Publications
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Filter Total Items: 31
CONUS404: The NCAR-USGS 4-km long-term regional hydroclimate reanalysis over the CONUS
A unique, high-resolution, hydroclimate reanalysis, 40-plus-year (October 1979–September 2021), 4 km (named as CONUS404), has been created using the Weather Research and Forecasting Model by dynamically downscaling of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate dataset (ERA5) over the conterminous United States. The p
Authors
R. M. Rasmussen, F. Chen, C. H. Liu, K. Ikeda, A. Prein, J. Kim, T. Schneider, A. Dai, D. Gochis, A. Dugger, Y. Zhang, A. Jaye, J. Dudhia, C. He, M. Harrold, L. Xue, S. Chen, A. Newman, E. Dougherty, R. Abolafia-Rozenzweig, N. Lybarger, Roland J. Viger, David P. Lesmes, Katherine Skalak, John Brakebill, Donald Walter Cline, Krista A. Dunne, K. Rasmussen, G. Miguez-Macho
Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations
Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations
Authors
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White
Preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York
Manganese and 1,4-dioxane in groundwater underlying Long Island, New York, were modeled with machine learning methods to demonstrate the use of these methods for mapping contaminants in groundwater in the Long Island aquifer system. XGBoost, a gradient boosted, ensemble tree method, was applied to data from 910 wells for manganese and 553 wells for 1,4-dioxane. Explanatory variables included soil
Authors
Leslie A. DeSimone
When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates
Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of err
Authors
Stanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth Lindsey
Simulating debris flow and levee formation in the 2D shallow flow model D-Claw: Channelized and unconfined flow
Debris flow runout poses a hazard to life and infrastructure. The expansion of human population into mountainous areas and onto alluvial fans increases the need to predict and mitigate debris flow runout hazards. Debris flows on unconfined alluvial fans can exhibit spontaneous self-channelization through levee formation that reduces lateral spreading and extends runout distances compared to unchan
Authors
Ryan P. Jones, Francis K. Rengers, Katherine R. Barnhart, David L. George, Dennis M. Staley, Jason W. Kean
National Land Cover Database 2019: A new strategy for creating clean leaf-on and leaf-off Landsat composite images
National Land Cover Database (NLCD) 2019 is a new epoch of national land cover products for the conterminous United States. Image quality is fundamental to the quality of any land cover product. Image preprocessing has often taken a considerable proportion of overall time and effort for this kind of national project. An approach to prepare image inputs for NLCD 2019 production was developed to ens
Authors
Suming Jin, Jon Dewitz, Patrick Danielson, Brian Granneman, Catherine Costello, Zhe Zhu
Toward consistent change detection across irregular remote sensing time series observations
The use of remote sensing in time series analysis enables wall-to-wall monitoring of the land surface and is critical for assessing and understanding land cover and land use change and for understanding the Earth system as a whole. However, variability in remote sensing observation frequency through time and across space presents challenges for producing consistent change detection results through
Authors
Heather J. Tollerud, Zhe Zhu, Kelcy Smith, Danika F. Wellington, Reza Hussain, Donna Viola
Simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States
While nonstationary flood frequency analysis (NSFFA) methods have proliferated, few studies have rigorously compared them for modeling changes in both the central tendency and variability of annual peak-flow series, also known as the annual maximum series (AMS), in hydrologically diverse areas. Through Monte Carlo experiments, we appraise five methods for updating estimates of 10- and 100-year flo
Authors
Jory Seth Hecht, Nancy A. Barth, Karen R. Ryberg, Angela Gregory
Development of the LCMAP annual land cover product across Hawai'i
Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the U.S. Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full Landsat records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the
Authors
Congcong Li, George Z. Xian, Danika F. Wellington, Kelcy Smith, Josephine Horton, Qiang Zhou
Tree regrowth duration map from LCMAP collection 1.0 land cover products in the conterminous United States, 1985–2017
Forest covers about one-third of the land area of the conterminous United States (CONUS) and plays an important role in offsetting carbon emissions and supporting local economies. Growing interest in forests as relatively cost-effective nature-based climate solutions, particularly restoration and reforestation activities, has increased the demand for information on forest regrowth and recovery fol
Authors
Qiang Zhou, George Z. Xian, Josephine Horton, Danika F. Wellington, Grant Domke, Roger F. Auch, Congcong Li, Zhe Zhu
Ignoring species availability biases occupancy estimates in single-scale occupancy models
Most applications of single-scale occupancy models do not differentiate between availability and detectability, even though species availability is rarely equal to one. Species availability can be estimated using multi-scale occupancy models; however, for the practical application of multi-scale occupancy models, it can be unclear what a robust sampling design looks like and what the statistical p
Authors
Graziella Vittoria Direnzo, David A. W. Miller, Evan H. Campbell Grant
Precision of headwater stream permanence estimates from a monthly water balance model in the Pacific Northwest, USA
Stream permanence classifications (i.e., perennial, intermittent, ephemeral) are a primary consideration to determine stream regulatory status in the United States (U.S.) and are an important indicator of environmental conditions and biodiversity. However, at present, no models or products adequately describe surface water presence for regulatory determinations. We modified the Thornthwaite monthl
Authors
Konrad Hafen, Kyle Blasch, Paul E. Gessler, Roy Sando, Alan H. Rea