Skip to main content
U.S. flag

An official website of the United States government


This list of Upper Midwest Water Science Center publications spans from 1899 to present. It includes both official USGS publications and journal articles authored by our scientists. To access the full, searchable catalog of USGS publications, please visit the USGS Publications Warehouse.

Filter Total Items: 2161

Simulation of monthly mean and monthly base flow of streamflow using random forests for the Mississippi River Alluvial Plain, 1901 to 2018

Improved simulations of streamflow and base flow for selected sites within and adjacent to the Mississippi River Alluvial Plain area are important for modeling groundwater flow because surface-water flows have a substantial effect on groundwater levels. One method for simulating streamflow and base flow, random forest (RF) models, was developed from the data at gaged sites and, in turn, was used t

The water cycle

An illustrated diagram of the water cycle. This is a modern, updated version of the widely used diagram featured on the USGS Water Science School. Notably, this new water cycle diagram depicts humans and major categories of human water use as key components of the water cycle, in addition to the key pools and fluxes of the hydrologic cycle. This product targets an 8th grade audience and is designe

Building a library of source samples for sediment fingerprinting – Potential and proof of concept

PurposeSediment fingerprinting of fluvial targets has proven useful to guide conservation management and prioritize sediment sources for Federal and State supported programs in the United States. However, the collection and analysis of source samples can make these studies unaffordable, especially when needed for multiple drainage basins. We investigate the potential use of source samples from a b

A framework for prioritizing contaminants in retrospective ecological assessments: Application in the Milwaukee Estuary (Milwaukee, WI)

Watersheds are subjected to diverse anthropogenic inputs, exposing aquatic biota to a wide range of chemicals. Detection of multiple, different chemicals can challenge natural resource managers who often have to determine where to allocate potentially limited resources. Here, we describe a weight-of-evidence framework for retrospectively prioritizing aquatic contaminants. To demonstrate framework

Creek and quarry water quality at Pipestone National Monument and pilot study of pathogen detection methods in waterfall mist at Winnewissa Falls, Pipestone, Minnesota, 2018–19

Pipestone National Monument is a 301-acre site sacred to many Native American Tribes, providing cultural exhibits and walking trails to Pipestone Creek, Winnewissa Falls, and historical pipestone quarries for numerous visitors each year. However, the Minnesota Pollution Control Agency has determined turbidity and fecal coliform bacteria occur in Pipestone Creek in high enough numbers to be a poten

Agricultural conservation practices could help offset climate change impacts on cyanobacterial harmful algal blooms in Lake Erie

Harmful algal blooms (HABs) are a recurring problem in many temperate large lake and coastal marine ecosystems, caused mainly by anthropogenic eutrophication. Implementation of agricultural conservation practices (ACPs) offers a means to reduce non-point source nutrient runoff and mitigate HABs. However, the effectiveness of ACPs in a changing climate remains uncertain. We used an integrated bioph

Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets?

Hydrological models are usually calibrated to in-situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged basins, the single-objective calibration to gaged streamflow cannot guarantee reliable forecasts because, as has been documented elsewhere, the inverse problem is m

Hydrologic change in the St. Louis River Basin from iron mining on the Mesabi Iron Range, northeastern Minnesota

This study compares the results of two regional steady-state U.S. Geological Survey Modular Three-Dimensional Finite-Difference Ground-Water Flow (MODFLOW) models constructed to quantify the hydrologic changes in the St. Louis River Basin from iron mining on the Mesabi Iron Range in northeastern Minnesota. The U.S. Geological Survey collaborated in this study with bands of the Minnesota Chippewa T

Estimating flood magnitude and frequency for unregulated streams in Wisconsin

Flood frequency characteristics and estimated flood discharges for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities were computed at 299 streamgaged locations in Wisconsin. The State was divided into four flood frequency regions using a cluster analysis to produce regions which are homogeneous with respect to physical basin characteristics. Regression equations

Juxtaposition of intensive agriculture, vulnerable aquifers, and mixed chemical/microbial exposures in private-well tapwater in northeast Iowa

In the United States and globally, contaminant exposure in unregulated private-well point-of-use tapwater (TW) is a recognized public-health data gap and an obstacle to both risk-management and homeowner decision making. To help address the lack of data on broad contaminant exposures in private-well TW from hydrologically-vulnerable (alluvial, karst) aquifers in agriculturally-intensive landscapes

Contaminant exposure and transport from three potential reuse waters within a single watershed

Global demand for safe and sustainable water supplies necessitates a better understanding of contaminant exposures in potential reuse waters. In this study, we compared exposures and load contributions to surface water from the discharge of three reuse waters (wastewater effluent, urban stormwater, and agricultural runoff). Results document substantial and varying organic-chemical contribution to

Machine learning for understanding inland water quantity, quality, and ecology

This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or analysis; generating and exploring hypotheses; est