Next Article in Journal
Combined Coagulation and Ultrafiltration Process to Counteract Increasing NOM in Brown Surface Water
Previous Article in Journal
Implication of Directly Connected Impervious Areas to the Mitigation of Peak Flows in Urban Catchments

Evaluating the Drivers of Seasonal Streamflow in the U.S. Midwest

Department of Geography, Loughborough University, Loughborough LE11 3TU, UK
IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IA 52242, USA
Author to whom correspondence should be addressed.
Water 2017, 9(9), 695;
Received: 3 August 2017 / Revised: 30 August 2017 / Accepted: 8 September 2017 / Published: 12 September 2017
Streamflows have increased notably across the U.S. Midwest over the past century, fueling a debate on the relative influences of changes in precipitation and land cover on the flow distribution. Here, we propose a simple modeling framework to evaluate the main drivers of streamflow rates. Streamflow records from 290 long-term USGS stream gauges were modeled using five predictors: precipitation, antecedent wetness, temperature, agriculture, and population density. We evaluated which predictor combinations performed best for every site, season and streamflow quantile. The goodness-of-fit of our models is generally high and varies by season (higher in the spring and summer than in the fall and winter), by streamflow quantile (best for high flows in the spring and winter, best for low flows in the fall, and good for all flow quantiles in summer), and by region (better in the southeastern Midwest than in the northwestern Midwest). In terms of predictors, we find that precipitation variability is key for modeling high flows, while antecedent wetness is a crucial secondary driver for low and median flows. Temperature improves model fits considerably in areas and seasons with notable snowmelt or evapotranspiration. Finally, in agricultural and urban basins, harvested acreage and population density are important predictors of changing streamflow, and their influence varies seasonally. Thus, any projected changes in these drivers are likely to have notable effects on future streamflow distributions, with potential implications for basin water management, agriculture, and flood risk management. View Full-Text
Keywords: streamflow; statistical modeling; attribution; time series; seasonal streamflow; statistical modeling; attribution; time series; seasonal
Show Figures

Figure 1

MDPI and ACS Style

Slater, L.J.; Villarini, G. Evaluating the Drivers of Seasonal Streamflow in the U.S. Midwest. Water 2017, 9, 695.

AMA Style

Slater LJ, Villarini G. Evaluating the Drivers of Seasonal Streamflow in the U.S. Midwest. Water. 2017; 9(9):695.

Chicago/Turabian Style

Slater, Louise J., and Gabriele Villarini. 2017. "Evaluating the Drivers of Seasonal Streamflow in the U.S. Midwest" Water 9, no. 9: 695.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop