Geology topics

R. Russell Lotspeich

Publications and source records attributed to R. Russell Lotspeich.

5 recordsLinked to original sources

USGS2025

RIce-Net: Integrating ground-based cameras and machine learning for automated river ice detection

River ice plays a critical role in controlling streamflow in cold regions. The U.S. Geological Survey (USGS) qualifies affected water-level measurements and inferred streamflow by ice conditions at a date later than the day of the actual measurements. This study introduces a novel computer vision-based framework, River Ice-Network (RIce-Net), that uses the USGS nationwide network of ground-based cameras whose images are published through the National Imagery Management System (NIMS). RIce-Net consists of a binary classifier to identify ice-affected images that are segmented to calculate the fraction of ice coverage, which is used to automatically generate a near real-time ice flag. RIce-Net was trained using images from selected NIMS stations collected in 2023 and tested using images collected in 2024. Also, the framework’s scalability and transferability were tested over another station that was not included in the training process. RIce-Net ice flags are well-aligned with those reported by USGS.

Environmental Engineering & Software↗

USGS2022

GPS data from 2019 and 2020 campaigns in the Chesapeake Bay region towards quantifying vertical land motions

The Chesapeake Bay is a region along the eastern coast of the United States where sea-level rise is confounded with poorly resolved rates of land subsidence, thus new constraints on vertical land motions (VLM) in the region are warranted. In this paper, we provide a description of two campaign-style Global Positioning System (GPS) datasets, explain the methods used in data collection and validation, and present the experiment designed to quantify a new baseline of VLM in the Chesapeake Bay region of eastern North America. Data from GPS campaigns in 2019 and 2020 are presented as ASCII RINEX2.11 files and logsheets for each observation from the campaigns. Data were quality checked using the open-source program TEQC, resulting in average multipath 1 and 2 values of 0.68 and 0.57, respectively. All data are archived and publicly available for open access at the geodesy facility UNAVCO to abide by Findable, Accessible, Interoperable, Reusable (FAIR) data principles.

Chesapeake Bay area↗

USGS2020

Near-field remote sensing of surface velocity and river discharge using radars and the probability concept at 10 USGS streamgages

Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m 3 /s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m 3 /s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge.

Alaska, Colorado, Montana, New Mexico, North Dakot↗

USGS2017

Characteristics of peak streamflows and extent of inundation in areas of West Virginia and southwestern Virginia affected by flooding, June 2016

Heavy rainfall occurred across central and southern West Virginia in June 2016 as a result of repeated rounds of torrential thunderstorms. The storms caused major flooding and flash flooding in central and southern West Virginia with Kanawha, Fayette, Nicholas, and Greenbrier Counties among the hardest hit. Over the duration of the storms, from 8 to 9.37 inches of rain was reported in areas in Greenbrier County. Peak streamflows were the highest on record at 7 locations, and streamflows at 18 locations ranked in the top five for the period of record at U.S. Geological Survey streamflow-gaging stations used in this study. Following the storms, U.S. Geological Survey hydrographers identified and documented 422 high-water marks in West Virginia, noting location and height of the water above land surface. Many of these high-water marks were used to create flood-inundation maps for selected communities of West Virginia that experienced flooding in June 2016. Digital datasets of the inundation areas, mapping boundaries, and water depth rasters are available online.

Virginia, West Virginia↗

USGS2017

The Surge, Wave, and Tide Hydrodynamics (SWaTH) network of the U.S. Geological Survey—Past and future implementation of storm-response monitoring, data collection, and data delivery

After Hurricane Sandy made landfall along the northeastern Atlantic coast of the United States on October 29, 2012, the U.S. Geological Survey (USGS) carried out scientific investigations to assist with protecting coastal communities and resources from future flooding. The work included development and implementation of the Surge, Wave, and Tide Hydrodynamics (SWaTH) network consisting of more than 900 monitoring stations. The SWaTH network was designed to greatly improve the collection and timely dissemination of information related to storm surge and coastal flooding. The network provides a significant enhancement to USGS data-collection capabilities in the region impacted by Hurricane Sandy and represents a new strategy for observing and monitoring coastal storms, which should result in improved understanding, prediction, and warning of storm-surge impacts and lead to more resilient coastal communities. As innovative as it is, SWaTH evolved from previous USGS efforts to collect storm-surge data needed by others to improve storm-surge modeling, warning, and mitigation. This report discusses the development and implementation of the SWaTH network, and some of the regional stories associated with the landfall of Hurricane Sandy, as well as some previous events that informed the SWaTH development effort. Additional discussions on the mechanics of inundation and how the USGS is working with partners to help protect coastal communities from future storm impacts are also included.

Connecticut, Delaware, Maine, Maryland, Massachuse↗