Jaqueline Ortiz | Search USGS

USGS2025

Exploring the news media and scientific conversations around water quality in a water-rich basin of the United States

Community concerns about water availability vary depending on local economic, regulatory, environmental, and ecological considerations. In water-rich basins, water quality is often the focus of community concerns. As such, understanding community priorities in the context of water quality is crucial for informing scientists working in water-rich basins. In this work, we compiled over 6,500 local news articles (public discourse) and 190 scientific abstracts (scientific discourse) related to water-quality issues in the water-rich Illinois River Basin (ILRB) published between 2018 and 2022. We applied a Structural Topic Model (STM) to identify key water-quality topics within both datasets and explore the variability of newspaper topics geographically across the basin. Prevalent topics in both the public (local news articles) and scientific (abstracts) discourses were agriculture, drinking water quality, PFAS (per- and polyfluoroalkyl substances), and river ecosystem/fish. Topics exclusive to public discourse included water infrastructure, community development, and public water supply, while the scientific discourse focused more heavily on a wider range of agricultural issues. Furthermore, the public discourse varied geographically across the basin. Some topics are correlated with land use or urban/rural divides within the basin, and the frequency of many topics clearly varied across state (political) boundaries. Understanding and quantifying public and scientific discourses related to water-quality are important for scientists and water managers working in the basin to improve communication of critical science to the public.

Illinois, Indiana, Michigan, Wisconsin↗

USGS2025

The joint effect of changes in urbanization and climate on trends in floods: A comparison of panel and single-station quantile regression approaches

Estimates of annual maximum (peak) flow quantiles are needed for basins undergoing changes in both urbanization and climate. Most previous work on the effect of urbanization on peak flows has considered urbanization alone and only the spatial variation in flood quantiles or its mean temporal effect, and most work on the effect of nonstationarity in climate has focused on single-station analyses, which give uncertain results for extreme quantiles. To address these gaps, three approaches to the statistical estimation of the joint effects of changes in impervious cover and climate on the estimation of peak-flow quantiles were compared: single-station quantile regression; a fixed effect panel-quantile regression (pQR) method using a location (mean) shift to homogenize the panel; and a location-scale panel regression model (pQRmom), which accounts for both scale (variance) and location effects. The different approaches were applied to a dataset consisting of instantaneous annual peak flows from 127 minimally nested basins in the midwestern United States with at least 4 % change in imperviousness. The annual maximum daily discharge from a water-balance model was selected as the primary climate predictor; in addition, to provide a comparison of climate predictors, precipitation was also considered. The coefficients from single-station regressions were usually sufficiently certain to determine the effects of climate variation but usually too uncertain to estimate the effects of urbanization. The panel-quantile regression approaches give much more certain results, but their estimates of quantile dependence differ: although both indicate urbanization effects decreasing with decreasing annual exceedance probability (AEP), the pQRmom urbanization coefficients are insignificantly different from zero for AEPs less than 0.10, whereas the pQR coefficients remain positive and are significant except for AEP = 0.01, the smallest AEP value considered. Although the location-scale structure of the pQRmom approach has less flexible quantile dependence than the pQR approach, the pQRmom approach has somewhat lower overall error, and it is found that by subsetting the dataset to homogenize the scale effects, the pQR and pQRmom results become similar, indicating the insignificant urbanization coefficients for small AEPs of the pQRmom results are likely correct for the study dataset.

Arkansas, Illinois, Indiana, Iowa, Michigan, Minne↗