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USGS2022

Extensive droughts in the conterminous United States during multiple centuries

Extensive and severe droughts have substantial effects on water supplies, agriculture, and aquatic ecosystems. To better understand these droughts, we used tree-ring-based reconstructions of the Palmer drought severity index (PDSI) for the period 1475–2017 to examine droughts that covered at least 33% of the conterminous United States (CONUS). We identified 37 spatially extensive drought events for the CONUS and examined their spatial and temporal patterns. The duration of the extensive drought events ranged from 3 to 12 yr and on average affected 43% of the CONUS. The recent (2000–08) drought in the southwestern CONUS, often referred to as the turn-of-the-century drought, is likely one of the longest droughts in the CONUS during the past 500 years. A principal components analysis of the PDSI data from 1475 through 2017 resulted in three principal components (PCs) that explain about 48% of the variability of PDSI and are helpful to understand the temporal and spatial variability of the 37 extensive droughts in the CONUS. Analyses of the relations between the three PCs and well-known climate indices, such as indices of El Niño–Southern Oscillation, indicate statistically significant correlations; however, the correlations do not appear to be large enough (all with an absolute value less than 0.45) to be useful for the development of drought prediction models.

Conterminous United States↗

USGS2012

Tools for quantifying isotopic niche space and dietary variation at the individual and population level.

Ecologists are increasingly using stable isotope analysis to inform questions about variation in resource and habitat use from the individual to community level. In this study we investigate data sets from 2 California sea otter ( Enhydra lutris nereis ) populations to illustrate the advantages and potential pitfalls of applying various statistical and quantitative approaches to isotopic data. We have subdivided these tools, or metrics, into 3 categories: IsoSpace metrics, stable isotope mixing models, and DietSpace metrics. IsoSpace metrics are used to quantify the spatial attributes of isotopic data that are typically presented in bivariate (e.g., δ 13 C versus δ 15 N) 2-dimensional space. We review IsoSpace metrics currently in use and present a technique by which uncertainty can be included to calculate the convex hull area of consumers or prey, or both. We then apply a Bayesian-based mixing model to quantify the proportion of potential dietary sources to the diet of each sea otter population and compare this to observational foraging data. Finally, we assess individual dietary specialization by comparing a previously published technique, variance components analysis, to 2 novel DietSpace metrics that are based on mixing model output. As the use of stable isotope analysis in ecology continues to grow, the field will need a set of quantitative tools for assessing isotopic variance at the individual to community level. Along with recent advances in Bayesian-based mixing models, we hope that the IsoSpace and DietSpace metrics described here will provide another set of interpretive tools for ecologists.

Journal of Mammalogy↗

USGS1999

Use of generalized linear models and digital data in a forest inventory of northern Utah

Forest inventories, like those conducted by the Forest Service's Forest Inventory and Analysis Program (FIA) in the Rocky Mountain Region, are under increased pressure to produce better information at reduced costs. Here we describe our efforts in Utah to merge satellite-based information with forest inventory data for the purposes of reducing the costs of estimates of forest population totals and providing spatial depiction of forest resources. We illustrate how generalized linear models can be used to construct approximately unbiased and efficient estimates of population totals while providing a mechanism for prediction in space for mapping of forest structure. We model forest type and timber volume of five tree species groups as functions of a variety of predictor variables in the northern Utah mountains. Predictor variables include elevation, aspect, slope, geographic coordinates, as well as vegetation cover types based on satellite data from both the Advanced Very High Resolution Radiometer (AVHRR) and Thematic Mapper (TM) platforms. We examine the relative precision of estimates of area by forest type and mean cubic-foot volumes under six different models, including the traditional double sampling for stratification strategy. Only very small gains in precision were realized through the use of expensive photointerpreted or TM-based data for stratification, while models based on topography and spatial coordinates alone were competitive. We also compare the predictive capability of the models through various map accuracy measures. The models including the TM-based vegetation performed best overall, while topography and spatial coordinates alone provided substantial information at very low cost.

Utah↗

USGS2016

Groundwater-flow model of the northern High Plains aquifer in Colorado, Kansas, Nebraska, South Dakota, and Wyoming

The High Plains aquifer is a nationally important water resource underlying about 175,000 square miles in parts of eight states: Colorado, Kansas, Oklahoma, Nebraska, New Mexico, South Dakota, Texas, and Wyoming. Droughts across much of the Northern High Plains from 2001 to 2007 have combined with recent (2004) legislative mandates to elevate concerns regarding future availability of groundwater and the need for additional information to support science-based water-resource management. To address these needs, the U.S. Geological Survey began the High Plains Groundwater Availability Study to provide a tool for water-resource managers and other stakeholders to assess the status and availability of groundwater resources. A transient groundwater-flow model was constructed using the U.S. Geological Survey modular three-dimensional finite-difference groundwater-flow model with Newton-Rhapson solver (MODFLOW–NWT). The model uses an orthogonal grid of 565 rows and 795 columns, and each grid cell measures 3,281 feet per side, with one variably thick vertical layer, simulated as unconfined. Groundwater flow was simulated for two distinct periods: (1) the period before substantial groundwater withdrawals, or before about 1940, and (2) the period of increasing groundwater withdrawals from May 1940 through April 2009. A soil-water-balance model was used to estimate recharge from precipitation and groundwater withdrawals for irrigation. The soil-water-balance model uses spatially distributed soil and landscape properties with daily weather data and estimated historical land-cover maps to calculate spatial and temporal variations in potential recharge. Mean annual recharge estimated for 1940–49, early in the history of groundwater development, and 2000–2009, late in the history of groundwater development, was 3.3 and 3.5 inches per year, respectively. Primary model calibration was completed using statistical techniques through parameter estimation using the parameter estimation suite of software with Tikhonov regularization. Calibration targets for the groundwater model included 343,067 groundwater levels measured in wells and 10,820 estimated monthly stream base flows at streamgages. A total of 1,312 parameters were adjusted during calibration to improve the match between calibration targets and simulated equivalents. Comparison of calibration targets to simulated equivalents indicated that, at the regional scale, the model correctly reproduced groundwater levels and stream base flows for 1940–2009. This comparison indicates that the model can be used to examine the likely response of the aquifer system to potential future stresses. Mean calibrated recharge for 1940–49 and 2000–2009 was smaller than that estimated with the soil-water-balance model. This indicated that although the general spatial patterns of recharge estimated with the soil-water-balance model were approximately correct at the regional scale of the Northern High Plains aquifer, the soil-water-balance model had overestimated recharge, and adjustments were needed to decrease recharge to improve the match of the groundwater model to calibration targets. The largest components of the simulated groundwater budgets were recharge from precipitation, recharge from canal seepage, outflows to evapotranspiration, and outflows to stream base flow. Simulated outflows to irrigation wells increased from 7 percent of total outflows in 1940–49 to 38 percent of 1970–79 total outflows and 49 percent of 2000–2009 total outflows.

Colorado, Kansas, Nebraska, South Dakota, Wyoming↗

USGS2002

Characterization and analysis of temporal and spatial variations in habitat and macroinvertebrate community structure, Fountain Creek basin, Colorado Springs and vicinity, Colorado, 1998-2001

The Fountain Creek Basin in and around Colorado Springs, Colorado, is affected by various land- and water-use activities. Biological, hydrological, water-quality, and land-use data were collected at 10 sites in the Fountain Creek Basin from April 1998 through April 2001 to provide a baseline characterization of macroinvertebrate communities and habitat conditions for comparison in subsequent studies; and to assess variation in macroinvertebrate community structure relative to habitat quality. Analysis of variance results indicated that instream and riparian variables were not affected by season, but significant differences were found among sites. Nine metrics were used to describe and evaluate macroinvertebrate community structure. Statistical analysis indicated that for six of the nine metrics, significant variability occurred between spring and fall seasons for 60 percent of the sites. Cluster analysis (unweighted pair group method average) using macroinvertebrate presence-absence data showed a well-defined separation between spring and fall samples. Six of the nine metrics had significant spatial variation. Cluster analysis using Sorenson?s Coefficient of Community values computed from macroinvertebrate density (number of organisms per square meter) data showed that macroinvertebrate community structure was more similar among tributary sites than main-stem sites. Canonical correspondence analysis identified a substrate particle-size gradient from site-specific species-abundance data and environmental correlates that decreased the 10 sites to 5 site clusters and their associated taxa.

Water-Resources Investigations Report↗

USGS2003

Environmental water-quality zones for streams: A regional classification scheme

Various approaches have been used to classify large geographical areas into smaller regions of similar water quality or extrapolate water-quality data from a few streams to other unmonitored streams. A combination of some of the strengths of existing techniques is used to develop a new approach for these purposes. In this new approach, referred to here as SPARTA (SPAtial Regression-Tree Analysis), environmental characteristics for each monitored stream are first quantified using a Geographic Information System (GIS) and then regression-tree analysis is used to determine which characteristics are most statistically important in describing the distribution of a specific water-quality constituent. GIS coverages of only the most statistically significant environmental characteristics are then used to subdivide the area of interest into relatively homogeneous environmental water-quality zones. Results from the regression-tree analysis not only define the most important environmental characteristics, but also describe how to subdivide the coverage of the specific characteristic (for example, areas with <26% or ???26% soil clay content). The resulting regionalization scheme is customized for each water-quality constituent based on the environmental characteristics most statistically related to that constituent. SPARTA was used to delineate areas of similar phosphorus, nitrogen, and sediment concentrations (by including land-use characteristics) and areas of similar potential water quality (by excluding land-use characteristics). The SPARTA approach reduced the variability in water-quality concentrations (phosphorus, total nitrogen, Kjeldahl nitrogen, and suspended sediment) within similarly classified zones from that obtained using the US Environmental Protection Agency's nutrient ecoregions.

Environmental Management↗

USGS2017

Human presence diminishes the importance of climate in driving fire activity across the United States

Growing human and ecological costs due to increasing wildfire are an urgent concern in policy and management, particularly given projections of worsening fire conditions under climate change. Thus, understanding the relationship between climatic variation and fire activity is a critically important scientific question. Different factors limit fire behavior in different places and times, but most fire-climate analyses are conducted across broad spatial extents that mask geographical variation. This could result in overly broad or inappropriate management and policy decisions that neglect to account for regionally specific or other important factors driving fire activity. We developed statistical models relating seasonal temperature and precipitation variables to historical annual fire activity for 37 different regions across the continental United States and asked whether and how fire-climate relationships vary geographically, and why climate is more important in some regions than in others. Climatic variation played a significant role in explaining annual fire activity in some regions, but the relative importance of seasonal temperature or precipitation, in addition to the overall importance of climate, varied substantially depending on geographical context. Human presence was the primary reason that climate explained less fire activity in some regions than in others. That is, where human presence was more prominent, climate was less important. This means that humans may not only influence fire regimes but their presence can actually override, or swamp out, the effect of climate. Thus, geographical context as well as human influence should be considered alongside climate in national wildfire policy and management.

PNAS↗

USGS2001

Map and map database of susceptibility to slope failure by sliding and earthflow in the Oakland area, California

Map data that predict the varying likelihood of landsliding can help public agencies make informed decisions on land use and zoning. This map, prepared in a geographic information system from a statistical model, estimates the relative likelihood of local slopes to fail by two processes common to an area of diverse geology, terrain, and land use centered on metropolitan Oakland. The model combines the following spatial data: (1) 120 bedrock and surficial geologic-map units, (2) ground slope calculated from a 30-m digital elevation model, (3) an inventory of 6,714 old landslide deposits (not distinguished by age or type of movement and excluding debris flows), and (4) the locations of 1,192 post-1970 landslides that damaged the built environment. The resulting index of likelihood, or susceptibility, plotted as a 1:50,000-scale map, is computed as a continuous variable over a large area (872 km2) at a comparatively fine (30 m) resolution. This new model complements landslide inventories by estimating susceptibility between existing landslide deposits, and improves upon prior susceptibility maps by quantifying the degree of susceptibility within those deposits. Susceptibility is defined for each geologic-map unit as the spatial frequency (areal percentage) of terrain occupied by old landslide deposits, adjusted locally by steepness of the topography. Susceptibility of terrain between the old landslide deposits is read directly from a slope histogram for each geologic-map unit, as the percentage (0.00 to 0.90) of 30-m cells in each one-degree slope interval that coincides with the deposits. Susceptibility within landslide deposits (0.00 to 1.33) is this same percentage raised by a multiplier (1.33) derived from the comparative frequency of recent failures within and outside the old deposits. Positive results from two evaluations of the model encourage its extension to the 10-county San Francisco Bay region and elsewhere. A similar map could be prepared for any area where the three basic constituents, a geologic map, a landslide inventory, and a slope map, are available in digital form. Added predictive power of the new susceptibility model may reside in attributes that remain to be explored?among them seismic shaking, distance to nearest road, and terrain elevation, aspect, relief, and curvature.

Miscellaneous Field Studies Map↗

USGS2013

Landscape influences on climate-related lake shrinkage at high latitudes

Climate-related declines in lake area have been identified across circumpolar regions and have been characterized by substantial spatial heterogeneity. An improved understanding of the mechanisms underlying lake area trends is necessary to predict where change is most likely to occur and to identify implications for high latitude reservoirs of carbon. Here, using a population of ca. 2300 lakes with statistically significant increasing and decreasing lake area trends spanning longitudinal and latitudinal gradients of ca. 1000 km in Alaska, we present evidence for a mechanism of lake area decline that involves the loss of surface water to groundwater systems. We show that lakes with significant declines in lake area were more likely to be located: (1) in burned areas; (2) on coarser, well-drained soils; and (3) farther from rivers compared to lakes that were increasing. These results indicate that postfire processes such as permafrost degradation, which also results from a warming climate, may promote lake drainage, particularly in coarse-textured soils and farther from rivers where overland flooding is less likely and downslope flow paths and negative hydraulic gradients between surface water and groundwater systems are more common. Movement of surface water to groundwater systems may lead to a deepening of subsurface flow paths and longer hydraulic residence time which has been linked to increased soil respiration and CO 2 release to the atmosphere. By quantifying relationships between statewide coarse resolution maps of landscape characteristics and spatially heterogeneous responses of lakes to environmental change, we provide a means to identify at-risk lakes and landscapes and plan for a changing climate.

Alaska↗

USGS2023

Landscape and connectivity metrics as a spatial tool to support invasive annual grass management decisions

The spatial patterns and context of invasions are increasingly recognized as important for successful and efficient management actions. Beyond mapping occurrence or percent cover in pixels, spatial summary information that describes the size and arrangement of patches in the context of a larger landscape (e.g., infested regions, connected patch networks) can add a depth of information for managing invasive grasses that threaten native ecosystems. Few invasive annual grass analyses have explored the use of landscape and circuit-based connectivity metrics to characterize and compare spatial patterns of invasion. To assess the transferability and applicability of these landscape ecology analyses, we calculated landscape metrics (4 area-based, 3 configuration) and a connectivity metric (circuit-based centrality), using a weighted-average map of invasive annual grass cover in the Great Basin, USA. We calculated metrics at local and regional scales, allowing invasion statistics to be compared across the landscape and illustrating varying patterns of invasion extent and connectedness. We found the metrics provided additional, complementary information at the sampled local and regional scales beyond abundance measures alone. We also illustrated how key metrics could be used to categorize and map areas needing different management strategies, for example, where strategies could proactively protect uninvaded cores, disconnect fine fuel patches, or contain established invasions. The landscape and connectivity metric approach can be applied across scales to spatially target patches locally, provide broader context within a single region, as well as to compare metrics and spatial variation in patterns among different regions.

Biological Invasions↗

USGS2014

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m −2 yr −1 and total NPP in the range of 318–490 Tg C yr −1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m −2 yr −1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m −2 yr −1 . MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.

Illinois, Indiana, Iowa, Kansas, Michigan, Minneso↗

USGS1997

Trout production dynamics and water quality in Minnesota streams

We sampled fish assemblages and quantified production dynamics of brook trout Salvelinus fontinalis , brown trout Salmo trutta , and rainbow trout Oncorhynchus mykiss in 13 southeastern Minnesota streams during 1988–1990 to examine the influence of water quality on fish populations in fertile trout streams. Fish assemblages in 15 stream reaches were abundant, but low in diversity; 13 species were collected. Parameter means (ranges) over the reaches were species richness, 4.1 (1–8); density, 29,490 (1,247–110,602) fish/ha; and biomass, 253.5 (49.6–568.6) kg/ha. Means (ranges) for salmonids were annual mean density, 2,279 (343–8,096) fish/ha; annual mean biomass, 162.0 (32.5–355.5) kg/ha; and annual production, 155.6 (36.7–279.6) kg/ha. Salmonid production and mean biomass were greater during the spring‐fall interval than during fall‐spring; young cohorts (ages 0–1) contributed the greatest proportion to population biomass and production. Salmonid annual production‐to‐mean‐biomass ratio (P/B) averaged 1.06 (0.64–1.42), and means were significantly different among species (1.03 for brown trout, 1.54 for brook trout, and 1.92 for rainbow trout). A significant linear model was developed that describes P/B as an inverse function of population age structure and may be used to improve accuracy in approximations of annual production from mean biomass. Fish density, biomass, or production were not correlated with eight water quality variables describing ionic and nutrient content in these streams, but when data from other United States streams with a wide range in alkalinity were incorporated, salmonid production was strongly, positively correlated with alkalinity. The wide range in fish population and production statistics and their lack of correlation with water quality suggest that no uniform fish carrying capacity exists among these streams and that factors other than water fertility limit fish density, biomass, and productivity at this spatial scale, but the overall maximum production rate in the region may be governed by water quality.

Minnesota↗

USGS1999

Summary of water-quality data for City of Albuquerque drinking-water supply wells, 1988-97

The City of Albuquerque has collected and analyzed more than 5,000 water-quality samples from 113 water-supply wells in the Albuquerque area, including many drinking-water supply wells, since May of 1988. As a result, a large water-quality data base has been compiled that includes data for major ions, nutrients, trace elements, carbon, volatile organic compounds, radiological constituents, and bacteria. These data are intended to improve the understanding and management of the ground-water resources of the region, rather than demonstrate compliance with Federal and State drinking-water standards. This report gives summary statistics for selected physical properties and chemical constituents for ground water from wells used by the City of Albuquerque for drinking-water supply between 1988 and 1997. Maps are provided to show the general spatial distribution of selected parameters and water types around the region. Although the values of some parameters vary substantially across the city, median values for all parameters included in this report are less than their respective maximum contaminant levels in each drinking-water supply well. The dominant water types are sodium plus potassium / carbonate plus bicarbonate in the western part of the city and calcium / carbonate plus bicarbonate in the eastern part of the city.

Open-File Report↗

USGS2014

Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006

Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y −1 during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management.

Remote Sensing↗

USGS2012

An accessible method for implementing hierarchical models with spatio-temporal abundance data

A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.

PLoS ONE↗

USGS2018

Interactive tool to estimate groundwater elevations in central and eastern North Dakota

This report describes an interactive tool (NDakGWtool) in which a statistical model is developed using locally weighted regression to estimate monthly mean groundwater elevations for a specified latitude and longitude, referred to as the “user-specified location.” For each user-specified location, seven models are developed for each month from April through October. Localized, high spatial-resolution maps of estimated monthly mean groundwater surface elevations are produced from the models. The tool was evaluated for glacial drift aquifers of the 32-county study area in central and eastern North Dakota. Although groundwater elevations from 1960 to 2017 were available to develop the tool, groundwater elevations from 1995 to 2015 were used for model testing and development of the model domain. There are 413 grid cells of 0.1-degree latitude by 0.1-degree longitude size in the model domain, and the tool produces maps of estimated monthly mean groundwater surface elevations for the cell containing the user-specified location. Additionally, the NDakGWtool produces maps of estimated groundwater depth below land surface and ArcGIS files of estimated groundwater surface elevations and groundwater depth below land surface. The tool is composed of four main components: data input, statistical model, output, and user-interactive process.

North Dakota↗

USGS2013

Simultaneous estimation of local-scale and flow path-scale dual-domain mass transfer parameters using geoelectrical monitoring

Anomalous solute transport, modeled as rate-limited mass transfer, has an observable geoelectrical signature that can be exploited to infer the controlling parameters. Previous experiments indicate the combination of time-lapse geoelectrical and fluid conductivity measurements collected during ionic tracer experiments provides valuable insight into the exchange of solute between mobile and immobile porosity. Here, we use geoelectrical measurements to monitor tracer experiments at a former uranium mill tailings site in Naturita, Colorado. We use nonlinear regression to calibrate dual-domain mass transfer solute-transport models to field data. This method differs from previous approaches by calibrating the model simultaneously to observed fluid conductivity and geoelectrical tracer signals using two parameter scales: effective parameters for the flow path upgradient of the monitoring point and the parameters local to the monitoring point. We use regression statistics to rigorously evaluate the information content and sensitivity of fluid conductivity and geophysical data, demonstrating multiple scales of mass transfer parameters can simultaneously be estimated. Our results show, for the first time, field-scale spatial variability of mass transfer parameters (i.e., exchange-rate coefficient, porosity) between local and upgradient effective parameters; hence our approach provides insight into spatial variability and scaling behavior. Additional synthetic modeling is used to evaluate the scope of applicability of our approach, indicating greater range than earlier work using temporal moments and a Lagrangian-based Damköhler number. The introduced Eulerian-based Damköhler is useful for estimating tracer injection duration needed to evaluate mass transfer exchange rates that range over several orders of magnitude.

Water Resources Research↗

USGS2017

Storms, channel changes, and a sediment budget for an urban-suburban stream, Difficult Run, Virginia, USA

Determining erosion and deposition rates in urban-suburban settings and how these processes are affected by large storms is important to understanding geomorphic processes in these landscapes. Sediment yields in the suburban and urban Upper Difficult Run are among the highest ever recorded in the Chesapeake Bay watershed, ranging from 161 to 376 Mg/km 2 /y. Erosion and deposition of streambanks, channel bed, and bars and deposition of floodplains were monitored between 1 March 2010 and 18 January 2013 in Upper Difficult Run, Virginia, USA. We documented the effects of two large storms, Tropical Storm Lee (September 2011), a 100-year event, and Super Storm Sandy (October 2012) a 5-year event, on channel erosion and deposition. Variability in erosion and deposition rates for all geomorphic features, temporally and spatially, are important conclusions of this study. Tropical Storm Lee was an erosive event, where erosion occurred on 82% of all streambanks and where 88% of streambanks that were aggrading before Tropical Storm Lee became erosional. Statistical analysis indicated that drainage area explains linear changes (cm/y) in eroding streambanks and that channel top width explains cross-sectional area changes (cm 2 /y) in eroding streambanks and floodplain deposition (mm/y). A quasi-sediment budget constructed for the study period using the streambanks, channel bed, channel bars, and floodplain measurements underestimated the measured suspended-sediment load by 61% (2130 Mg/y). Underestimation of the sediment load may be caused by measurement errors and to contributions from upland sediment sources, which were not measured but estimated at 36% of the gross input of sediment. Eroding streambanks contributed 42% of the gross input of sediment and accounted for 70% of the measured suspended-sediment load. Similar to other urban watersheds, the large percentage of impervious area in Difficult Run and direct runoff of precipitation leads to increased streamflow and streambank erosion. This study emphasizes the importance of streambanks in urban-suburban sediment budgets but also suggests that other sediment sources, such as upland sources, which were not measured in this study, can be an important source of sediment.

Virginia↗