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At least 379 records · Page 21Linked to original sources

USGS2017

Correlation of Alaskan varve thickness with climatic parameters, and use in paleoclimatic reconstruction

The thickness of varves in the sediments of Skilak Lake, Alaska, are correlated with the mean annual temperature (r = 0.574), inversely correlated with the mean annual cumulative snowfall (r = -0.794), and not correlated with the mean annual precipitation (r = 0.202) of the southern Alaska climatological division for the years 1907-1934 A.D. Varve thickness in Skilak Lake is sensitive to annual temperature and snowfall because Skilak Glacier, the dominant source of sediment for Skilak Lake, is sensitive to these climatic parameters. Trends of varve thickness are well correlated with trends of mean annual cumulative snowfall (r = -0.902) of the southern Alaska climatological division and with trends of mean annual temperature of the southern (r = 0.831) and northern (r = 0.786) Alaska climatological divisions. Trends of varve thickness also correlate with trends of annual temperature in Seattle and North Head, Washington (r = 0.632 and 0.850, respectively). Comparisons of trends of varve thickness with trends of annual temperature in California, Oregon, and Washington suggest no widespread regional correlation. Trends of annual snowfall in the southern Alaska climatological division and trends of annual temperature in the southern and northern Alaska climatological divisions are reconstructed for the years 1700-1906 A.D. Climatic reconstructions on the basis of varve thickness in Skilak Lake utilize equations derived from the regression of series of smoothed climatological data on series of smoothed varve thickness. Reconstruction of trends of mean annual cunulative snowfall in the southern Alaska climatological division suggests that snowfall during the 1700s and 1800s was much greater than that during the early and mid-1900s. The periods 1770-1790 and 1890-1906 show marked decreases in the mean annual snowfall. Reconstructed trends of the annual temperature of the northern and southern Alaska climatological divisions suggest that annual temperatures during the 1700s and 1800s were lower than those of the early and mid-1900s. Two periods of relatively high annual temperatures coincide with the periods of low annual snowfall thus determined. ?? 1983.

Quaternary Research↗

USGS2016

Insights into shallow magmatic processes at Kīlauea Volcano, Hawaiʻi, from a multiyear continuous gravity time series

Continuous gravity data collected near the summit eruptive vent at Kīlauea Volcano, Hawaiʻi, during 2011–2015 show a strong correlation with summit-area surface deformation and the level of the lava lake within the vent over periods of days to weeks, suggesting that changes in gravity reflect variations in volcanic activity. Joint analysis of gravity and lava level time series data indicates that over the entire time period studied, the average density of the lava within the upper tens to hundreds of meters of the summit eruptive vent remained low—approximately 1000–1500 kg/m 3 . The ratio of gravity change (adjusted for Earth tides and instrumental drift) to lava level change measured over 15 day windows rose gradually over the course of 2011–2015, probably reflecting either (1) a small increase in the density of lava within the eruptive vent or (2) an increase in the volume of lava within the vent due to gradual vent enlargement. Superimposed on the overall time series were transient spikes of mass change associated with inflation and deflation of Kīlauea's summit and coincident changes in lava level. The unexpectedly strong mass variations during these episodes suggest magma flux to and from the shallow magmatic system without commensurate deformation, perhaps indicating magma accumulation within, and withdrawal from, void space—a process that might not otherwise be apparent from lava level and deformation data alone. Continuous gravity data thus provide unique insights into magmatic processes, arguing for continued application of the method at other frequently active volcanoes.

Hawaii↗

USGS2011

Developing climate data records and essential climate variables from landsat data

The series of Landsat missions has compiled the longest record of satellite observation of the Earth’s land surface, extending for more than 38 years for most areas of the globe. Landsat data are particularly important as long term climate data records because the scale of observation is sufficient to differentiate between natural and human drivers of land cover change. The USGS has established consistent radiometric calibration across the Landsat TM and ETM+ sensors, and have extended the calibration back to the earlier MSS sensors. The USGS is developing capabilities to create fundamental climate data records (FCDRs), thematic climate data records (TCDRs), and essential climate variables (ECVs) from the Landsat data archive. Two high priority TCDRs were identified: surface reflectance and land surface temperature because they have direct application or are required as input to the generation of ECVs. We will focus development on a few of the terrestrial ECVs that have a high potential for being derived from Landsat data, that include land cover, albedo, fire disturbance, surface water, snow and ice, and leaf area index (LAI). We are collaborating with scientists who have demonstrated successful algorithm development and application of these science products to develop a framework of processing capabilities to support research projects and land management applications, along with an independent strategy for product validation. Our goal is to scale the creation and validation of these products from specific sites in the conterminous U.S. and Alaska, for extension to continental and global scales.

Conference Paper↗

USGS2018

Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of LST, the LST gt . We hypothesize that in geothermal areas the LSM time series will underestimate the LST as compared to the remote sensing data, since the former does not account for the geothermal component in its model. In order to extract LST gt , two approaches of different nature (physical based and data mining) were developed and tested in an area of about 560 × 560 km 2 centered at the Kenyan Rift. Pre-dawn data in the study area during the first 45 days of 2012 were analyzed. The results show consistent spatial and temporal LST gt patterns between the two approaches, and systematic differences of about 2 K. A geothermal area map from surface studies was used to assess LST gt inside and outside the geothermal boundaries. Spatial means were found to be higher inside the geothermal limits, as well as the relative frequency of occurrence of high LST gt . Results further show that areas with strong topography can result in anomalously high LST gt values (false positives), which suggests the need for a slope and aspect correction in the inputs to achieve realistic results in those areas. The uncertainty analysis indicates that large uncertainties of the input parameters may limit detection of LST gt anomalies. To validate the approaches, higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over the Olkaria geothermal field were used. An established method to estimate radiant geothermal flux was applied providing values between 9 and 24 W/m 2 in the geothermal area, which coincides with the LST gt flux rates obtained with the proposed approaches. The proposed approaches are a first step in estimating LST gt at large spatial coverage from remote sensing and LSM data series, and provide an innovative framework for future improvements.

Remote Sensing of Environment↗

USGS2018

Documentation of particle-size analyzer time series, and discrete suspended-sediment and bed-sediment sample data collection, Niobrara River near Spencer, Nebraska, October 2014

The U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers, monitored a sediment release by Nebraska Public Power District from Spencer Dam located on the Niobrara River near Spencer, Nebraska, during the fall of 2014. The accumulated sediment behind Spencer Dam ordinarily is released semiannually; however, the spring 2014 release was postponed until the fall. Because of the postponement, the scheduled fall sediment release would consist of a larger volume of sediment. The larger than normal sediment release expected in fall 2014 provided an opportunity for the USGS and U.S. Army Corps of Engineers to improve the understanding of sediment transport during reservoir sediment releases. A primary objective was to collect continuous suspended-sediment data during the first days of the sediment release to document rapid changes in sediment concentrations. For this purpose, the USGS installed a laser-diffraction particle-size analyzer at a site near the outflow of the dam to collect continuous suspended-sediment data. The laser-diffraction particle-size analyzer measured volumetric particle concentration and particle-size distribution from October 1 to 2 (pre-sediment release) and October 5 to 9 (during sediment release). Additionally, the USGS manually collected discrete suspended-sediment and bed-sediment samples before, during, and after the sediment release. Samples were collected at two sites upstream from Spencer Dam and at three bridges downstream from Spencer Dam. The resulting datasets and basic metadata associated with the datasets were published as a data release; this report provides additional documentation about the data collection methods and the quality of the data.

Nebraska↗

USGS2010

A parametric study of the January 2006 explosive eruptions of Augustine Volcano, using seismic, infrasonic, and lightning data

A series of 13 explosive eruptions occurred at Augustine Volcano, Alaska, from January 11–28, 2006. Each lasted 2.5 to 19 minutes and produced ash columns 3.8 to 13.5 km above mean sea level. We investigated various parameters to determine systematic trends, including durations, seismic amplitudes, frequency contents, signal characteristics, peak acoustic pressures, ash column heights, lightning occurrence, and lengths of pre-event and post-event quiescence. Individual tephra volumes are not known. There is no clear correlation between acoustic peak pressure and ash column height or between peak seismic amplitude and duration. However, several trends are evident. Two events, January 11 at 0444 AKST (1344 UTC) and January 27 at 2337 AKST (0837 UTC) are short (180 and 140 seconds) and have very impulsive onsets and high acoustic peak pressures of 93 and 105 Pa, as well as high peak seismic amplitudes. We interpret these to be mainly gas releases. Two of the largest events followed quiescent intervals of 3 days or longer: January 17 at 0758 AKST (1658 UTC), and January 27 at 2024 AKST (January 28 at 0524 UTC). These two events had reduced displacements ( D R ) of 11.4 and 7.5 cm 2 , respectively. Although these D R values are typical for eruptions with ash columns to 9 to 14 km, most other D R values of 1.6 to 3.6 cm 2 are low for the 7.0 to 10.5 km ash column heights observed. The combination of short durations, small D R and high ash columns suggests that these events are highly explosive, in agreement with Vulcanian eruption type. Several events had long durations on individual seismic stations but not on others; we interpret these to represent pyroclastic or other flows passing near the affected stations so that tractions or momentum exchange from the cloud or flow adds energy to the ground only near those stations. The eruption on January 27 at 2024 AKST had more than 300 lightning flashes, whereas the following eruptions on January 28 at 0204 AKST and 0742 AKST had only 28 and 6 lightning flashes. The 2024 AKST eruption had a longer duration (1,180 versus <460 seconds), a higher ash column height (10.5 versus 7.0–7.2 km) and higher acoustic peak pressure (83 versus 66 and 24 Pa). The data suggest that the lightning-rich 2024 AKST eruption produced more tephra than the following eruptions, hence there were more charge carriers injected to the atmosphere. Seismic signals preceded the infrasound signals by 0 to 5 seconds with no obvious pattern in terms of the above groupings. The explosive eruption phase overlapped with the subsequent continuous phase by about 2 days. Parametric data may be useful to estimate eruption conditions in near real time.

Alaska↗

USGS1997

Enhancements to the Branched Lagrangian Transport Modeling System

The Branched Lagrangian Transport Model (BLTM) has received wide use within the U.S. Geological Survey over the past 10 years. This report documents the enhancements and modifications that have been made to this modeling system since it was first introduced. The programs in the modeling system are arranged into five levels?programs to generate time-series of meteorological data (EQULTMP, SOLAR), programs to process time-series data (INTRP, MRG), programs to build input files for transport model (BBLTM, BQUAL2E), the model with defined reaction kinetics (BLTM, QUAL2E), and post processor plotting programs (CTPLT, CXPLT). An example application is presented to illustrate how the modeling system can be used to simulate 10 water-quality constituents in the Chattahoochee River below Atlanta, Georgia.

Water-Resources Investigations Report↗

USGS2011

Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data

Maps of irrigated areas are essential for Ghana's agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20-57% higher than irrigated areas reported by Ghana's Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs. ?? 2011 by the authors.

Remote Sensing↗

USGS1996

Long-term geochemical surveillance of fumaroles at Showa-Shinzan dome, Usu volcano, Japan

This study investigates 31 years of fumarole gas and condensate (trace elements) data from Showa-Shinzan, a dacitic dome-cryptodome complex that formed during the 1943-1945 eruption of Usu volcano. Forty-two gas samples were collected from the highest-temperature fumarole, named A-1, from 1954 (800??C) to 1985 (336??C), and from lower-temperature vents. Condensates were collected contemporaneously with the gas samples, and we reanalyzed ten of these samples, mostly from the A-1 vent, for 32 cations and three anions. Modeling using the thermochemical equilibrium program, SOLVGAS, shows that the gas samples are mild disequilibrium mixtures because they: (a) contain unequilibrated sedimentary CH4 and NH3; (b) have unequilibrated meteoric water; or (c) lost CO, either by air oxidation or by absorption by the sodium hydroxide sampling solution. SOLVGAS also enabled us to restore the samples by removing these disequilibrium effects, and to estimate their equilibrium oxygen fugacities and amounts of S2 and CH4. The restored compositions contain > 98% H2O with minor to trace amounts of CO2, H2, HCl, SO2, HF, H2S, CO, S2 and CH4. We used the restored gas and condensate data to test the hypotheses that these time-series compositional data from the dome's fumaroles provide: (1) sufficient major-gas data to analyze long-term degassing trends of the dome's magma-hydrothermal system without the influence of sampling or contamination effects; (2) independent oxygen fugacity-versus-temperature estimates of the Showa-Shinzan dacite; (3) the order of release of trace elements, especially metals, from magma; and (4) useful information for assessing volcanic hazards. The 1954-1985 restored A-1 gas compositions confirm the first hypothesis because they are sufficient to reveal three long-term degassing trends: (1) they became increasingly H2O-rich with time due to the progressive influx of meteoric water into the dome; (2) their C/S and S/Cl ratios decreased dramatically while their Cl/F ratios stayed roughly constant, indicating the progressive outgassing of less soluble components (F ??? Cl > S > C) from the magma reservoir; and (3) their H2O/H2, CO2/CO and H2S/SO2 ratios increased significantly in concert with equilibrium changes expected for the ??? 500??C temperature drop. When plotted against reciprocal temperature, the restored-gas log oxygen fugacities follow a tight linear trend from < NNO + 0.5 at > 800??C to NNO + 2.5 at ??? 400??C. This trend largely disproves the second hypothesis because the oxygen fugacities for the < 800??C restored gases can only be explained by mixing of hot magmatic gases with ??? 350??C steam from superheated meteoric water. But above 800??C this trend intersects the opposing linear trend for other Usu eruptive products, implying a log oxygen fugacity of -11.45 at 902??C for the Showa-Shinzan magma. The time-series trace-element data also disprove the third hypothesis because rock- and incrustation-particle contaminants in the condensates account for most of the trace-element variation. Nonetheless, highly volatile elements like B and As are relatively unaffected by this particle contamination, and they show similar time-series trends as Cl and F. Finally, except for infrequent sampling around the 1977 Usu eruption, the results generally confirm the fourth hypothesis, since the time-series trends for the major gases and selected trace elements indicate that, with time, the system cooled, degassed and was infiltrated by meteoric water, all of which are positive signs that volcanic activity declined over the 31-year history. This study also suggests that second boiling of shallow magma within and possibly beneath the cryptodome sustained magmatic degassing for at least 20 years after emplacement.

Journal of Volcanology and Geothermal Research↗

USGS2012

Estimating survival rates with time series of standing age‐structure data

It has long been recognized that age‐structure data contain useful information for assessing the status and dynamics of wildlife populations. For example, age‐specific survival rates can be estimated with just a single sample from the age distribution of a stable, stationary population. For a population that is not stable, age‐specific survival rates can be estimated using techniques such as inverse methods that combine time series of age‐structure data with other demographic data. However, estimation of survival rates using these methods typically requires numerical optimization, a relatively long time series of data, and smoothing or other constraints to provide useful estimates. We developed general models for possibly unstable populations that combine time series of age‐structure data with other demographic data to provide explicit maximum likelihood estimators of age‐specific survival rates with as few as two years of data. As an example, we applied these methods to estimate survival rates for female bison ( Bison bison ) in Yellowstone National Park, USA. This approach provides a simple tool for monitoring survival rates based on age‐structure data.

Ecology↗

USGS2007

Analysis of flood-magnitude and flood-frequency data for streamflow-gaging stations in the Delaware and North Branch Susquehanna River Basins in Pennsylvania

The Delaware and North Branch Susquehanna River Basins in Pennsylvania experienced severe flooding as a result of intense rainfall during June 2006. The height of the flood waters on the rivers and tributaries approached or exceeded the peak of record at many locations. Updated flood-magnitude and flood-frequency data for streamflow-gaging stations on tributaries in the Delaware and North Branch Susquehanna River Basins were analyzed using data through the 2006 water year to determine if there were any major differences in the flood-discharge data. Flood frequencies for return intervals of 2, 5, 10, 50, 100, and 500 years (Q2, Q5, Q10, Q50, Q100, and Q500) were determined from annual maximum series (AMS) data from continuous-record gaging stations (stations) and were compared to flood discharges obtained from previously published Flood Insurance Studies (FIS) and to flood frequencies using partial-duration series (PDS) data. A Wilcoxon signed-rank test was performed to determine any statistically significant differences between flood frequencies computed from updated AMS station data and those obtained from FIS. Percentage differences between flood frequencies computed from updated AMS station data and those obtained from FIS also were determined for the 10, 50, 100, and 500 return intervals. A Mann-Kendall trend test was performed to determine statistically significant trends in the updated AMS peak-flow data for the period of record at the 41 stations. In addition to AMS station data, PDS data were used to determine flood-frequency discharges. The AMS and PDS flood-frequency data were compared to determine any differences between the two data sets. An analysis also was performed on AMS-derived flood frequencies for four stations to evaluate the possible effects of flood-control reservoirs on peak flows. Additionally, flood frequencies for three stations were evaluated to determine possible effects of urbanization on peak flows. The results of the Wilcoxon signed-rank test showed a significant difference at the 95-percent confidence level between the Q100 computed from AMS station data and the Q100 determined from previously published FIS for 97 sites. The flood-frequency discharges computed from AMS station data were consistently larger than the flood discharges from the FIS; mean percentage difference between the two data sets ranged from 14 percent for the Q100 to 20 percent for the Q50. The results of the Mann-Kendall test showed that 8 stations exhibited a positive trend (i.e., increasing annual maximum peaks over time) over their respective periods of record at the 95-percent confidence level, and an additional 7 stations indicated a positive trend, for a total of 15 stations, at a confidence level of greater than or equal to 90 percent. The Q2, Q5, Q10, Q50, and Q100 determined from AMS and PDS data for each station were compared by percentage. The flood magnitudes for the 2-year return period were 16 percent higher when partial-duration peaks were incorporated into the analyses, as opposed to using only the annual maximum peaks. The discharges then tended to converge around the 5-year return period, with a mean collective difference of only 1 percent. At the 10-, 50-, and 100-year return periods, the flood magnitudes based on annual maximum peaks were, on average, 6 percent higher compared to corresponding flood magnitudes based on partial-duration peaks. Possible effects on flood peaks from flood-control reservoirs and urban development within the basin also were examined. Annual maximum peak-flow data from four stations were divided into pre- and post-regulation periods. Comparisons were made between the Q100 determined from AMS station data for the periods of record pre- and post-regulation. Two stations showed a nearly 60- and 20-percent reduction in the 100-year discharges; the other two stations showed negligible differences in discharges. Three stations within urban basins were compared to 38 stations

Pennsylvania↗

USGS2018

A Bayesian approach to predict sub-annual beach change and recovery

The upper beach, between the astronomical high tide and the dune-toe, supports habitat and recreation along many beaches, making predictions of upper beach change valuable to coastal managers and the public. We developed and tested a Bayesian network (BN) to predict the cross-shore position of an upper beach elevation contour (Z l D) following 1 month to 1-year intervals at Fire Island, New York. We combine hydrodynamic data with series of island-wide topographic data and spatially limited cross-shore profiles. First, we predicted beach configuration of Z l D positions at high spatial resolution (50 m) over intervals spanning 2005–2014. Compared to untrained model predictions, in which all six outcomes are equally likely (prior likelihood = 0.16), our prediction metrics (skill = 0.52; log likelihood ratio = 0.14; accuracy = 0.56) indicate the BN confidently predicts upper beach dynamics. Next, the BN forecasted three intervals of beach recovery following Hurricane Sandy. Results suggest the pre-Sandy training data is sufficiently robust to require only periodic updates to beach slope observations to maintain confidence for forecasts. Finally, we varied input data, using observations collected at a range of temporal (1–12 months) and spatial (50 m to > 1 km) resolutions to evaluate model skill. This experiment shows that data collection techniques with different spatial and temporal frequencies can be used to inform a single modeling framework and can provide insight to BN training requirements. Overall, results indicate that BNs and inputs can be developed for broad coastal change assessment or tailored to a set of predictive requirements, making this methodology applicable to a variety of coastal prediction scenarios.

Estuaries and Coasts↗

USGS2018

Promoting synergy in the innovative use of environmental data—Workshop summary

From December 2 to 4, 2015, NatureServe and the U.S. Geological Survey organized and hosted a biodiversity and ecological informatics workshop at the U.S. Department of the Interior in Washington, D.C. The workshop objective was to identify user-driven future directions and areas of collaboration in advanced applications of environmental data applied to forecasting and decision making for the sustainability of biodiversity and ecosystem services. Substantial effort to recruit attendees from diverse Federal, State, and private sector organizations successfully attracted participants from 20 Federal agencies and 48 different institutions in the academic, nonprofit, State government, and commercial sectors; the total number of attendees ranged from 100 to 144 during the 3-day workshop. The first one-half of the workshop was divided into 7 plenary sessions and 3 sets of lightning talk sessions organized by sector, providing 48 oral and visual plenary presentations that shared diverse perspectives on biodiversity and ecological informatics, including original biospatial analyses from 6 graduate student map contest winners. The second one-half of the workshop focused on 10 breakout sessions with participant-driven themes from the environmental data sphere and concluded with an address by the Director of the U.S. Fish and Wildlife Service. The workshop was structured to encourage interactivity. About 80–90 percent of attendees provided direct feedback using clicker devices for specific questions related to biodiversity and ecological data uses and needs, and 10 breakout session leaders shared the highlights of their group discussions during the final workshop plenary sessions. Participants were encouraged to use the Twitter hashtag #ShareUrData. Over lunch on day 2 there were 20 simultaneous presentations of tools and apps during a special “Tools Café” session. The 10 participant-defined breakout session topics are listed below: Ecosystem services and ecological indicators Inventory and monitoring Biogeographic map of the Nation Pollinators Invasive species Remote sensing Drivers of agricultural change Citizen science Climate Hydrology and watersheds Numerous common themes that emerged from the workshop include the following: The vital importance of completing foundational environmental datasets that are nationally consistent and are essential to multiple sectors, such as the Soil Survey Geographic database high-resolution soils data, a minimum 5-meter resolution digital elevation model, national hydrographic data, high-resolution land cover data, time series high-resolution spatial climate data from historical to future time steps, and a national wetland inventory. Improved, nationally consistent environmental datasets (integrated with targeted observations) will dramatically advance forecasting capacity and support early warning systems (that is, drought, forest disease); however, multiagency coordination should focus on decision support tools that convey appropriate actions and responses to adapt to, and mitigate, potential negative consequences. Digitizing and providing access to the vast stores of underused historical data that can be leveraged for this purpose is of national importance. Modern computational techniques and the ever-increasing flow of environmental data from ground and remote observations can support improved understanding of environmental change. Success of understanding patterns of change for decision making requires establishing baselines from which change can be measured. The value of digitized historical data is greater than ever before. There is a need to recognize the multifaceted potential of citizen science to engage the public in resource stewardship, to create the next generation of science, technology, engineering, math, and environmental leaders, and to have sufficient field personnel to monitor environmental trends, including early detection of alien invasive species, phenological shifts, shifting distribution and abundance of indicator species, and species inventories. The Federal government has an essential role in creating the infrastructure to dramatically improve mobilization of citizen science (and other) data by fostering the following: creation of data standards, creation of nationally consistent framework datasets, vertical integration of observation data, visualization and dissemination of aggregated datasets, and calculation and communication of derived trends. Current and near future trends in the availability of remotely sensed data (rapid expansion of satellite fleets and drones) is revolutionizing access to near-real-time ecological data. Targeted integration with ground-based observations and instrumentation has an extremely valuable role in validating remotely sensed data, filling data gaps, improving data quality, and fully realizing the potential of the near-real-time monitoring of environmental indicator trends. Integrated management of environmental data at the landscape scale is required even as specific actions on the ground are largely local in nature. The workshop highlighted numerous success stories; however, almost every breakout group pointed out the still-too-fragmented nature of the current data landscape. Management and delivery of the necessary data, tools, and analyses to sustain our Nation’s environmental capital must be a collaborative effort between Federal, State, and local governments, academia, nonprofits, and the commercial sector, even though the responsibilities of each sector are different.

Open-File Report↗

USGS2020

Investigating the effects of land use and land cover on the relationship between moisture and reflectance using Landsat Time Series

To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we investigate the effect of precipitation and LULC on the reflectance of the land surface in the northern U.S. Great Plains. We utilize time series satellite data from the 45 year Landsat archive. The length of the Landsat record allows for analysis of multiple periods of drought and wet conditions (reflecting climate, as well as weather), such that the precipitation-reflectance relationship can be investigated robustly for every individual pixel in the study area. The high spatial resolution of Landsat (30 m) allows for investigation of spatial patterns in weather (i.e., precipitation extremes) interactions with land surface reflectance at the scale of individual fields. Weather history is represented by a drought index that describes effective moisture availability, the Standardized Precipitation and Evaporation Index (SPEI). We find that effective moisture has a robust and consistent effect on reflectance over many types of land cover, with ∼90% of all pixels having significantly ( p < 0.01 "> p < 0.01 ) higher visible reflectance during dry periods than during wet, occurring in nearly all regional, temporal, and LULC categories investigated. In grassland, the relationship is especially strong; there is an average reflectance increase of more than a third between very wet and very dry conditions (red band), and ∼99% of pixels have a significant relationship. In cropland, the effective moisture-reflectance relationship is more variable, suggesting that management decisions are an important factor in cropland-reflectance relationships.

North Dakota, South Dakota↗

USGS1996

Testing and validating environmental models

Generally accepted standards for testing and validating ecosystem models would benefit both modellers and model users. Universally applicable test procedures are difficult to prescribe, given the diversity of modelling approaches and the many uses for models. However, the generally accepted scientific principles of documentation and disclosure provide a useful framework for devising general standards for model evaluation. Adequately documenting model tests requires explicit performance criteria, and explicit benchmarks against which model performance is compared. A model's validity, reliability, and accuracy can be most meaningfully judged by explicit comparison against the available alternatives. In contrast, current practice is often characterized by vague, subjective claims that model predictions show 'acceptable' agreement with data; such claims provide little basis for choosing among alternative models. Strict model tests (those that invalid models are unlikely to pass) are the only ones capable of convincing rational skeptics that a model is probably valid. However, 'false positive' rates as low as 10% can substantially erode the power of validation tests, making them insufficiently strict to convince rational skeptics. Validation tests are often undermined by excessive parameter calibration and overuse of ad hoc model features. Tests are often also divorced from the conditions under which a model will be used, particularly when it is designed to forecast beyond the range of historical experience. In such situations, data from laboratory and field manipulation experiments can provide particularly effective tests, because one can create experimental conditions quite different from historical data, and because experimental data can provide a more precisely defined 'target' for the model to hit. We present a simple demonstration showing that the two most common methods for comparing model predictions to environmental time series (plotting model time series against data time series, and plotting predicted versus observed values) have little diagnostic power. We propose that it may be more useful to statistically extract the relationships of primary interest from the time series, and test the model directly against them.

Science of the Total Environment↗

USGS2021

The AEMON-J “Hacking Limnology” workshop series & virtual summit: Incorporating data science and open science in aquatic research

Following the 2020 “Virtual Summit: Incorporating Data Science and Open Science in Aquatic Research” (DSOS; Meyer and Zwart 2020 ), a grassroots group of scientists convened the 2nd Virtual DSOS Summit on 22–23 July 2021. DSOS combined forces with the Aquatic Ecosystem MOdeling Network - Junior (AEMON-J; https://github.com/aemon-j) to host a 4-d “Hacking Limnology” Workshop Series prior to the summit (13–16 July 2021). The aim was to focus more deeply on skill development and networking among early career researchers (ECRs), both of which are key to growing a workforce of data-intensive aquatic scientists (López Moreira M et al. in press; Meyer et al. 2021 a ). To support ECRs further, we hosted a virtual job board, where participants could note if they were either looking for employment or hiring for a position. Like the 2020 summit, there was high enthusiasm for both the summit and the workshops. In total, 686 people from over 50 countries registered for the AEMON-J Workshop Series and the DSOS Summit. Countries with the highest number of registrants included the United States (41%), Nigeria (20%), Canada (6%), Brazil (6%), and Germany (5%) (Fig. 1). To increase accessibility, there were no registration costs for the workshops and summit, and we centralized introductory training materials, coding scripts, and presentation recordings in one community website (https://aquaticdatasciopensci.github.io/; Fig. 2), which we hope will continue to support the AEMON-J and DSOS communities over time.

Limnology and Oceanography Bulletin↗

USGS2004

Load estimator (LOADEST): a FORTRAN program for estimating constituent loads in streams and rivers

LOAD ESTimator (LOADEST) is a FORTRAN program for estimating constituent loads in streams and rivers. Given a time series of streamflow, additional data variables, and constituent concentration, LOADEST assists the user in developing a regression model for the estimation of constituent load (calibration). Explanatory variables within the regression model include various functions of streamflow, decimal time, and additional user-specified data variables. The formulated regression model then is used to estimate loads over a user-specified time interval (estimation). Mean load estimates, standard errors, and 95 percent confidence intervals are developed on a monthly and(or) seasonal basis. The calibration and estimation procedures within LOADEST are based on three statistical estimation methods. The first two methods, Adjusted Maximum Likelihood Estimation (AMLE) and Maximum Likelihood Estimation (MLE), are appropriate when the calibration model errors (residuals) are normally distributed. Of the two, AMLE is the method of choice when the calibration data set (time series of streamflow, additional data variables, and concentration) contains censored data. The third method, Least Absolute Deviation (LAD), is an alternative to maximum likelihood estimation when the residuals are not normally distributed. LOADEST output includes diagnostic tests and warnings to assist the user in determining the appropriate estimation method and in interpreting the estimated loads. This report describes the development and application of LOADEST. Sections of the report describe estimation theory, input/output specifications, sample applications, and installation instructions.

Techniques and Methods↗

USGS2012

Simulating the effects of ground-water withdrawals on streamflow in a precipitation-runoff model

Precipitation-runoff models are used to assess the effects of water use and management alternatives on streamflow. Often, ground-water withdrawals are a major water-use component that affect streamflow, but the ability of surface-water models to simulate ground-water withdrawals is limited. As part of a Hydrologic Simulation Program-FORTRAN (HSPF) precipitation-runoff model developed to analyze the effect of ground-water and surface-water withdrawals on streamflow in the Ipswich River in northeastern Massachusetts, an analytical technique (STRMDEPL) was developed for calculating the effects of pumped wells on streamflow. STRMDEPL is a FORTRAN program based on two analytical solutions that solve equations for ground-water flow to a well completed in a semi-infinite, homogeneous, and isotropic aquifer in direct hydraulic connection to a fully penetrating stream. One analytical method calculates unimpeded flow at the stream-aquifer boundary and the other method calculates the resistance to flow caused by semipervious streambed and streambank material. The principle of superposition is used with these analytical equations to calculate time-varying streamflow depletions due to daily pumping. The HSPF model can readily incorporate streamflow depletions caused by a well or surface-water withdrawal, or by multiple wells or surface-water withdrawals, or both, as a combined time-varying outflow demand from affected channel reaches. These demands are stored as a time series in the Watershed Data Management (WDM) file. This time-series data is read into the model as an external source used to specify flow from the first outflow gate in the reach where these withdrawals are located. Although the STRMDEPL program can be run independently of the HSPF model, an extension was developed to run this program within GenScn, a scenario generator and graphical user interface developed for use with the HSPF model. This extension requires that actual pumping rates for each well be stored in a unique WDM dataset identified by an attribute that associates each well with the model reach from which water is withdrawn. Other attributes identify the type and characteristics of the data. The interface allows users to easily add new pumping wells, delete exiting pumping wells, or change properties of the simulated aquifer or well. Development of this application enhanced the ability of the HSPF model to simulate complex water-use conditions in the Ipswich River Basin. The STRMDEPL program and the GenScn extension provide a valuable tool for water managers to evaluate the effects of pumped wells on streamflow and to test alternative water-use scenarios. Copyright ASCE 2004.

Conference Paper↗