An Empirical Reevaluation of Streamflow Recession Analysis at the Continental Scale

Abstract

Streamflow recession analysis is a widely used hydrologic tool that uses readily available discharge measurements to estimate otherwise unmeasurable watershed-scale properties, predict low flows, and parameterize many lumped hydrologic models. Traditional methods apply the simplifying assumptions of outflow from a Boussinesq aquifer, which predicts the slope of the recession curve relating streamflow to its derivative in log-log space to decrease from early-stage to late-stage recession. However, this prediction has not been validated in actual watersheds. Also, recent studies have shown that slopes of observed recession events are often much greater than traditional methods that predict with data point clouds. We analyze recession behavior of 1,027 streams from across the continental United States for periods of 10 to 118 years, identifying over 155,000 individual recession events. We find that the average slope of observed recession events is greater than that of the point cloud for all streams. Further, recession slopes of observed events decrease with time in only 10% of cases and instead increase with time in 74% of cases. We identify only nine watersheds where observed streamflow behavior often conforms to the predictions of traditional recession analysis, each of which is arid and flat with low permeability. Analysis of our extensive empirical results with a regionalization of catchment hydrologic characteristics indicates that heterogeneity of subsurface flow paths increases the nonlinearity and convexity of observed recession, likely as a function of watershed memory. The practical implications of our analysis are that streamflow is more stable during periods of extended drought than generally predicted.

Drought Propagation in Contiguous U.S. Watersheds: A Process‐Based Understanding of the Role of Climate and Watershed Properties

“…Thus, in the Great Plains and southwest U.S. watersheds, streamflow recedes rapidly following high flow events due to the nearly constant contribution of baseflow during and after storm events; whereas in watersheds of western and eastern United States, streamflow recedes more gradually due to the variability of baseflow. A similar pattern was also found in a recent study by Tashie, Pavelsky, and Band (2020), who analyzed the streamflow recession behavior of 1,027 streams across the CONUS. Zhang et al (2019) reported slower response of groundwater storage in response to precipitation in the Great Plains region and faster response in the western and eastern United States.…”

“…For instance, Sproles et al (2015) showed that the Upper Columbia watershed in the northwest United States, which has a steeper topography, exhibited a stronger variation in baseflow in response to the watershed storage as compared to the Snake river basin which has a flatter terrain. Tashie, Pavelsky, and Band (2020) showed that the flat terrain in the Great Plains region also contributed to lower variability of baseflow in the watersheds of this region. In terms of geological properties, Stoelzle et al (2014) showed that in watersheds with karstic and fractured aquifers, baseflow shows short‐term response to variations in storage whereas the watersheds with porous aquifers show a more delayed response of baseflow.…”

Spatial and Temporal Patterns in Baseflow Recession in the Continental United States

“…However, it is becoming increasingly common to interpret the data point-cloud as a mathematical artifact (Jachens et al, 2019;Sánchez-Murillo et al, 2015) and to acknowledge that point-cloud based regression methods systematically underestimate the nonlinearity of observed recession events (Santos et al, 2019;Tashie et al, 2020). Instead, many researchers have begun to assess watersheds according to the typical values of recession parameters calculated using individual recession events (e.g., Dralle et al, 2017;Shaw & Riha, 2012).…”

“…Instead, many researchers have begun to assess watersheds according to the typical values of recession parameters calculated using individual recession events (e.g., Dralle et al, 2017;Shaw & Riha, 2012). Promisingly, the typical nonlinearity of individual events strongly predicts the nonlinearity of data point clouds (as well as multiple baseflow indices), indicating that regional-scale physical mechanisms likely underlie recession nonlinearity (Tashie et al, 2020). However, these physical mechanisms have proven difficult to identify (Patnaik et al, 2018).…”

“…Previous studies have investigated the effects of geomorphologic, soil hydraulic, and climatologic properties on basin-average recession characteristic values at the regional scale (Mutzner et al, 2013;Patnaik et al, 2015Patnaik et al, , 2018Sánchez-Murillo et al, 2015;Tashie et al, 2020;Ye et al, 2014) and of temporally variable drivers like ET, PPT, soil moisture, or groundwater at the local scale (Karlsen et al, 2019;Shaw & Riha, 2012;Tashie et al, 2019). However, potential regional patterns and trends in the seasonal dynamics of recession characteristics (and their physical drivers) remain poorly understood.…”