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At least 361 records · Page 20Linked to original sources
USGS2024

Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model

The Chesapeake Bay Program’s Watershed Model (CBWM) has been used as an accounting tool for the Chesapeake Bay Total Maximum Daily Load (TMDL). However, some of the fundamental parameters that underpin the watershed model may not represent local watershed characteristics at all scales. Significant investments have been made by state and local governments, and other local stakeholders, who are interested in validating loads and progress in implementing measures to achieve the pollutant reductions called for in the TMDL through local monitoring data. For the purposes of this STAC workshop, local monitoring is considered any relevant data collected by a local, regional, state, or federal organization that has not been used previously in the development, calibration, or validation of the CBWM. Some of these local monitoring efforts have been collecting data over the past 5-10 years, with some datasets extending back over more than two decades. However, the data and the CBWM are often not directly comparable due to differences in temporal and spatial scales or because the water quality parameters being monitored are not those estimated by the model. Therefore, a Scientific and Technical Advisory Committee (STAC) workshop was convened to bring together Chesapeake Bay Program (CBP) modelers, local and state government stakeholders, and scientists who are monitoring and analyzing local water quality data to recommend ways in which local monitoring data can be used to inform the CBWM, identify gaps between modeled and monitored data, and validate model predictions at the local scale. The workshop, “Using Local Monitoring Results to Inform the Chesapeake Bay Program’s Watershed Model”, was held in March 2023 to provide insight on the scope of local water quality monitoring efforts within and outside of the Bay watershed that could be used to inform the CBWM. Scientists and managers developed recommendations that could be used by modelers for either calibration or knowledge generation to inform the Phase 7 version of the CBWM currently under development for a 2027 decision by the CBP, recommendations for how local monitoring efforts could be designed or altered to better inform the CBWM, and recommendations for how monitored trends could be used in management. The preliminary presentations for the workshop provided essential background information on the CBWM and data used to parameterize it. This information was the foundation for discussions on existing data gaps, the importance of current local monitoring networks, and best practices for developing future monitoring networks. More information on this STAC-funded effort including workshop presentation slides and recordings can be accessed on the workshop webpage. Confidence in the loading estimates of the CBWM is critical because of its role as the accounting mechanism for measuring progress toward the Bay TMDL’s nutrient and sediment reduction goals. Those who are being asked or required to pay for these reductions, from state and local government managers to farmers, property owners and developers, must have confidence in the scientific validity of the CBWM’s loading estimates or trust in the restoration effort will dissipate. Toward that end, several local entities have invested in extensive urban, suburban, and agricultural monitoring programs to characterize nutrient and sediment loading (among other water quality parameters) at a relatively fine scale (from a few acres to 5 square miles). Monitoring networks outside of the Bay watershed were also included as their relevance and similarities to Bay watershed landscapes, hydrology, and climate conditions can help build the body of knowledge necessary for better parameterization of the CBWM. Local monitoring results could be analyzed for loads and trends for calibration of Phase 7, comparison against trends, informing the structure and parameterization of the model, and potentially in policy evaluation. The effectiveness of management practices at the small watershed scale is a primary question of watershed managers that could be addressed by local monitoring, but to do so study design and statistical techniques may need to be altered if these datasets are intended to inform parameterization of the Bay modeling tools. The partnership would benefit from the redesign of some existing monitoring programs so that they are hypothesis-driven, with fully described inputs, outputs, and practices. New statistical tools could be applied to evaluate the relative importance of various drivers affecting water quality and influenced by hydrogeologic setting and watershed condition.

Chesapeake Bay watershed↗
USGS2017

Ad hoc instrumentation methods in ecological studies produce highly biased temperature measurements

In light of global climate change, ecological studies increasingly address effects of temperature on organisms and ecosystems. To measure air temperature at biologically relevant scales in the field, ecologists often use small, portable temperature sensors. Sensors must be shielded from solar radiation to provide accurate temperature measurements, but our review of 18 years of ecological literature indicates that shielding practices vary across studies (when reported at all), and that ecologists often invent and construct ad hoc radiation shields without testing their efficacy. We performed two field experiments to examine the accuracy of temperature observations from three commonly used portable data loggers (HOBO Pro, HOBO Pendant, and iButton hygrochron) housed in manufactured Gill shields or ad hoc, custom‐fabricated shields constructed from everyday materials such as plastic cups. We installed this sensor array (five replicates of 11 sensor‐shield combinations) at weather stations located in open and forested sites. HOBO Pro sensors with Gill shields were the most accurate devices, with a mean absolute error of 0.2°C relative to weather stations at each site. Error in ad hoc shield treatments ranged from 0.8 to 3.0°C, with the largest errors at the open site. We then deployed one replicate of each sensor‐shield combination at five sites that varied in the amount of urban impervious surface cover, which presents a further shielding challenge. Bias in sensors paired with ad hoc shields increased by up to 0.7°C for every 10% increase in impervious surface. Our results indicate that, due to variable shielding practices, the ecological literature likely includes highly biased temperature data that cannot be compared directly across studies. If left unaddressed, these errors will hinder efforts to predict biological responses to climate change. We call for greater standardization in how temperature data are recorded in the field, handled in analyses, and reported in publications.

Ecology and Evolution↗
USGS2019

Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma developed by using streamflow data through 2017

The U.S. Geological Survey (USGS), in cooperation with the Oklahoma Department of Transportation, updated peak-streamflow regression equations for estimating flows with annual exceedance probabilities from 50 to 0.2 percent for the State of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the State by use of a generalized least-squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for stream basins with drainage areas less than 2,510 square miles that are not affected by regulation. The standard model error ranged from 31.28 to 49.32 percent for the different annual exceedance probabilities that were computed. Annual-maximum peak flows observed at 212 USGS streamgages through water year 2017 were used for the regression analysis, excluding the Oklahoma Panhandle region. The USGS StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 212 streamgages in and near Oklahoma are provided in this report. This report contains descriptions of the methods that can be used to estimate peak streamflows at ungaged sites by using estimates from streamgages on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater-retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.

Arkansas, Kansas, Missouri, Oklahoma, Texas↗
USGS2018

Northeast

The distinct seasonality of the Northeast’s climate supports a diverse natural landscape adapted to the extremes of cold, snowy winters and warm to hot, humid summers. This natural landscape provides the economic and cultural foundation for many rural communities, which are largely supported by a diverse range of agricultural, tourism, and natural resource-dependent industries (see Ch. 10: Ag & Rural, Key Message 4) . The recent dominant trend in precipitation throughout the Northeast has been towards increases in rainfall intensity, with increases in intensity exceeding those in other regions of the contiguous United States. Further increases in rainfall intensity are expected, with increases in total precipitation expected during the winter and spring but with little change in the summer. Monthly precipitation in the Northeast is projected to be about 1 inch greater for December through April by end of century (2070–2100) under the higher scenario (RCP8.5). Ocean and coastal ecosystems are being affected by large changes in a variety of climate-related environmental conditions. These ecosystems support fishing and aquaculture, tourism and recreation, and coastal communities. Observed and projected increases in temperature, acidification, storm frequency and intensity, and sea levels are of particular concern for coastal and ocean ecosystems, as well as local communities and their interconnected social and economic systems. Increasing temperatures and changing seasonality on the Northeast Continental Shelf have affected marine organisms and the ecosystem in various ways. The warming trend experienced in the Northeast Continental Shelf has been associated with many fish and invertebrate species moving northward and to greater depths. Because of the diversity of the Northeast’s coastal landscape, the impacts from storms and sea level rise will vary at different locations along the coast. Northeastern cities, with their abundance of concrete and asphalt and relative lack of vegetation, tend to have higher temperatures than surrounding regions due to the urban heat island effect. During extreme heat events, nighttime temperatures in the region’s big cities are generally several degrees higher than surrounding regions, leading to higher risk of heat-related death. Urban areas are at risk for large numbers of evacuated and displaced populations and damaged infrastructure due to both extreme precipitation events and recurrent flooding, potentially requiring significant emergency response efforts and consideration of a long-term commitment to rebuilding and adaptation, and/or support for relocation where needed. Much of the infrastructure in the Northeast, including drainage and sewer systems, flood and storm protection assets, transportation systems, and power supply, is nearing the end of its planned life expectancy. Climate-related disruptions will only exacerbate existing issues with aging infrastructure. Sea level rise has amplified storm impacts in the Northeast (Key Message 2), contributing to higher surges that extend farther inland, as demonstrated in New York City in the aftermath of Superstorm Sandy in 2012. Service and resource supply infrastructure in the Northeast is at increasing risk of disruption, resulting in lower quality of life, economic declines, and increased social inequality. Loss of public services affects the capacity of communities to function as administrative and economic centers and triggers disruptions of interconnected supply chains (Ch. 16: International, Key Message 1) . Increases in annual average temperatures across the Northeast range from less than 1°F (0.6°C) in West Virginia to about 3°F (1.7°C) or more in New England since 1901. Although the relative risk of death on very hot days is lower today than it was a few decades ago, heat-related illness and death remain significant public health problems in the Northeast. For example, a study in New York City estimated that in 2013 there were 133 excess deaths due to extreme heat. These projected increases in temperature are expected to lead to substantially more premature deaths, hospital admissions, and emergency department visits across the Northeast. For example, in the Northeast we can expect approximately 650 additional premature deaths per year from extreme heat by the year 2050 under either a lower (RCP4.5) or higher (RCP8.5) scenario and from 960 (under RCP4.5) to 2,300 (under RCP8.5) more premature deaths per year by 2090. Communities, towns, cities, counties, states, and tribes across the Northeast are engaged in efforts to build resilience to environmental challenges and adapt to a changing climate. Developing and implementing climate adaptation strategies in daily practice often occur in collaboration with state and federal agencies (e.g., New Jersey Climate Adaptation Alliance 2017, New York Climate Clearinghouse 2017, Rhode Island STORMTOOLS 2017, EPA 2017, CDC 2015 ). Advances in rural towns, cities, and suburban areas include low-cost adjustments of existing building codes and standards. In coastal areas, partnerships among local communities and federal and state agencies leverage federal adaptation tools and decision support frameworks (for example, NOAA’s Digital Coast, USGS’s Coastal Change Hazards Portal, and New Jersey’s Getting to Resilience). Increasingly, cities and towns across the Northeast are developing or implementing plans for adaptation and resilience in the face of changing climate (e.g., EPA 2017 ). The approaches are designed to maintain and enhance the everyday lives of residents and promote economic development. In some cities, adaptation planning has been used to respond to present and future challenges in the built environment. Regional efforts have recommended changes in design standards when building, replacing, or retrofitting infrastructure to account for a changing climate.

Report↗
USGS2010

Nitrous oxide emission from denitrification in stream and river networks

Nitrous oxide (N 2 O) is a potent greenhouse gas that contributes to climate change and stratospheric ozone destruction. Anthropogenic nitrogen (N) loading to river networks is a potentially important source of N 2 O via microbial denitrification that converts N to N 2 O and dinitrogen (N 2 ). The fraction of denitrified N that escapes as N 2 O rather than N 2 (i.e., the N 2 O yield) is an important determinant of how much N 2 O is produced by river networks, but little is known about the N 2 O yield in flowing waters. Here, we present the results of whole-stream 15 N-tracer additions conducted in 72 headwater streams draining multiple land-use types across the United States. We found that stream denitrification produces N 2 O at rates that increase with stream water nitrate (NO 3 − ) concentrations, but that <1% of denitrified N is converted to N 2 O. Unlike some previous studies, we found no relationship between the N 2 O yield and stream water NO 3 − . We suggest that increased stream NO 3 − loading stimulates denitrification and concomitant N 2 O production, but does not increase the N 2 O yield. In our study, most streams were sources of N 2 O to the atmosphere and the highest emission rates were observed in streams draining urban basins. Using a global river network model, we estimate that microbial N transformations (e.g., denitrification and nitrification) convert at least 0.68 Tg·y −1 of anthropogenic N inputs to N 2 O in river networks, equivalent to 10% of the global anthropogenic N 2 O emission rate. This estimate of stream and river N 2 O emissions is three times greater than estimated by the Intergovernmental Panel on Climate Change. Humans have more than doubled the availability of fixed nitrogen (N) in the biosphere, particularly through the production of N fertilizers and the cultivation of N-fixing crops ( 1 ). Increasing N availability is producing unintended environmental consequences including enhanced emissions of nitrous oxide (N 2 O), a potent greenhouse gas ( 2 ) and an important cause of stratospheric ozone destruction ( 3 ). The Intergovernmental Panel on Climate Change (IPCC) estimates that the microbial conversion of agriculturally derived N to N 2 O in soils and aquatic ecosystems is the largest source of anthropogenic N 2 O to the atmosphere ( 2 ). The production of N 2 O in agricultural soils has been the focus of intense investigation (i.e., >1,000 published studies) and is a relatively well constrained component of the N 2 O budget ( 4 ). However, emissions of anthropogenic N 2 O from streams, rivers, and estuaries have received much less attention and remain a major source of uncertainty in the global anthropogenic N 2 O budget. Microbial denitrification is a large source of N 2 O emissions in terrestrial and aquatic ecosystems. Most microbial denitrification is a form of anaerobic respiration in which nitrate (NO 3 − , the dominant form of inorganic N) is converted to dinitrogen (N 2 ) and N 2 O gases ( 5 ). The proportion of denitrified NO 3 − that is converted to N 2 O rather than N 2 (hereafter referred to as the N 2 O yield and expressed as the mole ratio) partially controls how much N 2 O is produced via denitrification ( 6 ), but few studies provide information on the N 2 O yield in streams and rivers because of the difficulty of measuring N 2 and N 2 O production in these systems. Here we report rates of N 2 and N 2 O production via denitrification measured using whole-stream 15 NO 3 − -tracer experiments in 72 headwater streams draining different land-use types across the United States. This project, known as the second Lotic Intersite Nitrogen eXperiment (LINX II), provides unique whole-system measurements of the N 2 O yield in streams. Although N 2 O emission rates have been reported for streams and rivers ( 7 , 8 ), the N 2 O yield has been studied mostly in lentic freshwater and marine ecosystems, where it generally ranges between 0.1 and 1.0%, although yields as high as 6% have been observed ( 9 ). These N 2 O yields are low compared with observations in soils (0–100%) ( 10 ), which may be a result of the relatively lower oxygen (O 2 ) availability in the sediments of lakes and estuaries. However, dissolved O 2 in headwater streams is commonly near atmospheric equilibrium and benthic algal biofilms can produce O 2 at the sediment–water interface, resulting in strong redox gradients more akin to those in partially wetted soils. Thus, streams may have variable and often high N 2 O yields, similar to those in soils ( 11 ). The N 2 O yield in headwater streams is of particular interest because much of the NO 3 − input to rivers is derived from groundwater upwelling into headwater streams. Furthermore, headwater streams compose the majority of stream length within a drainage network and have high ratios of bioreactive benthic surface area to water volume ( 12 ).

Proceedings of the National Academy of Sciences of↗
USGS2011

Regional and climatic controls on seasonal dust deposition in the southwestern U.S.

Vertical dust deposition rates (dust flux) are a complex response to the interaction of seasonal precipitation, wind, changes in plant cover and land use, dust source type, and local vs. distant dust emission in the southwestern U.S. Seasonal dust flux in the Mojave–southern Great Basin (MSGB) deserts, measured from 1999 to 2008, is similar in summer–fall and winter–spring, and antecedent precipitation tends to suppress dust flux in winter–spring. In contrast, dust flux in the eastern Colorado Plateau (ECP) region is much larger in summer–fall than in winter–spring, and twice as large as in the MSGB. ECP dust is related to wind speed, and in the winter–spring to antecedent moisture. Higher summer dust flux in the ECP is likely due to gustier winds and runoff during monsoonal storms when temperature is also higher. Source types in the MSGB and land use in the ECP have important effects on seasonal dust flux. In the MSGB, wet playas produce salt-rich dust during wetter seasons, whereas antecedent and current moisture suppress dust emission from alluvial and dry-playa sources during winter–spring. In the ECP under drought conditions, dust flux at a grazed-and-plowed site increased greatly, and also increased at three annualized, previously grazed sites. Dust fluxes remained relatively consistent at ungrazed and currently grazed sites that have maintained perennial vegetation cover. Under predicted scenarios of future climate change, these results suggest that an increase in summer storms may increase dust flux in both areas, but resultant effects will depend on source type, land use, and vegetation cover.

Arizona, California, Nevada, Utah↗
USGS1982

Estimating peak discharges of small rural streams in Massachusetts

Floodflows on natural-flow streams in Massachusetts with drainage areas between 0.25 square miles and 260 square miles may be estimated from drainage area, main-channel slope, mean basin elevation, and the area of swamps, lakes, and ponds. Multiple-regression techniques were used to define the relationship between a suite of basin and climatic characteristics and flood peaks in three flood-frequency regions at a total of 95 sites. Station flood-frequency data were computed following guidelines in Bulletin I7A of the U.S. Water Resources Council. The frequency analyses are based upon weighted skew values, and adjustments for high and low outliers, and historic peak data. Regression equations for estimation of peak discharges for 0.5, 0.2, 0.1, 0.04, 0.02, and 0.01 exceedance probabilities are provided for ungaged sites. An improved sample of flood peaks and gaging stations and the definition of three flood-frequency regions reduced the standard error of estimate by about 5 percent over those for the 1977 relations. Included in this analysis were the synthetic flood-frequency data at eight sites computed using historic climatic data and 10 parameters optimized by calibration of the U.S. Geological Survey's rainfall-runoff model with storm data observed over II years. The equations are applicable to streams unaffected by regulation where the usable manmade storage is less than 4.5 million cubic feet per square mile, or by diversions or urbanization. The equations are restricted to sites where the basin indices are within a specified range outside of eastern Plymouth, Barnstable, Dukes or Nantucket Counties. In these areas, the available data do not adequately define the influence of high infiltration and storage capacities of drainage basins on floodflows.

Massachusetts↗
USGS1983

Estimating peak discharges of small, rural streams in Massachusetts

Floodflows on natural-flow streams in Massachusetts with drainage areas between 0.25 square miles and 260 square miles may be estimated from drainage area, main-channel slope, mean basin elevation, and the area of swamps, lakes, and ponds. Multiple-regression techniques were used to define the relationship between a suite of basin and climatic characteristics and flood peaks in three flood-frequency regions at a total of 95 sites. Station flood-frequency data were computed following guidelines in Bulletin 17A of the U.S. Water Resources Council. The frequency analyses are based upon weighted skew values, adjustments for high and low outliers, and historic peak data. Regression equations for estimation of peak discharges for 0.5, 0.2, 0.1, 0.04, 0.02, and 0.01 exceedance probabilities are provided for ungaged sites. An improved sample of flood peaks and gaging stations and the definition of three flood-frequency regions reduced the standard errors of estimate by about 5 percent over those for the 1977 relations. Included in this analysis were the synthetic flood-frequency data at 8 sites computed using historic climatic data and 10 parameters optimized by calibration of the U.S. Geological Survey's rainfall-runoff model with storm data observed over 11 years. The equations are applicable to streams unaffected by regulation where the usable manmade storage is less than 4.5 million cubic feet per square mile, or by diversions or urbanization. The equations are restricted to sites where the basin indices are within a specified range outside of eastern Plymouth, Barnstable, Dukes, or Nantucket Counties. In these areas, the available data do not adequately define the influence of high infiltration and storage capacities of drainage basins on floodflows.

Water Supply Paper↗
USGS2026

DRIFteRS: A dataset of drift invertebrate densities in streams and rivers across western North America, 1997–2024

Prey availability is among the most influential and highly variable determinants of fish growth and freshwater habitat carrying capacity, yet it remains understudied compared to physical habitat variables (Ouellet et al., 2025; Rosenfeld et al., 2014; Weber et al., 2017). We often lack a clear understanding of how much food is available to fishes, how it varies spatially and temporally, and how it influences responses to restoration (Ouellet et al., 2025; Rossi et al., 2024; Wipfli et al., 2010). Drift invertebrates—the primary food source for juvenile salmonids and other drift-foraging fishes—play a pivotal role in these dynamics. To better understand the spatiotemporal variability of drift invertebrate abundance and biomass across the freshwater range of drift-feeding salmonids in western North America, we compiled the DRift Invertebrates For salmonids in River Systems (DRIFteRS) dataset. The dataset encompasses 6125 samples of drift invertebrates, and, for a subset of drift samples, associated benthic invertebrate density data, collected from 1360 reaches on 459 unique rivers and streams spanning 55 river basins considered hydrologically independent (i.e., not nested within the same larger watershed) across British Columbia, Canada, and the U.S. states of Alaska, Arizona, California, Colorado, Idaho, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming. Sample sites represent a diverse array of river and stream habitats (e.g., headwater, mainstem, side channel), in watersheds with diverse land uses (e.g., urban, wilderness, agricultural), and disturbance histories (e.g., fire, restoration). Collected between 1997 and 2024, the data span the full calendar year and capture daily and seasonal patterns in drift abundance and biomass densities. When paired with water quality and quantity data as well as remotely sensed environmental landscape data, such as land use/land cover, climate, and disturbance history, channel morphology, and riparian vegetation composition, the DRIFteRS dataset can aid in identifying key drivers of drift invertebrate densities and mean body size and support predictive modeling in unsampled locations and times. The dataset may also be used to analyze aquatic-terrestrial resource flows, derive prey-encounter rates and profitability (mean prey size), and inform broader investigations of sit-and-wait foraging ecology, especially when paired with data on drift-foraging predators. For salmonid-focused applications, the dataset can be integrated into habitat evaluation models, including bioenergetic (e.g., Naman et al., 2019) and life cycle models (e.g., Beechie et al., 2023), to improve estimates of habitat capacity and population dynamics for river- and stream-rearing salmonids. Understanding prey availability dynamics is increasingly important, because rising water temperatures increase salmonid metabolic demands (Crozier et al., 2010). Flow regime transitions (i.e., snow or glacier dominated to rain dominated; Beechie et al., 2013), wildfire frequency and intensity (Hessburg et al., 2021), as well as plant community and phenology shifts (Cleland et al., 2007; Franklin et al., 2016) are all predicted to change with rising temperatures and are potential drivers of terrestrial and aquatic invertebrate prey quality and availability in lotic systems. These insights can ultimately inform restoration prioritization and design, helping managers consider food resource implications when evaluating restoration priorities and match habitat improvement to food supply. The data are released under the Creative Commons Attribution 4.0 International license and may be reused with attribution.

Alaska, Arizona, British Columbia, California, Col↗
USGS2001

U.S. Geological Survey Ground-Water Resources Program, 2001

Ground water is among the Nation's most important natural resources. It provides drinking water to urban and rural communities, supports irrigation and industry, sustains the flow of streams and rivers, and maintains riparian and wetland ecosystems. In many areas of the Nation, the future sustainability of ground-water resources is at risk from over use and contamination. Because ground-water systems typically respond slowly to human actions and climate variability, a long-term perspective is needed to manage this valuable resource. The U.S. Geological Survey Ground-Water Resources Program provides regional evaluations, fundamental data, and predictive tools to help assure the sustainability of our Nation's ground-water resources.

Fact Sheet↗
USGS2009

Magnitude and Frequency of Rural Floods in the Southeastern United States, through 2006: Volume 2, North Carolina

Reliable estimates of the magnitude and frequency of floods are required for the economical and safe design of transportation and water-conveyance structures. A multistate approach was used to update methods for estimating the magnitude and frequency of floods in rural, ungaged basins in North Carolina, South Carolina, and Georgia that are not substantially affected by regulation, tidal fluctuations, or urban development. In North Carolina, annual peak-flow data available through September 2006 were available for 584 sites; 402 of these sites had a total of 10 or more years of systematic record that is required for at-site, flood-frequency analysis. Following data reviews and the computation of 20 physical and climatic basin characteristics for each station as well as at-site flood-frequency statistics, annual peak-flow data were identified for 363 sites in North Carolina suitable for use in this analysis. Among these 363 sites, 19 sites had records that could be divided into unregulated and regulated/ channelized annual peak discharges, which means peak-flow records were identified for a total of 382 cases in North Carolina. Considering the 382 cases, at-site flood-frequency statistics are provided for 333 unregulated cases (also used for the regression database) and 49 regulated/channelized cases. The flood-frequency statistics for the 333 unregulated sites were combined with data for sites from South Carolina, Georgia, and adjacent parts of Alabama, Florida, Tennessee, and Virginia to create a database of 943 sites considered for use in the regional regression analysis. Flood-frequency statistics were computed by fitting logarithms (base 10) of the annual peak flows to a log-Pearson Type III distribution. As part of the computation process, a new generalized skew coefficient was developed by using a Bayesian generalized least-squares regression model. Exploratory regression analyses using ordinary least-squares regression completed on the initial database of 943 sites resulted in defining five hydrologic regions for North Carolina, South Carolina, and Georgia. Stations with drainage areas less than 1 square mile were removed from the database, and a procedure to examine for basin redundancy (based on drainage area and periods of record) also resulted in the removal of some stations from the regression database. Flood-frequency estimates and basin characteristics for 828 gaged stations were combined to form the final database that was used in the regional regression analysis. Regional regression analysis, using generalized least-squares regression, was used to develop a set of predictive equations that can be used for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance flows for rural ungaged, basins in North Carolina, South Carolina, and Georgia. The final predictive equations are all functions of drainage area and the percentage of drainage basin within each of the five hydrologic regions. Average errors of prediction for these regression equations range from 34.0 to 47.7 percent. Discharge estimates determined from the systematic records for the current study are, on average, larger in magnitude than those from a previous study for the highest percent chance exceedances (50 and 20 percent) and tend to be smaller than those from the previous study for the lower percent chance exceedances when all sites are considered as a group. For example, mean differences for sites in the Piedmont hydrologic region range from positive 0.5 percent for the 50-percent chance exceedance flow to negative 4.6 percent for the 0.2-percent chance exceedance flow when stations are grouped by hydrologic region. Similarly for the same hydrologic region, median differences range from positive 0.9 percent for the 50-percent chance exceedance flow to negative 7.1 percent for the 0.2-percent chance exceedance flow. However, mean and median percentage differences between the estimates from the previous and curre

North Carolina↗
USGS2016

Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014)

Remote sensing has proven a useful way of evaluating long-term trends in vegetation &ldquo;greenness&rdquo; through the use of vegetation indices like Normalized Differences Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In particular, analyses of greenness trends have been performed for large areas (continents, for example) in an attempt to understand vegetation response to climate. These studies have been most often used coarse resolution sensors like Moderate Resolution Image Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, trends in greenness are also important at more local scales, particularly in and around cities as vegetation offers a variety of valuable ecosystem services ranging from minimizing air pollution to mitigating urban heat island effects. To explore the ability to monitor greenness trends in and around cities, this paper presents a new way for analyzing greenness trends based on all available Landsat 5, 7, and 8 images and applies it to Guangzhou, China. This method is capable of including the effects of land cover change in the evaluation of greenness trends by separating the effects of abrupt and gradual changes, and providing information on the timing of greenness trends. An assessment of the consistency of surface reflectance from Landsat 8 with past Landsat sensors indicates biases in the visible bands of Landsat 8, especially the blue band. Landsat 8 NDVI values were found to have a larger bias than the EVI values; therefore, EVI was used in the analysis of greenness trends for Guangzhou. In spite of massive amounts of development in Guangzhou from 2000 to 2014, greenness was found to increase, mostly as a result of gradual change. Comparison of the greening magnitudes estimated from the approach presented here and a Simple Linear Trend (SLT) method indicated large differences for certain time intervals as the SLT method does not include consideration for abrupt land cover changes. Overall, this analysis demonstrates the importance of considering land cover change when analyzing trends in greenness from satellite time series in areas where land cover change is common.

Guangzhou↗
USGS2023

Assessing stormwater control measure inventories from 23 cities in the United States

Since the 1987 Clean Water Act Section 319 amendment, the United States Government has required and funded the development of nonpoint source pollution programs with about $5 billion dollars. Despite these expenditures, nonpoint source pollution from urban watersheds is still a significant cause of impaired waters in the United States. Urban stormwater management has rapidly evolved over recent decades with decision-making made at a local or city-scale. To address the need for a better understanding of how stormwater management has been implemented in different cities, we used stormwater control measure (SCM) network data from 23 United States cities and assessed what physical, climatic, socioeconomic, and/or regulatory explanatory variables, if any, are related to SCM assemblages at the municipal scale. Spearman's correlation and Wilcoxon rank-sum tests were used to investigate relationships between explanatory variables and SCM types and assemblages of SCMs in each city. The results from these analyses showed that for the cities assessed, physical explanatory variables (e.g., impervious percentage and depth to water table) explained the greatest portion of variability in SCM assemblages. Additionally, it was found that cities with combined sewers favored filters, swales and strips, and infiltrators over basins, and cities that are under consent decrees with the EPA tended to include filters more frequently in their SCM inventories. Future work can build on the SCM assemblages used in this study and their explanatory variables to better understand the differences and drivers of differences in SCM effectiveness across cities, improve watershed modeling, and investigate city- and watershed-scale impacts of SCM assemblages

Environmental Research: Infrastructure and Sustain↗
USGS2012

Groundwater quality in the Indian Wells Valley, California

Groundwater provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The Priority Basin Project of the GAMA Program provides a comprehensive assessment of the State’s groundwater quality and increases public access to groundwater-quality information. Indian Wells Valley is one of the study areas being evaluated. The Indian Wells study area is approximately 600 square miles (1,554 square kilometers) and includes the Indian Wells Valley groundwater basin (California Department of Water Resources, 2003). Indian Wells Valley has an arid climate and is part of the Mojave Desert. Average annual rainfall is about 6 inches (15 centimeters). The study area has internal drainage, with runoff from the surrounding mountains draining towards dry lake beds in the lower parts of the valley. Land use in the study area is approximately 97.0 percent (%) natural, 0.4% agricultural, and 2.6% urban. The primary natural land cover is shrubland. The largest urban area is the city of Ridgecrest (2010 population of 28,000). Groundwater in this basin is used for public and domestic water supply and for irrigation. The main water-bearing units are gravel, sand, silt, and clay derived from the Sierra Nevada to the west and from the other surrounding mountains. Recharge to the groundwater system is primarily runoff from the Sierra Nevada and to the west and from the other surrounding mountains. Recharge to the groundwater system is primarily runoff from the Sierra Nevada and direct infiltration from irrigation and septic systems. The primary sources of discharge are pumping wells and evapotranspiration near the dry lakebeds. The primary aquifers in the Indian Wells study area are defined as those parts of the aquifers corresponding to the perforated intervals of wells listed in the California Department of Public Health database. Public-supply wells in Indian Wells Valley are completed to depths between 240 and 800 feet (73 to 244 meters), consist of solid casing from the land surface to a depth of 180 to 260 feet (55 to 79 meters), and are screened or perforated below the solid casing.

California↗
USGS2006

Magnitude and Frequency of Floods on Nontidal Streams in Delaware

Reliable estimates of the magnitude and frequency of annual peak flows are required for the economical and safe design of transportation and water-conveyance structures. This report, done in cooperation with the Delaware Department of Transportation (DelDOT) and the Delaware Geological Survey (DGS), presents methods for estimating the magnitude and frequency of floods on nontidal streams in Delaware at locations where streamgaging stations monitor streamflow continuously and at ungaged sites. Methods are presented for estimating the magnitude of floods for return frequencies ranging from 2 through 500 years. These methods are applicable to watersheds exhibiting a full range of urban development conditions. The report also describes StreamStats, a web application that makes it easy to obtain flood-frequency estimates for user-selected locations on Delaware streams. Flood-frequency estimates for ungaged sites are obtained through a process known as regionalization, using statistical regression analysis, where information determined for a group of streamgaging stations within a region forms the basis for estimates for ungaged sites within the region. One hundred and sixteen streamgaging stations in and near Delaware with at least 10 years of non-regulated annual peak-flow data available were used in the regional analysis. Estimates for gaged sites are obtained by combining the station peak-flow statistics (mean, standard deviation, and skew) and peak-flow estimates with regional estimates of skew and flood-frequency magnitudes. Example flood-frequency estimate calculations using the methods presented in the report are given for: (1) ungaged sites, (2) gaged locations, (3) sites upstream or downstream from a gaged location, and (4) sites between gaged locations. Regional regression equations applicable to ungaged sites in the Piedmont and Coastal Plain Physiographic Provinces of Delaware are presented. The equations incorporate drainage area, forest cover, impervious area, basin storage, housing density, soil type A, and mean basin slope as explanatory variables, and have average standard errors of prediction ranging from 28 to 72 percent. Additional regression equations that incorporate drainage area and housing density as explanatory variables are presented for use in defining the effects of urbanization on peak-flow estimates throughout Delaware for the 2-year through 500-year recurrence intervals, along with suggestions for their appropriate use in predicting development-affected peak flows. Additional topics associated with the analyses performed during the study are also discussed, including: (1) the availability and description of more than 30 basin and climatic characteristics considered during the development of the regional regression equations; (2) the treatment of increasing trends in the annual peak-flow series identified at 18 gaged sites, with respect to their relations with maximum 24-hour precipitation and housing density, and their use in the regional analysis; (3) calculation of the 90-percent confidence interval associated with peak-flow estimates from the regional regression equations; and (4) a comparison of flood-frequency estimates at gages used in a previous study, highlighting the effects of various improved analytical techniques.

Scientific Investigations Report↗
USGS2021

Growth of the wildland-urban interface within and around U.S. National Forests and Grasslands, 1990-2010

The wildland-urban interface (WUI), where housing is in close proximity to or intermingled with wildland vegetation, is widespread throughout the United States, but it is unclear how this type of housing development affects public lands. We used a national dataset to examine WUI distribution and growth (1990–2010) in proximity to National Forests and created a typology to characterize each National Forest’s combination of WUI area and housing growth. We found that National Forests are hotspots for WUI growth, with a 38% increase in WUI area and 46% growth in WUI houses from 1990 to 2010, in excess of WUI growth for the conterminous U.S. Growth within National Forests was higher than the surrounding area. Diffuse intermix WUI, where houses are intermingled with wildland vegetation, is common within National Forests, but WUI houses around National Forests were primarily in denser interface WUI areas, which lack substantial wildland vegetation. WUI was more prevalent within and around National Forests in the East, while National Forests in the West experienced higher rates of WUI growth. National Forests with the most challenging WUI issues—extensive WUI area and rapid growth in intermix and interface—were found primarily in the South and interior West. Given the diversity of WUI landscapes, effectively responding to current and future WUI challenges will require both engagement with individual homeowners dispersed throughout National Forests, as well as increased emphasis on mitigating denser interface development around National Forests. At a time when wildfire risks are expected to intensify due to climate change, and 75% of privately owned land within and around National Forests is not yet WUI, understanding WUI growth patterns in proximity to public lands is vital for land management and human well-being.

Landscape and Urban Planning↗
USGS1997

Techniques for estimating peak-streamflow frequency for unregulated streams and streams regulated by small floodwater retarding structures in Oklahoma

Statewide regression equations for Oklahoma were determined for estimating peak discharge and flood frequency for selected recurrence intervals from 2 to 500 years for ungaged sites on natural unregulated streams. The most significant independent variables required to estimate peak-streamflow frequency for natural unregulated streams in Oklahoma are contributing drainage area, main-channel slope, and mean-annual precipitation. The regression equations are applicable for watersheds with drainage areas less than 2,510 square miles that are not affected by regulation from manmade works. Limitations on the use of the regression relations and the reliability of regression estimates for natural unregulated streams are discussed. Log-Pearson Type III analysis information, basin and climatic characteristics, and the peak-stream-flow frequency estimates for 251 gaging stations in Oklahoma and adjacent states are listed. Techniques are presented to make a peak-streamflow frequency estimate for gaged sites on natural unregulated streams and to use this result to estimate a nearby ungaged site on the same stream. For ungaged sites on urban streams, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow frequency. For ungaged sites on streams regulated by small floodwater retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow frequency. The statewide regression equations are adjusted by substituting the drainage area below the floodwater retarding structures, or drainage area that represents the percentage of the unregulated basin, in the contributing drainage area parameter to obtain peak-streamflow frequency estimates.

Oklahoma↗
USGS2014

1.13 – Emerging contaminants

Since the Industrial Revolution, a diversity of large-scale chemical innovations has impacted aquatic systems in urban environments. Beginning in the 1990s, there has been a growing scientific interest and public awareness of the effects of the chemicals used in domestic, commercial, industrial, and agricultural applications, referred to in this article as ‘emerging contaminants’ (ECs), on ecosystem and human health. The growing global population and its increasing demands on water supplies in conjunction with climate-induced changes in hydrologic regimes place stress on freshwater resources, resulting in a greater reliance on reuse of reclaimed municipal wastewater treatment plant (WWTP) effluents to meet human and environmental needs. WWTP effluents are a major source of ECs, and it is important to have an understanding of the chemical composition of the reclaimed water, because many ECs are biologically active and the effects of chronic exposure to low concentration complex mixtures are unknown. Several classes of ECs that have been shown to be widespread in the aquatic environment are discussed in this chapter, including surfactants, complexing agents, fragrances, antimicrobials, industrial chemicals, pharmaceuticals, natural and synthetic estrogens, and disinfection byproducts. All of these compounds are biologically active via a variety of modes of action, and can occur in aquatic systems at concentrations ranging from <0.001 to >100 μg l −1 .

Book chapter↗