Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG

Aho, Ken; Kriloff, Cathy; Godsey, Sarah E.; Ramos, R.; Wheeler, Chris; You, Yaqi; Warix, Sara R.; Derryberry, DeWayne R.; Zipper, Samuel C.; Hale, Rebecca L.; Bond, C.; Kuehn, Kevin A.

doi:10.1016/j.envsoft.2023.105775

Cited by 4 publications

(1 citation statement)

References 94 publications

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“…Botter et al (2021) described the concept of hierarchal activation of drainage networks, which were later used to develop a simple analytical model and a more complex stochastic model Durighetto, Mariotto, et al, 2022) of FDN dynamics. Recently, Aho et al (2023) used directed acyclic graphs to summarize and track non-perennial stream characteristics as FDN expand and contract. However, all these FDN modelling studies face challenges of limited FDN training data.…”

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confidence: 99%

Short‐term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

Bujak‐Ozga,

van Meerveld,

Rinaldo

et al. 2023

Hydrological Processes

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Headwater streams often experience intermittent flow. Consequently, the flowing drainage network expands and contracts and the flowing drainage density (DD) varies over time. Monitoring the DD dynamics is essential to understand the processes controlling it. However, our knowledge of the event‐scale DD dynamics is limited because high spatial and temporal resolution data on the DD remain sparse. Therefore, our team monitored the DD dynamics and hydrologic variables in two 5‐ha headwater catchments in the Swiss pre‐Alps in the summer of 2021, through mapping surveys of the flow state and a wireless streamwater level sensor network. We combined the two data sources to calculate the DD at the event‐time scale. Our so‐called CEASE method assumes that flow in a channel reach occurs above a set of water level thresholds, and it determined the DDs with accuracies >94%. DD responses to events differed for the two catchments, despite their proximity and similar size. DD ranged from 2.7 to 32.2 km km−2 in the flatter catchment (average slope: 15°). For this catchment, the discharge‐DD relationship became steeper when DD exceeded 20 km km−2 and DD increased substantially with relatively small increases in discharge. For rainfall events during dry conditions, the discharge‐DD relationship showed counterclockwise hysteresis, likely due to initially high groundwater discharge from the area near the catchment outlet; once rainfall stopped, DD remained high during the streamflow recession due to rising groundwater levels throughout the catchment. For events during wet conditions, the discharge and DD responded synchronously. In the steeper catchment (average slope: 24°), the DD varied only from 7.8 to 14.6 km km−2 and there was no hysteresis or threshold behaviour in the discharge‐DD relationship, likely because multiple groundwater springs maintained streamflow throughout the network during the monitoring period. These results highlight the high variability in DD and its dynamics across small headwater catchments.

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confidence: 99%

Short‐term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

Bujak‐Ozga,

van Meerveld,

Rinaldo

et al. 2023

Hydrological Processes

View full text Add to dashboard Cite

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Structural characteristics and spatiotemporal changes of a reticular river network based on complex network theory

Huang,

Wang,

Hua

et al. 2024

Journal of Hydrology

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Communication Distance and Bayesian Inference in Non‐Perennial Streams

Aho,

Derryberry,

Godsey

et al. 2023

Water Resources Research

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Non‐perennial streams are receiving increased attention from researchers, however, suitable methods for measuring their hydrologic connectivity remain scarce. To address this deficiency, we developed Bayesian statistical approaches for measuring both average active stream length, and a new metric called average communication distance. Average communication distance is a theoretical increased effective distance that stream‐borne materials must travel, given non‐continuous streamflow. Because it is the product of the inverse probability of surface water presence and stream length, the average communication distance of a non‐perennial stream segment will be greater than its actual physical length. As an application we considered Murphy Creek, a simple non‐perennial stream network in southwestern Idaho, USA. We used surface water presence/absence data obtained in 2019, and priors for the probability of surface water, based on predictions from an existing regional United States Geological Survey model. Average communication distance posterior distributions revealed locations where effective stream lengths increased dramatically due to flow rarity. We also found strong seasonal (spring, summer, fall) differences in network‐level posterior distributions of both average stream length and average communication distance. Our work demonstrates the unique perspectives concerning network drying provided by communication distance, and demonstrates the general usefulness of Bayesian approaches in the analysis of non‐perennial streams.

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Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG

Cited by 4 publications

References 94 publications

Short‐term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

Short‐term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

Structural characteristics and spatiotemporal changes of a reticular river network based on complex network theory

Communication Distance and Bayesian Inference in Non‐Perennial Streams

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