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At least 1,117 records · Page 62Linked to original sources
USGS2019

Groundwater recharge estimates for Maine using a Soil-Water-Balance model—25-year average, range, and uncertainty, 1991 to 2015

To address the lack of information on the spatial and temporal variability of recharge to groundwater systems in Maine, a study was initiated in cooperation with the Maine Geological Survey to use the U.S. Geological Survey Soil-Water-Balance model to evaluate annual average potential recharge across the State over a 25-year period from 1991 to 2015. The Maine Soil-Water-Balance model was calibrated using annual observations of recharge, runoff, and evapotranspiration for 32 calibration watersheds in the State during 2001–12 (902 total observations). Observations of recharge, runoff, and evapotranspiration were developed for each watershed to reduce the possibility of nonunique combinations of model parameters during the calibration. The Maine Soil-Water-Balance model was run using an optional evapotranspiration calculation method that provides more control for calibration than the standard method. The model was calibrated using the Parameter ESTimation software suite. The overall mean model error (average of all annual residuals for recharge, runoff, and precipitation) was 0.39 inch. The mean of the absolute value of the residuals, or the mean absolute error, was 2.32 inches. The root mean squared error for the calibrated model overall was 3.14 inches. Statistical tests indicated that the model residuals are normally distributed. To determine the potential uncertainty in the median annual potential recharge that results from uncertainty in the parameters as they relate to information contained in the observations, 300 alternate model realizations were run, and the standard deviation of the median potential recharge value at every pixel was calculated. Simulated 25-year median potential recharge across the State is widely variable; this variability closely follows patterns of precipitation, with additional variability contributed by the patchwork nature of the combinations of land-use class and hydrologic soil group inputs, and distribution of available water capacity in the soil across the State. Overall, the 25-year median annual potential recharge across the State is 7.5 inches, ranging from a low of about 5 inches to over 30 inches. The statewide range in the 25-year minimum values is from just over 2 inches to just over 20 inches. The statewide range in the 25-year maximum potential recharge is between 15 and 48 inches per year. The model areas with the highest simulated median potential recharge include areas underlain by type A soils (sandy and well drained), particularly those that also have land uses with low or little vegetation (blueberry barrens, developed, open space, scrub/shrub, and cropland, for example). The potential recharge values for these areas are similar to previously published values for comparable soil types. The 25-year average potential recharge grids were compared to recharge evaluated through groundwater-flow models or other methods in four hydrogeologic settings at six study areas in the State. A key factor in the ability of the Soil-Water-Balance model to reproduce the earlier study results was whether the available water-capacity data were an appropriate match for the hydrologic soil groups. The Maine Soil-Water-Balance model does a good job in representing an accurate potential recharge under circumstances where the surficial mapped soils extend below the surface to the water-table aquifer and where the available water-capacity data are in an appropriate range for the hydrologic soil group. One hydrogeologic setting that was challenging for the model was where a silt and clay layer was below a shallow soil unit that did not have available water-capacity data that were appropriate for the hydrologic soil group. In these cases, typically the available water-capacity data were very low, not accounting for the impedance of water flow provided by the underlying soil. The model also does not simulate well areas where bedrock surfaces are above the water table but below the plant rooting zone. The data products accompanying this report are intended to be used to provide first-cut estimates of recharge for geographic areas no smaller than the smallest watersheds used in the calibration of the model—or about 1.5 square miles. It is recommended that the grids are used to calculate an area-wide average potential recharge for any given area of study, and an uncertainty around the mean should be calculated from the standard deviation grid at the same time.

Maine↗
USGS2012

Effects of groundwater pumping in the lower Apalachicola-Chattahoochee-Flint River basin

USGS developed a groundwater-flow model of the Upper Floridan aquifer in lower Apalachicola-Chattahoochee-Flint River basin in southwest Georgia and adjacent parts of Alabama and Florida to determine the effect of agricultural groundwater pumping on aquifer/stream flow within the basin. Aquifer/stream flow is the sum of groundwater outflow to and inflow from streams, and is an important consideration for water managers in the development of water-allocation and operating plans. Specifically, the model was used to evaluate how agricultural pumping relates to 7Q10 low streamflow, a statistical low flow indicative of drought conditions that would occur during seven consecutive days, on average, once every 10 years. Argus ONETM, a software package that combines a geographic information system (GIS) and numerical modeling in an Open Numerical Environment, facilitated the design of a detailed finite-element mesh to represent the complex geometry of the stream system in the lower basin as a groundwater-model boundary. To determine the effects on aquifer/stream flow of pumping at different locations within the model area, a pumping rate equivalent to a typical center-pivot irrigation system (50,000 ft3/d) was applied individually at each of the 18,951 model nodes in repeated steady-state simulations that were compared to a base case representing drought conditions during October 1999. Effects of nodal pumping on aquifer/stream flow and other boundary flows, as compared with the base-case simulation, were computed and stored in a response matrix. Queries to the response matrix were designed to determine the sensitivity of targeted stream reaches to agricultural pumping. Argus ONE enabled creation of contour plots of query results to illustrate the spatial variation across the model area of simulated aquifer/streamflow reductions, expressed as a percentage of the long-term 7Q10 low streamflow at key USGS gaging stations in the basin. These results would enable water managers to assess the relative impact of agricultural pumping and drought conditions on streamflow throughout the basin, and to develop mitigation strategies to conserve water resources and preserve aquatic habitat.

Alabama, Georgia, Florida↗
USGS2015

Multiscale analysis of river networks using the R package linbin

Analytical tools are needed in riverine science and management to bridge the gap between GIS and statistical packages that were not designed for the directional and dendritic structure of streams. We introduce linbin, an R package developed for the analysis of riverscapes at multiple scales. With this software, riverine data on aquatic habitat and species distribution can be scaled and plotted automatically with respect to their position in the stream network or—in the case of temporal data—their position in time. The linbin package aggregates data into bins of different sizes as specified by the user. We provide case studies illustrating the use of the software for (1) exploring patterns at different scales by aggregating variables at a range of bin sizes, (2) comparing repeat observations by aggregating surveys into bins of common coverage, and (3) tailoring analysis to data with custom bin designs. Furthermore, we demonstrate the utility of linbin for summarizing patterns throughout an entire stream network, and we analyze the diel and seasonal movements of tagged fish past a stationary receiver to illustrate how linbin can be used with temporal data. In short, linbin enables more rapid analysis of complex data sets by fisheries managers and stream ecologists and can reveal underlying spatial and temporal patterns of fish distribution and habitat throughout a riverscape.

North American Journal of Fisheries Management↗
USGS2012

Yield of bedrock wells in the Nashoba terrane, central and eastern Massachusetts

The yield of bedrock wells in the fractured-bedrock aquifers of the Nashoba terrane and surrounding area, central and eastern Massachusetts, was investigated with analyses of existing data. Reported well yield was compiled for 7,287 wells from Massachusetts Department of Environmental Protection and U.S. Geological Survey databases. Yield of these wells ranged from 0.04 to 625 gallons per minute. In a comparison with data from 103 supply wells, yield and specific capacity from aquifer tests were well correlated, indicating that reported well yield was a reasonable measure of aquifer characteristics in the study area. Statistically significant relations were determined between well yield and a number of cultural and hydrogeologic factors. Cultural variables included intended water use, well depth, year of construction, and method of yield measurement. Bedrock geology, topography, surficial geology, and proximity to surface waters were statistically significant hydrogeologic factors. Yield of wells was higher in areas of granites, mafic intrusive rocks, and amphibolites than in areas of schists and gneisses or pelitic rocks; higher in valleys and low-slope areas than on hills, ridges, or high slopes; higher in areas overlain by stratified glacial deposits than in areas overlain by till; and higher in close proximity to streams, ponds, and wetlands than at greater distances from these surface-water features. Proximity to mapped faults and to lineaments from aerial photographs also were related to well yield by some measures in three quadrangles in the study area. Although the statistical significance of these relations was high, their predictive power was low, and these relations explained little of the variability in the well-yield data. Similar results were determined from a multivariate regression analysis. Multivariate regression models for the Nashoba terrane and for a three-quadrangle subarea included, as significant variables, many of the cultural and hydrogeologic factors that were individually related to well yield, in ways that are consistent with conceptual understanding of their effects, but the models explained only 21 percent (regional model for the entire terrane) and 30 percent (quadrangle model) of the overall variance in yield. Moreover, most of the explained variance was due to well characteristics rather than hydrogeologic factors. Hydrogeologic factors such as topography and geology are likely important. However, the overall high variability in the well-yield data, which results from the high variability in aquifer hydraulic properties as well as from limitations of the dataset, would make it difficult to use hydrogeologic factors to predict well yield in the study area. Geostatistical analysis (variograms), on the other hand, indicated that, although highly variable, the well-yield data are spatially correlated. The spatial continuity appears greater in the northeast-southwest direction and less in the southeast-northwest direction, directions that are parallel and perpendicular, respectively, to the regional geologic structural trends. Geostatistical analysis (kriging), used to estimate yield values throughout the study area, identified regional-scale areas of higher and lower yield that may be related to regional structural features—in particular, to a northeast-southwest trending regional fault zone within the Nashoba terrane. It also would be difficult to use kriging to predict yield at specific locations, however, because of the spatial variability in yield, particularly at small scales. The regional-scale analyses in this study, both with hydrogeologic variables and geostatistics, provide a context for understanding the variability in well yield, rather a basis for precise predictions, and site-specific information would be needed to understand local conditions.

Massachusetts↗
USGS2011

Spatial distribution and risk factors of highly pathogenic avian influenza (HPAI) H5N1 in China

Highly pathogenic avian influenza (HPAI) H5N1 was first encountered in 1996 in Guangdong province (China) and started spreading throughout Asia and the western Palearctic in 2004–2006. Compared to several other countries where the HPAI H5N1 distribution has been studied in some detail, little is known about the environmental correlates of the HPAI H5N1 distribution in China. HPAI H5N1 clinical disease outbreaks, and HPAI virus (HPAIV) H5N1 isolated from active risk-based surveillance sampling of domestic poultry (referred to as HPAIV H5N1 surveillance positives in this manuscript) were modeled separately using seven risk variables: chicken, domestic waterfowl population density, proportion of land covered by rice or surface water, cropping intensity, elevation, and human population density. We used bootstrapped logistic regression and boosted regression trees (BRT) with cross-validation to identify the weight of each variable, to assess the predictive power of the models, and to map the distribution of HPAI H5N1 risk. HPAI H5N1 clinical disease outbreak occurrence in domestic poultry was mainly associated with chicken density, human population density, and elevation. In contrast, HPAIV H5N1 infection identified by risk-based surveillance was associated with domestic waterfowl density, human population density, and the proportion of land covered by surface water. Both models had a high explanatory power (mean AUC ranging from 0.864 to 0.967). The map of HPAIV H5N1 risk distribution based on active surveillance data emphasized areas south of the Yangtze River, while the distribution of reported outbreak risk extended further North, where the density of poultry and humans is higher. We quantified the statistical association between HPAI H5N1 outbreak, HPAIV distribution and post-vaccination levels of seropositivity (percentage of effective post-vaccination seroconversion in vaccinated birds) and found that provinces with either outbreaks or HPAIV H5N1 surveillance positives in 2007–2009 appeared to have had lower antibody response to vaccination. The distribution of HPAI H5N1 risk in China appears more limited geographically than previously assessed, offering prospects for better targeted surveillance and control interventions.

PLoS Pathogens↗
USGS2024

Propensity score matching mitigates risk of faulty inferences in observational studies of effectiveness of restoration trials

Determining effectiveness of restoration treatments is an important requirement of adaptive management, but it can be non-trivial where only portions of large and heterogeneous landscapes of concern can be treated and sampled. Bias and non-randomness in the spatial deployment of treatment and thus sampling is nearly unavoidable in the data available for large-scale management trials, and the biophysical landscape characteristics underlying the bias are key but rare considerations in analyses of treatment effects. Treatment effects from large-scale management trials are typically estimated with multivariable regression (MVR) models. However, this method is unsuited to reliable estimations of treatment effects when treated and untreated areas differ in their underlying biophysical variability. An alternative to conventional regression is to use propensity score (PS) matching, which can limit the differences in confounding variables among treatment groups and assure the data collected or selected for analysis are more consistent with a randomized and unconfounded experiment. Thus, PS is expected to identify treatment effects more accurately. We used data from a large-scale monitoring effort of a megafire to evaluate the efficacy of PS matching in making inferences on treatment effects when treatments are applied non-randomly over a large heterogeneous area. We compared the resulting inference to both traditional MVR methods and to “naïve” methods that do not consider treatment allocation bias. Treatment effects varied between the different statistical methods for controlling selection bias and confounding biophysical factors. The PS-matched model revealed a weaker treatment effect of drill seeding and a greater effect of herbicide spraying on the cover of perennial bunchgrasses when compared to MVR or naïve modelled estimates. The inferences from the PS-matched model are considered more reliable because the treated and untreated plots are more similar in their underlying biophysical characteristics. Synthesis and applications . Failure to consider the non-random and selective deployment of restoration treatments by managers leads to faulty inference on their effectiveness. However, tools such as propensity-score matching can be used to remove the bias from analyses of the outcomes of management trials or to devise sampling plans that efficiently protect against the bias.

Journal of Applied Ecology↗
USGS2000

A hybrid orographic plus statistical model for downscaling daily precipitation in northern California

A hybrid (physical–statistical) scheme is developed to resolve the finescale distribution of daily precipitation over complex terrain. The scheme generates precipitation by combining information from the upper-air conditions and from sparsely distributed station measurements; thus, it proceeds in two steps. First, an initial estimate of the precipitation is made using a simplified orographic precipitation model. It is a steady-state, multilayer, and two-dimensional model following the concepts of Rhea. The model is driven by the 2.5° × 2.5° gridded National Oceanic and Atmospheric Administration–National Centers for Environmental Prediction upper-air profiles, and its parameters are tuned using the observed precipitation structure of the region. Precipitation is generated assuming a forced lifting of the air parcels as they cross the mountain barrier following a straight trajectory. Second, the precipitation is adjusted using errors between derived precipitation and observations from nearby sites. The study area covers the northern half of California, including coastal mountains, central valley, and the Sierra Nevada. The model is run for a 5-km rendition of terrain for days of January–March over the period of 1988–95. A jackknife analysis demonstrates the validity of the approach. The spatial and temporal distributions of the simulated precipitation field agree well with the observed precipitation. Further, a mapping of model performance indices (correlation coefficients, model bias, root-mean-square error, and threat scores) from an array of stations from the region indicates that the model performs satisfactorily in resolving daily precipitation at 5-km resolution.

California↗
USGS2026

Estimation, distribution, and development of a surrogate model for Escherichia coli in the New River, New River Gorge National Park and Preserve, West Virginia, 2021–23

The New River Gorge National Park and Preserve in West Virginia receives more than 1 million visitors each year, many of whom come to enjoy the New River, which is known for its whitewater recreation. However, most of the tributaries within the New River Gorge are impaired by fecal-coliform bacteria, which are at concentrations that may exceed recreational-contact standards, posing a potential health risk to the public and, therefore, creating a need to better understand the spatial and temporal distribution of fecal-coliform bacteria and to communicate this information to park visitors. Concentrations of Escherichia coli , a species of fecal-coliform bacteria, were monitored in the New River and selected tributaries from October 2021 through September 2023, with emphasis placed on the primary recreational-contact season from May through October. Composite and cross-sectional water samples were taken from three U.S. Geological Survey (USGS) monitoring locations: the New River at Highway 41 at Prince, West Virginia (USGS 03184905), New River at Thurmond, West Virginia (USGS 03185400; hereafter, Thurmond), and New River at Fayette, West Virginia (USGS 03186000). Periodic longitudinal transects included water samples collected below seven major tributaries of the New River within the gorge. Water-quality parameters, including water temperature, pH, specific conductance, dissolved oxygen, and turbidity, were recorded with each E. coli water sample. During the 2 years of sampling, E. coli concentrations in samples collected from the New River ranged from less than 1 to 1,100 most probable number (MPN) per 100 milliliters (MPN/100 mL). The recreational-contact standard, which is based on the U.S. Environmental Protection Agency 90th-percentile statistical threshold value for E. coli concentrations (320 MPN/100 mL), was exceeded in 11 of the 110 samples collected from the New River during this study. Water-quality parameter measurements and E. coli concentrations in collected samples were generally consistent among USGS monitoring locations throughout the New River Gorge; however, storm events created notable exceptions because they increased tributary streamflow and E. coli concentrations in samples, particularly at the New River below Piney Creek at McCreery, West Virginia (USGS 03185208), and New River Below Arbuckle Creek at Thurmond, West Virginia (USGS 03185440), monitoring locations. Escherichia coli concentrations of cross-sectional samples tended to be consistent across the New River, except for a few nearshore samples. Sample E. coli concentrations and corresponding measurements of continuous water-quality parameters, streamflow, and precipitation data from Thurmond and the Piney Creek at Raleigh, West Virginia (USGS 03185000; tributary to the New River) monitoring locations were evaluated for use in a near-real-time E. coli surrogate model. The antecedent mean 24-hour turbidity at Thurmond was selected as the best variable for a simple linear regression surrogate model for the log 10 E. coli concentration in the New River and had an adjusted coefficient of determination of 0.556 and a p-value of less than 0.001. The regression equation surrogate model suggests that the recreational-contact standard is exceeded when the antecedent mean 24-hour turbidity at Thurmond is 23.6 formazin nephelometric units or higher (with a 95-percent confidence interval of 19.4–30.7 formazin nephelometric units). Evaluated against a turbidity duration curve, this standard is exceeded 7.5 percent of the time at Thurmond. This surrogate model could help New River Gorge National Park and Preserve staff provide near-real-time information about E. coli concentrations and related recreational-contact risks to the public.

West Virginia↗
USGS2019

Monitoring drought impact on annual forage production in semi-arid grasslands: A case study of Nebraska sandhills

Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha −1 *yr −1 , was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R 2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses. View Full-Text

Nebraska↗
USGS2022

On the role of climate in monthly baseflow changes across the continental United States

Baseflow is the portion of streamflow that comes from groundwater and subsurface sources. Although baseflow is essential for sustaining streams during low flow and drought periods, we have little information about how and why it has changed over large regions of the continental United States. The objective of this study was to evaluate how changes in the climate system have affected observed monthly baseflow records at 3,283 USGS gauges over the last 30 years (1989–2019). We developed a statistical modeling framework to determine the relationship between monthly baseflow and monthly climate predictors (i.e., precipitation, temperature, and antecedent wetness). Overall, we found that baseflow trends and the factors influencing them vary by region and month. In the US Northeast, increases were detected earlier in the year (February and March) and in the summer (May and June), and were likely due to increasing precipitation, warmer temperature, and subsequent changes in snowmelt. Increasing baseflow in the US Pacific Northwest and Midwest were associated with increases in precipitation and antecedent wetness throughout the year. Decreasing trends were located in the US Southeast and Southwest. Baseflow trends in the US Southeast were only detected in March, possibly as a result of decreased precipitation during the spring. On the other hand, decreases in baseflow in the Central Southwestern United States occurred throughout the year. These trends were associated with a lack of precipitation and increases in temperature. Finally, we examined the relationship between monthly baseflow trends and changes in total water storage using monthly Gravity Recovery and Climate Experiment mascon products from the Jet Propulsion Laboratory. In this study, trends in total water storage were strongly associated with baseflow trends across the United States. The spatial and temporal variability in baseflow response to climate reported here can aid water managers in adapting to future climate change.

continental United States↗
USGS2008

Trends In Nutrient and Sediment Concentrations and Loads In Major River Basins of the South-Central United States, 1993-2004

Nutrient and sediment data collected at 115 sites by Federal and State agencies from 1993 to 2004 were analyzed by the U.S. Geological Survey to determine trends in concentrations and loads for selected rivers and streams that drain into the northwestern Gulf of Mexico from the south-central United States, specifically from the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf Basins. Trends observed in the study area were compared to determine potential regional patterns and to determine cause-effect relations with trends in hydrologic and human-induced factors such as nutrient sources, streamflow, and implementation of best management practices. Secondary objectives included calculation of loads and yields for the study period as a basis for comparing the delivery of nutrients and sediment to the northwestern Gulf of Mexico from the various rivers within the study area. In addition, loads were assessed at seven selected sites for the period 1980-2004 to give hydrologic perspective to trends in loads observed during 1993-2004. Most study sites (about 64 percent) either had no trends or decreasing trends in streamflow during the study period. The regional pattern of decreasing trends in streamflow during the study period appeared to correspond to moist conditions at the beginning of the study period and the influence of three drought periods during the study period, of which the most extreme was in 2000. Trend tests were completed for ammonia at 49 sites, for nitrite plus nitrate at 69 sites, and for total nitrogen at 41 sites. For all nitrogen constituents analyzed, no trends were observed at half or more of the sites. No regional trend patterns could be confirmed because there was poor spatial representation of the trend sites. Decreasing trends in flow-adjusted concentrations of ammonia were observed at 25 sites. No increasing trends in concentrations of ammonia were noted at any sites. Flow-adjusted concentrations of nitrite plus nitrate decreased at 7 sites and increased at14 sites. Flow-adjusted concentrations of total nitrogen decreased at 2 sites and increased at 12 sites. Improvements to municipal wastewater treatment facilities contributed to the decline of ammonia concentrations at selected sites. Notable increasing trends in nitrite plus nitrate and total nitrogen at selected study sites were attributed to both point and nonpointsources. Trend patterns in total nitrogen generally followed trend patterns in nitrite plus nitrate, which was understandable given that nitrite plus nitrate loads generally were 70-90 percent of the total nitrogen loads at most sites. Population data were used as a surrogate to understand the relation between changes in point sources and nutrient trends because data from wastewater treatment plants were inconsistent for this study area. Although population increased throughout the study area during the study period, there was no observed relation between increasing trends in nitrogen in study area streams and increasing trends in population. With respect to other nitrogen sources, statistical results did suggest that increasing trends in nitrogen could be related to increasing trends in nitrogen from either commercial fertilizer use and/or land application of manure. Loads of ammonia, nitrite plus nitrate, and total nitrogen decreased during the study period, but some trends in nitrogen loads were part of long-term decreases since 1980. For example, ammonia loads were shown to decrease at nearly all sites over the past decade, but at selected sites, these decreasing trends were part of much longer trends since 1980. The Mississippi and Atchafalaya Rivers contributed the highest nitrogen loads to the northwestern Gulf of Mexico as expected; however, nitrogen yields from smaller rivers had similar or higher yields than yields from the Mississippi River. Trend tests were completed for orthophosphorus at 34 sites and for total phosphorus at 52 sites. No trends were observed in abo

Scientific Investigations Report↗
USGS2021

Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013

The U.S. Geological Survey, in cooperation with the California Department of Water Resources (DWR), has constructed a new spatially detailed Precipitation-Runoff Modeling System (PRMS) model for the Merced River Basin, California, which is a tributary of the San Joaquin River in California. Operated through an Object User Interface (OUI) with Ensemble Streamflow Prediction (ESP) and daily climate distribution preprocessing functionality, the model is calibrated primarily to simulate (and eventually, forecast) year-to-year variations of inflows to Lake McClure during the critical April–July snowmelt season. The model is intended to become part of a suite of methods used by DWR for estimating daily streamflow from the Merced River Basin, especially during the snowmelt season. This study describes the results of the application of an analysis tool that simulates responses to climate and land-use variations at a higher spatial resolution than previously available to DWR. A geographic information system was used to delineate the model domain, that is, areas draining to a single outlet at U.S. Geological Survey streamflow-gaging station 11270900, Merced River below Merced Falls Dam, near Snell, CA (also known as California Data Exchange Center station MRC), and subdrainage areas, including four draining to internal gages used as calibration targets. Using this delineation, three contiguous subbasins were recognized and, along with the model domain and nested calibration targets, are the simulation units evaluated in this report. An auto-calibration tool, LUCA (Let Us CAlibrate), was used for each calibration node, from headwaters to basin outlet, and then parameters were manually adjusted to complete the calibration. The main objective was to match April–July snowmelt seasonal discharge values of simulated streamflow to observed (measured or reconstructed) discharge values. Calibration or validation periods used site-specific streamflows—mostly from October 1, 1988, through September 30, 2013—but differed according to the period-of-record available for the measurements collected at internal gages or reconstructed flows for the single outlet. The accuracy of the Merced PRMS streamflow simulations varied seasonally, as compared to observed values. Based on statistical results, the Merced PRMS model satisfactorily simulated snowmelt seasonal streamflows. April–July calibrations for all areas had small negative bias (not greater than 7 percent) and low relative error (less than 8 percent). Less satisfactory performance for other seasons was attributed to several factors: (1) high uncertainty in low or zero flows in summer and fall, (2) lack of accounting for basin withdrawals and anthropogenic water use, (3) unavailability and (or) inaccuracy of observed (measured) meteorological input data, and (4) uncertainty in reconstructed streamflow data. With some additional refinement, the Merced PRMS model may be used for forecasting seasonal and longer-term streamflow variations; evaluating forecasted and past climate and land cover changes; providing water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites within the basin; and aiding environmental studies, hydraulic design, water management, and water-quality projects in the Merced River Basin.

California↗
USGS2014

Palila abundance estimates and trends

The palila (Loxioides bailleui) population was surveyed annually during 1998−2014 on Mauna Kea Volcano to determine abundance, population trend, and spatial distribution. In the latest surveys, the 2013 population was estimated at 1,492−2,132 birds (point estimate: 1,799) and the 2014 population was estimated at 1,697−2,508 (point estimate: 2,070). Similar numbers of palila were detected during the first and subsequent counts within each year during 2012−2014, and there was no difference in their detection probability due to count sequence. This suggests that greater precision in population estimates can be achieved if future surveys include repeat visits. No palila were detected outside the core survey area in 2013 or 2014, suggesting that most if not all palila inhabit the western slope during the survey period. Since 2003, the size of the area containing all annual palila detections do not indicate a significant change among years, suggesting that the range of the species has remained stable; although this area represents only about 5% of its historical extent. During 1998−2003, palila numbers fluctuated moderately (coefficient of variation [CV] = 0.21). After peaking in 2003, population estimates declined steadily through 2011; since 2010, estimates have fluctuated moderately above the 2011 minimum (CV = 0.18). The average rate of decline during 1998−2014 was 167 birds per year with very strong statistical support for an overall declining trend in abundance. Over the 16-year monitoring period, the estimated rate of change equated to a 68% decline in the population.

Hawai'i↗
USGS1998

Stream habitat characteristics at selected sites in the Georgia-Florida coastal plain

Habitat characterization is part of a multidisciplinary approach to water-quality assessment implemented by the National Water-Quality Assessment Program. Habitat data were collected in the Georgia-Florida Coastal Plain study unit at 24 sites during 1993-95. Data were collected for habitat characteristics at three spatial scales: basin, segment, and reach. Basin data include physiography, land resource provinces, and land use, providing a description of the environmental setting at each site. Segment data include length, gradient, and sinuosity. A Kendall correlation analysis performed on segment characteristics and the log-of-basin area showed a correlation between segment gradient and the log-of-basin area and a correlation between sinuosity and segment length. Reach data consist of field-collected measurements of both instream and riparian habitats. Sand and detritus were the most common channel-bed substrates among the sampled sites. Measurements of channel width, water depth, and bank width and height were used to create cross-sectional profiles of each sampled area. Elevations of selected durations plotted on cross sections illustrated the percentage of time that the banks were inundated at each site. Sites were divided into two groups based on duration of bank inundation (less than or equal to 1 percent and greater than 1 percent). Bank woody vegetation was also sampled and a clustering algorithm known as Two-Way INdicator SPecies ANalysis (TWINSPAN) was used to analyze these data. TWINSPAN divided the sites into two groups based on their vegetation composition. A statistical comparison of the two types of site groups (duration of bank inundation and vegetation) was performed. The significant association between these groups was consistent with the hypothesis that inundation frequency affected riparian vegetation.

Florida, Georgia↗
USGS2019

Estimating minimum streamflow from measurements at ungauged sites in regions with streamflow‐gauging networks

Estimation of low flows in rivers continues to be a vexing problem despite advances in statistical and process‐based hydrological models. We develop a method to estimate minimum streamflow at seasonal to annual timescales from measured streamflow based on regional similarity in the deviations of daily streamflow from minimum streamflow for a period of interest. The method is applied to 1,019 gauged sites in the Western United States for June to December 2015. The gauges were clustered into six regions with distinct timing and magnitude of low flows. A gamma distribution was fit each day to the deviations in specific discharge (daily streamflow divided by drainage area) from minimum specific discharge for gauges in each region. The Kolmogorov–Smirnov test identified days when the gamma distribution was adequate to represent the distribution of deviations in a region. The performance of the gamma distribution was evaluated at gauges by comparing daily estimates of minimum streamflow with estimates from area‐based regression relations for minimum streamflow. Each region had at least 8 days during the period when streamflow measurements would provide better estimates than the regional regression equation, but the number of such days varied by region depending on aridity and homogeneity of streamflow within the region. Synoptic streamflow measurements at ungauged sites have value for estimating minimum streamflow and improving the spatial resolution of hydrological model in regions with streamflow‐gauging networks.

Hydrological Processes↗
USGS2021

Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement

African elephants (Loxodonta africana) are well-studied and inhabit diverse landscapes that are being transformed by both humans and natural forces. Most tools currently in use are limited in their ability to predict how elephants will respond to novel changes in the environment. Individual-, or agent-based modeling (ABM), may extend current methods in addressing and predicting spatial responses to environmental conditions over time. We developed a spatially explicit agent-based model to simulate elephant space use and validated the model with movement data from elephants in Kruger National Park (KNP) and Chobe National Park (CNP). We simulated movement at an hourly scale, as this scale can reflect switches in elephant behavior due to changes in internal states and short-term responses to the local availability and distribution of critical resources, including forage, water, and shade. Known internal drivers of elephant movement, including perceived temperature and the time since an individual last visited a water source, were linked to the external environment through behavior-based movement rules. Simulations were run on model landscapes representing the wet season and the hot, dry season for both parks. The model outputs, including home range size, daily displacement distance, net displacement distance, and maximum distance traveled from a permanent water source, were evaluated through qualitative and quantitative comparisons to actual elephant movement data from both KNP and CNP. The ABM was successful in reproducing the differences in daily displacements between seasons in each park, and in distances traveled from a permanent water source between parks and seasons. Other movement characteristics, including differences in home range sizes and net daily displacements, were partially reproduced. Out of the all the statistical comparisons made between the empirical and simulated movement patterns, the majority were classified as discrepancies of medium or small effect size. We have shown that a resource-driven model with relatively simple decision rules generates trajectories with movement characteristics that are mostly comparable to those calculated from empirical data. Simulating hourly movement (as our model does) may be useful in predicting how finer-scale patterns of space use, such as those created by foraging movements, are influenced by finer spatio-temporal changes in the environment.

Chobe National Park, Kruger National Park↗
USGS2018

Evaluating and monitoring forest fuel treatments using remote sensing applications in Arizona, U.S.A.

The practice of fire suppression across the western United States over the past century has led to dense forests, and when coupled with drought has contributed to an increase in large and destructive wildfires. Forest management efforts aimed at reducing flammable fuels through various fuel treatments can help to restore frequent fire regimes and increase forest resilience. Our research examines how different fuel treatments influenced burn severity and post-fire vegetative stand dynamics on the San Carlos Apache Reservation, in east-central Arizona, U.S.A. Our methods included the use of multitemporal remote sensing data and cloud computing to evaluate burn severity and post-fire vegetation conditions as well as statistical analyses. We investigated how forest thinning, commercial harvesting, prescribed burning, and resource benefit burning (managed wildfire) related to satellite measured burn severity (the difference Normalized Burn Ratio – dNBR) following the 2013 Creek Fire and used spectral measures of post-fire stand dynamics to track changes in land surface characteristics (i.e., brightness, greenness and wetness). We found strong negative relationships between dNBR and post-fire greenness and wetness, and a positive non-linear relationship between dNBR and brightness, with greater variability at higher severities. Fire severity and post-fire surface changes also differed by treatment type. Our results showed harvested and thinned sites that were not treated with prescribed fire had the highest severity fire. When harvesting was followed by a prescribed burn, the sites experienced lower burn severity and reduced post-fire changes in vegetation greenness and wetness. Areas that had previously experienced resource benefit burns had the lowest burn severities and the highest post-fire greenness measurements compared to all other treatments, except for where the prescribed burn had occurred. These results suggest that fire treatments may be most effective at reducing the probability of hazardous fire and increasing post-fire recovery. This research demonstrates the utility of remote sensing and spatial data t o inform forest management, and how various fuel treatments can influence burn severity and post-fire vegetation response within ponderosa pine forests across the southwestern U.S.

Arizona↗
USGS2012

Producing fractional rangeland component predictions in a sagebrush ecosystem, a Wyoming sensitivity analysis

Remote sensing information has been widely used to monitor vegetation condition and variations in a variety of ecosystems, including shrublands. Careful application of remotely sensed imagery can provide additional spatially explicit, continuous, and extensive data on the composition and condition of shrubland ecosystems. Historically, the most widely available remote sensing information has been collected by Landsat, which has offered large spatial coverage and moderate spatial resolution data globally for nearly three decades. Such medium-resolution satellite remote sensing information can quantify the distribution and variation of terrestrial ecosystems. Landsat imagery has been frequently used with other high-resolution remote sensing data to classify sagebrush components and quantify their spatial distributions (Ramsey and others, 2004; Seefeldt and Booth, 2004; Stow and others, 2008; Underwood and others, 2007). Modeling algorithms have been developed to use field measurements and satellite remote sensing data to quantify the extent and evaluate the quality of shrub ecosystem components in large geographic areas (Homer and others, 2009). The percent cover of sagebrush ecosystem components, including bare-ground, herbaceous, litter, sagebrush, and shrub, have been quantified for entire western states (Homer and others, 2012). Furthermore, research has demonstrated the use of current measurements with historical archives of Landsat imagery to quantify the variations of these components for the last two decades (Xian and others, 2012). The modeling method used to quantify the extent and spatial distribution of sagebrush components over a large area also has required considerable amounts of training data to meet targeted accuracy requirements. These training data have maintained product accuracy by ensuring that they are derived from good quality field measurements collected during appropriate ecosystem phenology and subsequently maximized by extrapolation on high-resolution remote sensing data (Homer and others, 2012). This method has proven its utility; however, to develop these products across even larger areas will require additional cost efficiencies to ensure that an adequate product can be developed for the lowest cost possible. Given the vast geographic extent of shrubland ecosystems in the western United States, identifying cost efficiencies with optimal training data development and subsequent application to medium resolution satellite imagery provide the most likely areas for methodological efficiency gains. The primary objective of this research was to conduct a series of sensitivity tests to evaluate the most optimal and practical way to develop Landsat scale information for estimating the extent and distribution of sagebrush ecosystem components over large areas in the conterminous United States. An existing dataset of sagebrush components developed from extensive field measurements, high-resolution satellite imagery, and medium resolution Landsat imagery in Wyoming was used as the reference database (Homer and others, 2012). Statistical analysis was performed to analyze the relation between the accuracy of sagebrush components and the amount and distribution of training data on Landsat scenes needed to obtain accurate predictions.

Wyoming↗