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USGS2026

Assessment of extreme subsurface hydrologic conditions captured during atmospheric river storms in the San Francisco Bay area (California, USA) with applications to shallow landslide early warning

An increase in soil pore water pressure is the typical trigger for the majority of landslides caused by rainfall. Atmospheric river storms, common to the west coast of North America during the winter season, can deliver landslide triggering rainfall resulting in severe impacts to coastal communities. Using a network of hydrologic monitoring stations situated within landslide-prone terrain in the San Francisco Bay area of California (USA), we assess the meteorologic conditions and resulting hydrologic and landslide response resulting from eight consecutive storm events that caused thousands of shallow landslides during the winter of 2022–2023. We find disparate hydrological responses and resultant degrees of observed landsliding ranging from < 1 landslide/km2 to 18 landslides/km2 at the monitoring sites that reflect the interplay and differences between rainfall delivery, subsurface hydrological characteristics, and geotechnical properties at each site. Antecedent soil moisture from both early season rainfall and the first storm in the sequence played a critical role in setting up some hillslopes for failure. Subsequent storms then generated elevated pore water pressures for several hours with associated landsliding. However, we find that the occurrence of widespread landsliding required not only sufficient pore water pressure magnitude in susceptible hillslopes, but also full and prolonged effective soil saturation throughout hillslope profiles. Landslides may still occur at lower values and durations of effective saturation but are likely to be less extensive regionally. We present these findings within the context of research directions and improvements to landslide early warning systems first suggested by researchers 40 years ago.

California↗

USGS1995

Simulation of hydrodynamics and solute transport in the Neuse River estuary

An investigation was conducted to characterize flow, circulation, and transport in the Neuse River estuary, North Carolina. The study included a detailed field-measurement program and the calibration, validation, and application of a physically realistic numerical model of hydro- dynamics and transport through a 40-kilometer reach of the estuary. Water level, salinity, water temperature, wind speed and direction, current velocity, and bathymetric data were collected during March 1988 through September 1992. Additional data from pre-existing continuous-record streamflow gaging stations and meteorological stations also were used. A two-dimensional, vertically averaged hydrodynamic and solute transport model was applied to the study reach. The model domain was discretized into 200- by 200-meter computational cells. Model calibration was achieved through adjustment of model parameters for the period June 1-24, 1991. Additional simulations for periods in 1989 and 1991 were run to validate the model. The model was calibrated and validated for water levels ranging from -0.104 to 0.908 meters, salinities ranging from 2.8 to 22.0 parts per thousand, and wind speed from calm to 9 meters per second. The model was tested for stratified and unstratified conditions. Simulated and observed level, salinity, and velocity data were used to evaluate the model. The calibrated model was applied to simulate flows, circulation, and salinity distributions for four simulation periods in 1989 and 1991. Comparisons of simulated results for a period in June 1991 were made between the Neuse and Pamlico models to characterize differences between the two systems.

Open-File Report↗

USGS2024

Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States

The application of Long Short-Term Memory (LSTM) models for streamflow predictions has been an area of rapid development, supported by advancements in computing technology, increasing availability of spatiotemporal data, and availability of historical data that allows for training data-driven LSTM models. Several studies have focused on improving the performance of LSTM models; however, few studies have assessed the applicability of these LSTM models across different hydroclimate regions. This study investigated the single-basin trained local (one model for each basin), multi-basin trained regional (one model for one region), and grand (one model for several regions) models for predicting daily streamflow in water-limited Great Basin (18 basins) and energy-limited New England (27 basins) regions in the United States using the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) data set. The results show a general pattern of higher accuracy in daily streamflow predictions from the regional model when compared to local or grand models for most basins in the New England region. For the Great Basin region, local models provided smaller errors for most basins and substantially lower for those basins with relatively larger errors from the regional and grand models. The evaluation of one-layer and three-layer LSTM network architectures trained with 1-day lag information indicates that the addition of model complexity by increasing the number of layers may not necessarily increase the model skill for improving streamflow predictions. Findings from our study highlight the strengths and limitations of LSTM models across contrasting hydroclimate regions in the United States, which could be useful for local and regional scale decisions using standalone or potential integration of data-driven LSTM models with physics-based hydrological models.

Machine Learning with Applications↗

USGS2003

Stable isotopes of oxygen and hydrogen in the Truckee River–Pyramid Lake surface‐water system. 2. A predictive model of δ18O and 182H in Pyramid Lake

A physically based model of variations in δ 18 O and δ 2 H in Pyramid Lake is presented. For inputs, the model uses measurements of liquid water inflows and outflows and their associated isotopic compositions and a set of meteorological data (radiative fluxes, air temperature, relative humidity, and windspeed). The model simulates change of lake volume, thermal and isotopic stratification, evaporation, and the isotopic composition of evaporation. A validation of the model for 1987–1989 and 1991 indicates that it can reproduce measured intra‐ and interannual variations of δ 18 O and δ 2 H. Three applications of the model demonstrate its ability to simulate longer term responses of δ 18 O to change in the hydrologic balance and hydrologic characteristics (opening and closing) of the lake.

Nevada↗

USGS1989

Physical habitat simulation system reference manual: version II

There are four major components of a stream system that determine the productivity of the fishery (Karr and Dudley 1978). These are: (1) flow regime, (2) physical habitat structure (channel form, substrate distribution, and riparian vegetation), (3) water quality (including temperature), and (4) energy inputs from the watershed (sediments, nutrients, and organic matter). The complex interaction of these components determines the primary production, secondary production, and fish population of the stream reach. The basic components and interactions needed to simulate fish populations as a function of management alternatives are illustrated in Figure I.1. The assessment process utilizes a hierarchical and modular approach combined with computer simulation techniques. The modular components represent the "building blocks" for the simulation. The quality of the physical habitat is a function of flow and, therefore, varies in quality and quantity over the range of the flow regime. The conceptual framework of the Incremental Methodology and guidelines for its application are described in "A Guide to Stream Habitat Analysis Using the Instream Flow Incremental Methodology" (Bovee 1982). Simulation of physical habitat is accomplished using the physical structure of the stream and streamflow. The modification of physical habitat by temperature and water quality is analyzed separately from physical habitat simulation. Temperature in a stream varies with the seasons, local meteorological conditions, stream network configuration, and the flow regime; thus, the temperature influences on habitat must be analysed on a stream system basis. Water quality under natural conditions is strongly influenced by climate and the geological materials, with the result that there is considerable natural variation in water quality. When we add the activities of man, the possible range of water quality possibilities becomes rather large. Consequently, water quality must also be analysed on a stream system basis. Such analysis is outside the scope of this manual, which concentrates on simulation of physical habitat based on depth, velocity, and a channel index. The results form PHABSIM can be used alone or by using a series of habitat time series programs that have been developed to generate monthly or daily habitat time series from the Weighted Usable Area versus streamflow table resulting from the habitat simulation programs and streamflow time series data. Monthly and daily streamflow time series may be obtained from USGS gages near the study site or as the output of river system management models.

Report↗

USGS2023

Development of a benchmark eddy flux evapotranspiration dataset for evaluation of satellite-driven evapotranspiration models over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

continental United States↗

USGS2023

VogCast: A framework for modeling volcanic air pollution and its application to the 2022 eruption of Mauna Loa Volcano, Hawai'i

Volcanic activity and the associated gas emissions into the atmosphere often result in adverse air quality conditions and present a hazard to human health and the environment. Building on a decade-long effort to provide operational surface sulfur dioxide and sulfate aerosol forecasts for the State of Hawai'i, we present an air quality modeling framework called VogCast. VogCast is designed to simplify ensemble air quality prediction on a regional scale by linking together multiple state-of-the-art models of meteorology, emissions, and dispersion. The framework is open-source and introduces a new dynamic plume-rise algorithm for distributing pollutants vertically. Using radar and satellite data, we demonstrate that VogCast reasonably captured the mean injection height, the location, and the general envelope of the vog plume during Mauna Loa's 2022 eruption. The results suggest that during the 12-day eruption period model performance varied between days with trade and non-trade wind conditions. Our findings also highlight the importance of sulfur dioxide emission rate and vent parameter inputs for improving forecast accuracy. The broad goal of this work is to better our understanding of vog dispersion and improve air quality prediction for impacted communities.

Hawaii↗

USGS2023

Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama

Long short-term memory (LSTM) models have been shown to be efficient for rainfall-runoff modeling, and to a lesser extent, for groundwater depth forecasting. In this study, LSTMs were applied to quantify the spatiotemporal evolution of surface and subsurface hydrographs in Alabama in the Southeastern United States, where water sustainability has not been fully quantified across spatiotemporal scales. First, the surface water LSTM model with extensive dynamic (precipitation and other weather variables) and static (basin characteristics) inputs predicted the main characteristics of streamflow for six years at 19 gauged basins in Alabama. The model tended to underestimate extremely high streamflow but adding drainage density as an input feature slightly improved the predictions of extreme events. Second, to predict the groundwater depth evolution, a groundwater LSTM (GW-LSTM) model was proposed and applied using static inputs capturing the aquifers' hydrogeological properties and dynamic inputs of meteorological information. Three precipitation scenarios were also explored to evaluate the groundwater hydrograph evolution in the next two decades. The GW-LSTM model predicted the general trend of daily groundwater depth fluctuations (at 21 wells distributed across Alabama from 1990 to 2021) including most extremely high groundwater levels, and recovered groundwater depth for locations withheld from model training and validation. This study, therefore, extended the application of LSTMs in quantifying the spatiotemporal evolution of surface water and groundwater, two manifestations of a single integrated resource.

Alabama↗

USGS2016

Synthesising empirical results to improve predictions of post-wildfire runoff and erosion response

Advances in research into wildfire impacts on runoff and erosion have demonstrated increasing complexity of controlling factors and responses, which, combined with changing fire frequency, present challenges for modellers. We convened a conference attended by experts and practitioners in post-wildfire impacts, meteorology and related research, including modelling, to focus on priority research issues. The aim was to improve our understanding of controls and responses and the predictive capabilities of models. This conference led to the eight selected papers in this special issue. They address aspects of the distinctiveness in the controls and responses among wildfire regions, spatiotemporal rainfall variability, infiltration, runoff connectivity, debris flow formation and modelling applications. Here we summarise key findings from these papers and evaluate their contribution to improving understanding and prediction of post-wildfire runoff and erosion under changes in climate, human intervention and population pressure on wildfire-prone areas.

International Journal of Wildland Fire↗

USGS2010

User's guide for MAGIC-Meteorologic and hydrologic genscn (generate scenarios) input converter

Meteorologic and hydrologic data used in watershed modeling studies are collected by various agencies and organizations, and stored in various formats. Data may be in a raw, un-processed format with little or no quality control, or may be checked for validity before being made available. Flood-simulation systems require data in near real-time so that adequate flood warnings can be made. Additionally, forecasted data are needed to operate flood-control structures to potentially mitigate flood damages. Because real-time data are of a provisional nature, missing data may need to be estimated for use in floodsimulation systems. The Meteorologic and Hydrologic GenScn (Generate Scenarios) Input Converter (MAGIC) can be used to convert data from selected formats into the Hydrologic Simulation System-Fortran hourly-observations format for input to a Watershed Data Management database, for use in hydrologic modeling studies. MAGIC also can reformat the data to the Full Equations model time-series format, for use in hydraulic modeling studies. Examples of the application of MAGIC for use in the flood-simulation system for Salt Creek in northeastern Illinois are presented in this report.

Open-File Report↗

USGS2013

Representing the effects of alpine grassland vegetation cover on the simulation of soil thermal dynamics by ecosystem models applied to the Qinghai-Tibetan Plateau

Soil surface temperature is a critical boundary condition for the simulation of soil temperature by environmental models. It is influenced by atmospheric and soil conditions and by vegetation cover. In sophisticated land surface models, it is simulated iteratively by solving surface energy budget equations. In ecosystem, permafrost, and hydrology models, the consideration of soil surface temperature is generally simple. In this study, we developed a methodology for representing the effects of vegetation cover and atmospheric factors on the estimation of soil surface temperature for alpine grassland ecosystems on the Qinghai-Tibetan Plateau. Our approach integrated measurements from meteorological stations with simulations from a sophisticated land surface model to develop an equation set for estimating soil surface temperature. After implementing this equation set into an ecosystem model and evaluating the performance of the ecosystem model in simulating soil temperature at different depths in the soil profile, we applied the model to simulate interactions among vegetation cover, freeze-thaw cycles, and soil erosion to demonstrate potential applications made possible through the implementation of the methodology developed in this study. Results showed that (1) to properly estimate daily soil surface temperature, algorithms should use air temperature, downward solar radiation, and vegetation cover as independent variables; (2) the equation set developed in this study performed better than soil surface temperature algorithms used in other models; and (3) the ecosystem model performed well in simulating soil temperature throughout the soil profile using the equation set developed in this study. Our application of the model indicates that the representation in ecosystem models of the effects of vegetation cover on the simulation of soil thermal dynamics has the potential to substantially improve our understanding of the vulnerability of alpine grassland ecosystems to changes in climate and grazing regimes.

Qinghai;Tibet↗

USGS1997

Nowcast model for hazardous material spill prevention and response, San Francisco Bay, California

The National Oceanic and Atmospheric Administration (NOAA) installed the Physical Oceanographic Real-time System (PORTS) in San Francisco Bay, California, to provide real-time observations of tides, tidal currents, and meteorological conditions to, among other purposes, guide hazardous material spill prevention and response. Integrated with nowcast modeling techniques and dissemination of real-time data and the nowcasting results through the Internet on the World Wide Web, emerging technologies used in PORTS for real-time data collection forms a nowcast modeling system. Users can download tides and tidal current distribution in San Francisco Bay for their specific applications and/or for further analysis.

Conference Paper↗

USGS2009

An empirical algorithm for estimating agricultural and riparian evapotranspiration using MODIS Enhanced Vegetation Index and ground measurements of ET. II. Application to the lower Colorado River, U.S.

Large quantities of water are consumed by irrigated crops and riparian vegetation in western U.S. irrigation districts. Remote sensing methods for estimating evaporative water losses by soil and vegetation (evapotranspiration, ET) over wide river stretches are needed to allocate water for agricultural and environmental needs. We used the Enhanced Vegetation Index (EVI) from MODIS sensors on the Terra satellite to scale ET over agricultural and riparian areas along the Lower Colorado River in the southwestern U.S., using a linear regression equation between ET of riparian plants and alfalfa measured on the ground, and meteorological and remote sensing data, with an error or uncertainty of about 20%. The algorithm was applied to irrigation districts and riparian areas from Lake Mead to the U.S./Mexico border. The results for agricultural crops were similar to results produced by crop coefficients developed for the irrigation districts along the river. However, riparian ET was only half as great as crop coefficient estimates set by expert opinion, equal to about 40% of reference crop evapotranspiration. Based on reported acreages in 2007, agricultural crops (146,473 ha) consumed 2.2 × 10 9 m 3 yr −1 of water. All riparian shrubs and trees (47,014 ha) consumed 3.8 × 10 8 m 3 yr −1 , of which saltcedar, the dominant riparian shrub (25,044 ha), consumed 1.8 × 10 8 m 3 yr −1 , about 1% of the annual flow of the river. This method could supplement existing protocols for estimating ET by providing an estimate based on the actual state of the canopy as determined by frequent-return satellite data.

Arizona, California, Nevada↗

USGS2023

Comparing snowpack meteorological inputs to support regional wet snow avalanche forecasting

Wet snow avalanches are predicted to increase in frequency with climate change and are often difficult to forecast. Improving our understanding of wet snow avalanche timing will help with current forecasting challenges. The onset of wet snow avalanching is closely tied to the temporal progression of liquid water flow through the seasonal snowpack. Measuring the flow of water through the snowpack in-situ is difficult due to the spatial variability of snow depth and structure. However, physical snowpack models can potentially simulate this process. The accuracy of snowpack models is heavily dependent upon the quality of the meteorological input data. A thorough investigation of model output differences using several different meteorological inputs for forecasting water movement and wet snow avalanches has not yet been thoroughly investigated. Here, we evaluate indicators of regional wet snow avalanches produced by the SNOWPACK model using different meteorological input. We compare the accuracy of SNOWPACK modeled outputs driven by two different numerical weather prediction (NWP) forecast models: the High-Resolution Deterministic Prediction System (HRDPS) and the North American Model (NAMnest). We leverage hourly automated weather station data, daily operational avalanche observations along the Going-to-the-Sun Road in Glacier National Park, Montana, United States, and in-situ snow stratigraphy and wetness profile observations to validate the SNOWPACK modeled outputs. This research is directly applicable to avalanche forecasting operations and future avalanche research as wet snow avalanche timing evolves due to climate change.

Montana↗

USGS2026

Towards a refined definition of “tidal influence” for the coastal wetland sciences

The astronomical tide relates to lunar (and to a lesser extent solar) influences on rising and falling water bodies, and is a key control of many coastal processes. However, tides manifest along coastal margins in different ways to muddle application beyond Sir Isaac Newton’s first descriptions. Here, we argue that tide, or specifically “tidal,” has not been adequately defined for the coastal wetland sciences to facilitate contemporary interdisciplinary distinction for rapidly changing coastlines and future climate adaptation planning. We provide a brief history of the tidal concept, and we describe tides in a diversity of uncommonly recognized marginal coastal environments. Common astronomical tide manifestations are described through hydrographic examples, then expanded to include those tidal environments exposed additionally to fluvial, meteorological (wind), or groundwater influences. Our expanded definition of “tidal” is intended to be a forward-looking construct as environments will inevitably change with sea-level rise, water extraction, and anthropogenic flow alteration. Our definition of wetland tidal influence, which we broaden, includes those “wetlands affected by astronomic water level fluctuations within coastally restricted environments causing frequent or infrequent surface inundation or groundwater tidal variation that affects the biogeochemistry of the soil (e.g., ion exchange capacity, oxygen state, mineralization processes)”. Astronomic water level fluctuations are often amplified or dampened by non-astronomical events, such as wind. Expanding the definition of what is considered tidal by the scientific community is critical for improved understanding of coastal wetland function and the suite of ecosystem services they provide people and society, including climate mitigation opportunity, management, and adaptation.

Wetlands↗

USGS2000

A Community Hydrometeorology Laboratory for Fostering Collaborative Research by the Atmospheric and Hydrologic Sciences

A new community laboratory for fostering collaborative research between the atmospheric and hydrologie sciences communities is described. This facility, located at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, allows scientists from both communities to more easily focus resources and attention on interdisciplinary problems in atmospheric, hydrologic, and other related sciences. Researchers can remotely access the computing tools to use them or to download them to their own facility, or they can visit NCAR and use the laboratory with other scientists in joint research projects. An application of this facility is described, where scientists from NCAR, the University of Colorado, and the United States Geological Survey used quantitative precipitation estimates from weather radar to simulate a flash flood in the Buffalo Creek watershed in the mountainous Front Range near Denver, Colorado.

Bulletin of the American Meteorological Society↗

USGS2024

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R 2 ) and mean absolute error values of 0.73- and 0.95-mm d −1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

California↗

USGS2010

Seasonal groundwater contribution to crop-water use assessed with lysimeter observations and model simulations

Groundwater evaporation can play an important role in crop-water use where the water table is shallow. Lysimeters are often used to quantify the groundwater evaporation contribution influenced by a broad range of environmental factors. However, it is difficult for such field facilities, which are operated under limited conditions within limited time, to capture the whole spectrum of capillary upflow with regard to the inter-seasonal variability of climate, especially rainfall. Therefore, in this work, the method of combining lysimeter and numerical experiments was implemented to investigate seasonal groundwater contribution to crop-water use. Groundwater evaporation experiments were conducted through a weighing lysimeter at an agricultural experiment station located within an irrigation district in the lower Yellow River Basin for two winter wheat growth seasons. A HYDRUS-1D model was first calibrated and validated with weighing lysimeter data, and then was employed to perform scenario simulations of groundwater evaporation under different depths to water table (DTW) and water input (rainfall plus irrigation) driven by long term meteorological data. The scenario simulations revealed that the seasonally averaged groundwater evaporation amount was linearly correlated to water input for different values of DTW. The linear regression could explain more than 70% of the variability. The seasonally averaged ratio of the groundwater contribution to crop-water use varied with the seasonal water input and DTW. The ratio reached as high as 75% in the case of DTW=1.0. m and no irrigation, and as low as 3% in the case of DTW=3.0. m and three irrigation applications. The results also revealed that the ratio of seasonal groundwater evaporation to potential evapotranspiration could be fitted to an exponential function of the DTW that may be applied to estimate seasonal groundwater evaporation. In this case study of multilayered soil profile, the depth at which groundwater may evaporate at potential rate was 0.60-0.65. m, and the extinction depth of groundwater evaporation was approximately 3.8. m. ?? 2010 Elsevier B.V.

Journal of Hydrology↗