4.7 Article

Simulation of dynamic expansion, contraction, and connectivity in a mountain stream network

期刊

ADVANCES IN WATER RESOURCES
卷 114, 期 -, 页码 64-82

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.advwatres.2018.01.018

关键词

River corridor; Hyporheic; Solute tracer; Riparian; Network; Stream

资金

  1. National Science Foundation's (NSF's) Long-Term Ecological Research Program [DEB 1440409]
  2. US Forest Service Pacific Northwest Research Station
  3. Oregon State University
  4. NSF [EAR 1417603, EAR 1652293, EAR 1505309, EAR 1331906]
  5. Indiana University Office of the Vice Provost for Research
  6. Indiana Water Resources Research Center
  7. Lilly Endowment, Inc., through its Indiana University (IU) Pervasive Technology Institute
  8. Indiana METACyt Initiative
  9. Lilly Endowment, Inc.
  10. Division Of Earth Sciences [1505309] Funding Source: National Science Foundation
  11. Division Of Environmental Biology [1440409] Funding Source: National Science Foundation

向作者/读者索取更多资源

Headwater stream networks expand and contract in response to changes in stream discharge. The changes in the extent of the stream network are also controlled by geologic or geomorphic setting - some reaches go dry even under relatively wet conditions, other reaches remain flowing under relatively dry conditions. While such patterns are well recognized, we currently lack tools to predict the extent of the stream network and the times and locations where the network is dry within large river networks. Here, we develop a perceptual model of the river corridor in a headwater mountainous catchment, translate this into a reduced-complexity mechanistic model, and implement the model to examine connectivity and network extent over an entire water year. Our model agreed reasonably well with our observations, showing that the extent and connectivity of the river network was most sensitive to hydrologic forcing under the lowest discharges (Q(gauge) < 1 L s(-1)), that at intermediate discharges (1 L s(-1) < Q(gauge) < 10 L s(-1)) the extent of the network changed dramatically with changes in discharge, and that under wet conditions (Q(gauge) > 10 L s(-1)) the extent of the network was relatively insensitive to hydrologic forcing and was instead determined by the network topology. We do not expect that the specific thresholds observed in this study would be transferable to other catchments with different geology, topology, or hydrologic forcing. However, we expect that the general pattern should be robust: the dominant controls will shift from hydrologic forcing to geologic setting as discharge increases. Furthermore, our method is readily transferable as the model can be applied with minimal data requirements (a single stream gauge, a digital terrain model, and estimates of hydrogeologic properties) to estimate flow duration or connectivity along the river corridor in unstudied catchments. As the available information increases, the model could be better calibrated to match site-specific observations of network extent, locations of dry reaches, or solute break through curves as demonstrated in this study. Based on the low initial data requirements and ability to later tune the model to a specific site, we suggest example applications of this parsimonious model that may prove useful to both researchers and managers. (C) 2018 Elsevier Ltd. All rights reserved.

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