Geology topics

Timothy J. Stagnitta

Publications and source records attributed to Timothy J. Stagnitta.

7 recordsLinked to original sources

USGS2026

National-scale remotely sensed lake surface temperature in the contiguous United States

Temperature is a master variable in aquatic ecosystems. Despite its importance for ecosystem function, our capacity to measure temperature is often limited to focal systems with intensive in situ measurements or larger systems with readily accessible data from disparate data streams. These limitations are consequential for holistically understanding aquatic ecosystem status and change, especially considering that most lakes globally do not have consistent in situ data. To empower researchers with macroscale surface temperature observations, we present the first remotely sensed lake temperature dataset that includes measurements from Landsat 8 made every ~ 16 d for 137,394 lakes of at least 4 ha in surface area throughout the contiguous United States from 2013 through 2025. By aggregating data in a cloud computing framework, the dataset is amenable for continual update, thereby allowing end users to track local-to-continental scale changes in lake water temperature over time.

contiguous United States↗

USGS2025

Random forest regression models for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island

This report is currently being updated with re‑calibrated random‑forest models for StreamStats implementation. The basin‑characteristic inputs were modified to align with StreamStats requirements. Abstract Models to estimate low-streamflow statistics at ungaged locations in New York, excluding Long Island and including hydrologically connected basins from bordering States, were developed for the first time by the U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation. A total of 224 basin characteristics were developed for 213 unaltered streamgages (locations where the human effects on streamflow were limited), across the following categories: basin geometry, climate, land cover, soils, surficial geology, and other characteristics. The basins with unaltered streamgages were evaluated for potential redundancy, and streamgages in close proximity and with similar drainage areas were flagged and removed from the testing and cross-validation datasets to prevent data leaking from the training dataset to the testing dataset. Random forest regression models were created by using basin characteristics as predictor variables and by developing a workflow to train, tune, and test the model. Models were developed to estimate the ungaged lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years (7Q10 and 30Q10). The top four basin characteristics used for the 7Q10 and 30Q10 models were drainage area, total stream length, perimeter of the basin, and length of the longest flow path. Results for the 7Q10 and 30Q10 models had coefficients of determination ( R 2 ) of 0.796 and 0.853, respectively. The output model results were bias-corrected for ungaged locations across New York and are available within the interactive StreamStats tool.

New York↗

USGS2025

The Long Island Sound and Watershed Metadata map application

The Long Island Sound and its watershed encompass an area of about 17,000 square miles and include the Connecticut, Housatonic, and Thames Rivers, which all drain to the sound. Dozens of organizations from government agencies, nonprofits, and Tribal Nations have developed projects and monitoring programs to analyze and protect the water resources of the watershed and sound. The abundance of data and lack of an existing searchable index require a centralized metadata repository to allow users to find water resources data more efficiently. The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency and the Long Island Sound Study, has created an interactive map application to visualize and search for metadata information across organizations working to monitor and protect the Long Island Sound.

Connecticut, Massachusetts, New Hampshire, New Yor↗

USGS2025

ARCHI: A new R package for automated imputation of regionally correlated hydrologic records

Missing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in “target” records by linear regression using more complete “reference” records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-acceptance criteria and applying geospatial, correlation, and group-based filters to control reference selection. ARCHI provides additional functions for visualizing results, clustering records with similar correlation structures, evaluating holdout data, and interactive parameterization with an accessible and intuitive graphical user interface (GUI). This methods brief provides an overview of the ARCHI package, modeling guidelines, and benchmarking on two regional groundwater-level datasets from the Central Valley, CA and Long Island, NY. We evaluate ARCHI alongside widely used multivariate imputation software to highlight and contextualize its computational efficiency, imputation accuracy, and model transparency when applied to large, groundwater-level datasets.

California, New York↗

USGS2024

Estimating domestic self-supplied water use in Rhode Island, 2014–21

Water withdrawal from private groundwater wells is often unaccounted for in water planning studies, and water from private wells can be a source of exposure to environmental contaminants. The sizes of populations that depend on private wells for domestic water use and the amounts of water that are withdrawn from these wells are generally poorly represented in data collection efforts because of the challenges of locating, metering, or gathering withdrawal information from individual property owners. To address this problem, the U.S. Geological Survey, in cooperation with the Rhode Island Water Resources Board, estimated the volume of water withdrawn from domestic self-supply wells and the populations who use them for the State of Rhode Island at a 30-meter pixel spatial resolution and one-month temporal resolution between July 2014 and June 2021. The number of people reliant on domestic self-supply wells has increased in Rhode Island over the study period; however, the statewide estimate of total water withdrawal has not statistically increased. Withdrawals from private wells are largest in the inland areas of the western part of the State, and the towns of Scituate and Charlestown have the highest estimated withdrawals. Statewide monthly withdrawals ranged from 3.987 million gallons per day in March 2018 to 7.767 million gallons per day in September 2016. The median per capita domestic water use rate was 46.0 gallons per capita per day.

Rhode Island↗

USGS2024

Low-flow statistics for selected streams in New York, excluding Long Island

The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, updated low-streamflow statistics for New York, excluding Long Island and including hydrologically connected watersheds in bordering States, for the first time since 1972. Historical daily streamflow data for active and inactive gages were considered for this study with periods of record as recent as March 31, 2022, adding 50 years of data to the last published low-streamflow statistics report for New York and including 119 new gages where low-streamflow statistics are calculated for the first time. Gages were evaluated across several criteria to identify gages that were not suitable for low-streamflow frequency analysis. In addition, gages were evaluated for the presence of alteration within the streamflow period of record based on previous studies and U.S. Geological Survey National Water Information System site metadata including peak flow codes. A trend analysis was performed using the Wilcoxon rank-sum hypothesis test comparing data from the most recent 30 years of record to data from 30 years and earlier for each long-record gage (30 years or more of available data). Results from the trend analysis indicated that 45 unaltered and 32 altered long-record sites had a statistically significant trend for the annual minimum n -day time series; most gages showed increasing trends in the annual minimum n -day time series. Low-streamflow statistics were calculated using the most recent 30 years of record for gages with a statistically significant trend. Before and after 1972, the lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years (7Q10 and 30Q10 statistics respectively) increased significantly at 41 unaltered gages and decreased significantly at 3 unaltered gages where data were available.

New York↗

USGS2023

Assessment of factors that influence human water demand for Providence, Rhode Island

To determine the most relevant climatic and economic factors driving water demand for Providence, Rhode Island, and to further the understanding of human interactions with water availability, linear regression models were developed to estimate single-family and multifamily residential, commercial, and industrial water demand for the service area of Providence Water for 2014–21. Monthly water use delivery data were provided by Providence Water. An array of climatic and economic data, the drought index, and binary variables to represent seasonal water use and the onset of the coronavirus (COVID–19) were investigated as possible explanatory variables for the water demand models. The water demand model with the best fit with the least amount of error was the single-family residential water demand followed in descending order of accuracy by the commercial, multifamily residential, and industrial water demand. Seasonal variables were significant in all models, and the COVID–19 binary variable was significant in the commercial and industrial models. One or two economic variables were significant in all models and one climatic variable was significant in all models except the commercial model. Overall residential, commercial, and industrial water demand in the Providence, Rhode Island, service area has decreased during the study period most likely because of widescale drought conditions and policies designed to improve water efficiencies. The linear regression models developed for single-family and multifamily residential, commercial, and industrial water use explained 94, 85, 91, and 77 percent, respectively, of the variability in monthly water use. Multifamily residential water demand displayed a less distinct seasonal trend than that observed for single-family residential customers, likely because multifamily homes tend to use less water outdoors. The commercial water-demand model included no climatic variables, one economic variable, the COVID–19 pandemic variable, and the high and low water use seasonal variables—the latter two variables indicating the importance of seasonal fluctuations in water use. The COVID–19 pandemic and a concomitant State executive order had the immediate effect of severely reducing commercial water use. The industrial water-demand model did not perform as well as the other models because industrial water delivery data display a greater range of values, both seasonally and for the overall study period.

Rhode Island↗