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At least 271 records · Page 15Linked to original sources

USGS2021

A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17

Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse. To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow. The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [ R 2 ]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period. The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature. The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance ( R 2 =0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance ( R 2 =0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent ( R 2 =0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations. The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.

Wyoming↗

USGS2007

Frequency-duration analysis of dissolved-oxygen concentrations in two southwestern Wisconsin streams

Historically, dissolved-oxygen (DO) data have been collected in the same manner as other water-quality constituents, typically at infrequent intervals as a grab sample or an instantaneous meter reading. Recent years have seen an increase in continuous water-quality monitoring with electronic dataloggers. This new technique requires new approaches in the statistical analysis of the continuous record. This paper presents an application of frequency-duration analysis to the continuous DO records of a cold and a warm water stream in rural southwestern Wisconsin. This method offers a quick, concise way to summarize large time-series data bases in an easily interpretable manner. Even though the two streams had similar mean DO concentrations, frequency-duration analyses showed distinct differences in their DO-concentration regime. This type of analysis also may be useful in relating DO concentrations to biological effects and in predicting low DO occurrences.

Wisconsin↗

USGS2019

Using the precipitation-runoff modeling system to predict seasonal water availability in the upper Klamath River basin, Oregon and California

Accurate forecasts of the streamflow expected during late spring and summer in the Upper Klamath River Basin in southern-central Oregon and northern California are used by water management agencies to balance water allocations for agriculture, aquatic habitat, and hydropower-production needs. Streamflow forecasts are also used by irrigation farmers for planning. The forecasts are typically made twice a month starting as early in the water year as December. Multiple regression equations relating real-time snowpack and precipitation conditions to seasonal streamflow volumes have been used for many years in forecasting. However, with warming temperature trends and lower snowpack, such forecasts based on historical data could become less reliable in the future. If the timing and relation of snowpack and precipitation are outside of the range of the historical data used to create the equations, the forecasts become extrapolations. Statistical forecast equations are also limited in their ability to forecast streamflow in groundwater-dominated basins having inter-annual lag. As an additional method for seasonal streamflow forecasting, a physical-process-based hydrologic model employing the Precipitation-Runoff Modeling System (PRMS) was developed in cooperation with the U.S. Bureau of Reclamation for the Upper Klamath Basin in this study. The model was calibrated for the portion of the basin draining into Upper Klamath Lake. PRMS is a deterministic, distributed-parameter, physical-process-based modeling system developed by the U.S. Geological Survey. It simulates daily streamflow, snow, solar radiation, evapotranspiration, surface-water, and groundwater processes within the basin. A model calibration and validation period for water years 2000–15 and water years 1984–99, respectively, was used. The model was calibrated and validated using measured streamflow, snowpack, evapotranspiration, and solar radiation data sets. Interpolated daily precipitation and air temperature data from 32 meteorological stations within and surrounding the Upper Klamath Basin were used as model input. Performance statistics, used to evaluate how well simulated daily streamflow matched with measured streamflow included percent bias, percent relative error, and root-mean-square error. The statistics were computed annually, monthly, for October–March, and for April–September. With the exception of the October–March period, percent bias statistics were all within plus or minus 5-percent for both the calibration and validation periods. Limitations to using the model are error in the precipitation and air temperature input time series data, which include measurement error and error in the spatial interpolation method. Other errors include measured daily streamflow data, which were adjusted for consumptive use losses to make them more closely resemble natural streamflow for calibration. The model developed for the Upper Klamath Basin can be used to forecast streamflow from the Sprague and Williamson River Basins and inflow to Upper Klamath Lake. Reliable forecasts at these locations are needed for managing water for irrigation, ecosystem health, and power production. Using the models in a forecast application requires assembling model input data sets of anticipated daily precipitation and minimum and maximum air temperature for the period after the date the forecast is made and the end of the forecasted period. These climate data sets can be based on historical or synthetic records, at the discretion of the forecaster. With the Ensemble Streamflow Prediction method, a suite of streamflow scenarios is simulated using multiple years of climate data as model input. The forecasted streamflow is determined from knowing the exceedance probabilities of the simulated streamflows. In this study, the model and the Ensemble Streamflow Prediction method were used to forecast the volume of inflow to Upper Klamath Lake for a 6-month period from April 1, 2015, to September 30, 2015, using a range of climate data sets based on El Niño Southern Oscillation (ENSO) criteria. Because 2015 was a warm phase ENSO period, climate data for 10 warm phase ENSO years from 1980 to 2010 were used as input to the model. The simulated April–September 2015 UKL inflow volume based on measured 2015 climate data was 482,000 acre-feet, which was very close to the 50th percent exceedance probability computed from 10 simulated scenarios that used warm phase ENSO climate input data from 1980–2010.

California, Oregon↗

USGS2019

Quantitative guidance for efficient vertical flow measurements at the sediment-water interface using temperature-depth profiles

Upward discharge to surface water bodies can be quantified using analytical models based on temperature-depth (T-z) profiles. The use of sediment T-z profiles is attractive as discharge estimates can be obtained using point-in-time data that are collected inexpensively and rapidly. Previous studies have identified that T-z methods can only be applied at times of the year when there is significant difference between the streambed-water interface and deeper sediment temperatures (e.g., winter and summer). However, surface water temperatures also vary diurnally, and the influence of these variations on discharge estimates from T-z methods is poorly understood. For this study, synthetic T-z profiles were generated numerically using measured streambed interface temperature data to assess the influence of diurnal temperature variations on discharge estimation and provide insight into the suitable application of T-z methods. Results show that the time of day of data collection can have a substantial influence on vertical flux estimates using T-z methods. For low groundwater discharge fluxes (e.g. 0.1 m d-1), daily transience in streambed temperatures led to relatively large errors in estimated flow magnitude and direction. For higher discharge fluxes (1.5 m d-1), the influence of transient streambed temperatures on discharge estimates was strongly reduced. Discharge estimates from point-in-time T-z profiles were most accurate when the uppermost point in the T-z profile was near the bed interface daily mean (two time periods daily). Where temperature time series data are available, daily averaged T-z profiles can produce accurate discharge estimates across a wide range of discharge rates. Seasonality in shallow groundwater temperature generally had a negligible influence on vertical flow estimates. These findings can be used to plan field campaigns and provide guidance on the optimal application of T-z methods to quantify vertical groundwater discharge to surface water bodies.

Hydrological Processes↗

USGS2010

An approach to quantify sources, seasonal change, and biogeochemical processes affecting metal loading in streams: Facilitating decisions for remediation of mine drainage

Historical mining has left complex problems in catchments throughout the world. Land managers are faced with making cost-effective plans to remediate mine influences. Remediation plans are facilitated by spatial mass-loading profiles that indicate the locations of metal mass-loading, seasonal changes, and the extent of biogeochemical processes. Field-scale experiments during both low- and high-flow conditions and time-series data over diel cycles illustrate how this can be accomplished. A low-flow experiment provided spatially detailed loading profiles to indicate where loading occurred. For example, SO 4 2 - was principally derived from sources upstream from the study reach, but three principal locations also were important for SO 4 2 - loading within the reach. During high-flow conditions, Lagrangian sampling provided data to interpret seasonal changes and indicated locations where snowmelt runoff flushed metals to the stream. Comparison of metal concentrations between the low- and high-flow experiments indicated substantial increases in metal loading at high flow, but little change in metal concentrations, showing that toxicity at the most downstream sampling site was not substantially greater during snowmelt runoff. During high-flow conditions, a detailed temporal sampling at fixed sites indicated that Zn concentration more than doubled during the diel cycle. Monitoring programs must account for diel variation to provide meaningful results. Mass-loading studies during different flow conditions and detailed time-series over diel cycles provide useful scientific support for stream management decisions.

Applied Geochemistry↗

USGS2009

Trends in pesticide concentrations in corn-belt streams, 1996-2006

Trends in the concentrations of commonly occurring pesticides in the Corn Belt of the United States were assessed, and the performance and application of several statistical methods for trend analysis were evaluated. Trends in the concentrations of 11 pesticides with sufficient data for trend assessment were assessed at up to 31 stream sites for two time periods: 1996–2002 and 2000–2006. Pesticides included in the trend analyses were atrazine, acetochlor, metolachlor, alachlor, cyanazine, EPTC, simazine, metribuzin, prometon, chlorpyrifos, and diazinon. The statistical methods applied and compared were (1) a modified version of the nonparametric seasonal Kendall test (SEAKEN), (2) a modified version of the Regional Kendall test, (3) a parametric regression model with seasonal wave (SEAWAVE), and (4) a version of SEAWAVE with adjustment for streamflow (SEAWAVE-Q). The SEAKEN test is a statistical hypothesis test for detecting monotonic trends in seasonal time-series data such as pesticide concentrations at a particular site. Trends across a region, represented by multiple sites, were evaluated using the regional seasonal Kendall test, which computes a test for an overall trend within a region by computing a score for each season at each site and adding the scores to compute the total for the region. The SEAWAVE model is a parametric regression model specifically designed for analyzing seasonal variability and trends in pesticide concentrations. The SEAWAVE-Q model accounts for the effect of changing flow conditions in order to separate changes caused by hydrologic trends from changes caused by other factors, such as pesticide use. There was broad, general agreement between unadjusted trends (no adjustment for streamflow effects) identified by the SEAKEN and SEAWAVE methods, including the regional seasonal Kendall test. Only about 10 percent of the paired comparisons between SEAKEN and SEAWAVE indicated a difference in the direction of trend, and none of these had differences significant at the 10-percent significance level. This consistency of results supports the validity and robustness of all three approaches as trend analysis tools. The SEAWAVE method is favored, however, because it has less restrictive data requirements, enabling analysis for more site/pesticide combinations, and can incorporate adjustment for streamflow (SEAWAVE-Q) with substantially fewer measurements than the flow-adjustment procedure used with SEAKEN. Analysis of flow-adjusted trends is preferable to analysis of non-adjusted trends for evaluating potential effects of changes in pesticide use or management practices because flow-adjusted trends account for the influence of flow-related variability. Analysis of flow-adjusted trends by SEAWAVE-Q showed that all of the pesticides assessed, except simazine and acetochlor, were dominated by varying degrees of concentration downtrends in one or both analysis periods. Atrazine, metolachlor, alachlor, cyanazine, EPTC, and metribuzin—all major corn herbicides, as well as prometon and chlorpyrifos, showed more prevalent concentration downtrends during 1996–2002 compared to 2000–2006. Diazinon had no clear trends during 1996–2002, but had predominantly downward trends during 2000–2006. Acetochlor trends were mixed during 1996–2002 and slightly upward during 2000–2006, but most of the trends were not statistically significant. Simazine concentrations trended upward at most sites during both 1996–2002 and 2000–2006. Comparison of concentration trends to agricultural-use trends indicated similarity in direction and magnitude for acetochlor, metolachlor, alachlor, cyanazine, EPTC, and metribuzin. Concentration downtrends for atrazine, chlorpyrifos, and diazinon were steeper than agricultural-use downtrends at some sites, indicating the possibility that agricultural management practices may have increasingly reduced transport to streams (particularly atrazine) or, for chlorpyrifos and diazinon, that nonagricultural uses declined substantially. Concentration uptrends for simazine generally were steeper than agricultural-use uptrends, indicating the possibility that nonagricultural uses of this herbicide increased during the study period.

Scientific Investigations Report↗

USGS1976

Technical manual for estimating low-flow frequency characteristics of streams in the Susquehanna River basin

This report presents procedures for estimating low-flow frequency characteristics for streams in the Susquehanna River basin. The techniques can be used at ungaged sites as well as sites where insufficient data are available to make a reliable estimate. Streams have been divided intp two types--major and minor. Major streams are the Susquehanna, West Branch Susquehanna, Juniata, and Chemung Rivers. Points on these streams with drainage areas of more than 2,000 mi 2 (5,180 km 2 ) are included in this category. Points on these streams with drainage areas of less than 2,000 mi 2 fall into the minor stream category. Generally minor streams are herein defined as those draining less than 2,000 mi 2 (5,180 km 2 ). Multiple -regression techniques have been used to develop relations for estimating the 1-, 3-, 7-, 30-, and 183-day duration low flows at recurrence intervals of 10, 20, SO and 100 years for annual series data and the 1 - , 3-, 7-, and 30-day duration low flows, at the same recurrence intervals, for six individual months, May through October, inclusive.

Water-Resources Investigations Report↗

USGS2021

Relative risk of groundwater-quality degradation near California (USA) oil fields estimated from 3H, 14C, and 4He

Relative risks of groundwater-quality degradation near selected California oil fields are estimated by examining spatial and temporal patterns in chemical and isotopic data in the context of groundwater-age categories defined by tritium and carbon-14. In the Coastal basins, western San Joaquin Valley (SJV), and eastern SJV; 82, 76, and 0% of samples are premodern (pre-1953 recharge), respectively; and 3, 0, and 31% are modern (recharged during or after 1953), respectively. Carbon-14 and helium-4 data indicate most premodern samples are 1000 to 10,000 (33%) or >10,000 (50%) years old. Organic chemicals that could be associated with deeper hydrocarbon reservoirs (e.g. thermogenic gases and benzene) occur most frequently in premodern groundwater, suggesting premodern groundwater has a higher risk of degradation from upward migration of hydrocarbons than modern and mixed-age groundwater. Low sulfate concentrations in some premodern groundwater containing high thermogenic-methane concentrations (>28 mg/L) indicate methane attenuation associated with sulfate reduction can be limited in premodern groundwater. The more common occurrence of manufactured compounds, like tetrachloroethene, in modern and mixed-age groundwater than in premodern groundwater indicates modern and mixed-age groundwater has a higher risk of degradation from land-surface sources than premodern groundwater. Time-series data for chloride in groundwater affected by disposal of oil-field water in unlined ponds indicate some modern and mixed-age groundwater are susceptible to chemical migration within 2–3 km of surface sources. Timescales for diluting chloride concentrations in groundwater with fresh recharge once disposal ponds are decommissioned are shorter in mixed-age groundwater with large fractions of modern water (9–14 years in one example) than in mixed-age groundwater with large fractions of premodern water (no evidence of dilution after 12 years of monitoring in one example). The presence of predominantly premodern groundwater in the Coastal basins and western SJV indicates these areas have relatively high risk from upward migration of hydrocarbons, reduced methane attenuation capacity, and long dilution times, whereas predominantly modern- and mixed-age groundwater in the eastern SJV indicates this area has relatively high risk from chemical migration from land-surface sources and subsequent extensive spreading. Age-based characterizations of relative risk could inform the design of groundwater-monitoring programs near oil fields in terms of the spatial distribution of monitoring points relative to source areas and monitoring frequency and duration.

California↗

USGS2023

Estimating streamflow permanence with the watershed erosion prediction project model: Implications for surface water presence modeling and data collection

Many data collection efforts and modeling studies have focused on providing accurate estimates of streamflow while fewer efforts have sought to identify when and where surface water is present and the duration of surface water presence in stream channels, hereafter referred to as streamflow permanence. While physically-based hydrological models are frequently used to explore how water quantity may be influenced by various climatic and basin characteristics at local, regional, national, and global extents they are less often used to explore streamflow permanence. Herein, the Watershed Erosion Prediction Project (WEPP) hydrological model is applied to watersheds in the humid H. J. Andrews Experimental Forest (HJA) and watersheds of the arid Willow and Whitehorse creeks (WW), both in Oregon, to simulate daily (WW) and annual (HJA and WW) streamflow permanence. One thousand parameter combinations were tested to calibrate WEPP to observed streamflow in the HJA watersheds and one hundred parameter combinations were tested to calibrate WEPP to observed surface water presence time series data in WW watersheds. When calibrated to observed streamflow, WEPP correctly classified annual streamflow permanence for 83% of HJA stream reaches. In the WW, WEPP simulations correctly classified 63–93% of daily streamflow permanence observations and 59-87% of annual streamflow permanence classifications. Inclusion of a dry-day threshold (the maximum number of days a stream reach could be modeled ‘dry’ but still classified as permanent for the year) improved annual accuracy in three WW watersheds from 2-10%. Parameter sets that produced the best daily accuracies in WW resulted in poor annual accuracies. Results highlight the importance of evaluating physically-based streamflow permanence models on both permanent and nonpermanent streams at daily and annual time scales to ensure evaluation metrics are appropriate for interpretation purposes. Additionally, results suggest that strategic collection of surface water presence observations and streamflow observations may support robust calibration of physically based models to simulate streamflow permanence moving forward.

Oregon↗

USGS2004

Cormorant predation and the population dynamics of walleye and yellow perch in Oneida Lake

Double-crested Cormorants ( Phalacrocorax auritus ) increased dramatically in North America during the 1990s, providing the opportunity to study the effects of an increase of a top predator on an existing predator-prey system. In Oneida Lake, New York, USA, Double-crested Cormorants were first observed nesting in 1984 and had increased to over 360 nesting pairs by 2000. Concomitant with this increase in piscivorous birds was a decrease in the adult walleye ( Stizostedion vitreum ) and yellow perch ( Perca flavescens ) populations. Analysis of a 40-yr data series shows higher mortality of subadults (age 1-2 yr perch and age 1-3 yr walleye) for both species in the 1990s compared to the previous three decades. Cormorant diet was investigated from 1995 to 2000 using a combination of cast pellets, regurgitants, and stomach analysis. Walleye and yellow perch were a major portion of the cormorant diet during these years (40-82% by number). The number of subadult walleye and yellow perch consumed by cormorants suggests that the increase in subadult mortality can be explained by predation from cormorants. Mean mortality rates of adult percids attributed to cormorant predation were 1.1% per year for walleye and 7.7% per year for yellow perch. Our analysis suggests that predation by cormorants on subadult percids is a major factor contributing to the decline in both the walleye and the yellow perch populations in Oneida Lake. Other ecosystem changes (zebra mussels, lower nutrient loading, decrease in alternate prey) are not likely explanations because the potential mechanisms involved are not consistent with auxiliary data from the lake and would not affect subadult mortality. The likely impact of bird predation on percid populations in Oneida Lake occurs because cormorants feed on larger fish that are beyond the size range where compensatory mechanisms are important.

New York↗

USGS2011

Summary of oceanographic and water–quality measurements in West Falmouth Harbor and Buzzards Bay, Massachusetts, 2009–2010

This data report presents oceanographic and water-quality observations made at six locations in West Falmouth Harbor and Buzzards Bay, Massachusetts, from August 2009 to September 2010. Both Buzzards Bay and West Falmouth Harbor are estuarine embayments; the input of freshwater on the eastern margin of Buzzards Bay adjacent to Cape Cod and West Falmouth Harbor is largely due to groundwater. In West Falmouth Harbor, the groundwater that seeps into the harbor is characterized by relatively high levels of nitrate. This high nitrate load has modified the ecology of the harbor (Howes and others, 2006) and may be a significant source of nitrate to Buzzards Bay during seasons with low biological nitrate uptake. The U.S. Geological Survey undertook these measurements to improve understanding of circulation, residence time, and water quality in the harbor and bay. We set up and monitored multiple sites in both Buzzards Bay and West Falmouth Harbor, measuring depth, water velocity,salinity, pH, dissolved oxygen, chlorophyll-a, and nitrate concentration. In this report we present the processed time-series data at these locations and provide access to the data and metadata. The results will be used to understand circulation mechanisms and verify numerical models of hydrodynamics and biogeochemistry.

Massachusetts↗

USGS2012

Computing daily mean streamflow at ungaged locations in Iowa by using the Flow Anywhere and Flow Duration Curve Transfer statistical methods

The U.S. Geological Survey (USGS) maintains approximately 148 real-time streamgages in Iowa for which daily mean streamflow information is available, but daily mean streamflow data commonly are needed at locations where no streamgages are present. Therefore, the USGS conducted a study as part of a larger project in cooperation with the Iowa Department of Natural Resources to develop methods to estimate daily mean streamflow at locations in ungaged watersheds in Iowa by using two regression-based statistical methods. The regression equations for the statistical methods were developed from historical daily mean streamflow and basin characteristics from streamgages within the study area, which includes the entire State of Iowa and adjacent areas within a 50-mile buffer of Iowa in neighboring states. Results of this study can be used with other techniques to determine the best method for application in Iowa and can be used to produce a Web-based geographic information system tool to compute streamflow estimates automatically. The Flow Anywhere statistical method is a variation of the drainage-area-ratio method, which transfers same-day streamflow information from a reference streamgage to another location by using the daily mean streamflow at the reference streamgage and the drainage-area ratio of the two locations. The Flow Anywhere method modifies the drainage-area-ratio method in order to regionalize the equations for Iowa and determine the best reference streamgage from which to transfer same-day streamflow information to an ungaged location. Data used for the Flow Anywhere method were retrieved for 123 continuous-record streamgages located in Iowa and within a 50-mile buffer of Iowa. The final regression equations were computed by using either left-censored regression techniques with a low limit threshold set at 0.1 cubic feet per second (ft3/s) and the daily mean streamflow for the 15th day of every other month, or by using an ordinary-least-squares multiple linear regression method and the daily mean streamflow for the 15th day of every other month. The Flow Duration Curve Transfer method was used to estimate unregulated daily mean streamflow from the physical and climatic characteristics of gaged basins. For the Flow Duration Curve Transfer method, daily mean streamflow quantiles at the ungaged site were estimated with the parameter-based regression model, which results in a continuous daily flow-duration curve (the relation between exceedance probability and streamflow for each day of observed streamflow) at the ungaged site. By the use of a reference streamgage, the Flow Duration Curve Transfer is converted to a time series. Data used in the Flow Duration Curve Transfer method were retrieved for 113 continuous-record streamgages in Iowa and within a 50-mile buffer of Iowa. The final statewide regression equations for Iowa were computed by using a weighted-least-squares multiple linear regression method and were computed for the 0.01-, 0.05-, 0.10-, 0.15-, 0.20-, 0.30-, 0.40-, 0.50-, 0.60-, 0.70-, 0.80-, 0.85-, 0.90-, and 0.95-exceedance probability statistics determined from the daily mean streamflow with a reporting limit set at 0.1 ft 3 /s. The final statewide regression equation for Iowa computed by using left-censored regression techniques was computed for the 0.99-exceedance probability statistic determined from the daily mean streamflow with a low limit threshold and a reporting limit set at 0.1 ft 3 /s. For the Flow Anywhere method, results of the validation study conducted by using six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 1,016 to 138 ft 3 /s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 1,690 to 237 ft 3 /s. Values of the percent root-mean-square error ranged from 115 percent to 26.2 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 13.0 to 5.3 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.80 to 0.40. Percent-bias values ranged from 25.4 to 4.0 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.35. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.86 to 0.56. For the streamgage with the best agreement between observed and estimated streamflow, higher streamflows appear to be underestimated. For the streamgage with the worst agreement between observed and estimated streamflow, low flows appear to be overestimated whereas higher flows seem to be underestimated. Estimated cumulative streamflows for the period October 1, 2004, to September 30, 2009, are underestimated by -25.8 and -7.4 percent for the closest and poorest comparisons, respectively. For the Flow Duration Curve Transfer method, results of the validation study conducted by using the same six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 437 to 93.9 ft 3 /s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 906 to 169 ft 3 /s. Values of the percent root-mean-square-error ranged from 67.0 to 25.6 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 12.5 to 4.4 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.79 to 0.40. Percent-bias values ranged from 22.7 to 0.94 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.38. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.89 to 0.48. For the streamgage with the closest agreement between observed and estimated streamflow, there is relatively good agreement between observed and estimated streamflows. For the streamgage with the poorest agreement between observed and estimated streamflow, streamflows appear to be substantially underestimated for much of the time period. Estimated cumulative streamflow for the period October 1, 2004, to September 30, 2009, are underestimated by -9.3 and -22.7 percent for the closest and poorest comparisons, respectively.

Illinois;Iowa;Minnesota;Missouri;Nebraska;Wisconsi↗

USGS2021

Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models

Re-examination of previously published dissolved iron time-series data from Ocean Station Papa in the central Gulf of Alaska (GoA) reveals 33-70% increases in the dissolved iron inventories occurring between September and February of successive years, implying a source of Fe to this region during autumn or early winter. Because I can virtually rule out many possible iron sources at this time of year, I suggest Alaskan glacial dust is the likely iron source. Large plumes of such dust are known to be generated regularly in the autumn by anomalous offshore winds and channelled through mountain gaps, simultaneously from several locations spanning ∼1000 km of the northern Gulf of Alaska coastline. Large dust flux events occur when below-freezing, low-humidity air temperatures persist for many days during the autumn. I suggest that existing state-of-the-art global dust models fail to reproduce this Alaskan dust flux because the model spatial resolution is too coarse to resolve the high winds through the narrow mountain gaps that generate the dust. Future work that could help to confirm this Fe source to the central GoA includes time-series profiles of iron concentrations, and ancillary information from sensor-equipped profiling floats. If this mechanism of Fe supply to the central GoA were confirmed, it would imply this Alaskan dust is transported ≥ 1100 km from the coast, more than twice as far as has been visually documented from satellite observations.

Alaska↗

USGS1998

Coalbed methane resource potential and current prospects in Pennsylvania

Coalbed methane gas content analyses from exploratory coal cores and existing data indicate that gas content generally increases with increasing depth and rank. The coal beds studied are from the Main Bituminous field of Pennsylvania (which currently contains 24 coalbed methane pools) and the Northern and Southern Anthracite coal fields. They range from the Middle Pennsylvanian Allegheny Group to the Late Pennsylvanian-Early Permian Dunkard Group. Previous US Bureau of Mines studies revealed gas contents from 0.4 to 13.8 cm3/g at depths of 99 to 432 m for the bituminous coal beds of the Allegheny Group. More recent core data from the Allegheny Group yielded gas contents from 2.2 to 8.9 cm3/g at depths from 167 to 387 m. In the Anthracite region of eastern Pennsylvania, the little data that are available show that gas content is anomalously high or low. Gas yields from test holes in eastern Pennsylvania are low with or without artificial stimulation mainly due to the lack of a good cleat system. Overall estimates of coalbed methane resources indicate there may be 1.7 Tm3 (61 Tcf) of gas-in-place contained in the Northern Appalachian coal basin. The amount of technically recoverable coalbed methane resources is projected by the US Geological Survey National Oil and Gas Resource Assessment Team [US Geological Survey National Oil and Gas Resource Assessment Team, 1996. 1995 National assessment of United States oil and gas resources-results, methodology, and supporting data, US Geological Survey Digital Data Series DDS-30, CD-ROM, Denver, CO, 80 pp.] and Lyons [Lyons, P.C., 1997. Central-northern Appalachian coalbed methane flow grows. Oil and Gas Journal 95 (27) 76-79] at 0.3 Tm3 (11.48 Tcf). This includes portions of Pennsylvania, Ohio, West Virginia, and a small part of Maryland. Consequently, a mapping investigation was conducted to evaluate the regional geology of the bituminous coal-bearing intervals in southwestern Pennsylvania and its influence on coalbed methane potential. Phase I of this study involved the entire Pennsylvanian coal-bearing interval of southwestern Pennsylvania. Phase II focused on a stratigraphic delineation and evaluation of Allegheny Group coal beds and associated sandstones. Several prospective coal beds and associated facies relationships with channel-fill sandstones were determined. Possible non-coal scenarios for coalbed methane include erosional contacts between coal beds and overlying channel-fill sandstones and areas of stacked channel-fill sandstones. Repetitive sequences of coal accumulation are stacked, commonly with shale interburden, and are also potential coalbed methane targets. Additional Pennsylvania Geological Survey drilling/coalbed methane sampling occurred in Armstrong, Beaver, Cambria, Greene, Lawrence, Somerset, and Washington Counties. Raw coalbed methane desorption data tables/graphical displays of gas contents versus depth, thickness, and time, and average composition and heating values from coal beds of the Allegheny Group to the Dunkard Group are available at the Pennsylvania Geological Survey. Further information on cross-sections, isopleth maps, isopach maps, raw drillhole data, and ownership issues can also be obtained from the same source.A mapping of the regional geology of the bituminous coal-bearing intervals in southwestern Pennsylvania reveal several prospective coal beds and associated facies relationships with channel-fill sandstones. Possible non-coal scenarios for coalbed methane include erosional contacts between coalbeds and overlying channel-fill sandstones and areas of stacked channel-fill sandstones. Repetitive sequences of coal accumulation are stacked, commonly with shale interburden. and are also potential coalbed methane targets.

International Journal of Coal Geology↗

USGS2015

Water Quality, Cyanobacteria, and Environmental Factors and Their Relations to Microcystin Concentrations for Use in Predictive Models at Ohio Lake Erie and Inland Lake Recreational Sites, 2013-14

Harmful cyanobacterial “algal” blooms (cyanoHABs) and associated toxins, such as microcystin, are a major water-quality issue for Lake Erie and inland lakes in Ohio. Predicting when and where a bloom may occur is important to protect the public that uses and consumes a water resource; however, predictions are complicated and likely site specific because of the many factors affecting toxin production. Monitoring for a variety of environmental and water-quality factors, for concentrations of cyanobacteria by molecular methods, and for algal pigments such as chlorophyll and phycocyanin by using optical sensors may provide data that can be used to predict the occurrence of cyanoHABs. To test these monitoring approaches, water-quality samples were collected at Ohio recreational sites during May–November in 2013 and 2014. In 2013, samples were collected monthly at eight sites at eight lakes to facilitate an initial assessment and select sites for more intensive sampling during 2014. In 2014, samples were collected approximately weekly at five sites at three lakes. Physical water-quality parameters were measured at the time of sampling. Composite samples were preserved and analyzed for dissolved and total nutrients, toxins, phytoplankton abundance and biovolume, and cyanobacterial genes by molecular methods. Molecular assays were done to enumerate (1) general cyanobacteria, (2) general Microcystis and Dolichospermum ( Anabaena ), (3) mcyE genes for Microcystis , Dolichospermum ( Anabaena ), and Planktothrix targeting deoxyribonucleic acid (DNA), and (4) mcyE transcripts for Microcystis , Dolichospermum ( Anabaena ), and Planktothrix targeting ribonucleic acid (RNA).The DNA assays for the mcyE gene provide data on cyanobacteria that have the potential to produce microcystin, whereas the RNA assays provide data on cyanobacteria that are actively transcribing the toxin gene. Environmental data were obtained from available online sources. Quality-control (QC) samples were collected and analyzed for all constituents to characterize bias and variability; however, QC data for molecular assays were examined in more detail than for the other constituents. The QC data for molecular assays suggested that sampling variability and qPCR variability were small in comparison with the combined variability associated with sample filtering, extraction and purification, and the matrix itself. A total of 46 water-quality samples were collected during 2013 at 8 beach sites—Buck Creek, Buckeye Crystal, Deer Creek, Harsha Main, Maumee Bay State Park (MBSP) Inland (negative control site), MBSP Lake Erie, Port Clinton, and Sandusky Bay. Microcystin was detected in 67–100 percent of samples at all sites except for MBSP Inland, where microcystin was detected in only 20 percent of samples. Microcystin concentrations ranged from <0.10 to 48 micrograms per liter (µ/L), with the widest range found at MBSP Lake Erie and the highest concentrations found at Buckeye Crystal. Saxitoxin was detected in five samples, and cylindrospermopsin was not detected in any samples. A total of 65 water-quality samples were collected during 2014 at 5 sites on 3 lakes—Buckeye Fairfield and Onion Island, Harsha Main and Campers, and MBSP Lake Erie beach. Four of the sites were bathing beaches and one site, Onion Island, was an offshore boater swim area. Concentrations of microcystin ranged from <0.10 to 240 µ/L and, as in 2013, the widest range was found at MBSP Lake Erie. At Buckeye Lake, microcystin concentrations were consistently high (greater than 20 µ/L), ranging from 23 to 81 µ/L. At Harsha Main and Campers, microcystin concentrations ranged from <0.10 to 15 µ/L. Saxitoxin was detected in four samples collected at MBSP Lake Erie. Throughout the 2014 season, the cyanobacterial community, as determined by molecular and microscopy methods, and the dominance associated with the highest microcystin concentrations were unique to individual lakes. At Buckeye Lake, Planktothrix dominated the cyanobacterial community throughout the season and Planktothrix DNA and RNA were found in 100 percent of samples; Microcystis mcyE DNA was found in low concentrations. At Harsha Lake, Dolichospermum and Microcystis were a substantial percentage of the community from late May through August, and the highest microcystin concentrations occurred in June and July. At MBSP Lake Erie, Microcystis generally dominated from mid-July through early November, and the highest microcystin concentrations occurred in August. Spearman’s correlation coefficient (rho) was computed to determine the relations between environmental and water-quality factors and microcystin concentrations at four sites—Buckeye Fairfield, Buckeye Onion Island, Harsha Main, and MBSP Lake Erie. Factors were evaluated for use as potential independent variables in two types of predictive models—daily and long-term models. Easily or continuously measured water-quality factors and available environmental data are used for daily predictions that do not require a site visit. Data from factors used in daily predictions and results from samples collected and analyzed in a laboratory are used for long-term predictions (a few days to several weeks). A few statistically significant correlations (p ≤ 0.05) between microcystin concentrations and factors for both daily and long-term predictions were found at Buckeye Onion Island, and many were found at Harsha Main and MBSP Lake Erie. There were only a few statistically significant factors for daily predictions at Buckeye Fairfield, likely because of the lack of variability in microcystin concentrations. Among factors for daily predictions, phycocyanin had the highest Spearman’s correlation to microcystin concentrations (rho = 0.79 to 0.93) at all sites except for Buckeye Fairfield. Turbidity, pH, algae category, and Secchi depth were significantly correlated to microcystin concentrations at Harsha Main and MBSP Lake Erie. Algae categories were observational categories from 0 (none) to 4 (extreme). Several discharge variables (Maumee River at Waterville, river mouth is approximately 3.5 miles from the beach) at MBSP Lake Erie were promising environmental factors for daily predictions. In addition to discrete water-quality measurements recorded at Harsha Main at the time of sampling, many manipulated measurements (factors derived from mathematical manipulation of time-series data) available from a nearby continuous monitor were strongly correlated to microcystin concentrations; the highest correlation was found for the relation between microcystin concentrations and the antecedent 7-day average phycocyanin (rho = 0.98). For long-term predictions, the most highly correlated molecular assays were Planktothrix mcyE DNA at Buckeye Onion Island and Microcystis mcyE DNA at Harsha Main and MBSP Lake Erie. Concentrations of several nutrient constituents were significantly correlated to microcystin concentrations including total nitrogen at Buckeye Onion Island, ammonia and nitrate plus nitrite (both negatively correlated) at Harsha Main and MBSP Lake Erie, and total phosphorus at MBSP Lake Erie. The results of this study showed that water-quality and environmental variables are promising for use in site-specific daily or long-term predictive models. In order to develop more accurate models to predict toxin concentrations at freshwater lake sites, data need to be collected more frequently and for consecutive days in future studies.

Ohio↗

USGS2017

Long-term monitoring data provide evidence of declining species richness in a river valued for biodiversity conservation

Free-flowing river segments provide refuges for many imperiled aquatic biota that have been extirpated elsewhere in their native ranges. These biodiversity refuges are also foci of conservation concerns because species persisting within isolated habitat fragments may be particularly vulnerable to local environmental change. We have analyzed long-term (14- and 20-y) survey data to assess evidence of fish species declines in two southeastern U.S. rivers where managers and stakeholders have identified potentially detrimental impacts of current and future land uses. The Conasauga River (Georgia and Tennessee) and the Etowah River (Georgia) form free-flowing headwaters of the extensively dammed Coosa River system. These rivers are valued in part because they harbor multiple species of conservation concern, including three federally endangered and two federally threatened fishes. We used data sets comprising annual surveys for fish species at multiple, fixed sites located at river shoals to analyze occupancy dynamics and temporal changes in species richness. Our analyses incorporated repeated site-specific surveys in some years to estimate and account for incomplete species detection, and test for species-specific (rarity, mainstem-restriction) and year-specific (elevated frequencies of low- or high-flow days) covariates on occupancy dynamics. In the Conasauga River, analysis of 26 species at 13 sites showed evidence of temporal declines in colonization rates for nearly all taxa, accompanied by declining species richness. Four taxa (including one federally endangered species) had reduced occupancy across the Conasauga study sites, with three of these taxa apparently absent for at least the last 5 y of the study. In contrast, a similar fauna of 28 taxa at 10 sites in the Etowah River showed no trends in species persistence, colonization, or occupancy. None of the tested covariates showed strong effects on persistence or colonization rates in either river. Previous studies and observations identified contaminants, nutrient loading, or changes in benthic habitat as possible causes for fish species declines in the Conasauga River. Our analysis provides baseline information that could be used to assess effectiveness of future management actions in the Conasauga or Etowah rivers, and illustrates the use of dynamic occupancy models to evaluate evidence of faunal decline from time-series data.

Georgia↗

USGS2005

Hydrogeology of the Mogollon Highlands, central Arizona

The Mogollon Highlands, 4,855 square miles of rugged, mountainous terrain at the southern edge of the Colorado Plateau in central Arizona, is characterized by a bedrock-dominated hydrologic system that results in an incompletely integrated regional ground-water system, flashy streamflow, and various local water-bearing zones that are sensitive to drought. Increased demand on the water resources of the area as a result of recreational activities and population growth have made necessary an increased understanding of the hydrogeology of the region. The U.S. Geological Survey conducted a study of the geology and hydrology of the region in cooperation with the Arizona Department of Water Resources under the auspices of the Arizona Rural Watershed Initiative, a program launched in 1998 to assist rural areas in dealing with water-resources issues. The study involved the analysis of geologic maps, surface-water and ground-water flow, and water and rock chemical data and spatial relationships to characterize the hydrogeologic framework. The study area includes the southwestern corner of the Colorado Plateau and the Mogollon Rim, which is the eroded edge of the plateau. A 3,000- to 4,000-foot sequence of early to late Paleozoic sedimentary rocks forms the generally south-facing scarp of the Mogollon Rim. The area adjacent to the edge of the Mogollon Rim is an erosional landscape of rolling, step-like terrain exposing Proterozoic metamorphic and granitic rocks. Farther south, the Sierra Ancha and Mazatzal Mountain ranges, which are composed of various Proterozoic rocks, flank an alluvial basin filled with late Cenozoic sediments and volcanic flows. Eight streams with perennial to intermittent to ephemeral flow drain upland regions of the Mogollon Rim and flow into the Salt River on the southern boundary or the Verde River on the western boundary. Ground-water flow paths generally are controlled by large-scale fracture systems or by karst features in carbonate rocks. Stream channels are also largely controlled by structural features, such as regional joint or fault systems. Precipitation, which shows considerable variability in amount and intensity, recharges the ground-water system along the crest of the Mogollon Rim and to a lesser extent along the crests and flanks of the rim and the Mazatzal Mountains and Sierra Ancha. Flashy runoff in the mainly bedrock stream channels is typical. Springs are distributed throughout the region, typically discharging at or above the contact of variably permeable formations along the face of the Mogollon Rim with a scattering of low-discharge springs in the Proterozoic rocks below the rim. The surface of the Colorado Plateau is the primary recharge area for the C aquifer in which ground-water flows north toward the Little Colorado River and south toward the Mogollon Highlands. Within the study area, flow from the C aquifer primarily discharges from large, stable springs in the upper East Verde River, Tonto Creek, and Canyon Creek Basins along the top of the Mogollon Rim and to the west as base flow in West Clear Creek. On the basis of chemical evidence and the distribution and flow characteristics of springs and perennial streams, the C aquifer is also the source of water for the limestone aquifer that discharges from carbonate rocks near the base of the Mogollon Rim. Vertical flow from the C aquifer, the base of which is in the Schnebly Hill Formation, recharges the limestone aquifer that discharges mainly at Fossil Springs in the western part of the study area and as base flow in Cibecue Creek on the eastern edge of the study area. Local, generally shallow aquifers of variable productivity occur in plateau and mesa-capping basalts in the sedimentary rocks of the Schnebly Hill and Supai Formations, in fractured zones of the Proterozoic Payson granite, and in the alluvium of the lower Tonto Creek Basin. Where time series data exist, such water-bearing zones are shown to be sensitive to short-

Scientific Investigations Report↗

USGS2017

A new sulfur and carbon degassing inventory for the Southern Central American Volcanic Arc: The importance of accurate time-series datasets and possible tectonic processes responsible for temporal variations in arc-scale volatile emissions

This work presents a new database of SO 2 and CO 2 fluxes from the Southern Central American Volcanic Arc (SCAVA) for the period 2015–2016. We report ∼300 SO 2 flux measurements from 10 volcanoes and gas ratios from 11 volcanoes in Costa Rica and Nicaragua representing the most extensive available assessment of this ∼500 km arc segment. The SO 2 flux from SCAVA is estimated at 6,240 ± 1,150 T/d, about a factor of three higher than previous estimations (1972–2013). We attribute this increase in part to our more complete assessment of the arc. Another consideration in interpreting the difference is the context of increased volcanic activity, as there were more eruptions in 2015–2016 than in any period since ∼1980. A potential explanation for increased degassing and volcanic activity is a change in crustal stress regime (from compression to extension, opening volcanic conduits) following two large (Mw > 7) earthquakes in the region in 2012. The CO 2 flux from the arc is estimated at 22,500 ± 4,900 T/d, which is equal to or greater than estimates of C input into the SCAVA subduction zone. Time‐series data sets for arc degassing need to be improved in temporal and spatial coverage to robustly constrain volatile budgets and tectonic controls. Arc volatile budgets are strongly influenced by short‐lived degassing events and arc systems likely display significant short‐term variations in volatile output, calling for expansion of nascent geochemical monitoring networks to achieve spatial and temporal coverage similar to traditional geophysical networks.

Geochemistry, Geophysics, Geosystems↗