An automated knickzone selection algorithm (KZ‐Picker) to analyze transient landscapes: Calibration and validation

Streams commonly respond to base‐level fall by localizing erosion within steepened, convex knickzone reaches. Localized incision causes knickzone reaches to migrate upstream. Such migrating knickzones dictate the pace of landscape response to changes in tectonics or erosional efficiency and can help...

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Vydáno v:Journal of geophysical research. Earth surface Ročník 122; číslo 6; s. 1236 - 1261
Hlavní autoři: Neely, A. B., Bookhagen, B., Burbank, D. W.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Washington Blackwell Publishing Ltd 01.06.2017
Témata:
Accuracy
Algorithms
Automation
Balances (scales)
California
Catchments
Climate
Climate change
Convergence
Digital Elevation Models
Drainage
Dynamics
Earthquake construction
Earthquakes
Efficiency
Environmental changes
Environmental conditions
Erosion
Fluvial morphology
Frameworks
Geological structures
geophysics
Gradients
Height
incision
knickpoint
knickzone
Land use
Landforms
Landscape
landscapes
Mathematical models
Morphology
Mountains
Oceans
Parameter identification
Parameters
Position (location)
relict landscape
River channels
River morphology
Rivers
Santa Cruz Island
Scripts
Sea level changes
Seismic activity
Shape
Slopes
Streams
Structures
Technology
Tectonics
topographic slope
Training
transient
Uplift
Upstream
Watersheds
California
ISSN:2169-9003, 2169-9011
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Abstract Streams commonly respond to base‐level fall by localizing erosion within steepened, convex knickzone reaches. Localized incision causes knickzone reaches to migrate upstream. Such migrating knickzones dictate the pace of landscape response to changes in tectonics or erosional efficiency and can help quantify the timing and source of base‐level fall. Identification of knickzones typically requires individual selection of steepened reaches: a process that is tedious and subjective and has no efficient means to measure knickzone size. We construct an algorithm to automate this procedure by selecting the bounds of knickzone reaches in a χ‐space (drainage‐area normalized) framework. An automated feature calibrates algorithm parameters to a subset of knickzones handpicked by the user. The algorithm uses these parameters as consistent criteria to identify knickzones objectively, and then the algorithm measures the height, length, and slope of each knickzone reach. We test the algorithm on 1, 10, and 30 m resolution digital elevation models (DEMs) of six catchments (trunk‐stream lengths: 2.1–5.4 km) on Santa Cruz Island, southern California. On the 1 m DEM, algorithm‐selected knickzones confirm 93% of handpicked knickzone positions (n = 178) to a spatial accuracy of ≤100 m, 88% to an accuracy within 50 m, and 46% to an accuracy within 10 m. Using 10 and 30 m DEMs, accuracy is similar: 88–86% to ≤100 m and 82% to ≤50 m (n = 38 and 36, respectively). The algorithm enables efficient regional comparison of the size and location of knickzones with geologic structures, mapped landforms, and hillslope morphology, thereby facilitating approaches to characterize the dynamics of transient landscapes. Plain Language Summary The shape of rivers reflects the environments that they flow through and the environments that they link together: mountains and oceans. Anywhere along the length of a river, changes in environmental conditions are propagated upstream and downstream as the river changes its morphology to match the new environmental conditions. Commonly, rivers steepen as land uplifts faster in regions of high tectonic convergence. The steepening of river gradients is propagated upstream and can be mapped to trace zones of high tectonic activity across landscapes and estimate the source and timing of environmental change. Such insights may indicate regions where earthquakes have become more frequent in the recent past and how rivers respond to these changes. In this submission, we detail an algorithm that can use digital topographic data (similar to google earth), to automatically map and measure anomalously steep river reaches across continental scales. This technology can highlight areas that have experienced recent sustained changes in environmental conditions, evident by changes in the morphology of rivers. Such environmental conditions could be changes in tectonic uplift and earthquake activity, changes in sea level, changes in land‐use, or changes in climate, all factors that can produce measurable differences in river morphology over time. Key Points Algorithm uses a training data set to objectively select and measure river knickzones as bounded reaches of sustained high channel steepness index Algorithm‐selected knickzones confirm ~80–90% of 178 manually selected knickzones on 1 to 30 m DEMs, and the measured height of confirmed knickzones matches to >90% Algorithm is embedded in a set of automated scripts and can be applied to any region with DEM coverage
AbstractList Streams commonly respond to base-level fall by localizing erosion within steepened, convex knickzone reaches. Localized incision causes knickzone reaches to migrate upstream. Such migrating knickzones dictate the pace of landscape response to changes in tectonics or erosional efficiency and can help quantify the timing and source of base-level fall. Identification of knickzones typically requires individual selection of steepened reaches: a process that is tedious and subjective and has no efficient means to measure knickzone size. We construct an algorithm to automate this procedure by selecting the bounds of knickzone reaches in a χ-space (drainage-area normalized) framework. An automated feature calibrates algorithm parameters to a subset of knickzones handpicked by the user. The algorithm uses these parameters as consistent criteria to identify knickzones objectively, and then the algorithm measures the height, length, and slope of each knickzone reach. We test the algorithm on 1, 10, and 30 m resolution digital elevation models (DEMs) of six catchments (trunk-stream lengths: 2.1-5.4 km) on Santa Cruz Island, southern California. On the 1 m DEM, algorithm-selected knickzones confirm 93% of handpicked knickzone positions (n = 178) to a spatial accuracy of ≤100 m, 88% to an accuracy within 50 m, and 46% to an accuracy within 10 m. Using 10 and 30 m DEMs, accuracy is similar: 88-86% to ≤100 m and 82% to ≤50 m (n = 38 and 36, respectively). The algorithm enables efficient regional comparison of the size and location of knickzones with geologic structures, mapped landforms, and hillslope morphology, thereby facilitating approaches to characterize the dynamics of transient landscapes. Plain Language Summary The shape of rivers reflects the environments that they flow through and the environments that they link together: mountains and oceans. Anywhere along the length of a river, changes in environmental conditions are propagated upstream and downstream as the river changes its morphology to match the new environmental conditions. Commonly, rivers steepen as land uplifts faster in regions of high tectonic convergence. The steepening of river gradients is propagated upstream and can be mapped to trace zones of high tectonic activity across landscapes and estimate the source and timing of environmental change. Such insights may indicate regions where earthquakes have become more frequent in the recent past and how rivers respond to these changes. In this submission, we detail an algorithm that can use digital topographic data (similar to google earth), to automatically map and measure anomalously steep river reaches across continental scales. This technology can highlight areas that have experienced recent sustained changes in environmental conditions, evident by changes in the morphology of rivers. Such environmental conditions could be changes in tectonic uplift and earthquake activity, changes in sea level, changes in land-use, or changes in climate, all factors that can produce measurable differences in river morphology over time. Key Points Algorithm uses a training data set to objectively select and measure river knickzones as bounded reaches of sustained high channel steepness index Algorithm-selected knickzones confirm ~80-90% of 178 manually selected knickzones on 1 to 30 m DEMs, and the measured height of confirmed knickzones matches to >90% Algorithm is embedded in a set of automated scripts and can be applied to any region with DEM coverage
Streams commonly respond to base‐level fall by localizing erosion within steepened, convex knickzone reaches. Localized incision causes knickzone reaches to migrate upstream. Such migrating knickzones dictate the pace of landscape response to changes in tectonics or erosional efficiency and can help quantify the timing and source of base‐level fall. Identification of knickzones typically requires individual selection of steepened reaches: a process that is tedious and subjective and has no efficient means to measure knickzone size. We construct an algorithm to automate this procedure by selecting the bounds of knickzone reaches in a χ‐space (drainage‐area normalized) framework. An automated feature calibrates algorithm parameters to a subset of knickzones handpicked by the user. The algorithm uses these parameters as consistent criteria to identify knickzones objectively, and then the algorithm measures the height, length, and slope of each knickzone reach. We test the algorithm on 1, 10, and 30 m resolution digital elevation models (DEMs) of six catchments (trunk‐stream lengths: 2.1–5.4 km) on Santa Cruz Island, southern California. On the 1 m DEM, algorithm‐selected knickzones confirm 93% of handpicked knickzone positions (n = 178) to a spatial accuracy of ≤100 m, 88% to an accuracy within 50 m, and 46% to an accuracy within 10 m. Using 10 and 30 m DEMs, accuracy is similar: 88–86% to ≤100 m and 82% to ≤50 m (n = 38 and 36, respectively). The algorithm enables efficient regional comparison of the size and location of knickzones with geologic structures, mapped landforms, and hillslope morphology, thereby facilitating approaches to characterize the dynamics of transient landscapes. Plain Language Summary The shape of rivers reflects the environments that they flow through and the environments that they link together: mountains and oceans. Anywhere along the length of a river, changes in environmental conditions are propagated upstream and downstream as the river changes its morphology to match the new environmental conditions. Commonly, rivers steepen as land uplifts faster in regions of high tectonic convergence. The steepening of river gradients is propagated upstream and can be mapped to trace zones of high tectonic activity across landscapes and estimate the source and timing of environmental change. Such insights may indicate regions where earthquakes have become more frequent in the recent past and how rivers respond to these changes. In this submission, we detail an algorithm that can use digital topographic data (similar to google earth), to automatically map and measure anomalously steep river reaches across continental scales. This technology can highlight areas that have experienced recent sustained changes in environmental conditions, evident by changes in the morphology of rivers. Such environmental conditions could be changes in tectonic uplift and earthquake activity, changes in sea level, changes in land‐use, or changes in climate, all factors that can produce measurable differences in river morphology over time. Key Points Algorithm uses a training data set to objectively select and measure river knickzones as bounded reaches of sustained high channel steepness index Algorithm‐selected knickzones confirm ~80–90% of 178 manually selected knickzones on 1 to 30 m DEMs, and the measured height of confirmed knickzones matches to >90% Algorithm is embedded in a set of automated scripts and can be applied to any region with DEM coverage
Streams commonly respond to base‐level fall by localizing erosion within steepened, convex knickzone reaches. Localized incision causes knickzone reaches to migrate upstream. Such migrating knickzones dictate the pace of landscape response to changes in tectonics or erosional efficiency and can help quantify the timing and source of base‐level fall. Identification of knickzones typically requires individual selection of steepened reaches: a process that is tedious and subjective and has no efficient means to measure knickzone size. We construct an algorithm to automate this procedure by selecting the bounds of knickzone reaches in a χ‐space (drainage‐area normalized) framework. An automated feature calibrates algorithm parameters to a subset of knickzones handpicked by the user. The algorithm uses these parameters as consistent criteria to identify knickzones objectively, and then the algorithm measures the height, length, and slope of each knickzone reach. We test the algorithm on 1, 10, and 30 m resolution digital elevation models (DEMs) of six catchments (trunk‐stream lengths: 2.1–5.4 km) on Santa Cruz Island, southern California. On the 1 m DEM, algorithm‐selected knickzones confirm 93% of handpicked knickzone positions (n = 178) to a spatial accuracy of ≤100 m, 88% to an accuracy within 50 m, and 46% to an accuracy within 10 m. Using 10 and 30 m DEMs, accuracy is similar: 88–86% to ≤100 m and 82% to ≤50 m (n = 38 and 36, respectively). The algorithm enables efficient regional comparison of the size and location of knickzones with geologic structures, mapped landforms, and hillslope morphology, thereby facilitating approaches to characterize the dynamics of transient landscapes.
Author Neely, A. B.
Burbank, D. W.
Bookhagen, B.
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Snippet Streams commonly respond to base‐level fall by localizing erosion within steepened, convex knickzone reaches. Localized incision causes knickzone reaches to...
Streams commonly respond to base-level fall by localizing erosion within steepened, convex knickzone reaches. Localized incision causes knickzone reaches to...
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SubjectTerms Accuracy
Algorithms
Automation
Balances (scales)
California
Catchments
Climate
Climate change
Convergence
Digital Elevation Models
Drainage
Dynamics
Earthquake construction
Earthquakes
Efficiency
Environmental changes
Environmental conditions
Erosion
Fluvial morphology
Frameworks
Geological structures
geophysics
Gradients
Height
incision
knickpoint
knickzone
Land use
Landforms
Landscape
landscapes
Mathematical models
Morphology
Mountains
Oceans
Parameter identification
Parameters
Position (location)
relict landscape
River channels
River morphology
Rivers
Santa Cruz Island
Scripts
Sea level changes
Seismic activity
Shape
Slopes
Streams
Structures
Technology
Tectonics
topographic slope
Training
transient
Uplift
Upstream
Watersheds
Title An automated knickzone selection algorithm (KZ‐Picker) to analyze transient landscapes: Calibration and validation
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2F2017JF004250
https://www.proquest.com/docview/1917358273
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Volume 122
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