Harnessing hyperspectral imagery to map surface water presence and hyporheic flow properties of headwater stream networks DOI Creative Commons
David Dralle, Dana Lapides,

Daniell Rempe

et al.

EarthArXiv (California Digital Library), Journal Year: 2022, Volume and Issue: unknown

Published: Oct. 15, 2022

Growth and contraction of headwater stream networks determine the extent quality ecologically critical habitat, open a window into storage dynamics catchments. A fundamental challenge is observation process itself: wetted channel highly dynamic in space time, with length sometimes varying by orders magnitude over course single storm event To date, observational datasets are produced from boots-on-the-ground campaigns, drone imaging, or flow presence sensors, which often laborious limited their spatial temporal extents. Here, we evaluate high-resolution, multi-band satellite imagery as means to detect via machine learning methods trained using existing surveys. Even where features smaller than resolution imagery, absence surface water may nevertheless be imprinted upon spectral signature an individual pixel. We leverage surveys at two oak savanna catchments northern California minimal riparian canopy cover due small subsurface capacity saturation overland flow. train random forest model on high-resolution ($\sim$5 m pixel) RapidEye captured contemporaneously Withheld test data indicates prediction accuracy wet vs. dry $>$91\%. This predictive ability used produce length-discharge (L-Q) relations calculate spatially distributed estimates hyporheic exchange. sharp break properties occurs transition main stem channels lower order tributaries, resulting stepped L-Q relationship that cannot traditionally power law models. Remotely sensed powerful tool for producing maps high ($\sim$10 this study $>$ 0.01 km$^2$ contributing area).

Language: Английский

Stream Network Dynamics of Non‐Perennial Rivers: Insights From Integrated Surface‐Subsurface Hydrological Modeling of Two Virtual Catchments DOI Creative Commons
Francesca Zanetti, Gianluca Botter, Matteo Camporese

et al.

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(2)

Published: Feb. 1, 2024

Abstract Understanding the spatio‐temporal dynamics of runoff generation in headwater catchments is challenging, due to intermittent and fragmented nature surface flows. The active stream network non‐perennial rivers contracts expands, with a dynamic behavior that depends on complex interplay among climate, topography, geology. In this work, CATchment HYdrology, an integrated surface–subsurface hydrological model (ISSHM), used simulate two virtual same, spatially homogeneous, subsurface characteristics (hydraulic conductivity, porosity, water retention curves) but different morphology. We run sets simulations reproduce sequence steady‐states at catchment wetness levels transient conditions analyze joint variations length ( L ) discharge outlet Q high resolutions. shape curves differs does not depend climate forcing, as it mainly controlled by underlying topography. then analyzed suitability topographic index contributing area identify spatial configuration maximum catchments. These morphometric parameters provided good estimate distribution flowing both study Our numerical indicate ISSHMs have potential accurately describe networks processes driving such that, overall, they can be useful tools gain insights into main physical drivers streams.

Language: Английский

Citations

11
Characterizing Space‐Time Channel Network Dynamics in a Mediterranean Intermittent Catchment of Central Italy Combining Visual Surveys and Cameras DOI Creative Commons
Simone Noto, Nicola Durighetto, Flavia Tauro

et al.

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(1)

Published: Jan. 1, 2024

Abstract Non‐perennial streams have a global prevalence, but quantitative knowledge of the temporal dynamics their flowing length—namely extent wet portion stream network—remains limited, as monitoring spatiotemporal configuration channels is challenging in most settings. This work combines high spatial resolution visual surveys and camera‐based approaches to reconstruct space‐time network 3.7 km 2 Mediterranean catchment central Italy. Information on hydrological status derived from 40 field sub‐hourly images collected with 21 stage‐cameras are combined exploiting hierarchical principle. The latter postulates existence Bayesian chain, defined local persistence nodes that dictates wetting/drying order during expansion/retraction cycles network. Our results highlight complexity study area: while number decreases dry season increases season, persistency exhibits highly heterogeneous non‐monotonic pattern, originating dynamically disconnected Despite this heterogeneity, model well approximates evolution state nodes, an accuracy exceeds 99%. Crucially, allows reconstruction even cases which part was not observed. provides novel conceptual approach for poorly accessible sites.

Language: Английский

Citations

6
On the Relation Between Active Network Length and Catchment Discharge DOI
Nicola Durighetto, Gianluca Botter

Geophysical Research Letters, Journal Year: 2022, Volume and Issue: 49(14)

Published: July 19, 2022

The ever-changing hydroclimatic conditions of the landscape induce ceaseless variations in wet channel length (

Language: Английский

Citations

21
Dynamics of streamflow permanence in a headwater network: Insights from catchment-scale model simulations DOI Creative Commons
David Tyler Mahoney, Jay R. Christensen, Heather E. Golden

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 620, P. 129422 - 129422

Published: March 22, 2023

Language: Английский

Citations

11
Mapping Surface Water Presence and Hyporheic Flow Properties of Headwater Stream Networks With Multispectral Satellite Imagery DOI Creative Commons
David Dralle, Dana Lapides, Daniella Rempe

et al.

Water Resources Research, Journal Year: 2023, Volume and Issue: 59(9)

Published: Aug. 16, 2023

Abstract Growth and contraction of headwater stream networks determine habitat extent, open a window to the hyporheic zone. A fundamental challenge is observation this process: wetted channel extent dynamic in space time, with length varying by orders magnitude over course single storm event catchments. To date, observational data sets are produced from boots‐on‐the‐ground campaigns, drone imaging, or flow presence sensors, which often laborious limited their spatial temporal extents. Here, we evaluate satellite imagery as means detect via machine learning methods trained on local surveys extent. Even where features smaller than imagery's resolution, surface water may be imprinted upon spectral signature an individual pixel. For two catchments northern California minimal riparian canopy cover highly train random forest model RapidEye captured contemporaneously existing predict (accuracy >91%). The used produce length‐discharge (L‐Q) relations calculate spatially distributed estimates capacity exchange. sharp break occurs main stem channels lower order tributaries, resulting stepped L‐Q relationship that cannot traditionally power law models. Remotely sensed powerful tool for mapping at high resolution.

Language: Английский

Citations

11
Biogeochemical and community ecology responses to the wetting of non-perennial streams DOI
Adam N. Price, Margaret Zimmer, Anna Bergstrom

et al.

Nature Water, Journal Year: 2024, Volume and Issue: 2(9), P. 815 - 826

Published: Sept. 19, 2024

Language: Английский

Citations

4
Integrating spatially-and temporally-heterogeneous data on river network dynamics using graph theory DOI Creative Commons
Nicola Durighetto, Simone Noto, Flavia Tauro

et al.

iScience, Journal Year: 2023, Volume and Issue: 26(8), P. 107417 - 107417

Published: July 23, 2023

The study of non-perennial streams requires extensive experimental data on the temporal evolution surface flow presence across different nodes channel networks. However, consistency and homogeneity available datasets is threatened by empirical burden required to map stream network expansions contractions. Here, we developed a data-driven, graph-theory framework aimed at representing hierarchical structuring dynamics (i.e., order node activation/deactivation during expansion/retraction) through directed acyclic graph. method enables estimation configuration active portion based limited number observed nodes, can be utilized combine with resolutions spatial coverage. A proof-of-concept application seasonally-dry catchment in central Italy demonstrated ability approach reduce effort for monitoring efficiently extrapolate observations space time.

Language: Английский

Citations

9
Improving calibration of groundwater flow models using headwater streamflow intermittence DOI
Ronan Abhervé, Clément Roques, Jean‐Raynald de Dreuzy

et al.

Hydrological Processes, Journal Year: 2024, Volume and Issue: 38(6)

Published: June 1, 2024

Abstract Non‐perennial streams play a crucial role in ecological communities and the hydrological cycle. However, key parameters processes involved stream intermittency remain poorly understood. While climatic conditions, geology land use are well identified, assessment modelling of groundwater controls on streamflow intermittence challenge. In this study, we explore new opportunities to calibrate process‐based 3D flow models designed simulate hydrographic network dynamics groundwater‐fed headwaters. Streamflow measurements maps considered together constrain effective hydraulic properties aquifer hydrogeological models. The simulations were then validated using visual observations water presence/absence, provided by national monitoring France (ONDE). We tested methodology two pilot unconfined shallow crystalline catchments, Canut Nançon catchments (Brittany, France). found that both expansion/contraction required simultaneously estimate conductivity porosity with low uncertainties. calibration allowed good prediction intermittency, terms spatial extent. For studied, Nançon, is close reaching 1.5 × 10 −5 m/s 4.5 m/s, respectively. they differ more their storage capacity, estimated at 0.1% 2.2%, Lower capacity leads higher level fluctuations, shorter response times, an increase proportion intermittent reduction perennial flow. This framework for predicting headwater can be deployed improve our understanding different geomorphological, geological contexts. It will benefit from advances remote sensing crowdsourcing approaches generate observational data products high temporal resolution.

Language: Английский

Citations

3
Extending Active Network Length Versus Catchment Discharge Relations to Temporarily Dry Outlets DOI Creative Commons
Gianluca Botter, J. P. McNamara, Nicola Durighetto

et al.

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(1)

Published: Jan. 1, 2024

Abstract River networks are not steady blue lines drawn in a map, since they continuously change their shape and extent response to climatic drivers. Therefore, the flowing length of rivers ( L ) corresponding catchment‐scale streamflow Q sur co‐evolve dynamically. This paper analyzes relationship between wet channel river basin, formulating general analytical model that includes case temporarily dry outlets. In particular, framework relaxes common assumption when discharge at outlet tends zero upstream approaches zero. Different expressions for law derived cases (a) perennial outlet; (b) non‐perennial dries out only whole network is dry; (c) outlet, experiences surface flow less time than other nodes. all cases, controlled by distribution specific subsurface capacity along network. For outlets, however, relation might depend on an unknown shifting factor. Three real‐world examples presented demonstrate flexibility robustness theory. Our results indicate be empirically observable if significant fraction or some reaches experience longer gauging station. The study provides basis integrating empirical data gathered diverse sites.

Language: Английский

Citations

2
Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG DOI Creative Commons
Ken Aho, Cathy Kriloff, Sarah E. Godsey

et al.

Environmental Modelling & Software, Journal Year: 2023, Volume and Issue: 167, P. 105775 - 105775

Published: July 5, 2023

Language: Английский

Citations

5