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Title: Evaluating the drivers of seasonal streamflow in the U.S. Midwest
Authors: Slater, Louise
Villarini, Gabriele
Keywords: Streamflow
Statistical modeling
Antecedent wetness
Population density
Issue Date: 2017
Publisher: MDPI (© The Authors)
Citation: SLATER, L and VILLARINI, G. Evaluating the drivers of seasonal streamflow in the U.S. Midwest. Water, 9(9), 695
Abstract: 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, 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. Last, 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.
Description: This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Sponsor: This study was supported in part by the Broad Agency Announcement (BAA) Program and the Engineer Research and Development Center (ERDC)–Cold Regions Research and Engineering Laboratory (CRREL) under Contract No. W913E5-16-C-0002, and the National Science Foundation under CAREER Grant AGS-1349827.
Version: Accepted for publication
DOI: 10.3390/w9090695
URI: https://dspace.lboro.ac.uk/2134/26462
Publisher Link: https://doi.org/10.3390/w9090695
ISSN: 2073-4441
Appears in Collections:Published Articles (Geography and Environment)

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