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At least 217 records · Page 12Linked to original sources
USGS2018

A framework for identifying and characterising coral reef “oases” against a backdrop of degradation

Human activities have led to widespread ecological decline; however, the severity of degradation is spatially heterogeneous due to some locations resisting, escaping, or rebounding from disturbances. We developed a framework for identifying oases within coral reef regions using long‐term monitoring data. We calculated standardised estimates of coral cover ( z ‐scores) to distinguish sites that deviated positively from regional means. We also used the coefficient of variation (CV) of coral cover to quantify how oases varied temporally, and to distinguish among types of oases. We estimated “coral calcification capacity” (CCC), a measure of the coral community's ability to produce calcium carbonate structures and tested for an association between this metric and z ‐scores of coral cover. We illustrated our z ‐score approach within a modelling framework by extracting z ‐scores and CVs from simulated data based on four generalized trajectories of coral cover. We then applied the approach to time‐series data from long‐term reef monitoring programmes in four focal regions in the Pacific (the main Hawaiian Islands and Mo'orea, French Polynesia) and western Atlantic (the Florida Keys and St. John, US Virgin Islands). Among the 123 sites analysed, 38 had positive z ‐scores for median coral cover and were categorised as oases. Synthesis and applications . Our framework provides ecosystem managers with a valuable tool for conservation by identifying “oases” within degraded areas. By evaluating trajectories of change in state (e.g., coral cover) among oases, our approach may help in identifying the mechanisms responsible for spatial variability in ecosystem condition. Increased mechanistic understanding can guide whether management of a particular location should emphasise protection, mitigation or restoration. Analysis of the empirical data suggest that the majority of our coral reef oases originated by either escaping or resisting disturbances, although some sites showed a high capacity for recovery, while others were candidates for restoration. Finally, our measure of reef condition (i.e., median z ‐scores of coral cover) correlated positively with coral calcification capacity suggesting that our approach identified oases that are also exceptional for one critical component of ecological function.

Florida, Hawaii↗
USGS2014

Evaluating the efficiency of environmental monitoring programs

Statistical uncertainty analyses can be used to improve the efficiency of environmental monitoring, allowing sampling designs to maximize information gained relative to resources required for data collection and analysis. In this paper, we illustrate four methods of data analysis appropriate to four types of environmental monitoring designs. To analyze a long-term record from a single site, we applied a general linear model to weekly stream chemistry data at Biscuit Brook, NY, to simulate the effects of reducing sampling effort and to evaluate statistical confidence in the detection of change over time. To illustrate a detectable difference analysis, we analyzed a one-time survey of mercury concentrations in loon tissues in lakes in the Adirondack Park, NY, demonstrating the effects of sampling intensity on statistical power and the selection of a resampling interval. To illustrate a bootstrapping method, we analyzed the plot-level sampling intensity of forest inventory at the Hubbard Brook Experimental Forest, NH, to quantify the sampling regime needed to achieve a desired confidence interval. Finally, to analyze time-series data from multiple sites, we assessed the number of lakes and the number of samples per year needed to monitor change over time in Adirondack lake chemistry using a repeated-measures mixed-effects model. Evaluations of time series and synoptic long-term monitoring data can help determine whether sampling should be re-allocated in space or time to optimize the use of financial and human resources.

Ecological Indicators↗
USGS2026

Working group on American Eel (WGAMEEL; outputs from 2024 meeting)

The Working Group on American Eel (WGAMEEL) met virtually three times in 2022-2024 to address the five Terms of Reference (ToRs) of its three-year term. The first two ToRs tasked WGAMEEL with listing and evaluating data on American eel landings, abundance indices, and spatial and habitat data and also to describe assessment methods used in Canada and the US. Canada-wide American eel trajectory was estimated using 12 fishery-independent datasets. Generally, longer datasets had more negative trends than shorter ones. Limiting data to the post-2000 years produced fewer negative trends that did not differ from zero, suggesting the observed declines occurred pre-2000. Spatial modelling for American eel requires knowledge and mapping that covers the breadth of habitat types occupied by the species, including freshwater, estuarine, and marine environments. In recent years there has been an expansion of online databases with data from the aquatic environment, particularly in freshwater, with estuarine and marine data less consistently documented. This report broadly compiles abiotic data series of relevance to American eel. A larger challenge for spatial modelling will be acquiring enough high quality, georeferenced biological data sets with suitable observations to assess occurrence, abundance, and trends over time in a spatial framework. The third ToR considered Indigenous Knowledge Systems for American eel. A survey reaching First Nations representatives from four Canadian provinces confirmed the cultural importance of eels in Indigenous communities, and that Indigenous knowledge possessed by the participant groups are place-based and contextual, especially regarding threats impacting eels. The final two ToRs focused on identifying stock assessment modelling approaches applicable to American and European eel, and assessing whether any of these approaches might be appropriate for American eel management moving forward. WGAMEEL evaluated the various approaches for assessing American eel or providing management advice. Two approaches that could be completed in the next few years because of their minimal data needs are index-based methods and catch-only method. A suite of approaches considered by WGAMEEL that would take more time and data were spatial or habitat models, management strategy evaluation, and spawner-per-recruit (SPR) models potentially paired with meta-population models.

ICES Scientific Reports↗
USGS2022

Comparison of indices to infer population dynamics of black brant

To aid managers in assessing status of Pacific black brant Branta bernicla nigricans (hereafter brant), I examined pre-existing long-term data series from summer, fall staging, and wintering areas to infer overall population processes and assessed the utility of the various data sources. Variation in demographic parameters measured in subarctic and Arctic locations suggests some form of metapopulation structure likely exists for brant. I used serial autocorrelation coefficients to assess the ability of various indices to track population processes. Based on this approach, the Lincoln–Petersen estimator and the fall aerial survey estimate partitioned using age ratios of staging brant at Izembek Lagoon, Alaska, appear to be the best indicators. However, these two indexes show different trends for the overall brant population. The Lincoln–Petersen estimates showed biologically implausible changes in size among sequential years, whereas the fall Izembek index did not. Annual estimates of survival and productivity fit the patterns of annual variation in the fall Izembek index better than the Lincoln–Petersen estimates. I conclude that the fall age–partitioned Izembek Lagoon index appears to be the best for tracking population processes in brant.

Journal of Fish and Wildlife Management↗
USGS2020

Caltech/USGS Southern California Seismic Network (SCSN) and Southern California Earthquake Data Center (SCEDC): Data availability for the 2019 Ridgecrest sequence

The 2019 M6.4 and M7.1 Ridgecrest earthquake sequence occurred in the eastern California shear zone (ECSZ). The mainshock ruptured the Little Lake fault zone and aftershocks extended from the Garlock fault in the south, to the southern end of the 1872 M7.5 Owens Valley earthquake rupture in the north. We present data from the Southern California Seismic Network (SCSN) and partner seismic networks recorded by the SCSN in the region. These time series data and related products such as the SCSN earthquake picks and catalogs, available from the Southern California Earthquake Data Center (SCEDC), provide the most comprehensive seismic datasets for the 2019 Ridgecrest earthquake sequence.

California↗
USGS2002

Selected micrometeorological and soil-moisture data at Amargosa Desert Research Site in Nye County near Beatty, Nevada, 1998-2000

Selected micrometeorological and soil-moisture data were collected at the Amargosa Desert Research Site adjacent to a low-level radioactive waste and hazardous chemical waste facility near Beatty, Nev., 1998-2000. Data were collected in support of ongoing research studies to improve the understanding of hydrologic and contaminant-transport processes in arid environments. Micrometeorological data include precipitation, air temperature, solar radiation, net radiation, relative humidity, ambient vapor pressure, wind speed and direction, barometric pressure, soil temperature, and soil-heat flux. All micrometeorological data were collected using a 10-second sampling interval by data loggers that output daily mean, maximum, and minimum values, and hourly mean values. For precipitation, data output consisted of daily, hourly, and 5-minute totals. Soil-moisture data included periodic measurements of soil-water content at nine neutron-probe access tubes with measurable depths ranging from 5.25 to 29.75 meters. The computer data files included in this report contain the complete micrometeorological and soil-moisture data sets. The computer data consists of seven files with about 14 megabytes of information. The seven files are in tabular format: (1) one file lists daily mean, maximum, and minimum micrometeorological data and daily total precipitation; (2) three files list hourly mean micrometeorological data and hourly precipitation for each year (1998-2000); (3) one file lists 5-minute precipitation data; (4) one file lists mean soil-water content by date and depth at four experimental sites; and (5) one file lists soil-water content by date and depth for each neutron-probe access tube. This report highlights selected data contained in the computer data files using figures, tables, and brief discussions. Instrumentation used for data collection also is described. Water-content profiles are shown to demonstrate variability of water content with depth. Time-series data are plotted to illustrate temporal variations in micrometeorological and soil-water content data. Substantial precipitation at the end of an El Ni?o cycle in early 1998 resulted in measurable water penetration to a depth of 1.25 meters at one of the four experimental soil-monitoring sites.

Nevada↗
USGS2025

Groundwater-level elevations in the Denver Basin bedrock aquifers and Upper Black Squirrel Creek alluvial aquifer, El Paso County, Colorado, 2021–24

El Paso County is the second-most populous county in Colorado and is projected to grow another 15 percent by 2030. Within El Paso County is the Upper Black Squirrel Creek Designated Groundwater Basin (Black Squirrel Basin), an area where surface water is scarce and water users rely primarily on groundwater from five different aquifers (the Upper Black Squirrel Creek alluvial aquifer and four bedrock aquifers within the Denver Basin aquifer system: the lower Dawson, Denver, Arapahoe, and Laramie-Fox Hills aquifers) to meet their needs. Currently (2024), land within the Upper Black Squirrel Creek Basin is primarily used for rural grazing and agriculture; however, municipal development is ongoing. In 2021, the U.S. Geological Survey, in cooperation with the Upper Black Squirrel Creek Ground Water Management District, began a study to establish a baseline dataset and assess the groundwater resources of the aquifers within the Black Squirrel Basin. A network of 39 wells was established in 2021; discrete groundwater-level measurements were made bimonthly. Nine of the 39 wells were equipped with pressure transducers to record hourly groundwater-level data. Seven wells had statistically significant seasonal trends, and trends at 3 wells were negative. For the discrete data, 16 wells had a significant trend for the study period, and 4 wells had negative trends. For the time-series data, 8 wells had significant trends, and 3 wells had negative trends. Potentiometric surface maps were created for this study using discrete, static groundwater levels measured in April 2023. These maps showed the estimated groundwater flow direction from the north-northwest to the south-southeast in the alluvial aquifer and from the northwest to the east-southeast for the lower Dawson and Denver aquifer wells. This study indicates the potential benefit of monitoring wells in the areas near municipal pumping. Additional monitoring could lead to a better understanding of connectivity between aquifers and be an important tool for assessing long-term sustainability of groundwater use.

Colorado↗
USGS2019

Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA

We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories ( https://doi.org/10.6071/M3FH3D ).

California↗
USGS2016

Annual accumulation over the Greenland ice sheet interpolated from historical and newly compiled observation data

The estimation of ice/snow accumulation is of great significance in quantifying the mass balance of ice sheets and variation in water resources. Improving the accuracy and reducing uncertainty has been a challenge for the estimation of annual accumulation over the Greenland ice sheet. In this study, we kriged and analyzed the spatial pattern of accumulation based on an observation data series including 315 points used in a recent research, plus 101 ice cores and snow pits and newly compiled 23 coastal weather station data. The estimated annual accumulation over the Greenland ice sheet is 31.2 g cm −2 yr −1 , with a standard error of 0.9 g cm −2 yr −1 . The main differences between the improved map developed in this study and the recently published accumulation maps are in the coastal areas, especially southeast and southwest regions. The analysis of accumulations versus elevation reveals the distribution patterns of accumulation over the Greenland ice sheet.

Geografiska Annaler, Series A: Physical Geography↗
USGS2005

Hydratools, a MATLAB® based data processing package for Sontek Hydra data

The U.S. Geological Survey (USGS) has developed a set of MATLAB tools to process and convert data collected by Sontek Hydra instruments to netCDF, which is a format used by the USGS to process and archive oceanographic time-series data. The USGS makes high-resolution current measurements within 1.5 meters of the bottom. These data are used in combination with other instrument data from sediment transport studies to develop sediment transport models. Instrument manufacturers provide software which outputs unique binary data formats. Multiple data formats are cumbersome. The USGS solution is to translate data streams into a common data format: netCDF. The Hydratools toolbox is written to create netCDF format files following EPIC conventions, complete with embedded metadata. Data are accepted from both the ADV and the PCADP. The toolbox will detect and remove bad data, substitute other sources of heading and tilt measurements if necessary, apply ambiguity corrections, calculate statistics, return information about data quality, and organize metadata. Standardized processing and archiving makes these data more easily and routinely accessible locally and over the Internet. In addition, documentation of the techniques used in the toolbox provides a baseline reference for others utilizing the data.

Conference Paper↗
USGS2019

MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types

Innovative, open, and rapid methods to map crop types over large areas are needed for long-term cropland monitoring. We developed two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific classification. The classification approaches use similarities and dissimilarities in crop type phenologyderived from NDVI time-series data for the two approaches. The year-specific approach uses the training samples from one year and classifies crop types for that year only, whereas the generalized classification approach uses above-average, average, and below-average precipitation years for training to produce crop type maps for one or multiple years more robustly. We produced annual crop type maps using the generalized classification approach for 2001–2014 and the year-specific approach for 2008, 2010, 2011 and 2012. The year-specific classification had overall accuracies > 78%, while the generalized classifier had accuracies > 75% for the conterminous U.S. for 2008, 2010, 2011, and 2012. The generalized classifier enables automated and routine crop type mapping without repeated and expensive ground sample collection year after year. The resulting crop type maps for years prior to 2007 are new and especially important for long-term cropland monitoring and food security analysis because no other map products are currently available for 2001–2007.

Remote Sensing of Environment↗
USGS2006

Use of an ADCP to compute suspended-sediment discharge in the tidal Hudson River, New York

Acoustic Doppler current profilers (ADCPs) can provide data needed for computation of suspended-sediment discharge in complex river systems, such as tidal rivers, in which conventional methods of collecting time-series data on suspended-sediment concentration (SSC) and water discharge are not feasible. Although ADCPs are not designed to measure SSC, ADCP data can be used as a surrogate under certain environmental conditions. However, the software for such computation is limited, and considerable post-processing is needed to correct and normalize ADCP data for this use. This report documents the sampling design and computational procedure used to calibrate ADCP measures of echo intensity to SSC and water velocity to discharge in the computation of suspended-sediment discharge at the study site on the Hudson River near Poughkeepsie, New York. The methods and procedures described may prove useful to others doing similar work in different locations; however, they are specific to this study site and may have limited applicability elsewhere.

Connecticut, New York↗
USGS2020

Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as "duplicate interpretations"). The annual land cover reference class labels for the 1984-2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time; and 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30 year period. Protocols to quantify and enhance interpreter consistency are critical elements to document and ensure quality of these reference data.

Remote Sensing of Environment↗
USGS2022

Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018

This remote sensing section is based on Nagler et al. (in preparation for the journal Hydrological Processes) and is a summary of the USGS preliminary findings to date. This report documents the changes in green foliage density (greenness) as measured by satellite vegetation index (VI) data and corresponding evapotranspiration (ET) in the riparian corridor of the Colorado River delta associated with the Minutes 319 and 323 environmental water deliveries using time-series data from 2013 through 2018. The report focuses on what happened only within the riparian corridor’s seven reaches since the 2014 flows, and despite being a continuation of measuring greenness and ET after the 2017 end of Minute 319, this study continued the tracking of these two variables, greenness and ET, in these original riparian corridor focal areas. Two spatial scales are used here: (1) Landsat satellite imagery at 30 m pixels and (2) the EOS-1 satellite sensor the Moderate Resolution Imaging Spectrometer (MODIS) with a resolution of 250 m pixels. The focal period includes 2013 (prepulse flow) and the years 2014-2018, with a focus on imagery collected from the Summer growing seasons 2014 through 2018 (one-year, pre-pulse and several post-pulse years, respectively). This report re-creates the 2013-2017 Landsat-based results from Jarchow et al. (2017a, b) by using the same region of interest (ROI). The report now provides revised and re-created results using all new imagery acquisition and processing techniques, as well as extraction code, created by the Vegetation Index and Phenology (VIP) Lab of the Biosystems Engineering Department of the University of Arizona (UofA). In 2018, methods employed by the VIP lab (and not ArcGIS) were used. ArcGIS was only used in the newly processed data to display the final difference maps. The entire spatial tile data from NASA was downloaded and processed at the VIP Lab using satellite imagery at two resolutions: 250 m MODIS and 30 m Landsat using three sensors, Landsat 5, Landsat 7 ETM+ and Landsat 8 Operational Land Imager (OLI), with added scenes for each year based on new clear atmosphere requirements. The VIP lab clipped the river boundary and seven riparian reaches from the previously existing ROI used in Jarchow et al. (2017 a, b) for the analyses done under Minute 319. The NASA image datasets for this riparian corridor ROI in seven reaches were re-processed to produce additional vegetation index (VI) information for years 2013 to 2018 for this report. At the same time, the report acquired and processed imagery from 2000- 2018 (data outside the scope of this report and data not shown here). The additional VIs (NDVI, scaled NDVI, EVI, EVI2) were analyzed so that new assessments of greenness and ET could be produced from the imagery datasets following methods in Nagler et al. (2013). These VI choices were based on previous performance comparisons between biophysical ground-based data and radiometric satellite-based data collected from this riparian ecosystem (Nagler et al., 2001) as well as performance related to ET estimation (Nagler et al., 2005a, b) and current advancements in VIs such as EVI2.

Colorado River Delta↗
USGS2009

Using a coupled groundwater/surface-water model to predict climate-change impacts to lakes in the Trout Lake Watershed, northern Wisconsin

A major focus of the U.S. Geological Survey’s Trout Lake Water, Energy, and Biogeochemical Budgets (WEBB) project is the development of a watershed model to allow predictions of hydrologic response to future conditions including land-use and climate change. The coupled groundwater/surface-water model GSFLOW was chosen for this purpose because it could easily incorporate an existing groundwater flow model and it provides for simulation of surface-water processes. The Trout Lake watershed in northern Wisconsin is underlain by a highly conductive outwash sand aquifer. In this area, streamflow is dominated by groudwater contributions, however, surface runoff occurs during intense rainfall periods and spring snowmelt. Surface runoff also occurs locally near stream/lake areas where the unsaturated zone is thin. A diverse data set, collected from 1992 to 2007 for the Trout Lake WEBB project and the co-located and NSF-funded North Temperate Lake LTER project, includes snowpack, solar radiation, potential evapotranspiration, lake levels, groundwater levels, and streamflow. The time-series processing software TSPROC (Doherty 2001)was used to distill the large time series data set to a smaller set of observations and summary statistics that captured the salient hydrologic information. The time-series processing reduced hundreds of thousands of observations to less than 5,000. Model calibration included specific predictions for several lakes in the study area using the PEST parameter estimation suit of software (Doherty 2007). The calibrated model was used to simulate the hydrologic response in the study lakes to a variety of climate change scenarios culled from the IPCC Fourth Assessment Report of the Intergovernmental Panel of Climate Change (Solomon et al. 2007). Results from the simulations indicate climate change could result in substantial changes to the lake levels and components of the hydrologic budget of a seepage lake in the flow system. For a drainage lake lower in the flow system, the impacts of climate change are diminished.

Wisconsin↗
USGS2019

Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015

Water levels in the Great Lakes fluctuate substantially because of complex interactions among inputs (precipitation and streamflow), outputs (evaporation and outflow), and other factors. This report by the U.S. Geological Survey in cooperation with the Great Lakes Restoration Initiative was completed to describe trends in climate, streamflow, lake levels, and major water-budget components within the Great Lakes Basin for water years (WYs) 1960–2015 (study period). Resulting trends are applicable only to the study period and should not be considered indicative of longer-term trends. Analyses of climate trends used monthly data from the Parameter-elevation Regressions on Independent Slopes Model, which are available only for the United States. Trend tests were completed for annual and seasonal time series of monthly means for total precipitation, daily minimum air temperature ( T min ), and daily maximum air temperature ( T max ). Statistical significance for all time-trend tests (climate, streamflow, and lake levels) was determined using the Mann‑Kendall test for probability values less than or equal to 0.10. Trend analyses were completed without adjustments for serial correlation; however, a modified Mann-Kendall test was subsequently used to examine potential effects of short-term persistence in time-series data. Effects of short-term persistence were considered inconsequential for climate data and minor for streamflow data; however, the presence of short-term persistence in water-budget components had more substantial effects on trend analyses. Spatial distributions of trends in climatic data for WYs 1960–2015 for the U.S. part of the Great Lakes Basin (land only) indicate (1) generally ubiquitous upward trends in T min and (2) a sharp transition from neutral or downward trends in precipitation northwest of Lake Michigan to generally upward trends east of Lake Michigan. Trends in T max were not statistically significant. Analyses of annual climatic data aggregated for the U.S. land part of the Great Lakes Basin indicated statistically significant upward trends for precipitation and T min , and similar statistically significant trends existed for all the individual lake subbasins except Lake Superior. Of 103 U.S. Geological Survey streamgages analyzed for streamflow trends, 71 had significant annual trends (54 upward and 17 downward). Downward trends in annual streamflow are concentrated northwest of Lake Michigan (16 streamgages), and upward trends are concentrated east of Lake Michigan (53 streamgages). Of the 71 streamgages with significant annual trends, 70 had at least one season with a significant trend that matched the annual trend direction. Of 35 Environment and Climate Change Canada streamgages analyzed, 22 had significant upward trends in annual streamflow, and all but 1 of these 22 had at least one season with a significant upward trend. None of the Environment and Climate Change Canada streamgages had significant downward annual trends, and only one had a significant downward seasonal trend. Trends in lake levels and several major water-budget components affecting lake levels were analyzed for the study period. Significant downward trends in lake level and outflow for Lake Superior are driven primarily by low lake levels and outflows during WYs 1998–2014. A significant downward trend in runoff from the contributing drainage area also is indicated, which is consistent with numerous streamgages northwest of Lake Michigan with significant downward trends in annual streamflow. A significant upward trend in annual overlake evaporation also is indicated, which is consistent with the spatially distributed upward trends in annual T min . The sum of overlake precipitation and runoff from the contributing drainage area for each of the Great Lakes, less overlake evaporation, composes a variable called net basin supply (NBS). A significant downward trend in NBS is indicated for Lake Superior, which is consistent with significant trends for individual components of runoff (downward) and evaporation (upward) that contributed to a significant downward trend for lake outflow. Statistically significant upward trends in NBS for Lake Saint Clair and Lake Ontario offset the downward trend for Lake Superior and combine with nonsignificant upward trends in NBS for Lakes Michigan and Huron and Lake Erie to produce a neutral trend in NBS for the basin. A predictable pattern in monthly mean lake levels is noted for Lake Superior, with the minimum for each year usually during or near March and the maximum commonly during or near September or October. When an October lake level is in a period of substantial decline, potential for an ensuing short-term period of below-mean lake levels is enhanced. Downstream from Lake Superior, monthly lake levels have sawtooth patterns that somewhat resemble those for Lake Superior but with decreased predictability in timing. Similar to Lake Superior, Lakes Michigan and Huron, Lake Saint Clair, and Lake Erie all have a prolonged period of low lake levels around WYs 1998–2014; however, a significant downward trend is indicated only for Lakes Michigan and Huron. All these lakes also have a period of low lake levels before about WY 1968, when minimum lake levels were lower than during WYs 1998–2014. The significant downward trend of outflow from Lake Superior is carried downstream into Lakes Michigan and Huron; however, trends in outflow from the next three lakes downstream (Lakes Saint Clair, Erie, and Ontario) are offset by increased precipitation and runoff and are not significant.

Scientific Investigations Report↗
USGS2024

Concept of a satellite cross-calibration radiometer for in-orbit calibration of commercial optical satellites

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing number of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9 and Sentinel-2, and planned future missions, like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA)’s Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from reference systems to client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

Remote Sensing↗
USGS2006

User's Guide, software for reduction and analysis of daily weather and surface-water data: Tools for time series analysis of precipitation, temperature, and streamflow data

The software described here is used to process and analyze daily weather and surface-water data. The programs are refinements of earlier versions that include minor corrections and routines to calculate frequencies above a threshold on an annual or seasonal basis. Earlier versions of this software were used successfully to analyze historical precipitation patterns of the Mojave Desert and the southern Colorado Plateau regions, ecosystem response to climate variation, and variation of sediment-runoff frequency related to climate (Hereford and others, 2003; 2004; in press; Griffiths and others, 2006). The main program described here (Day_Cli_Ann_v5.3) uses daily data to develop a time series of various statistics for a user specified accounting period such as a year or season. The statistics include averages and totals, but the emphasis is on the frequency of occurrence in days of relatively rare weather or runoff events. These statistics are indices of climate variation; for a discussion of climate indices, see the Climate Research Unit website of the University of East Anglia (http://www.cru.uea.ac.uk/projects/stardex/) and the Climate Change Indices web site (http://cccma.seos.uvic.ca/ETCCDMI/indices.html). Specifically, the indices computed with this software are the frequency of high intensity 24-hour rainfall, unusually warm temperature, and unusually high runoff. These rare, or extreme events, are those greater than the 90th percentile of precipitation, streamflow, or temperature computed for the period of record of weather or gaging stations. If they cluster in time over several decades, extreme events may produce detectable change in the physical landscape and ecosystem of a given region. Although the software has been tested on a variety of data, as with any software, the user should carefully evaluate the results with their data. The programs were designed for the range of precipitation, temperature, and streamflow measurements expected in the semiarid Southwest United States. The user is encouraged to review the examples provided with the software. The software is written in Fortran 90 with Fortran 95 extensions and was compiled with the Digital Visual Fortran compiler version 6.6. The executables run on Windows 2000 and XP, and they operate in a MS-DOS console window that has only very simple graphical options such as font size and color, background color, and size of the window. Error trapping was not written into the programs. Typically, when an error occurs, the console window closes without a message.

Open-File Report↗