Geology topics

Ken Eng

Publications and source records attributed to Ken Eng.

At least 19 recordsLinked to original sources

USGS2025

Integrating theory and empirical patterns: Fish body size distributions, life history traits, and environmental flows in streams

Individual size distributions (ISDs) are prominent in ecological research and may support resource managers with ecosystem-scale objectives. We use a database of individual size measurements for US stream fishes to test for direct and indirect effects of traits, flow regimes, and land use on the interspecific ISD exponent. Path analysis indicates that traits have strong, direct effects on ISD. Flow and land use effects on the exponent are largely indirectly mediated by their influences on species traits. ISD exponents increase (abundances of larger-bodied individuals increase, relative to smaller-bodied) when environments favor higher trophic levels, warmer thermal tolerances, and periodic life histories. Alternatively, ISD exponents decrease in systems that favor opportunistic life histories. Our flexible modeling framework that includes direct and indirect effects of traits, flow regimes, and land use on ISD could be expanded to incorporate additional variables that interact with flow (e.g., temperature and physical habitat) to assess of effects of multiple stressors on aquatic ecosystem functioning.

Science Advances↗

USGS2024

Predictability and behavior of water transfers across basin boundaries

Inter-basin water transfers (IBTs) are important components of water balances of basins, and they can have substantial impact on regional water availability. Flow information is often not available at locations with known IBTs, which is a drawback in several published IBT databases. Few, if any, studies examine whether IBT flow behavior can be generalized, and if these behaviors can be predicted at undocumented locations or known IBT locations with no flow information. In this study, we employ a clustering method based on image matching to identify similar classes of flow behavior of IBTs. Machine learning models are used to assess how well IBT flow characteristics (e.g., average flow) associated with these behaviors can be predicted. These evaluations of IBTs are done for two regions in the United States. Three primary classes of IBTs (seasonal, nonseasonal/not mixed, and seasonal/mixed) are identified across the two regions analyzed. The IBT flow characteristics are accurately predicted in the northeast region. In the Colorado region, however, only the flow characteristics related to timing were accurately predicted. These results indicate that the proposed modeling framework can be used to identify generalizable IBT flow characteristics. This framework is shown to predict flow characteristics with a reasonable amount of accuracy to undocumented locations and improves previously published IBT databases by backfilling flow information to locations with a known IBT presence.

Arizona, Colorado, Delaware, Kansas, Maryland, Mas↗

USGS2024

Streamflow characterization and hydromodification, Indian and Kill Creek Basins, Johnson County, Kansas, 1985–2018

Urban stream restoration requires a quantitative understanding of hydromodification to provide a scientific basis for establishing, prioritizing, and monitoring stream quality improvement goals. A study by the U.S. Geological Survey, in cooperation with the Johnson County Urban stream restoration benefits from a quantitative understanding of hydromodification to provide a scientific basis for establishing, prioritizing, and monitoring stream quality improvement goals. A study by the U.S. Geological Survey, in cooperation with the Johnson County Stormwater Management Program, began in 2017 to assess streamflow conditions at U.S. Geological Survey streamgages along Indian and Kill Creeks in Johnson County, Kansas. These streams represent the most urban (Indian Creek) and least urban (Kill Creek) drainage basins in the county. The assessment used 40 streamflow indicators to characterize streamflow conditions for both streams and quantify the degree of hydromodification for Indian Creek. The 40 streamflow indicators consisted of 35 commonly used indicators for characterizing streamflow, 2 less common seasonality indicators, and 3 other indicators based on duration curves, runoff hydrographs, and streamflow percentile classes. The indicators represented five key components of the natural streamflow regime: magnitude, frequency, duration, timing, and rate of change. As part of the study, indicators were evaluated as to general utility for characterizing streamflow conditions, quantifying hydromodification, and assessing the effectiveness of implemented management practices intended to restore urban streams. Results identifying indicators that serve these purposes could be applied more generally to other streams in Johnson County to assess hydromodification and potential restoration opportunities. Although the same set of streamflow indicators may not apply to other regions, methods and results presented in this report provide guidance, techniques, and perspective for future related or similar studies elsewhere, particularly those designed to quantify hydromodification of urban streams and monitor the effectiveness of restoration efforts. Compared to Kill Creek, which, for the purposes of this study, was considered representative of a least disturbed rural reference condition, Indian Creek hydrology was determined to be substantially modified because of urbanization. Of the 35 streamflow indicators evaluated, 19 indicated a generally consistent and substantial difference between the 2 streams. Hydromodification of Indian Creek was characterized by larger annual mean and monthly mean streamflows (and, thus, larger streamflow volumes), larger low streamflows of shorter duration, larger high streamflows with increased frequency and shorter duration, faster rise and fall rates, and decreased seasonality of high and low streamflows. For the two seasonality indicators, seasonality of high and low streamflows decreased. Duration curves, runoff event hydrographs, and streamflow percentile classes also indicated differences between the two streams for specific ranges of streamflow. Indicators that were useful in identifying generally consistent and substantial differences between the two streams, and therefore demonstrating they collectively or individually may be indicators of hydromodification, included annual median and mean flows; monthly mean flows for February, July, August, September, October, November, and December; all the minimum mean flow indictors (1-day, 3-day, 7-day, 30-day, and 90-day); annual number and mean magnitude of peak flows; some of the flow pulse indicators; and rise and fall rates. Indicators determined to be marginally useful or not useful for identifying consistent and substantial streamflow differences between streams included the flashiness indicators Richards-Baker flashiness index and the fraction of the year the daily mean flow is greater than the annual mean flow, which was not expected. Municipalities are challenged by the need to restore stream quality in urbanized areas where options are limited because of existing development. Understanding hydromodification effects and implications for stream quality can help managers plan urban development that minimizes degradation of stream quality and provides insights for implementing effective management practices. Streamflow indicators identified in this report can be used to guide urban stream restoration. In particular, the most useful indicators could form the basis of numeric criteria for restoration goals aimed at achieving or progressing toward more natural streamflow conditions—and, by extension, more healthy ecosystems—by characterizing flow conditions, quantifying hydromodification, establishing stream-restoration goals, and monitoring progress toward achieving those goals as management practices are implemented.

Kansas↗

USGS2024

Assessing the added value of antecedent streamflow alteration information in modeling stream biological condition

In stream systems, disentangling relationships between biology and flow and subsequent prediction of these relationships to unsampled streams is a common objective of large-scale ecological modeling. Often, streamflow metrics are derived from aggregating continuous streamflow records available at a subset of stream gages into long-term flow regime descriptors. Despite demonstrated value, shortcomings of these long-term approaches include spatial restriction to locations with long-term continuous flow records (commonly, biased toward larger systems) and omission of potentially ecologically important short-term (i.e., ≤1 year) antecedent streamflow information. We used long-term flow regime and short-term antecedent streamflow alteration information to evaluate relative performance in modeling stream fish biological condition. We compared results to understand whether short-term antecedent streamflow information improved models of fish biological condition. Results indicated that models incorporating short-term antecedent data performed better than those relying solely on long-term flow regime data (kappa statistic = 0.29 and 0.23, respectively) and improved prediction accuracy among stream sizes and in six of nine ecoregions. Additionally, models relying solely on short-term streamflow information performed similarly to those with only long-term streamflow information (kappa = 0.23). Incorporating short-term antecedent streamflow metrics may provide added ecological information not fully captured by long-term flow regime summaries in macroscale modeling efforts or perform similarly to long-term streamflow data when long-term data are not available.

Science of the Total Environment↗

USGS2023

Predicting baseflow recession characteristics at ungauged stream locations using a physical and machine learning approach

Prediction of short- (i.e., aquifer is near or at saturated conditions) and long-time (i.e., aquifer is not near or at saturated conditions) baseflow recession characteristics at ungauged stream locations is a current challenge that has been primarily addressed by empirical approaches that relate these characteristics to basin attributes. However, the performance of these models is often only fair with coefficient of determination values ranging from 0.5 to 0.7. In this study, we propose a hybrid physical and machine learning approach to predict the long- and short-time baseflow recession characteristics at ungauged stream locations. This approach is compared to a machine learning method, random forest regression, that relates baseflow recession characteristics to basin attributes in 582 basins across the western and eastern United States. The new approach resulted in lower median and inner quartile ranges (IQR) of absolute normalized errors in predicting long-time baseflow recession characteristics (western: 23%, IQR=32%; eastern: 30%, IQR=39%) compared to estimates of those properties based on random forest regressions (western: 27%, IQR=34%; eastern: 38%, IQR=50%). For the short-time baseflow recession characteristics, the hybrid approach resulted in substantially lower median errors and IQR values (western: 79%, IQR=143%; eastern: 83%, IQR=140%) compared to estimates from random forest regressions (western: 1,577%, IQR=8,887%; eastern: 341%, IQR=2,154%). In addition, this approach identified four major regions in the western United States and three in the eastern United States where the baseflow recession characteristics are mostly constant, and these characteristics only vary based on the geometric properties of aquifers. Lastly, the inter-basin variability of the baseflow recession characteristics was not found to be strongly related to metrics measuring interstorm arrival periods, average number of storms, and average length of storms.

Advances in Water Resources↗

USGS2022

Evaluation of machine learning approaches for predicting streamflow metrics across the conterminous United States

Few regional or national scale studies have evaluated machine learning approaches for predicting streamflow metrics at ungaged locations. Most such studies are limited by the number of dimensions of the streamflow regime investigated. This study, in contrast, provides a comprehensive evaluation of the streamflow regime based on three widely available machine learning approaches (support vector regression, random forest, and cubist regression) and on multiple linear regression to predict 106 natural streamflow metrics at ungaged locations. This evaluation is done for 545 streamgages across the northwest United States for recurrence-interval flood metrics and for 1,851 sites in the conterminous United States for non-flood metrics. The results indicate that for flood metrics, predictions by cubist regression and support vector regressions have substantially less error than the other approaches. For all the remaining streamflow metrics, random forest models outperform the other methods.

Scientific Investigations Report↗

USGS2019

Severity and extent of alterations to natural streamflow regimes based on hydrologic metrics in the conterminous United States, 1980–2014

Alteration of the natural streamflow regime by land and water management, such as land-cover change and dams, is associated with aquatic ecosystem degradation. The severity and geographic extent of streamflow alteration at regional and national scales, however, remain largely unquantified. The primary goal of this study is to characterize the severity and extent of alterations to natural streamflow regimes for 1980–2014 based on hydrologic metrics at 3,355 U.S. Geological Survey streamgages in the conterminous United States. Twelve hydrologic metrics with known relevance to aquatic ecosystem health were used to characterize the streamflow regime. Alterations to the 12 hydrologic metrics were quantified by taking ratios of the metrics calculated from observed daily streamflow records divided by the same metrics predicted for natural conditions by random forest statistical models. Some level of streamflow alteration (diminishment or inflation of hydrologic metrics) compared to natural conditions was indicated at about 80 percent of the assessed streamgages across the conterminous United States. The severity of alteration differed among ecoregions because of differences in dominant land and water management practices. Finally, when compared over the period 1980–2014, climate variability generally played a minor role in the alteration of streamflows across the United States when compared to the effects of land and water management.

Conterminous United States↗

USGS2019

Flow modification in the Nation’s streams and rivers

This report summarizes a national assessment of flowing waters conducted by the U.S. Geological Survey’s (USGS) National Water-Quality Assessment (NAWQA) Project and addresses several pressing questions about the modification of natural flows in streams and rivers. The assessment is based on the integration, modeling, and synthesis of monitoring data collected by the USGS and the U.S. Environmental Protection Agency at more than 7,000 streams and rivers across the conterminous United States from 1980 to 2014. Key findings include the following. First, flow in many of the Nation’s streams and rivers is different from what it would be under natural conditions. In particular, low flows are more frequent, are of shorter duration, and vary less from one year to the next than they would naturally. In addition, high flows have been reduced in magnitude, are of shorter duration, are less frequent, and vary less from one year to the next than they would naturally. Other characteristics of natural flows also have been modified. Second, over the last 60 years (1955–2014), climatic trends have caused a change of 50 percent or more in one or more streamflow attributes at two-thirds of climate-sensitive streamgaging sites. However, these climate-induced changes have been less influential on streamflow modification than have land and water-management practices. Third, in every region assessed, streamflow modification was associated with reduced ecological health, as indicated by two biological communities—invertebrates and fish. Biological communities were increasingly likely to be impaired (defined as having lost a statistically significant number of species) in streams with flows most different from natural conditions. Finally, several case studies are presented that illustrate viable management strategies for balancing the water needs of people and ecosystems.

Circular↗

USGS2019

Regional-scale associations between indicators of biological integrity and indicators of streamflow modification

Although streamflow is widely recognized as a controlling factor in stream health, empirical relations between indicators of anthropogenic modification of streamflow and ecological indicators have been elusive. The objective of this report is to build upon specific findings reported in recent publications by providing a library of empirical models that describe the relations between streamflow modification and indicators of biological integrity. Biological monitoring data from 812 streams and rivers across the United States were matched with sites where daily streamflow was also monitored by the U.S. Geological Survey. Of these sites, 118 were sampled by the U.S. Geological Survey along gradients of streamflow modification within 3 regional focus studies. The integrity of invertebrate and fish communities was expressed as a binary variable, “impaired” or “unimpaired,” signifying whether or not the composition and structure of the biological community was statistically reduced relative to regional reference sites. Streamflow modification at each gaged site was quantified with 509 streamflow statistics scaled to express the ratio of observed streamflow conditions to site-specific expected conditions in the absence of human influences on watershed hydrology. For each region, generalized additive modeling was used to examine relations between each indicator of streamflow modification and indicators of biological integrity (response variable). In every region examined, statistically defensible and ecologically realistic relations were found between indicators of streamflow modification and indicators of biological integrity. These findings can aid practitioners and managers seeking to (1) propose empirically based hypotheses about the specific components of streamflow regimes that are critical to aquatic communities, which can subsequently be explored in detail in a region or river basin of interest; and (2) predict biological responses to anthropogenic modification of specific components of the streamflow regime.

Open-File Report↗

USGS2019

Regionalization of surface-water statistics using multiple linear regression

This report serves as a reference document in support of the regionalization of surface-water statistics using multiple linear regression. Streamflow statistics are quantitative characterizations of hydrology and are often derived from observed streamflow records. In the absence of observed streamflow records, as at unmonitored or ungaged locations, other techniques are required. Multiple linear regression is one tool that is widely used to regionalize or transfer information from gaged to ungaged locations. This report provides the background to support regression-based regionalization of streamflow statistics. This background includes tools for data assembly, exploratory data analysis, model estimation in a least-squares framework, and model evaluation.

Techniques and Methods↗

USGS2018

How hydrologic connectivity regulates water quality in river corridors

Downstream flow in rivers is repeatedly delayed by hydrologic exchange with off‐channel storage zones where biogeochemical processing occurs. We present a dimensionless metric that quantifies river connectivity as the balance between downstream flow and the exchange of water with the bed, banks, and floodplains. The degree of connectivity directly influences downstream water quality — too little connectivity limits the amount of river water exchanged and leads to biogeochemically inactive water storage, while too much connectivity limits the contact time with sediments for reactions to proceed. Using a metric of reaction significance based on river connectivity, we provide evidence that intermediate levels of connectivity, rather than the highest or lowest levels, are the most efficient in removing nitrogen from Northeastern United States’ rivers. Intermediate connectivity balances the frequency, residence time, and contact volume with reactive sediments, which can maximize the reactive processing of dissolved contaminants and the protection of downstream water quality. Our simulations suggest denitrification dominantly occurs in riverbed hyporheic zones of streams and small rivers, whereas vertical turbulent mixing in contact with sediments dominates in mid‐size to large rivers. The metrics of connectivity and reaction significance presented here can facilitate scientifically based prioritizations of river management strategies to protect the values and functions of river corridors.

Journal of the American Water Resources Associatio↗

USGS2018

Thresholds of lake and reservoir connectivity in river networks control nitrogen removal

Lakes, reservoirs, and other ponded waters are ubiquitous features of the aquatic landscape, yet their cumulative role in nitrogen removal in large river basins is often unclear. Here we use predictive modeling, together with comprehensive river water quality, land use, and hydrography datasets, to examine and explain the influences of more than 18,000 ponded waters on nitrogen removal through river networks of the Northeastern United States. Thresholds in pond density where ponded waters become important features to regional nitrogen removal are identified and shown to vary according to a ponded waters’ relative size, network position, and degree of connectivity to the river network, which suggests worldwide importance of these new metrics. Consideration of the interacting physical and biological factors, along with thresholds in connectivity, reveal where, why, and how much ponded waters function differently than streams in removing nitrogen, what regional water quality outcomes may result, and in what capacity management strategies could most effectively achieve desired nitrogen loading reduction.

Connecticut, Delaware, District of Columbia, Maine↗

USGS2017

Streamflow alteration at selected sites in Kansas

An understanding of streamflow alteration in response to various disturbances is necessary for the effective management of stream habitat for a variety of species in Kansas. Streamflow alteration can have negative ecological effects. Using a modeling approach, streamflow alteration was assessed for 129 selected U.S. Geological Survey streamgages in the State for which requisite streamflow and basin-characteristic information was available. The assessment involved a comparison of the observed condition from 1980 to 2015 with the predicted expected (least-disturbed) condition for 29 streamflow metrics. The metrics represent various characteristics of streamflow including average flow (annual, monthly) and low and high flow (frequency, duration, magnitude). Streamflow alteration in Kansas was indicated locally, regionally, and statewide. Given the absence of a pronounced trend in annual precipitation in Kansas, a precipitation-related explanation for streamflow alteration was not supported. Thus, the likely explanation for streamflow alteration was human activity. Locally, a flashier flow regime (typified by shorter lag times and more frequent and higher peak discharges) was indicated for three streamgages with urbanized basins that had higher percentages of impervious surfaces than other basins in the State. The combination of localized reservoir effects and regional groundwater pumping from the High Plains aquifer likely was responsible, in part, for diminished conditions indicated for multiple streamflow metrics in western and central Kansas. Statewide, the implementation of agricultural land-management practices to reduce runoff may have been responsible, in part, for a diminished duration and magnitude of high flows. In central and eastern Kansas, implemented agricultural land-management practices may have been partly responsible for an inflated magnitude of low flows at several sites.

Kansas↗

USGS2017

Streamflow alteration and habitat ramifications for a threatened fish species in the Central United States

In the Central United States, the Arkansas darter ( Etheostoma cragini ) is listed as a threatened fish species by the State of Kansas. Survival of the darter is threatened by loss of habitat caused by changing streamflow conditions, in particular flow depletion. Future management of darter populations and habitats requires an understanding of streamflow conditions and how those conditions may have changed over time in response to natural and anthropogenic factors. In Kansas, streamflow alteration was assessed at 9 U.S. Geological Survey streamgages in 6 priority basins with no pronounced long-term trends in precipitation. The assessment was based on a comparison of observed (O) and predicted expected (E) reference conditions for 29 flow metrics. The O/E results indicated a likely or possible diminished flow condition in 2 basins; the primary cause of which is groundwater-level declines resulting from groundwater pumping for irrigated agriculture. In these 2 basins, habitat characteristics adversely affected by flow depletion may include stream connectivity, pools, and water temperature. The other 4 basins were minimally affected, or unaffected, by flow depletion and therefore may provide the best opportunity for preservation of darter habitat. Through the O/E analysis, anthropogenic streamflow alteration was quantified and the results will enable better-informed decisions pertaining to the future management of darters in Kansas.

Kansas↗

USGS2017

Predictability and selection of hydrologic metrics in riverine ecohydrology

The natural flow regime is critical to the health of riverine ecosystems. Many hydrologic metrics (HMs) have been developed to describe natural flow regimes, quantify flow alteration, and provide the hydrologic foundation for the development of environmental flow standards. Many applications require the use of models to predict expected natural values of HMs from basin characteristics at sites with no observed records of unimpaired flows. However, the error associated with HM estimation has not been evaluated. The primary goal of our study was to provide guidance for river scientists and managers in the selection, use, and interpretation of HMs for stream classification and hydroecological investigations of river ecosystems. We evaluated the predictability of a broad suite of HMs for the conterminous USA based on random forest statistical models. We also examined how the predictability of metrics varied among unique components of the flow regime. Roughly 40% of 612 HMs we examined could be predicted reliably from basin characteristics. The predictable metrics were disproportionately represented in 5 flow components: asymmetry, seasonality, magnitude, variability, and average monthly flows. Most metrics that represent extreme hydrological events (i.e., high and low flows) could not be reliably predicted. Roughly ⅔ of the evaluated HMs were incalculable or highly biased at intermittent/ephemeral streams because of the need for logarithmic transformations or scaling by other HMs, such as mean flows or percentile flow thresholds. Scaling metrics by drainage area tended to improve predictability. We recommend that the predictability of HMs be given greater consideration in studies and applications in which they are used to characterize and assess alteration of streamflow regimes.

Freshwater Science↗

USGS2017

Biological relevance of streamflow metrics: Regional and national perspectives

Protecting the health of streams and rivers requires identifying ecologically significant attributes of the natural flow regime. Streamflow regimes are routinely quantified using a plethora of hydrologic metrics (HMs), most of which have unknown relevance to biological communities. At regional and national scales, we evaluated which of 509 commonly used HMs were associated with biological indicators of fish and invertebrate community integrity. We quantified alteration of each HM by using statistical models to predict site-specific natural baseline values for each of 728 sites across the USA where streamflow monitoring data were available concurrent with assessments of invertebrate or fish community integrity. We then ranked HMs according to their individual association with biological integrity based on random forest models that included HMs and other relevant covariates, such as land cover and stream chemistry. HMs were generally the most important predictors of biological integrity relative to the covariates. At a national scale, the most influential HMs were measures of depleted high flows, homogenization of flows, and erratic flows. Unique combinations of biologically relevant HMs were apparent among regions. We discuss the implications of our findings to the challenge of selecting HMs for streamflow research and management.

Freshwater Science↗

USGS2015

Sensitivity of intermittent streams to climate variations in the USA

There is a great deal of interest in the literature on streamflow changes caused by climate change because of the potential negative effects on aquatic biota and water supplies. Most previous studies have primarily focused on perennial streams, and there have been only a few studies examining the effect of climate variability on intermittent streams. Our objectives in this study were to (1) identify regions of similar zero-flow behavior, and (2) evaluate the sensitivity of intermittent streams to historical variability in climate in the United States. This study was carried out at 265 intermittent streams by evaluating: (1) correlations among time series of flow metrics (number of zero-flow events, the average of the central 50% and largest 10% of flows) with climate (magnitudes, durations and intensity), and (2) decadal changes in the seasonality and long-term trends of these flow metrics. Results identified five distinct seasonality patterns in the zero-flow events. In addition, strong associations between the low-flow metrics and historical changes in climate were found. The decadal analysis suggested no significant seasonal shifts or decade-to-decade trends in the low-flow metrics. The lack of trends or changes in seasonality is likely due to unchanged long-term patterns in precipitation over the time period examined.

River Research and Applications↗

USGS2013

River flow changes related to land and water management practices across the conterminous United States

The effects of land and water management practices (LWMP)—such as the construction of dams and roads—on river flows typically have been studied at the scale of single river watersheds or for a single type of LWMP. For the most part, assessments of the relative effects of multiple LWMP within many river watersheds across regional and national scales have been lacking. This study assesses flow alteration—quantified as deviation of several flow metrics from natural conditions—at 4196 gauged rivers affected by a variety of LWMP across the conterminous United States. The most widespread causes of flow changes among the LWMP considered were road density and dams. Agricultural development and wastewater discharges also were associated with flow changes in some regions. Dams generally reduced most attributes of flow, whereas road density, agriculture and wastewater discharges tended to be associated with increased flows compared to their natural condition.

Science of the Total Environment↗