Lauren Koenig
Lauren Koenig, Ph.D., is a data scientist and ecologist in the USGS Water Resources Mission Area.
I am excited about working collaboratively to translate large, complex datasets into better understanding of freshwater ecosystems and water resources. As a data scientist in the Analysis and Prediction Branch of the Water Resources Mission Area, I develop reproducible workflows that combine diverse observational datasets and modeling approaches to predict water quality in streams and rivers. Prior to joining USGS, I earned my Ph.D. from the University of New Hampshire, where I studied how water, energy, and nutrients move and cycle within river networks.
Professional Experience
2021 – Present Data Scientist, U.S. Geological Survey
2021 Postdoctoral Associate, Flathead Lake Biological Station, University of Montana
2017 – 2021 Postdoctoral Associate, University of Connecticut
Education and Certifications
Ph.D. Earth and Environmental Science, 2017. University of New Hampshire
B.S. Aquatic Biology, 2010. University of California – Santa Barbara
Science and Products
Downscaling and multi-scale modeling of stream temperature in five watersheds of the Delaware River Basin, 1979-2021
This model archive (Fan et al. 2025a) provides all data, code, and model outputs used in the corresponding manuscript (Fan et al. 2025b) to test machine learning (ML) methods for downscaling and multi-scale modeling of stream temperature to combine an ML model and/or input data at coarse spatial resolution with an ML model and/or input data at fine spatial resolution to predict stream...
Distance matrices and river-network crosswalks for the Geospatial Fabric v1.1 to support data-driven models of water quality in U.S. rivers
This data release contains hydrologic network information to support national modeling of water quality in streams and rivers, including distance matrices that represent the spatial relationships among modeled river reaches. These data can be used as inputs to data-driven models that leverage information about spatial proximity to make predictions of water quantity or quality. The...
Estimated seasonal nitrogen and phosphorus loads in selected streams of the conterminous United States, 1999 - 2020
Estimated seasonal total nitrogen and total phosphorus loads during 1999 through 2020 in selected streams of the conterminous United States and water-quality and stream flow data used to generate those estimates are presented in this dataset. Loads were generated as part of the Integrated Water-Availability Assessment (IWAA) Program of the U.S. Geological Survey (www.usgs.gov/mission...
Delaware River Basin Stream Salinity Machine Learning Models and Data
This model archive contains the input data, model code, and model outputs for machine learning models that predict daily non-tidal stream salinity (specific conductance) for a network of 459 modeled stream segments across the Delaware River Basin (DRB) from 1984-09-30 to 2021-12-31. There are a total of twelve models from combinations of two machine learning models (Random Forest and...
Data and model code used to evaluate a process-guided deep learning approach for in-stream dissolved oxygen prediction
This model archive contains data and code used to assess the use of process-informed multi-task deep learning models for predicting in-stream dissolved oxygen concentrations. Three holdout experiments were run to assess model performance, including a temporal holdout experiment, a spatial holdout experiment with similar sites held out, and a spatial holdout experiment with dissimilar...
Long-term water-quality trends for rivers and streams within the contiguous United States using Weighted Regressions on Time, Discharge, and Season (WRTDS) (ver. 1.1, March 2025)
The U.S. Geological Survey (USGS) Water Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand. It is also improving understanding of the natural and human factors affecting the balance between supply and demand. A key...
Model Code, Outputs, and Supporting Data for Approaches to Process-Guided Deep Learning for Groundwater-Influenced Stream Temperature Predictions
This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately incorporate groundwater-discharge processes. We assessed the performance of an existing process-guided deep learning stream temperature model of the Delaware River Basin (USA) and explored four...
Distribution, frequency, and global extent of hypoxia in rivers
To assess the distribution, frequency, and global extent of riverine hypoxia, we compiled 118 million paired dissolved oxygen (DO) and water temperature measurements from 125,158 unique locations in rivers in 93 countries and territories across the globe. The dataset also includes site characteristics derived from StreamCat, the National Hydrography and HydroAtlas datasets and proximal...
Data to support water quality modeling efforts in the Delaware River Basin
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this...
Predictive understanding of stream salinization in a developed watershed using machine learning
Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a...
Authors
Jared David Smith, Lauren Elizabeth Koenig, Margaux Jeanne Sleckman, Alison P. Appling, Jeffrey M Sadler, Vincent T. DePaul, Zoltan Szabo
Evaluating a process-guided deep learning approach for predicting dissolved oxygen in streams
Dissolved oxygen (DO) is a critical water quality constituent that governs habitat suitability for aquatic biota, biogeochemical reactions and solubility of metals in streams. Recently introduced high-frequency sensors have increased our ability to measure DO, but we still lack the capacity to understand and predict DO concentrations at high spatial resolutions or in unmonitored...
Authors
Jeffrey M Sadler, Lauren Elizabeth Koenig, Galen Gorski, Alice M. Carter, Robert O. Hall
Train, inform, borrow, or combine? Approaches to process-guided deep learning for groundwater-influenced stream temperature prediction
Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the...
Authors
Janet R. Barclay, Simon Nemer Topp, Lauren Elizabeth Koenig, Margaux Jeanne Sleckman, Alison P. Appling
Extent, patterns, and drivers of hypoxia in the world's streams and rivers
Hypoxia in coastal waters and lakes is widely recognized as a detrimental environmental issue, yet we lack a comparable understanding of hypoxia in rivers. We investigated controls on hypoxia using 118 million paired observations of dissolved oxygen (DO) concentration and water temperature in over 125,000 locations in rivers from 93 countries. We found hypoxia (DO
Authors
Joanna R Blaszczak, Lauren E Koenig, Francine H. Mejia, Alice M. Carter, Lluis Gómez-Gener, Christoper L Dutton, Nancy B. Grimm, Judson Harvey, Ashley M. Helton, Matthew J. Cohen
Light and flow regimes regulate the metabolism of rivers
Mean annual temperature and mean annual precipitation drive much of the variation in productivity across Earth's terrestrial ecosystems but do not explain variation in gross primary productivity (GPP) or ecosystem respiration (ER) in flowing waters. We document substantial variation in the magnitude and seasonality of GPP and ER across 222 US rivers. In contrast to their terrestrial...
Authors
Emily. S Bernhardt, Philip Savoy, Michael J Vlah, Alison Paige Appling, Lauren E Koenig, Robert O Hall Jr., Maite Arroita, Joanna Blaszczak, Alice M. Carter, Matthew J. Cohen, Judson Harvey, James B. Heffernan, Ashley M. Helton, J.D. Hosen, Lily Kirk, William H. McDowell, Emily H. Stanley, Charles Yackulic, Nancy B. Grimm
Non-USGS Publications**
Wollheim WM, Harms TK, Robison AL, Koenig LE, Helton AM, Song C, et al. Superlinear scaling of riverine biogeochemical function with watershed size. Nature communications [Internet]. 2022;13(1):1–9. Available from: https://doi.org/10.1038/s41467-022-28630-z
Bernhardt ES, Savoy P, Vlah MJ, Appling AP, Koenig LE, Hall Jr RO, et al. Light and flow regimes regulate the metabolism of rivers. Proceedings of the National Academy of Sciences [Internet]. 2022;119(8):e2121976119. Available from: https://doi.org/10.1073/pnas.2121976119
Rollinson CR, Finley AO, Alexander MR, Banerjee S, Dixon Hamil KA, Koenig LE, et al. Working across space and time: nonstationarity in ecological research and application. Frontiers in Ecology and the Environment [Internet]. 2021;19(1):66–72. Available from: https://doi.org/10.1002/fee.2298
Granville KE, Ooi SK, Koenig LE, Lawrence BA, Elphick CS, Helton AM. Seasonal patterns of denitrification and N2O production in a southern New England salt marsh. Wetlands [Internet]. 2021;41(1):1–13. Available from: https://doi.org/10.1007/s13157-021-01393-x
Koenig LE, Helton AM, Savoy P, Bertuzzo E, Heffernan JB, Hall Jr RO, et al. Emergent productivity regimes
of river networks. Limnology and Oceanography Letters [Internet]. 2019;4(5):173–81. Available from: https://doi.org/10.1002/lol2.10115
of river networks. Limnology and Oceanography Letters [Internet]. 2019;4(5):173–81. Available from: https://doi.org/10.1002/lol2.10115
Coble AA, Koenig LE, Potter JD, Parham LM, McDowell WH. Homogenization of dissolved organic matter within a river network occurs in the smallest headwaters. Biogeochemistry [Internet]. 2019;143(1):85–104. Available from: https://doi.org/10.1007/s10533-019-00551-y
Wollheim WM, Bernal S, Burns DA, Czuba JA, Driscoll CT, Hansen AT, et al. River network saturation concept: factors influencing the balance of biogeochemical supply and demand of river networks. Biogeochemistry [Internet]. 2018;141(3):503–21. Available from: https://doi.org/10.1007/s10533-018-0488-0
Song C, Dodds WK, Rüegg J, Argerich A, Baker CL, Bowden WB, et al. Continental-scale decrease in net primary productivity in streams due to climate warming. Nature Geoscience [Internet]. 2018;11(6):415–20. Available from: https://doi.org/10.1038/s41561-018-0125-5
Farrell KJ, Rosemond AD, Kominoski JS, Bonjour SM, Rüegg J, Koenig LE, et al. Variation in detrital resource stoichiometry signals differential carbon to nutrient limitation for stream consumers across biomes. Ecosystems [Internet]. 2018;21(8):1676–91. Available from: https://doi.org/10.1007/s10021-018-0247-z
Koenig LE, Song C, Wollheim WM, Rüegg J, McDowell WH. Nitrification increases nitrogen export from a tropical river network. Freshwater Science [Internet]. 2017;36(4):698–712. Available from: https://doi.org/10.1086/694906
Koenig LE, Shattuck MD, Snyder LE, Potter JD, McDowell WH. Deconstructing the effects of flow on DOC, nitrate, and major ion interactions using a high-frequency aquatic sensor network. Water Resources Research [Internet]. 2017;53(12):10655–73. Available from: https://doi.org/10.1002/2017WR020739
Rüegg J, Dodds WK, Daniels MD, Sheehan KR, Baker CL, Bowden WB, et al. Baseflow physical characteristics differ at multiple spatial scales in stream networks across diverse biomes. Landscape ecology [Internet]. 2016;31(1):119–36. Available from: https://doi.org/10.1007/s10980-015-0289-y
Mineau MM, Wollheim WM, Buffam I, Findlay SE, Hall Jr RO, Hotchkiss ER, et al. Dissolved organic carbon uptake in streams: A review and assessment of reach-scale measurements. Journal of Geophysical Research: Biogeosciences [Internet]. 2016;121(8):2019–29. Available from: https://doi.org/10.1002/2015JG003204
Koenig LE, Baumann AJ, McDowell WH. Improving automated phosphorus measurements in freshwater: an analytical approach to eliminating silica interference. Limnology and Oceanography: Methods [Internet]. 2014;12(4):223–31. Available from: https://doi.org/10.4319/lom.2014.12.223
Heffernan JB, Soranno PA, Angilletta Jr MJ, Buckley LB, Gruner DS, Keitt TH, et al. Macrosystems ecology: understanding ecological patterns and processes at continental scales. Frontiers in Ecology and the Environment [Internet]. 2014;12(1):5–14. Available from: https://doi.org/10.1890/130017
Albertson LK, Koenig LE, Lewis BL, Zeug SC, Harrison LR, Cardinale BJ. How Does Restored Habitat for Chinook Salmon (oncorhynchus Tshawytscha) in the Merced River in California Compare with Other Chinook Streams? River Research and Applications [Internet]. 2013 [cited 2022 Aug 29];29(4):469–82. Available from: https://doi.org/10.1002/rra.1604
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Analysis of monthly water supply for the United States for water years 2010-2020 using modeled states and fluxes
This pipeline analyzes estimates of streamflow, evapotranspiration, soil moisture, and snow water equivalent among other hydrologic states and fluxes from two national hydrologic models: the National Hydrologic Model application of the Precipitation-Runoff Modeling System (NHM-PRMS) and the Weather Research and Forecasting hydrologic modeling system (WRF-Hydro). Both of these models are...
Science and Products
Downscaling and multi-scale modeling of stream temperature in five watersheds of the Delaware River Basin, 1979-2021
This model archive (Fan et al. 2025a) provides all data, code, and model outputs used in the corresponding manuscript (Fan et al. 2025b) to test machine learning (ML) methods for downscaling and multi-scale modeling of stream temperature to combine an ML model and/or input data at coarse spatial resolution with an ML model and/or input data at fine spatial resolution to predict stream...
Distance matrices and river-network crosswalks for the Geospatial Fabric v1.1 to support data-driven models of water quality in U.S. rivers
This data release contains hydrologic network information to support national modeling of water quality in streams and rivers, including distance matrices that represent the spatial relationships among modeled river reaches. These data can be used as inputs to data-driven models that leverage information about spatial proximity to make predictions of water quantity or quality. The...
Estimated seasonal nitrogen and phosphorus loads in selected streams of the conterminous United States, 1999 - 2020
Estimated seasonal total nitrogen and total phosphorus loads during 1999 through 2020 in selected streams of the conterminous United States and water-quality and stream flow data used to generate those estimates are presented in this dataset. Loads were generated as part of the Integrated Water-Availability Assessment (IWAA) Program of the U.S. Geological Survey (www.usgs.gov/mission...
Delaware River Basin Stream Salinity Machine Learning Models and Data
This model archive contains the input data, model code, and model outputs for machine learning models that predict daily non-tidal stream salinity (specific conductance) for a network of 459 modeled stream segments across the Delaware River Basin (DRB) from 1984-09-30 to 2021-12-31. There are a total of twelve models from combinations of two machine learning models (Random Forest and...
Data and model code used to evaluate a process-guided deep learning approach for in-stream dissolved oxygen prediction
This model archive contains data and code used to assess the use of process-informed multi-task deep learning models for predicting in-stream dissolved oxygen concentrations. Three holdout experiments were run to assess model performance, including a temporal holdout experiment, a spatial holdout experiment with similar sites held out, and a spatial holdout experiment with dissimilar...
Long-term water-quality trends for rivers and streams within the contiguous United States using Weighted Regressions on Time, Discharge, and Season (WRTDS) (ver. 1.1, March 2025)
The U.S. Geological Survey (USGS) Water Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand. It is also improving understanding of the natural and human factors affecting the balance between supply and demand. A key...
Model Code, Outputs, and Supporting Data for Approaches to Process-Guided Deep Learning for Groundwater-Influenced Stream Temperature Predictions
This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately incorporate groundwater-discharge processes. We assessed the performance of an existing process-guided deep learning stream temperature model of the Delaware River Basin (USA) and explored four...
Distribution, frequency, and global extent of hypoxia in rivers
To assess the distribution, frequency, and global extent of riverine hypoxia, we compiled 118 million paired dissolved oxygen (DO) and water temperature measurements from 125,158 unique locations in rivers in 93 countries and territories across the globe. The dataset also includes site characteristics derived from StreamCat, the National Hydrography and HydroAtlas datasets and proximal...
Data to support water quality modeling efforts in the Delaware River Basin
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this...
Predictive understanding of stream salinization in a developed watershed using machine learning
Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a...
Authors
Jared David Smith, Lauren Elizabeth Koenig, Margaux Jeanne Sleckman, Alison P. Appling, Jeffrey M Sadler, Vincent T. DePaul, Zoltan Szabo
Evaluating a process-guided deep learning approach for predicting dissolved oxygen in streams
Dissolved oxygen (DO) is a critical water quality constituent that governs habitat suitability for aquatic biota, biogeochemical reactions and solubility of metals in streams. Recently introduced high-frequency sensors have increased our ability to measure DO, but we still lack the capacity to understand and predict DO concentrations at high spatial resolutions or in unmonitored...
Authors
Jeffrey M Sadler, Lauren Elizabeth Koenig, Galen Gorski, Alice M. Carter, Robert O. Hall
Train, inform, borrow, or combine? Approaches to process-guided deep learning for groundwater-influenced stream temperature prediction
Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the...
Authors
Janet R. Barclay, Simon Nemer Topp, Lauren Elizabeth Koenig, Margaux Jeanne Sleckman, Alison P. Appling
Extent, patterns, and drivers of hypoxia in the world's streams and rivers
Hypoxia in coastal waters and lakes is widely recognized as a detrimental environmental issue, yet we lack a comparable understanding of hypoxia in rivers. We investigated controls on hypoxia using 118 million paired observations of dissolved oxygen (DO) concentration and water temperature in over 125,000 locations in rivers from 93 countries. We found hypoxia (DO
Authors
Joanna R Blaszczak, Lauren E Koenig, Francine H. Mejia, Alice M. Carter, Lluis Gómez-Gener, Christoper L Dutton, Nancy B. Grimm, Judson Harvey, Ashley M. Helton, Matthew J. Cohen
Light and flow regimes regulate the metabolism of rivers
Mean annual temperature and mean annual precipitation drive much of the variation in productivity across Earth's terrestrial ecosystems but do not explain variation in gross primary productivity (GPP) or ecosystem respiration (ER) in flowing waters. We document substantial variation in the magnitude and seasonality of GPP and ER across 222 US rivers. In contrast to their terrestrial...
Authors
Emily. S Bernhardt, Philip Savoy, Michael J Vlah, Alison Paige Appling, Lauren E Koenig, Robert O Hall Jr., Maite Arroita, Joanna Blaszczak, Alice M. Carter, Matthew J. Cohen, Judson Harvey, James B. Heffernan, Ashley M. Helton, J.D. Hosen, Lily Kirk, William H. McDowell, Emily H. Stanley, Charles Yackulic, Nancy B. Grimm
Non-USGS Publications**
Wollheim WM, Harms TK, Robison AL, Koenig LE, Helton AM, Song C, et al. Superlinear scaling of riverine biogeochemical function with watershed size. Nature communications [Internet]. 2022;13(1):1–9. Available from: https://doi.org/10.1038/s41467-022-28630-z
Bernhardt ES, Savoy P, Vlah MJ, Appling AP, Koenig LE, Hall Jr RO, et al. Light and flow regimes regulate the metabolism of rivers. Proceedings of the National Academy of Sciences [Internet]. 2022;119(8):e2121976119. Available from: https://doi.org/10.1073/pnas.2121976119
Rollinson CR, Finley AO, Alexander MR, Banerjee S, Dixon Hamil KA, Koenig LE, et al. Working across space and time: nonstationarity in ecological research and application. Frontiers in Ecology and the Environment [Internet]. 2021;19(1):66–72. Available from: https://doi.org/10.1002/fee.2298
Granville KE, Ooi SK, Koenig LE, Lawrence BA, Elphick CS, Helton AM. Seasonal patterns of denitrification and N2O production in a southern New England salt marsh. Wetlands [Internet]. 2021;41(1):1–13. Available from: https://doi.org/10.1007/s13157-021-01393-x
Koenig LE, Helton AM, Savoy P, Bertuzzo E, Heffernan JB, Hall Jr RO, et al. Emergent productivity regimes
of river networks. Limnology and Oceanography Letters [Internet]. 2019;4(5):173–81. Available from: https://doi.org/10.1002/lol2.10115
of river networks. Limnology and Oceanography Letters [Internet]. 2019;4(5):173–81. Available from: https://doi.org/10.1002/lol2.10115
Coble AA, Koenig LE, Potter JD, Parham LM, McDowell WH. Homogenization of dissolved organic matter within a river network occurs in the smallest headwaters. Biogeochemistry [Internet]. 2019;143(1):85–104. Available from: https://doi.org/10.1007/s10533-019-00551-y
Wollheim WM, Bernal S, Burns DA, Czuba JA, Driscoll CT, Hansen AT, et al. River network saturation concept: factors influencing the balance of biogeochemical supply and demand of river networks. Biogeochemistry [Internet]. 2018;141(3):503–21. Available from: https://doi.org/10.1007/s10533-018-0488-0
Song C, Dodds WK, Rüegg J, Argerich A, Baker CL, Bowden WB, et al. Continental-scale decrease in net primary productivity in streams due to climate warming. Nature Geoscience [Internet]. 2018;11(6):415–20. Available from: https://doi.org/10.1038/s41561-018-0125-5
Farrell KJ, Rosemond AD, Kominoski JS, Bonjour SM, Rüegg J, Koenig LE, et al. Variation in detrital resource stoichiometry signals differential carbon to nutrient limitation for stream consumers across biomes. Ecosystems [Internet]. 2018;21(8):1676–91. Available from: https://doi.org/10.1007/s10021-018-0247-z
Koenig LE, Song C, Wollheim WM, Rüegg J, McDowell WH. Nitrification increases nitrogen export from a tropical river network. Freshwater Science [Internet]. 2017;36(4):698–712. Available from: https://doi.org/10.1086/694906
Koenig LE, Shattuck MD, Snyder LE, Potter JD, McDowell WH. Deconstructing the effects of flow on DOC, nitrate, and major ion interactions using a high-frequency aquatic sensor network. Water Resources Research [Internet]. 2017;53(12):10655–73. Available from: https://doi.org/10.1002/2017WR020739
Rüegg J, Dodds WK, Daniels MD, Sheehan KR, Baker CL, Bowden WB, et al. Baseflow physical characteristics differ at multiple spatial scales in stream networks across diverse biomes. Landscape ecology [Internet]. 2016;31(1):119–36. Available from: https://doi.org/10.1007/s10980-015-0289-y
Mineau MM, Wollheim WM, Buffam I, Findlay SE, Hall Jr RO, Hotchkiss ER, et al. Dissolved organic carbon uptake in streams: A review and assessment of reach-scale measurements. Journal of Geophysical Research: Biogeosciences [Internet]. 2016;121(8):2019–29. Available from: https://doi.org/10.1002/2015JG003204
Koenig LE, Baumann AJ, McDowell WH. Improving automated phosphorus measurements in freshwater: an analytical approach to eliminating silica interference. Limnology and Oceanography: Methods [Internet]. 2014;12(4):223–31. Available from: https://doi.org/10.4319/lom.2014.12.223
Heffernan JB, Soranno PA, Angilletta Jr MJ, Buckley LB, Gruner DS, Keitt TH, et al. Macrosystems ecology: understanding ecological patterns and processes at continental scales. Frontiers in Ecology and the Environment [Internet]. 2014;12(1):5–14. Available from: https://doi.org/10.1890/130017
Albertson LK, Koenig LE, Lewis BL, Zeug SC, Harrison LR, Cardinale BJ. How Does Restored Habitat for Chinook Salmon (oncorhynchus Tshawytscha) in the Merced River in California Compare with Other Chinook Streams? River Research and Applications [Internet]. 2013 [cited 2022 Aug 29];29(4):469–82. Available from: https://doi.org/10.1002/rra.1604
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Analysis of monthly water supply for the United States for water years 2010-2020 using modeled states and fluxes
This pipeline analyzes estimates of streamflow, evapotranspiration, soil moisture, and snow water equivalent among other hydrologic states and fluxes from two national hydrologic models: the National Hydrologic Model application of the Precipitation-Runoff Modeling System (NHM-PRMS) and the Weather Research and Forecasting hydrologic modeling system (WRF-Hydro). Both of these models are...