dams alter hydrograph dynamics. Although the magnitudes of peak flows are not substantially affected by the beaver dams considered in this study, results show that beaver dams temporarily impound a considerable amount of water throughout the duration of storms, which slows water conveyance to downstream reaches. Steady-state streamflow simulations at several streamflow magnitudes were also used to assess how beaver dams affect stream depths, velocities, and inundated areas, which are important factors affecting aquatic habitats. Results show that beaver dams result in a more hydraulically diverse stream, with substantially more inundated area, lower velocities, and greater depths than corresponding undammed scenarios. However, these differences diminish as streamflows increase and the channels overflow their banks and become hydraulically connected to adjacent floodplains. Together, these results confirm that beaver dams can fundamentally change urban stream channel hydraulics, but the influence of these dams is bounded by local geomorphic controls and is diminished at large streamflows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255039B","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"White, J.S., Jones, K.L., and Rounds, S.A., 2025, Effects of beaver dams and ponds on hydrologic and hydraulic responses of storm flows in urban streams of the Tualatin River Basin, northwestern Oregon, chap. B of Jones, K.L., and Smith, C.D., eds., Beavers in the Tualatin River Basin, northwestern Oregon: U.S. Geological Survey Scientific Investigations Report 2025–5039–B, 38 p., https://doi.org/10.3133/sir20255039B.","productDescription":"Report: viii, 38 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-164816","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":495978,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5039/b/sir20255039b.pdf","text":"Report","size":"6.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5039-B"},{"id":495981,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5039/b/images","text":"USGS data release","description":"USGS data release"},{"id":495982,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5039/b/sir20255039b.XML"},{"id":495977,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5039/b/coverthb.jpg"},{"id":495980,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1VZGC3Z","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydraulic models of two beaver affected reaches in the Tualatin Basin, Oregon"},{"id":495979,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255039b/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5039-B"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.375\n ],\n [\n -122.5,\n 45.375\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, Oregon 97204
Beaver dams can help streams connect to their floodplains. These floodplain connections can expand the range of available aquatic habitats and aid in the restoration of stream and floodplain function and processes. American beavers (Castor canadensis) occupy a wide variety of aquatic habitats; however, their ability to build dams, the agent of stream and floodplain change, is constrained in large part by three physical variables—local vegetation, topography, and hydrology.
These three physical variables are combined in the Beaver Restoration Assessment Tool (BRAT), a geographic information system-based utility that uses a Fuzzy Inference System (FIS) to estimate the capacity of each reach within a stream network to support beaver dams. In this study, version 1.0 of BRAT was adapted and applied to the entire perennial stream network of Tualatin River Basin in northwestern Oregon. Beaver-dam locations in the Tualatin River Basin were compiled to (1) define the distribution of dams in the basin during 2013–16 and (2) provide necessary data for calibrating and validating BRAT predictions. BRAT was calibrated to the current known distribution of dams, as compiled in the inventory. The input FIS equations of the original BRAT model were adjusted to account for local topographic conditions; specifically, the low gradient of many streams in the basin, although subsequent updates to BRAT may obviate the need for these changes.
Results from this modified BRAT model reasonably simulated the dam inventory. Results show that beavers can currently build the greatest density of dams, defined as number of dams per kilometer of stream, in the higher-gradient forested streams of the basin, whereas they can build the fewest number of dams per kilometer in urban streams along the lower-gradient valley floor. Estimated dam density was generally 5-15 dams per kilometer (km) for forested streams and 2-4 dams/km for urban streams. Improving riparian vegetation along urban streams may allow beavers to build on average four additional dams per kilometer compared to current conditions. Results from this study may help inform local stream and stormwater management by (1) identifying stream reaches with the most potential to support beaver dams, (2) determining the likely factors limiting potential for dam building, and (3) identifying potential areas where dam building may affect human infrastructure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255039A","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"White, J.S., Smith, C.D., Jones, K.L., and Rounds, S.A., 2025, Stream network capacity to support beaver dams in the Tualatin River Basin, northwestern Oregon, chap. A of Jones, K.L., and Smith, C.D., eds., Beavers in the Tualatin River Basin, northwestern Oregon: U.S. Geological Survey Scientific Investigations Report 2025–5039–A, 20 p., https://doi.org/10.3133/sir20255039A.","productDescription":"Report: viii, 20 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-102303","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":495854,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5039/a/sir20255039a.XML"},{"id":495853,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5039/a/images"},{"id":495852,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1SURYZ4","text":"USGS data release","description":"USGS data release","linkHelpText":"Stream network capacity to support beaver dams, Tualatin River Basin, northwest Oregon"},{"id":496245,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PZ57QP","text":"USGS data release","description":"USGS data release","linkHelpText":"Beaver dam locations and beaver activity in the Tualatin Basin, Oregon"},{"id":495851,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255039a/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5039-A"},{"id":495850,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5039/a/sir20255039a.pdf","size":"5.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5039-A"},{"id":495849,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5039/a/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.375\n ],\n [\n -122.5,\n 45.375\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, Oregon 97204
Growing interest in beaver-assisted restoration in the Tualatin River Basin of northwestern Oregon motivated a series of studies by the U.S. Geological Survey to assess the capacity of the stream network to support beaver dams and to evaluate the effects of beaver dams and ponds on urban streams. This multichapter volume describes the data collection from 2016–17 and the findings of these studies, which were done in partnership with Clean Water Services. Chapter A documents the locations of beaver dams in the Tualatin River Basin and how many beaver dams the stream network could support with existing and improved riparian vegetation. Beaver dam capacity was estimated by modifying existing tools to account for the low gradient of many streams in the Tualatin River Basin. Chapter B describes the effects of beaver dams and ponds on hydrologic and hydraulic responses of storm flows. Hydrologic and hydraulic responses for two urban stream reaches were compared with and without beaver dams and ponds and for a range of streamflow conditions using two-dimensional hydraulic models. Chapter C characterizes the effects of beaver dams and ponds on the transport and deposition of suspended sediment. Continuous turbidity, discrete suspended-sediment samples, and streamflow measurements collected during storms and base-flow periods were used to assess: (1) suspended-sediment loads upstream and downstream from two beaver-affected reaches, and (2) seasonal and longitudinal turbidity patterns. Chapter D describes the effects of beaver dams and ponds on longitudinal, spatial, and seasonal water-quality patterns. Continuous and synoptic water-quality data were collected along urban stream reaches, and net ecosystem production was calculated for two beaver-affected reaches. The findings of these studies illustrate that the effects of beaver dams and ponds on hydrology, hydraulics, suspended-sediment transport and deposition, and water quality are dependent on the characteristics of a stream reach (for example, channel gradient, groundwater exchange, and riparian vegetation) and the characteristics of beaver dams and ponds along that reach. This information can be used to consider the implications of beaver-assisted restoration in the Tualatin River Basin and the effects of beaver dams and ponds in urban streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255039","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"Jones, K.L, and Smith, C.D., eds., Beavers in the Tualatin River Basin, northwestern Oregon: U.S. Geological Survey Scientific Investigations Report 2025–5039, https://doi.org/10.3133/sir20255039.","productDescription":"Chapters A-D","onlineOnly":"Y","costCenters":[],"links":[{"id":496209,"rank":2,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.3133/fs20253022","text":"Fact Sheet 2025-3022","description":"FS 2025-3022","linkHelpText":"- Beaver dams and their effects on urban streams in the Tualatin River Basin, northwestern Oregon"},{"id":496091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5039/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.375\n ],\n [\n -122.5,\n 45.375\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, Oregon 97204
This report describes the techniques and methods for computing the mean-channel velocity and discharge using the entropy-based probability concept (probability concept). The method is an alternative to or augments standard streamgaging methods adopted by the U.S. Geological Survey (USGS). Although sensor technology for measuring the mean velocity and discharge has advanced, standard streamgaging and computational methods have remained relatively unchanged since the USGS established its first streamgage at the Rio Grande at Embudo, New Mexico in 1889.
Standard streamgaging methods rely on integrating velocities and depths measured at multiple verticals at a channel cross section (standard cross section) to compute a discharge. The probability concept computes discharge at a single vertical (y-axis) using the ratio of the mean-channel velocity (mean velocity) and maximum velocity, the measured maximum velocity, and the area as a function of stage at the standard cross section. Proper siting and operation and maintenance are required. If siting is conducted appropriately, the probability concept parameters and the y-axis stationing will be similar for different streamflow conditions. The timing of operation and maintenance visits should be based on hydrologic and meteorologic occurrences and seasonality and should capture low, medium, high, and opportunistic streamflow conditions.
Advantages of the probability concept are the capacity to (1) compute discharge time series immediately after streamgage siting, (2) compute discharge for complex streamflow conditions that cannot be quantified by stage-discharge methods, (3) augment time-series data where gaps exist, and (4) integrate with surface velocity sensors such as Doppler velocity radars and cameras, which are not subject to damage caused by ice, debris, and flood flows. Potential sources of bias in discharge derived from the probability concept include (1) rain, (2) wind, and (3) geomorphologic and hydraulic instabilities. Recommendations to address these biases are provided.
This report guides users through the steps to parameterize the probability concept, process field data, and compute the mean velocity and discharge using the probability concept.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm3A26","usgsCitation":"Fulton, J.W., Engel, F.L., Eggleston, J.R., and Chiu, C.-L., 2025, Computing discharge using the entropy-based probability concept: U.S. Geological Survey Techniques and Methods book 3, chap. A26, 66 p., https://doi.org/10.3133/tm3A26.","productDescription":"Report: viii, 66 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-138301","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":496208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/03/a26/tm3a26.pdf","text":"Report","size":"6.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2-A26"},{"id":496210,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/03/a26/Appendix_3_Wind_Bias.csv","text":"Appendix 3","size":"8.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"T and M 2-A26 Appendix 3","linkHelpText":"Correction for Wind Bias"},{"id":496207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/03/a26/coverthb.jpg"}],"contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
This report describes the steps and the theory to compute the speed and flow of water in streams using the probability concept.
","publicationDate":"2025-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Fulton, John W, 0000-0002-5335-0720","orcid":"https://orcid.org/0000-0002-5335-0720","contributorId":213630,"corporation":false,"usgs":true,"family":"Fulton","given":"John","middleInitial":"W,","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engel, Frank L. 0000-0002-4253-2625","orcid":"https://orcid.org/0000-0002-4253-2625","contributorId":218208,"corporation":false,"usgs":true,"family":"Engel","given":"Frank","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eggleston, Jack R. 0000-0001-6633-3041","orcid":"https://orcid.org/0000-0001-6633-3041","contributorId":204628,"corporation":false,"usgs":true,"family":"Eggleston","given":"Jack R.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chiu, Chao-Lin","contributorId":361821,"corporation":false,"usgs":false,"family":"Chiu","given":"Chao-Lin","affiliations":[{"id":86362,"text":"Emeritus - University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":949521,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70271978,"text":"70271978 - 2025 - Validation of gridded precipitation datasets for flood-typing in select conterminous U.S. basins","interactions":[],"lastModifiedDate":"2025-09-29T15:03:21.41738","indexId":"70271978","displayToPublicDate":"2025-09-26T07:58:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Validation of gridded precipitation datasets for flood-typing in select conterminous U.S. basins","docAbstract":"Gridded precipitation datasets are required for flood-typing historical annual peak streamflow events in basins across the Conterminous United States. Selected gridded precipitation datasets were validated over the period 1981–2013 through comparisons with gage data from the NOAA Global Historical Climatology Network daily (GHCNd). The ability of each gridded dataset to capture the spatiotemporal characteristics of daily precipitation, including multi-day extremes over six selected regions, was assessed using the Kling-Gupta Efficiency metric and its component statistics. Overall, the Parameter-elevation Regression on Independent Slopes Model and Livneh-unsplit were found to best match the spatiotemporal variability of the GHCNd precipitation data, including extremes. The Analysis of Record for Calibration was found to be the third best-performing dataset in most regions except in the western U.S. The performance of reanalysis datasets evaluated appears to be poor compared to gage-based datasets. The reanalysis datasets might not be able to skillfully capture precipitation amounts at the correct location and time. Gage- and radar-based datasets were found to have relatively small biases (within +/-10% on an annual basis), while reanalysis datasets were found to have larger positive apparent biases, especially in winter and spring in most regions. It is possible that the apparent overestimation of winter and spring precipitation in the reanalysis datasets might reflect snow undercatch at gages especially in the central U.S. An overall deterioration of performance for correlation and/or variability was also observed for the summer season compared to other seasons in the reanalysis datasets. Various precipitation datasets might need to be used for flood-typing during different periods from the late 19th century to present. Datasets from different sources have different biases and errors and might have to be homogenized using downscaling and bias-adjustment methods. Alternatively, precipitation thresholds used in some flood-typing schemes might have to be adjusted as a function of time.","language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/JHYEFF.HEENG-6500","usgsCitation":"Irizarry-Ortiz, M.M., and Murphy, S.Y., 2025, Validation of gridded precipitation datasets for flood-typing in select conterminous U.S. basins: Journal of Hydrologic Engineering, v. 30, no. 6, 04025042, 13 p., https://doi.org/10.1061/JHYEFF.HEENG-6500.","productDescription":"04025042, 13 p.","ipdsId":"IP-167576","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":496323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/jhyeff.heeng-6500","text":"Publisher Index Page"},{"id":496227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous 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Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Future forest conditions under alternative management and hydrological scenarios in the Upper Mississippi River floodplain","docAbstract":"Floodplain forests are being transformed by multiple pressures, prompting widespread management and restoration efforts. It is uncertain how disturbances, including hydrologic change, and management actions will interact to influence the ecology of these threatened forests.
This study examined the effects of alternative management and hydrologic regimes on forest succession at an Upper Mississippi River floodplain site with a restoration project in planning.
We used the spatially explicit forest landscape model, LANDIS-II, to simulate forest succession for 100 years under four hydrogeomorphic management scenarios, three forest management scenarios, and two scenarios of future hydrologic conditions. We evaluated changes in forest biomass and composition over time and assessed the relative importance of management actions and hydrologic change on succession.
Forest aboveground biomass decreased in all management-hydrology scenarios, especially in the wetter hydrological scenario. Intensified hydrogeomorphic and forest management scenarios reduced the magnitude and extent of biomass declines; however, they were unable to prevent overall declines in biomass or cause large shifts in tree species composition. Silver maple (Acer saccharinum) was projected to decrease in biomass, while increases in biomass were projected for several late-successional species including swamp white oak (Quercus bicolor). Among the factors influencing variation in biomass, forest management had the largest influence in the first 50 years of our simulations, but hydrological regime became the most important factor by the end of the century.
Our simulations indicate that management actions could play an important role in the conservation of floodplain forests, but their effectiveness will likely be limited if recent upward trends in flooding conditions in this system continue in the future. Thus, our results highlight both the potential benefits and limitations of management actions in the face of hydrologic change.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-025-02144-7","usgsCitation":"Trumper, M., De Jager, N.R., Van Appledorn, M., and Meier, A.R., 2025, Future forest conditions under alternative management and hydrological scenarios in the Upper Mississippi River floodplain: Landscape Ecology, v. 40, 186, 21 p., https://doi.org/10.1007/s10980-025-02144-7.","productDescription":"186, 21 p.","ipdsId":"IP-173189","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":496735,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-025-02144-7","text":"Publisher Index Page"},{"id":496589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, Wisconsin","otherGeospatial":"Reno Bottoms study area, Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.39940329445804,\n 43.68624998546463\n ],\n [\n -91.39940329445804,\n 43.44291861394157\n ],\n [\n -91.13016274447915,\n 43.44291861394157\n ],\n [\n -91.13016274447915,\n 43.68624998546463\n ],\n [\n -91.39940329445804,\n 43.68624998546463\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2025-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Trumper, Matthew Lewis 0000-0002-9881-7742","orcid":"https://orcid.org/0000-0002-9881-7742","contributorId":357508,"corporation":false,"usgs":true,"family":"Trumper","given":"Matthew Lewis","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meier, Andrew R.","contributorId":362320,"corporation":false,"usgs":false,"family":"Meier","given":"Andrew","middleInitial":"R.","affiliations":[{"id":16919,"text":"U.S. Army Corps of Engineers, St. Paul District","active":true,"usgs":false}],"preferred":false,"id":950316,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272041,"text":"70272041 - 2025 - Quantifying groundwater response and uncertainty in beaver-influenced mountainous floodplains using machine learning-based model calibration","interactions":[],"lastModifiedDate":"2025-11-14T15:42:24.888398","indexId":"70272041","displayToPublicDate":"2025-09-25T08:36:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying groundwater response and uncertainty in beaver-influenced mountainous floodplains using machine learning-based model calibration","docAbstract":"Beavers (Castor canadensis) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model-data integration workflow to address uncertainty in groundwater response to beaver-induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning-based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down-valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down-valley underflow, from 2% during dry pond periods to 20% during wet periods, serving as an analogy for conditions without and with beaver ponds. The study highlights the influence of floodplain structure on groundwater storage, water balance, and water quality impacted by beaver ponds. A thick gravel bed layer, with a large down-valley underflow, minimizes the effect of beaver-induced inundation on water quality. We emphasize the need for field-scale measurements of floodplain structure and improved characterization of evapotranspiration changes to reduce uncertainty in groundwater response.
","language":"English","publisher":"Wiley","doi":"10.1029/2024WR039192","usgsCitation":"Wang, L., Babey, T., Perzan, Z., Pierce, S., Briggs, M., Boye, K., and Maher, K., 2025, Quantifying groundwater response and uncertainty in beaver-influenced mountainous floodplains using machine learning-based model calibration: Water Resources Research, v. 61, no. 9, e2024WR039192, 28 p., https://doi.org/10.1029/2024WR039192.","productDescription":"e2024WR039192, 28 p.","ipdsId":"IP-175143","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":496711,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr039192","text":"Publisher Index Page"},{"id":496487,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Crested Butte","otherGeospatial":"Oh‐Be‐Joyful Creek‐Slate River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.04714288137164,\n 38.91446258506133\n ],\n [\n -107.04714288137164,\n 38.87739197906146\n ],\n [\n -106.97879177179699,\n 38.87739197906146\n ],\n [\n -106.97879177179699,\n 38.91446258506133\n ],\n [\n -107.04714288137164,\n 38.91446258506133\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"61","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Lijing","contributorId":258127,"corporation":false,"usgs":false,"family":"Wang","given":"Lijing","email":"","affiliations":[],"preferred":false,"id":949830,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Babey, Tristan 0000-0002-6897-3162","orcid":"https://orcid.org/0000-0002-6897-3162","contributorId":215172,"corporation":false,"usgs":false,"family":"Babey","given":"Tristan","email":"","affiliations":[{"id":39190,"text":"Université de Rennes","active":true,"usgs":false}],"preferred":false,"id":949831,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perzan, Zach","contributorId":362023,"corporation":false,"usgs":false,"family":"Perzan","given":"Zach","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":949832,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pierce, Samuel","contributorId":245448,"corporation":false,"usgs":false,"family":"Pierce","given":"Samuel","email":"","affiliations":[{"id":36408,"text":"SLAC National Accelerator Laboratory","active":true,"usgs":false}],"preferred":false,"id":949833,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222756,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":949834,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boye, Kristin","contributorId":219255,"corporation":false,"usgs":false,"family":"Boye","given":"Kristin","email":"","affiliations":[{"id":39977,"text":"Stanford Synchrotron Radiation Lightsource (SSRL)","active":true,"usgs":false}],"preferred":false,"id":949835,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maher, Kate","contributorId":245461,"corporation":false,"usgs":false,"family":"Maher","given":"Kate","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":949836,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70271947,"text":"70271947 - 2025 - Linking stream-reach nitrogen loads and groundwater “reachsheds” to inform wastewater-nitrogen management actions, Cape Cod, Massachusetts","interactions":[],"lastModifiedDate":"2025-09-25T14:08:33.913615","indexId":"70271947","displayToPublicDate":"2025-09-24T08:58:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Linking stream-reach nitrogen loads and groundwater “reachsheds” to inform wastewater-nitrogen management actions, Cape Cod, Massachusetts","docAbstract":"Salt marshes are dynamic biogeomorphic systems reliant on autochthonous and allochthonous input to maintain their three-dimensional configuration. Sea-level rise, subsidence, and sediment deficits can lead to submergence, open-water expansion, and ultimately loss of the vegetated marsh plain and associated ecosystem services. Widely used management-focused models focus on vegetation zonation in response to sea level but neglect sediment transport processes and geomorphic change. Process-based research models attempt to represent complex physical and biogeomorphic interactions but operate on spatiotemporal scales that are not directly transferable to restoration or management. Here we bridge these two paradigms and present a novel geomorphic model (UBMorph) based on the sediment-based lifespan concept that accounts for sea-level rise and open-water expansion to predict changes in salt marsh area in Chesapeake Bay. Model parameters such as surface accretion rate and elevation-to-areal loss fraction are selected using a separate, fully coupled biogeomorphic model (MarshMorpho2D) and the predicted lifespan is then compared with high marsh coverage from a zonation model (SLAMM). Across all of Chesapeake Bay, UBMorph estimates an overall loss of 404 km2 (37%) of vegetated marsh area under a dynamic 3–12 mm/y sea-level rise scenario (between 2010 and 2110). We then demonstrate a management-focused application of UBMorph and SLAMM used in tandem, for developing both a marsh condition and restoration model of the Chesapeake Bay portion of Maryland. The restoration model, which includes hydrologic intervention and sediment placement actions, indicates that ~ 400 km2 of marsh require either no intervention or low effort hydrologic intervention presently, whereas if no action is taken, over 700 km2 will require high effort intervention by 2070. This synthesis of research models with management-focused decision models demonstrates a tangible advance in bridging the gap between process-based research and restoration needs.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-025-01618-w","usgsCitation":"Ganju, N., Ackerman, K., Defne, Z., Mariotti, G., Curson, D., Posnik, Z., Carr, J., and Grand, J., 2025, A simple predictive model for salt marsh internal deterioration under sea-level rise and sediment deficits: Application to Chesapeake Bay: Estuaries and Coasts, v. 48, 178, 19 p., https://doi.org/10.1007/s12237-025-01618-w.","productDescription":"178, 19 p.","ipdsId":"IP-177384","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":496166,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-025-01618-w","text":"Publisher Index Page"},{"id":496080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.56007683480865,\n 39.662435478342644\n ],\n [\n -77.02265802866081,\n 39.662435478342644\n ],\n [\n -77.02265802866081,\n 36.851268885158845\n ],\n [\n -75.56007683480865,\n 36.851268885158845\n ],\n [\n -75.56007683480865,\n 39.662435478342644\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationDate":"2025-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":949455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Kate 0000-0003-3925-721X","orcid":"https://orcid.org/0000-0003-3925-721X","contributorId":293631,"corporation":false,"usgs":true,"family":"Ackerman","given":"Kate","email":"","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":949456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Defne, Zafer 0000-0003-4544-4310 zdefne@usgs.gov","orcid":"https://orcid.org/0000-0003-4544-4310","contributorId":5520,"corporation":false,"usgs":true,"family":"Defne","given":"Zafer","email":"zdefne@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":949457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mariotti, Giulio","contributorId":207541,"corporation":false,"usgs":false,"family":"Mariotti","given":"Giulio","email":"","affiliations":[{"id":37557,"text":"Louisiana State University, Baton Rouge LA","active":true,"usgs":false}],"preferred":false,"id":949458,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Curson, David","contributorId":361793,"corporation":false,"usgs":false,"family":"Curson","given":"David","affiliations":[{"id":86352,"text":"Audubon Mid-Atlantic","active":true,"usgs":false}],"preferred":false,"id":949459,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Posnik, Zachary","contributorId":361794,"corporation":false,"usgs":false,"family":"Posnik","given":"Zachary","affiliations":[{"id":27800,"text":"National Audubon Society","active":true,"usgs":false}],"preferred":false,"id":949460,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carr, Joel 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":220098,"corporation":false,"usgs":true,"family":"Carr","given":"Joel","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":949461,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grand, Joanna","contributorId":291964,"corporation":false,"usgs":false,"family":"Grand","given":"Joanna","email":"","affiliations":[{"id":27800,"text":"National Audubon Society","active":true,"usgs":false}],"preferred":false,"id":949462,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70271731,"text":"ofr20251050 - 2025 - Upper Mississippi River Restoration future hydrology meeting series","interactions":[],"lastModifiedDate":"2026-02-03T15:30:40.283753","indexId":"ofr20251050","displayToPublicDate":"2025-09-22T12:13:11","publicationYear":"2025","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":"2025-1050","displayTitle":"Upper Mississippi River Restoration Future Hydrology Meeting Series","title":"Upper Mississippi River Restoration future hydrology meeting series","docAbstract":"The Upper Mississippi River Restoration (UMRR) program, a broad partnership of State and Federal agencies administered by the U.S. Army Corps of Engineers, integrates ecosystem monitoring, research, and modeling to rehabilitate habitat and evaluate ecosystem trends over time in the Upper Mississippi River System. Hydrologic data are integral to the UMRR program because they are used in scientific research, decision-making, and restoration project planning. However, a lack of quantitative hydrologic data representing potential future conditions limits the ability to complete informative research on how future conditions may affect river ecology, achieve management goals, and design restoration projects for 50-year horizons.
The U.S. Geological Survey and the U.S. Army Corps of Engineers led a series of workshops with UMRR partners to (1) prioritize needs for understanding future hydrology, (2) discuss appropriate datasets that could address these needs, and (3) develop a plan for acquiring and distributing a hydrologic dataset of potential future conditions. Agency priorities for understanding future hydrology were broad, spanning ecologic, geomorphic, resource management, and engineering disciplines, and were identified for a range of spatial (project site, navigation pool, reach, system) and temporal (daily, seasonal, annual) scales. The LOcalized Constructed Analogs-Variable Infiltration Capacity-mizuRoute hydrologic data products were identified as a potential source of off-the-shelf data to meet UMRR priority needs but warranted a robust quantitative evaluation. The final meeting in the series scoped a proposal to evaluate the LOcalized Constructed Analogs-Variable Infiltration Capacity-mizuRoute hydrologic data products for use in UMRR applications, including contingencies if the data were determined to be unreliable.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251050","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Van Appledorn, M., and Sawyer, L., 2025, Upper Mississippi River Restoration future hydrology meeting series: U.S. Geological Survey Open-File Report 2025–1050, 93 p., https://doi.org/10.3133/ofr20251050.","productDescription":"vii, 93 p.","numberOfPages":"106","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-144284","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":495840,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1050/coverthb.jpg"},{"id":495844,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251050/full"},{"id":495843,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1050/images/"},{"id":495842,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1050/ofr20251050.XML"},{"id":495841,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1050/ofr20251050.pdf","text":"Report","size":"4.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1050"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.2103238355043,\n 37.043422473537504\n ],\n [\n -87.69596012242188,\n 41.69069025518516\n ],\n [\n -89.11501122546446,\n 44.87351523241463\n ],\n [\n -89.14678376857329,\n 46.12049934311611\n ],\n [\n -92.37672035941334,\n 46.134727618766064\n ],\n [\n -94.31468231391703,\n 47.910742701911886\n ],\n [\n -96.86686171848238,\n 47.08812323847167\n ],\n [\n -94.08172387608387,\n 40.82297944373079\n ],\n [\n -89.40096330837447,\n 37.051916822243555\n ],\n [\n -89.2103238355043,\n 37.043422473537504\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, Wisconsin 54603
A series of workshops was held so participants from several agencies could work together to prioritize needs for understanding future hydrologic scenarios, discuss appropriate datasets that could address these needs, and develop a plan for acquiring and distributing a hydrologic dataset representing potential future conditions. Agency priorities for understanding future hydrology spanned ecologic, geomorphic, resource management, and engineering disciplines and were identified for a range of spatial (project site, navigation pool, reach, system) and temporal (daily, seasonal, annual) scales. Participants described desired characteristics of a hydrologic dataset of potential future conditions that could meet agency priority needs and developed a workflow to evaluate a readily available data product.
","publicationDate":"2025-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":949216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sawyer, Lucie","contributorId":345904,"corporation":false,"usgs":false,"family":"Sawyer","given":"Lucie","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":949217,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70274071,"text":"70274071 - 2025 - Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities","interactions":[],"lastModifiedDate":"2026-02-23T15:34:30.951807","indexId":"70274071","displayToPublicDate":"2025-09-20T09:23:24","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"title":"Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities","docAbstract":"There is increasing awareness of the global diversity of tidal forested wetlands (TFWs) and their significance in the provision of ecosystem services. These ecosystems, including mangrove forests, tidal freshwater forested wetlands, supratidal forests and transitional forests together span multiple climatic zones, geomorphic settings, and inundation and salinity regimes. We utilise case studies across five continents to demonstrate the state of knowledge among TFWs. Intertidal mangroves are the best-defined of the TFWs thanks to decades of research on their geomorphology, hydrology and ecology across their broad distribution. Non-mangrove forest settings, however, demonstrate more diverse hydrological, biochemical and vegetation conditions. In many cases, non-mangrove forests are situated at upper intertidal or supratidal elevations, where surface waters and groundwater are subject to interactions between tides freshwater inputs. Salinity datasets show variations ranging from tidal freshwater forested wetlands and ‘low-salinity mangroves’ to mesohaline or marine salinities, often with high temporal variability. While the floristic composition of non-mangrove forests vary among biogeographic regions, locally dominant TFW species are commonly distributed beyond the tidal niche into non-tidal wetland and upland forests. This presents challenges for traditional remote sensing approaches to ecosystem mapping, which are mostly lacking for non-mangrove forests. Geomorphic approaches and developments in machine learning offer opportunities to address this.
","language":"English","publisher":"Earth ArXiv","doi":"10.31223/X5ZF2B","usgsCitation":"Kelleway, J.J., Noe, G.E., Krauss, K., Brophy, L., Conner, W.H., Duberstein, J.A., Friess, D.A., Gedan, K., White, E., Adame, M.F., Adams, J.B., Carvalho, R.C., Freddie, A., Ikenna, I.N., Ocasio, E.R., Owers, C.J., Sasmito, S., Swales, A., Stewart-Sinclair, P., Ward, R.D., Zabarte-Maeztu, I., 2025, Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities, preprint posted September 20, 2025, https://doi.org/10.31223/X5ZF2B.","productDescription":"85 p.","ipdsId":"IP-185904","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":500404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kelleway, J. 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Overall, TE water withdrawal and consumption trends declined across CONUS by 14,335 and 278 million liters/day, respectively. Decreasing water withdrawal and consumption trends for TE plants are driven largely by switching from coal-fired plants to other generation technologies. TE plant cooling system technology has also changed, with large declining trends for TE plants using once-through cooling systems and small increasing consumption trends for TE plants using recirculating tower cooling systems. Fifteen hydrologic regions have decreasing trends in withdrawals and consumption. The largest decreases are for coal-fired plants using once-through freshwater cooling systems in the Great Lakes and Ohio hydrologic regions. Natural gas combined cycle plants with recirculating tower cooling systems have increased water consumption trends across most of the CONUS hydrologic regions. Some TE plants with recirculating tower or once-through cooling systems withdraw water volumes that on average are close to or exceed average simulated streamflows. Most of these situations occur in the central and eastern U.S., potentially leading to water availability issues among competing water needs, ecosystem impacts from thermal pollution, and power generation constraints.
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Massachusetts","interactions":[],"lastModifiedDate":"2026-02-03T15:29:05.375527","indexId":"sir20255082","displayToPublicDate":"2025-09-19T09:50:00","publicationYear":"2025","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":"2025-5082","displayTitle":"Methods for Estimating Selected Low-Flow Statistics at Gaged and Ungaged Stream Sites in Massachusetts","title":"Methods for estimating selected low-flow statistics at gaged and ungaged stream sites in Massachusetts","docAbstract":"The U.S. Geological Survey, in cooperation with the Massachusetts Department of Conservation and Recreation, Office of Water Resources, computed selected at-site streamflow statistics at U.S. Geological Survey streamgages in and near Massachusetts and developed regional regression equations for estimating selected streamflows at ungaged stream sites in Massachusetts. Two sets of regional regression equations were developed: (1) the “mainland” equations, for mainland Massachusetts excluding the area covered by the second set, and (2) the “southeastern” equations, for the Plymouth-Carver-Kingston-Duxbury aquifer area in southeastern Massachusetts and for Cape Cod. The regression equations and at-site statistics may be used by Federal, State, and local water managers in addressing water-resources issues relevant in Massachusetts.
Regional regression analyses for the mainland equations were developed to estimate the following 27 streamflow statistics: 99-, 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, and 50-percent flow durations; monthly June, July, August, and September 90- and 50-percent flow durations; February, June, and August median of the monthly means; harmonic mean; and medians of the following annual low-flow frequency statistics: 7-day; 7-day, 2-year; 7-day, 10-year; 30-day, 2-year; and 30-day, 10-year. The analyses used 81 streamgages with minimal to no regulations in and near Massachusetts. The regression analyses determined that four basin characteristics—drainage area, combined hydrologic soils A and B, streamflow variability index, and annual mean temperature—were the only significant explanatory variables for the different mainland equations.
Regional regression equations were developed for the Plymouth-Carver-Kingston-Duxbury aquifer area in southeastern Massachusetts and Cape Cod, because surface-water drainage areas and groundwater contributing areas do not always coincide in this area of the State. The regression analyses to estimate 10 flow durations from the 99th to 50th percentiles used 18 streamflow sites with some occasional minor regulations—because there are few unregulated streams in southeastern Massachusetts. The analyses determined that groundwater contributing area and storage (combined water bodies and wetlands) were the only significant explanatory variables in the southeastern equations.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Forecasts of streamflow drought, when streamflow declines below typical levels, are notably less available than for floods or meteorological drought, despite widespread impacts. To address this gap, we apply machine learning (ML) models to forecast streamflow drought 1-13 weeks into the future at > 3,000 streamgage locations across the conterminous United States (CONUS). We applied two ML methods (Long short-term memory (LSTM) neural networks; Light Gradient-Boosting Machine - LightGBM) and two benchmark model approaches (persistence; Autoregressive Integrated Moving Average - ARIMA) to predict weekly streamflow percentiles with independent models for each forecast horizon. To explore whether a training focus on dry weeks improved performance, both ML models were trained using all percentiles (LSTM-all, LightGBM-all) and only percentiles below 30% (LSTM<30, LightGBM<30). We evaluated model performance regionally and nationally for drought occurrence (the classification performance for a future date) and for drought onset/termination (performance identifying drought starts and ends). ML models generally performed worse than the persistence model for discrete classification (moderate, severe, extreme drought) of drought occurrence but exceeded the benchmark models for onset/termination. ML models outperformed benchmarks in predicting continuous streamflow percentiles below 30%. Occurrence performance was better for less intense droughts and shorter forecast horizons, with the ML models having predictive power at 1-4 week horizons for severe droughts (10th percentile threshold). All models struggled to forecast onset, though the best ML model was the LSTM<30 (sensitivity of 22%). Termination performance was greater, with the drought termination performance greatest for the LightGBM-all model. When estimating model uncertainty, the LSTM<30 model had the narrowest 90% percentile interval with closest to optimal capture. This work highlights the challenges and opportunities to further advance hydrological drought forecasting and supports an experimental operational streamflow drought assessment and forecast tool.
","language":"English","publisher":"Earth ArXiv","doi":"10.31223/X56X77","usgsCitation":"Hammond, J., Goodling, P.J., Diaz, J.A., Corson-Dosch, H.R., Heldmyer, A.J., Hamshaw, S.D., McShane, R., Ross, J.C., Sando, R., Simeone, C., Smith, E., Staub, L.E., Watkins, D., Wieczorek, M., Wnuk, K., and Zwart, J.A., 2025, Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations: EarthArXiv, https://doi.org/10.31223/X56X77.","productDescription":"55 p.","ipdsId":"IP-179826","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":495839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Continental United States","geographicExtents":"{\n 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method for inferring image acquisition time from shadow orientation","interactions":[],"lastModifiedDate":"2025-09-19T14:56:19.138181","indexId":"70271686","displayToPublicDate":"2025-09-18T09:55:14","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Sundial: A method for inferring image acquisition time from shadow orientation","docAbstract":"Aerial photography and satellite imagery can be used to characterize landscape change over time and help to understand how these changes are related to climate and hydrology. Publicly available optical imagery from sources such as the United States National Agricultural Imagery Program (NAIP) is particularly valuable in this context due to its high temporal and spatial resolution. However, the exact time an image was acquired is often unknown, which complicates, if not precludes, linking images with other types of high temporal resolution data, such as streamflow records. In this letter, we propose a ‘sundial method’ to infer image acquisition time from shadow orientation. This approach involves measuring the direction of a shadow on the image and using solar geometry calculated for the known image date and location to infer the former sun position. Time estimates for 16 Worldview satellite and six NAIP aerial images based on 407 independent measurements of shadow orientation demonstrate the sundial method had an error of 2.1 ± 3.4 min, indicating that image acquisition times can be inferred with a high degree of accuracy and precision. Sensitivity analyses confirm the robustness of the method across different object types, shadow lengths, and solar zenith angles, while also providing practical guidelines regarding the number of measurements required and errors associated with uncertainty in the image date.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.70157","usgsCitation":"Bae, I., Legleiter, C.J., and Yager, E., 2025, Sundial: A method for inferring image acquisition time from shadow orientation: Earth Surface Processes and Landforms, v. 50, no. 12, e70157, 10 p., https://doi.org/10.1002/esp.70157.","productDescription":"e70157, 10 p.","ipdsId":"IP-173049","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":495797,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Bae, Inhyeok 0000-0003-3942-4110","orcid":"https://orcid.org/0000-0003-3942-4110","contributorId":347541,"corporation":false,"usgs":false,"family":"Bae","given":"Inhyeok","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":949025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":949024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yager, Elowyn 0000-0002-3382-2356","orcid":"https://orcid.org/0000-0002-3382-2356","contributorId":347542,"corporation":false,"usgs":false,"family":"Yager","given":"Elowyn","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":949026,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70272608,"text":"70272608 - 2025 - Native crayfish shows high desiccation tolerance and potential to outcompete invader","interactions":[],"lastModifiedDate":"2025-11-24T15:46:26.826539","indexId":"70272608","displayToPublicDate":"2025-09-18T08:40:23","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Native crayfish shows high desiccation tolerance and potential to outcompete invader","docAbstract":"Biological invasions threaten global biodiversity, with aquatic systems being particularly susceptible. Invasive crayfish drive native crayfish imperilment in North America and worldwide. Despite the probable increase in extreme hydrological events, the synergistic effects from invasive species and drought on crayfish are understudied. The invasion of Faxonius neglectus chaenodactylus in the Spring River drainage (AR, MO) has likely contributed to native crayfish displacement through mechanisms related to stream drying. F. n. chaenodactylus may further expand its range, posing a threat to other native species like Faxonius marchandi, the Mammoth Spring crayfish, a narrow-ranged endemic. We used stream mesocosms to examine (1) effects of invasive species on F. marchandi growth and survival, (2) responses of both species to simulated stream drying, and (3) additive effects of invasion and drought on F. marchandi. Additionally, we assessed differential desiccation tolerance using environmental chambers. We found no significant interaction between drought and competition nor any significant main effects on crayfish mass change or survival; however, interspecific competition significantly reduced length change in F. n. chaenodactylus. All populations showed differential desiccation tolerance, with survival rates varying significantly (p < 0.05) and carapace length (CL) positively influencing survival (p < 0.01). Understanding the effects of drought, invasion, and their interactions on native crayfish is essential, particularly given the potential expansion of an invader and increasing drought intensity from future climate change.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10530-025-03675-5","usgsCitation":"Bayer, L.M., and Magoulick, D.D., 2025, Native crayfish shows high desiccation tolerance and potential to outcompete invader: Biological Invasions, v. 27, 216, 16 p., https://doi.org/10.1007/s10530-025-03675-5.","productDescription":"216, 16 p.","ipdsId":"IP-170452","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":496826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri","otherGeospatial":"Spring River drainage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.82880316322702,\n 36.78950866025838\n ],\n [\n -92.82880316322702,\n 35.94152912534426\n ],\n [\n -91.54070609793327,\n 35.94152912534426\n ],\n [\n -91.54070609793327,\n 36.78950866025838\n ],\n [\n -92.82880316322702,\n 36.78950866025838\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","noUsgsAuthors":false,"publicationDate":"2025-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Bayer, Leah M.","contributorId":363007,"corporation":false,"usgs":false,"family":"Bayer","given":"Leah","middleInitial":"M.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":950908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":950909,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264311,"text":"70264311 - 2025 - Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning","interactions":[],"lastModifiedDate":"2025-11-26T16:47:08.362588","indexId":"70264311","displayToPublicDate":"2025-09-17T10:34:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning","docAbstract":"Large-scale hydrologic models are increasingly being developed for operational use in the forecasting and planning of water resources. However, the predictive strength of such models depends on how well they resolve various functions of catchment hydrology, which are influenced by gradients in climate, topography, soils, and land use. Most assessments of hydrologic model uncertainty have been limited to traditional statistical methods. Here, we present a proof-of-concept approach that uses interpretable machine learning techniques to provide post hoc assessment of model sensitivity and process deficiency in hydrologic models. We train a random forest model to predict the Kling–Gupta efficiency (KGE) of National Water Model (NWM) and National Hydrologic Model (NHM) streamflow predictions for 4383 stream gauges in the conterminous United States. Thereafter, we explain the local and global controls that 48 catchment attributes exert on KGE prediction using interpretable Shapley values. Overall, we find that soil water content is the most impactful feature controlling successful model performance, suggesting that soil water storage is difficult for hydrologic models to resolve, particularly for arid locations. We identify nonlinear thresholds beyond which predictive performance decreases for NWM and NHM. For example, soil water content less than 210 mm, precipitation less than 900 mm yr−1, road density greater than 5 km km−2, and lake area percent greater than 10 % contributed to lower KGE values. These results suggest that improvements in how these influential processes are represented could result in the largest increases in NWM and NHM predictive performance. This study demonstrates the utility of interrogating process-based models using data-driven techniques, which has broad applicability and potential for improving the next generation of large-scale hydrologic models.
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