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

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, 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.

Enhancing the Predictability of Seasonal Streamflow With a Statistical‐Dynamical Approach

“…We implement the Generalized Additive Models for Location, Scale and Shape (GAMLSS) using the gamlss package in R (Rigby & Stasinopoulos, 2005). However, previous work has shown that the inclusion of additional predictors, such as antecedent climatic conditions and land cover, enhances the model fit to seasonal streamflow data (Slater & Villarini, 2017). However, previous work has shown that the inclusion of additional predictors, such as antecedent climatic conditions and land cover, enhances the model fit to seasonal streamflow data (Slater & Villarini, 2017).…”

“…Agricultural land cover data are obtained from the U.S. Department of Agriculture's National Agricultural Statistics Services quickstats database. Time series are computed to reflect the average population density in each catchment, which varies from 0.6 pers/km 2 to 1339 pers/km 2 (see Slater & Villarini, 2017). Population density statistics are from the U.S. Census Bureau's Population Estimates Program.…”

“…Previous work has shown that the use of corn and soybean catchment extent as a predictor improves seasonal streamflow model fits in the summer and fall, alongside antecedent precipitation (Slater & Villarini, 2017). Previous work has shown that the use of corn and soybean catchment extent as a predictor improves seasonal streamflow model fits in the summer and fall, alongside antecedent precipitation (Slater & Villarini, 2017).…”

On the decadal predictability of the frequency of flood events across the U.S. Midwest

“…Data‐driven approaches of this kind are recognized to be straightforward to implement, especially given the availability of climate data, and they may yield results as accurate as those obtained by physically based models (Pagano et al ., ; Duethmann et al ., ). One key component in statistical flood forecasting is identifying which physical drivers are responsible for the observed inter‐annual variability: there have been growing efforts towards attribution studies of this kind, indicating that precipitation and antecedent moisture conditions are dominant flood drivers (e.g., Mallakpour and Villarini, ; Slater and Villarini, ; ; ). Building on the insights gained from these attribution studies, the research questions we aim to answer in this study are: Is it possible to skilfully predict the frequency of flood events across the central United States?…”

Identification of Hydrological Drought in Eastern China Using a Time-Dependent Drought Index

“…The Generalized Additive Models for Location, Scale and Shape (GAMLSS), proposed by Rigby and Stasinopoulos [26], has recently gained popularity in modeling nonstationarity time series in hydrology [27]. This model provides a high degree of flexibility in addressing nonstationarity probabilistic modeling.…”