Geology topics

J. William Lund

Publications and source records attributed to J. William Lund.

14 recordsLinked to original sources

USGS2026

The electric vehicle “EV”-crane—A next-generation system for fluvial sediment and water-quality sampling using an electric cart with integrated crane and accessories

The U.S. Geological Survey (USGS), with support from the Federal Interagency Sedimentation Project (FISP), developed the electric vehicle (EV)-crane, a modified electric cart equipped with a rigid swivel-mounted crane and integrated accessory holders. The EV-crane was developed to improve the efficiency and safety of fluvial sediment and water-quality sampling from bridges. USGS field staff have relied on foldable cranes on a four-wheeled base or nonstandardized platforms, such as utility terrain vehicle (UTV) mounted cranes, which highlight the need for a safer, standardized, and more adaptable solution. This redesigned platform replaces the foldable cranes on a four-wheeled base with a compact, powered system that reduces physical strain, enhances operator safety, and streamlines field operations on bridges. Dedicated holders for samplers, a controller, batteries, counterweights, and a field computer or tablet create a versatile, organized workspace that supports a wide range of sampling and heavy-lift applications. The EV-crane described here is version 1 of the prototype. Evaluation of the design is ongoing, and the findings summarized here are from initial qualitative user feedback and early quantitative testing. Potential plans may include additional testing and refinement to inform subsequent design iterations.

Open-File Report↗

USGS2026

Development of a two-dimensional hydraulic model for the Kalamazoo River between the Trowbridge and Allegan City Dams, Michigan

The U.S. Geological Survey developed a two-dimensional hydraulic model for a 9.2-mile reach of the Kalamazoo River between the Trowbridge and Allegan City Dams. The model simulates streamflow conditions with spatial coverage and resolution that would be difficult or dangerous to document with field measurements, enabling assessments of habitat connectivity and substrate stability to support dam removal and restoration planning. The model was calibrated with surveyed water surface elevation (WSE) profiles, streamgage WSE time series, and measured depth-average velocities. Modeled WSE profiles had root mean square error (RMSE) values of 0.20 and 0.32 foot. Cross-sectional average velocities were slightly underpredicted, with RMSE of 0.28 and 0.30 foot per second (ft/s). Channel roughness varied with stage, and the high-flow model reproduced streamgage WSE time series with an RMSE of 0.04 foot. Quasi-steady simulations at 4,000 cubic feet per second (ft 3 /s), about the 50-percent annual exceedance probability streamflow, indicated that cross-sectional average velocities within 3.4 miles downstream from Trowbridge Dam were commonly between 3 and 4 ft/s, occasionally exceeding 4 ft/s. Farther downstream, velocities seldom exceeded 3 ft/s. Simulated shear stresses were used to estimate substrate stability in the reach. At 4,000 ft 3 /s, the minimum stable grain size along most of the main channel was predicted to be in the pebble range (4–64 millimeters), and sands and silts were predicted to be stable in the floodplain and backwaters.

Michigan↗

USGS2025

Disentangling geomorphic equifinality in sediment and hydrologic connectivity through the analyses of landscape drivers of hysteresis

Sources, transport mechanisms and pathways of fine sediment in river systems are dependent on a multitude of climatic, geomorphic and anthropogenic factors, resulting in geomorphic equifinality, in which it is difficult to parse how different landscape processes affect sediment transport across different spatiotemporal scales. The objectives of this study are to 1) provide a conceptual model to consider how differing spatial distributions and hydrologic timing of sediment sources, both upland and in-channel, can result in equifinal sediment transport outcomes, and 2) utilize analytical methods with widely available environmental datasets to infer sediment processes from stream gaging data. Hysteretic patterns of observed storm events were classified based on their direction and timing of peak sediment concentration, relative to streamflow, using records from 35 U.S. Geological Survey stream gages in the period between 2007 and 2023 within two different physiographic regions: the Mid-Atlantic Delaware River Basin (DRB) and the Midwestern Illinois River Basin (IRB). The DRB contains mixed forest, urban, suburban and agricultural watersheds over diverse topography, and the IRB is primarily an intensively managed agricultural watershed on flat terrain. We use principal component analysis and linear discriminant analysis to infer regional hydrologic relations with turbidity dynamics, and to identify the primary hydrologic and land surface characteristics most effective at distinguishing between hysteretic classes in each region. These analyses reveal underlying regional relations in storm event hydrodynamics and landscape characteristics that contribute to varying patterns in sediment dynamics. Incorporating these sediment dynamic relations with spatial distributions and hydrologic timing of sediment sources could help to improve process understanding and predictive capability of fine sediment transport in watersheds.

Delaware, Illinois, Indiana, New Jersey, New York,↗

USGS2025

Beach nourishment response and recent morphological evolution of Minnesota Point, Lake Superior

Beach nourishments are a popular nature-based alternative to armoring for shoreline erosion mitigation, but nourishments have been criticized due to their environmental impacts and uncertain sustainability. Monitoring is often nonexistent or insufficient to constrain nourishment longevity and inform the renourishment interval required to maintain shoreline protection. This study uses a combination of topobathymetric surveys, high-resolution satellite-derived shorelines, and coastal engineering analyses to investigate the recent evolution of Minnesota Point and the fate of three beach nourishments constructed adjacent to littoral barriers. We use semi-empirical formulations for sediment compatibility, wave runup, and longshore sediment transport to inform the observed nourishment behavior. Minnesota Point experienced widespread foredune retreat averaging 7±2.8 m from 2009–2019 and 130,000 (70,000–140,000) m 3 of sediment was eroded during this interval. The 2019 nourishment at the Superior Entry was rapidly eroded by strong storms, losing >80% of the added beach width by the following spring. The 2020 and 2021 nourishments at the Duluth Entry retained >80% of the nourishment material at the time of the last topobathymetric survey in the fall of 2022, and satellite-derived shorelines indicate that the beach remained 10 m wider than pre-nourishment conditions at the end of 2023. Modeled longshore transport rates over the period 2009–2022 averaged 11,400 m 3 yr −1 northwestward at the Superior Entry, nearly 3x greater than the 4000 m 3 yr −1 southeastward transport modeled at the Duluth Entry. These observations show that differences in shoreline orientation, littoral sediment supply, and grain size compatibility can lead to contrasting beach nourishment longevities, and this study provides additional measurements of Minnesota Point’s long-term morphological change which can help inform coastal resiliency efforts.

Minnesota, Wisconsin↗

USGS2025

Suspended sediment and bedload transport along the Main and South Branches, Wild Rice River, northwestern Minnesota, 1979 through 2023

The geologic history and anthropogenic modifications of Minnesota’s Wild Rice River have caused major morphological adjustments, which induce erosion and excess fluvial sediment transport. The excess sediment deposits in the lower Wild Rice River, exacerbating flooding. To help mitigate these problems, the Wild Rice Watershed District has future plans to implement a river restoration on the lower Wild Rice River. The Wild Rice Watershed District collaborated with the U.S. Geological Survey to measure and analyze sediment transport along the Wild Rice River’s Main and South Branches to assess any potential changes in sediment transport among sites and time periods. Time differencing results indicated that all suspended-sediment constituents showed a significant difference between the two sampling periods at one South Branch site but not at the Main Branch site. Piecewise regression analysis better matched the suspended-sediment constituents transport process at most sites by differentiating no relation between suspended-sediment constituents at lower streamflows and a positive relation at higher streamflows at most Wild Rice River sites. Five of the sites showed elevated sediment transport with increasing streamflow. In contrast, the site farthest downstream showed a negative relation with increasing streamflow, indicating that that the lower Wild Rice River is supply limited and deposition is likely occurring upstream and (or) near the site. Overall, the uncertainty in results indicates the complexity of sediment transport in a river when using streamflow as the sole explanatory variable and suggests a need for multisite, multiyear, and multifaceted data.

Minnesota↗

USGS2025

Using machine learning in Minnesota’s StreamStats to predict fluvial sediment

A thorough understanding of fluvial sediment transport is essential for addressing key environmental issues such as aquatic habitat degradation, flooding, excess nutrients, and challenges with river restoration. Fluvial sediment samples are valuable for addressing these concerns, but their collection is often impractical across all rivers and timeframes of interest. In addition, previously used analytical and numerical methods have not allowed for the transfer of knowledge from sites that have data to sites that do not have data. To overcome this limitation, the U.S. Geological Survey developed machine learning models to predict suspended-sediment concentrations and bedload transport in Minnesota rivers that lack physical sediment data and integrated them into the U.S. Geological Survey StreamStats web application.

Minnesota↗

USGS2023

A novel suspended-sediment sampling method: Depth-Integrated Grab (DIG)

Measuring suspended sediment in fluvial systems is critical to understanding and managing water resources. Sampling suspended sediment has been the primary means of understanding fluvial suspended sediment. Specialized samplers, sampling methods, and laboratory methods developed by select U.S. Federal Agencies are more representative of river and stream conditions than commonly used grab sampling and total suspended solids (TSS) laboratory methods but are not widely used because they are expensive, time consuming, and not required as part of water quality standards in the United States. A new suspended-sediment sampling method called a depth-integrated grab (DIG) was developed by combining certain elements from both grab and depth-integrating sampling methods and suspended-sediment concentration (SSC) laboratory methods. The goal of the DIG method was to provide more accurate results than Grab-TSS while being easier and cheaper to sample than specialized samplers and methods. Approximately 50 paired comparison samples were collected at 9 sites in Minnesota from 2018 through 2019. Results showed no significant difference between the DIG and specialized sampling methods and a significant difference between both methods and the Grab-TSS method. The DIG-SSC provided an improved alternative to the Grab-TSS method, but additional research and testing is important to evaluate if this method is appropriate in different conditions than were observed in this study.

Minnesota↗

USGS2023

How machine learning can improve predictions and provide insight into fluvial sediment transport in Minnesota

Understanding fluvial sediment transport is critical to addressing many environmental concerns such as exacerbated flooding, degradation of aquatic habitat, excess nutrients, and the economic challenges of restoring aquatic systems. However, fluvial sediment transport is difficult to understand because of the multitude of factors controlling the potential sources, delivery, mechanics, and storage of sediment in aquatic systems. While physical fluvial sediment samples are an integral part of developing solutions for these environmental concerns, samples cannot be collected at every river and time of interest. Therefore, accurate and cost-effective estimates of sediment loading are needed to manage riverine sediment transport at a multitude of scales (Ellison et al. 2016); also needed are methods to estimate sediment transport at sites where little or no physical samples have been collected (Gray & Simes 2008). The application of machine learning (ML) approaches to estimate sediment transport has grown over the past two decades (Afan et al. 2016). ML used in sediment transport research has shown multiple benefits over traditional approaches, such as increased prediction accuracy, the ability to learn complex linear and non-linear relations amongst the dataset and providing the ability to interpret these complex relations with important features used in the model (Cisty et al. 2021; Francke et al. 2008; Khan et al. 2021; Zounemat-Kermani et al. 2020; Cutler et al. 2007).

Minnesota↗

USGS2023

Comparing empirical sediment transport modeling approaches in Michigan rivers

Excess or limited fluvial sediment transport can contribute to and exacerbate many environmental issues including nutrient loading, aquatic habitat degradation, flooding, channel navigation dredging, dam operation, and stream degradation or aggradation. However, fluvial sediment transport is difficult and expensive to comprehensively characterize because it can vary substantially both temporally and spatially. Having better estimates of fluvial sediment transport is important for understanding and solving these environmental issues when it is not possible to collect fluvial sediment samples. Different modeling approaches can be used to help estimate suspended sediment when sampling data are limited or unavailable. This study compared dimensionless sediment rating curves (DSRCs) developed in Pagosa Springs Colorado, Minnesota, and Michigan to determine if these DSRCs were suitable to make predictions of suspended sediment for Michigan rivers. Approximately 3,000 suspended sediment samples collected in or near Michigan from the mid-1960s through August 2022 were used to develop two DSRC models. The DSRCs developed in Michigan include a pooled DSRC model which uses nonlinear least squares regression, and a mixed-effects DSRC model which uses a mixed-effects modeling approach. In general, there was not a noticeable improvement in the performance of the Michigan mixed-effects DSRC model over the Michigan pooled DSRC model. The two Michigan DSRCs were evaluated against DSRCs developed for Pagosa Springs and Minnesota. The results showed DSRC models developed from Minnesota and Michigan were similar to each other. In contrast, the Pagosa Springs DSRC predicts higher suspended-sediment concentration (SSC) at low flows and increases at a higher rate due to having a greater exponent. The Pagosa Springs DSRC produces higher SSC predictions that do not approximate the observed data well at most of the Michigan sites in the study. The results suggest that the Pagosa Springs DSRC was not suitable to make predictions of suspended sediment for Michigan rivers. The similarity of the DSRC equations developed for Minnesota and Michigan compared to the Pagosa Springs DSRC equation suggest that there may be regional patterns of SSC in the upper Midwest rivers that differ from those in other areas of the country like Pagosa Springs. A regionally applicable model could be developed and strengthened by combining data from additional midwestern states. Since the Michigan DSRCs goodness-of-fit metrics were comparable to the site-specific simple linear regressions (SLRs) and outperformed them in the aggregate goodness-of-fit metrics, the Michigan DSRCs are suitable to make predictions of suspended sediment in Michigan rivers with limited data. However, the availability of the DSRCs from this study should not diminish the value of collecting physical samples and exploring alternative modeling approaches because of the uncertainty associated with using DSRCs. Approximately 3,000 suspended sediment samples collected in or near Michigan from the mid-1960s through August 2022 were used to develop two DSRC models. The DSRCs developed in Michigan include a pooled DSRC model which uses nonlinear least squares regression, and a mixed-effects DSRC model which uses a mixed-effects modeling approach. In general, there was not a noticeable improvement in the performance of the Michigan mixed-effects DSRC model over the Michigan pooled DSRC model. The two Michigan DSRCs were evaluated against DSRCs developed for Pagosa Springs and Minnesota. The results showed DSRC models developed from Minnesota and Michigan were similar to each other. In contrast, the Pagosa Springs DSRC predicts higher suspended-sediment concentration (SSC) at low flows and increases at a higher rate due to having a greater exponent. The Pagosa Springs DSRC produces higher SSC predictions that do not approximate the observed data well at most of the Michigan sites in the study. The results suggest that the Pagosa Springs DSRC was not suitable to make predictions of suspended sediment for Michigan rivers. The similarity of the DSRC equations developed for Minnesota and Michigan compared to the Pagosa Springs DSRC equation suggest that there may be regional patterns of SSC in the upper Midwest rivers that differ from those in other areas of the country like Pagosa Springs. A regionally applicable model could be developed and strengthened by combining data from additional midwestern states. Since the Michigan DSRCs goodness-of-fit metrics were comparable to the site-specific simple linear regressions (SLRs) and outperformed them in the aggregate goodness-of-fit metrics, the Michigan DSRCs are suitable to make predictions of suspended sediment in Michigan rivers with limited data. However, the availability of the DSRCs from this study should not diminish the value of collecting physical samples and exploring alternative modeling approaches because of the uncertainty associated with using DSRCs.

Michigan↗

USGS2022

Using machine learning to improve predictions and provide insight into fluvial sediment transport

A thorough understanding of fluvial sediment transport is critical to addressing many environmental concerns such as exacerbated flooding, degradation of aquatic habitat, excess nutrients, and the economic challenges of restoring aquatic systems. Fluvial sediment samples are integral for addressing these environmental concerns but cannot be collected at every river and time of interest. Therefore, to gain a better understanding for rivers where direct measurements have not been made, extreme gradient boosting machine learning (ML) models were developed and trained to predict suspended sediment and bedload from sampling data collected in Minnesota, United States (U.S.), by the U.S. Geological Survey. Approximately 400 watershed (full upstream area), catchment (nearby landscape), near-channel, channel, and streamflow features were retrieved or developed from multiple sources, reduced to approximately 30 uncorrelated features, and used in the final ML models. The results indicate suspended sediment and bedload ML models explain approximately 70% of the variance in the datasets. Important features used in the models were interpreted with Shapley additive explanation (SHAP) plots, which provided insight into sediment transport processes. The most important features in the models were developed to normalize streamflow by the 2-year recurrence interval and quantify the rate of change in streamflow (slope), which helped account for sediment hysteresis. Generally, this study also showed a combination of mostly watershed and catchment geospatial features were important in ML models that predict sediment transport from physical samples. This study is a promising step forward in making fluvial sediment transport predictions using machine learning models trained by physically collected samples. The approach developed here can be used wherever similar datasets exists and will be useful for landscape and water management.

Minnesota↗

USGS2022

Sediment monitoring and streamflow modeling before and after a stream restoration in Rice Creek, Minnesota, 2010–2019

The Rice Creek Watershed District (RCWD) cooperated with the U.S. Geological Survey to establish a 10-year suspended sediment and bedload monitoring and streamflow modeling study to evaluate the effects of two restored meander sections on middle Rice Creek in Arden Hills, Minnesota. The RCWD goals of this stream restoration were to reduce water quality impairments, improve aquatic habitat, and reduce associated costs of dredging a sedimentation pond. During the study there were several factors that introduced uncertainty in the sampling results; however, the sampling results indicated there was an increase in the post-stream restoration sediment data because of higher streamflows during the post-stream than the pre-stream restoration monitoring period. The negative relation between suspended fines and streamflow was explained by a reduction in the supply of fines with increasing streamflows. The positive relation among suspended sand, bedload, and streamflow was because of those constituents having a functional relation with the hydraulic properties of flow and a consistent supply of sand. Two-dimensional flow modeling simulations indicated the downstream restored section had less shear stress, more pools, and could access the floodplain at a lower streamflow than the original channel. Overall, the uncertainty of the sampling results indicates the complexity of sediment transport in a river and suggests a need for multisite, multifaceted, multiyear data, and tools to simulate those data to effectively evaluate river restorations.

Minnesota↗

USGS2022

The role of hydraulic and geomorphic complexity in predicting invasive carp spawning potential: St. Croix River, Minnesota and Wisconsin, United States

Since they were first introduced to the United States more than 50 years ago, invasive carp have rapidly colonized rivers of the Mississippi River Basin, with detrimental effects on native aquatic species. Their continued range expansion, and potential for subsequent invasion of the Great Lakes, has led to increased concern for the susceptibility of as-yet uncompromised lotic and lentic systems in the central United States. Because invasive carp eggs and larvae must drift in the river current for the first several days following spawning, numerical drift modeling has emerged as a useful technique for determining whether certain river systems and reaches have the potential to support suspension-to-hatching survival of invasive carp eggs, a critical first step in recruitment. Here we use one such numerical modeling approach, the Fluvial Egg Drift Simulator (FluEgg), to estimate bighead carp ( Hypophthalmichthys nobilis ) egg hatching success and larval retention in a 47.8-kilometer (km) reach of the multi-thread St. Croix River, Minnesota and Wisconsin, United States. We explore three approaches for obtaining the hydraulic data required by FluEgg, parameterizing the model with either (a) field hydraulic data collected within the main channel during a high-flow event, or hydraulic data output from a one-dimensional hydrodynamic model with both (b) steady, and (c) unsteady flows. We find that the three approaches, along with the range of water temperatures and discharge used in simulations, produce vastly different predictions of streamwise transport and in-river egg hatching probability (0% for field data, 0 to 96% for steady-state hydraulic modeling, and 1.8 to 65% for unsteady modeling). However, all FluEgg simulations, regardless of the source of hydraulic data, predicted that no larvae reach the gas bladder inflation stage within the study reach where nursery habitat is abundant. Overall, these results indicate that the lower St. Croix River is suitable for invasive carp spawning and egg suspension until hatching for a range of discharge and water temperatures. These results highlight the role of complex channel hydraulics and morphology, particularly multi-thread reaches, and their inclusion in ecohydraulic-suitability modeling to determine susceptibility of river systems for invasive carp reproduction. Our work also emphasizes the scientific value of multi-dimensional hydrodynamic models that can capture the spatial heterogeneity of flow fields in geomorphically complex rivers. This work may help to guide management efforts based on the targeted monitoring and control and improve invasive carp egg and larvae sampling efficiency.

Minnesota, Wisconsin↗

USGS2022

The use of continuous sediment-transport measurements to improve sand-load estimates in a large sand-bedded river: The Lower Chippewa River, WI

Accurately determining sediment loads is necessary for managing river environments but is difficult because multiple processes can lead to large discharge-independent changes in sediment transport. Thus, estimations of sediment load using discharge–sediment rating curves fit to sparse or historical sediment-transport measurements can be inaccurate, necessitating alternative approaches to reduce uncertainty. Continuous sediment-transport measurements reduce uncertainty because they can be used to detect discharge-independent changes in transport and are therefore unaffected by hysteresis. We used largely continuous approaches to measure sand transport in the lower Chippewa River, a large sand-supplying tributary to the Mississippi River. We used side-looking acoustic-Doppler profilers to continuously measure suspended-sand concentration, and bedform-tracking techniques to episodically measure bedload transport. Bedload transport was then continuously estimated using a discharge-dependent ratio of bedload to suspended-sand transport. This approach allowed determination of sand loads that were not estimated based only on water discharge. Our continuous suspended-sand measurements show that hysteresis between discharge and suspended-sand concentration occurs during most floods. Quasi-continuous bed-elevation measurements using a scour monitor show that lags between discharge and dune geometric adjustment is also common, causing hysteresis between discharge and bedload transport during floods. Furthermore, comparisons of our measurements with historical sediment-transport measurements indicate large discharge-independent declines in both suspended-sand and bedload transport since the 1980s. These findings indicate that sand transport is a non-stationary function of water discharge over timescales ranging from within individual floods to decades. Consequently, although our continuous-measurement approach yields only a ~20–30% improvement over rating-curve estimates of sand load over multi-year periods, our approach yields up to a factor-of-five improvement in sand-load estimates over the shorter, i.e., within a flood, timescales over which the largest discharge-independent changes in sand transport occur.

Wisconsin↗

USGS2019

Using acoustic Doppler velocity meters to estimate suspended sediment along the lower Minnesota and Mississippi Rivers

Lake Pepin is the largest naturally formed lake on the Mississippi River and has complex management needs to satisfy economic, environmental, and cultural demands. Lake Pepin is filling in with sediment at a rapid rate compared to conditions before settlement by European immigrants and intense agricultural cultivation. Accordingly, the Minnesota Pollution Control Agency has developed aggressive plans to prioritize sediment sources, understand transport mechanisms, and implement large-scale strategies to reduce sedimentation in Lake Pepin. The Minnesota River is the primary sediment source to Lake Pepin, and reductions in sediment loading from the Minnesota River are needed to reduce sedimentation in Lake Pepin. Current loading estimates were calculated from grab sampling and total suspended solids laboratory methods that greatly underestimate the actual concentrations in the rivers when compared to U.S. Geological Survey width and depth integrated sampling and laboratory methods for determining suspended-sediment concentration (SSC). Therefore, the U.S. Geological Survey, with funding from the Environment and Natural Resources Trust Fund and in cooperation with the U.S. Army Corps of Engineers, Lower Minnesota River Watershed District, Minnesota Pollution Control Agency, and Minnesota Department of Natural Resources, collected SSCs and acoustic backscatter data from acoustic Doppler velocity meters over a 2-year period at nine sites. The purpose of the study was to improve understanding of sediment-transport processes and increase accuracy of estimating SSCs and suspended-sediment loads for the lower Minnesota River and the Mississippi River compared to traditional measures. The study results indicated that acoustic backscatter worked well in estimating SSCs at sites not regulated by locks, dams, and lakes. The results also confirmed previous studies that determined most of the suspended-sediment loading into the Mississippi River is from the Minnesota River and the largest sediment sink is Lake Pepin. Suspended-sediment loading from site to site and year to year was often variable when compared to streamflow, which has been traditionally used to estimate SSC. As a result, this study demonstrates the value in having high temporal and spatial resolution of continuous sediment monitoring from acoustic devices to help manage the sources of sediment into Lake Pepin.

Minnesota River, Mississippi River↗