Modeling wet headwater stream networks across multiple flow conditions in the Appalachian Highlands

Abstract

Despite the advancement of remote sensing and geospatial technology in recent decades, maps of headwater streams continue to have high uncertainty and fail to adequately characterize temporary streams that expand and contract in the wet length. However, watershed management and policy increasingly require information regarding the spatial and temporal variability of flow along streams. We used extensive field data on wet stream length at different flows to create logistic regression models of stream network dynamics for four physiographic provinces of the Appalachian Highlands: New England, Appalachian Plateau, Valley and Ridge, and Blue Ridge. The topographic wetness index (TWI) was the most important parameter in all four models, and the topographic position index (TPI) further improved model performance in the Appalachian Plateau, Valley and Ridge, and Blue Ridge. We included stream runoff at the catchment outlet as a model predictor to represent the wetness state of the catchment, but adjustment of the probability threshold defining wet stream presence/absence to high values for low flows was the primary mechanism for approximating network extent at multiple flow conditions. Classification accuracy was high overall (> 0.90), and McFadden's pseudo R2 values ranged from 0.69 for the New England model to 0.79 in the Appalachian Plateau. More notable errors included an overestimation of wet stream length in wide valleys and inaccurate reach locations amid boulder deposits and along headwardly eroding tributaries. Logistic regression was generally successful for modeling headwater streams at high and low flows with only a few simple terrain metrics. Modification and application of this modeling approach to other regions or larger areas would be relatively easy and provide a more accurate portrayal of temporary headwaters than existing datasets. © 2018 John Wiley & Sons, Ltd.

Intraseasonal Drainage Network Dynamics in a Headwater Catchment of the Italian Alps

“…These overlapping dynamics may be in turn controlled by two distinct hydrological processes: (i) quick subsurface flow in the root zone feeding temporary streams and (ii) slower groundwater flow generated by the aquifers supplying water to the less dynamical reaches of the river network. The superposition of dynamics characterized by different time scales could lie at the basis of the hysteresis frequently observed in the relationship between discharge and ADNL (Jensen et al, ; Prancevic & Kirchner, ; Shaw et al, ; Ward et al, ). In spite of the empirical nature of the link between ADNL and precipitation provided in this paper, we believe that our results could provide a preliminary basis to incorporate the simulation of network expansion and contraction in hydrological models using climatic data.…”

Topographic Controls on the Extension and Retraction of Flowing Streams

“…This wide range in β indicates that some stream networks extend dramatically as catchments become wetter, while others remain nearly fixed in place ( Figure 1). For example, subsurface transport capacity can be sensitive to valley slope (Jensen et al, 2018;Whiting & Godsey, 2016;Wondzell, 2011), sediment size (Costigan et al, 2016;Wondzell, 2011), tectonic structures (Kennedy et al, 1984;Whiting & Godsey, 2016), and river behavior (e.g., incision versus aggradation; Costigan et al, 2016), but attempts to quantitatively relate these properties to stream network dynamics are rare. For example, subsurface transport capacity can be sensitive to valley slope (Jensen et al, 2018;Whiting & Godsey, 2016;Wondzell, 2011), sediment size (Costigan et al, 2016;Wondzell, 2011), tectonic structures (Kennedy et al, 1984;Whiting & Godsey, 2016), and river behavior (e.g., incision versus aggradation; Costigan et al, 2016), but attempts to quantitatively relate these properties to stream network dynamics are rare.…”

“…The tendency for some networks to be much more dynamic than others must reflect differences in the landscape properties that bring water to the surface. Two recent studies have attempted to model spatial and temporal patterns of stream connectivity and intermittency in a few well-studied catchments (Jensen et al, 2018;Ward et al, 2018), but these models cannot easily be transferred to other catchments. Two recent studies have attempted to model spatial and temporal patterns of stream connectivity and intermittency in a few well-studied catchments (Jensen et al, 2018;Ward et al, 2018), but these models cannot easily be transferred to other catchments.…”

Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development

“…Least-square regression equations using catchment-scale indicators have been used to differentiate flow duration classes in Idaho, where the annual minimum flow is a surrogate for flow duration [38]. Related to SDAMs, logistic models with catchment-scale indicators have also been used to predict the probability of stream reaches being wet or dry at different parts of the year [189]. Straka et al [175] developed an SDAM index (i.e., Biodrought index) that discriminated among three flow duration classes (perennial, near-perennial, and intermittent) in minimally disturbed streams of the Czech Republic.…”