Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques

Schwanghart, Wolfgang; Scherler, Dirk

doi:10.5194/esurf-5-821-2017

Cited by 143 publications

(98 citation statements)

References 65 publications

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“…By filling these depressions, researchers bypassed this technical challenge to begin analysing the structure of drainage networks and continue to make new discoveries based on this approach (e.g. Hooshyar et al, 2017;Seybold et al, 2017). However, all of these analyses rely on an assumption of full drainage connectivity.…”

Section: Background and Motivationsmentioning

confidence: 99%

Computing water flow through complex landscapes – Part 1: Incorporating depressions in flow routing using FlowFill

Callaghan

Wickert

2019

Earth Surf. Dynam.

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Calculating flow routing across a landscape is a routine process in geomorphology, hydrology, planetary science, and soil and water conservation. Flow-routing calculations often require a preprocessing step to remove depressions from a DEM to create a "flow-routing surface" that can host a continuous, integrated drainage network. However, real landscapes contain natural depressions that trap water. These are an important part of the hydrologic system and should be represented in flow-routing surfaces. Historically, depressions (or "pits") in DEMs have been viewed as data errors, but the rapid expansion of high-resolution, high-precision DEM coverage increases the likelihood that depressions are real-world features. To address this long-standing problem of emerging significance, we developed FlowFill, an algorithm that routes a prescribed amount of runoff across the surface in order to flood depressions if enough water is available. This mass-conserving approach typically floods smaller depressions and those in wet areas, integrating drainage across them, while permitting internal drainage and disruptions to hydrologic connectivity. We present results from two sample study areas to which we apply a range of uniform initial runoff depths and report the resulting filled and unfilled depressions, the drainage network structure, and the required compute time. For the reach-to watershed-scale examples that we ran, FlowFill compute times ranged from approximately 1 to 30 min, with compute times per cell of 0.0001 to 0.006 s.

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Section: Background and Motivationsmentioning

confidence: 99%

Computing water flow through complex landscapes – Part 1: Incorporating depressions in flow routing using FlowFill

Callaghan

Wickert

2019

Earth Surf. Dynam.

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“…Hillslope gradient and M χ are calculated for individual pixels in the river network. Due to DEM errors and artifacts, we filtered both metrics with a regularized smoothing technique developed for river networks (Schwanghart & Scherler, ; see Text S1).…”

Section: Methodsmentioning

confidence: 99%

Topographic and Seismic Constraints on the Vulnerability of Himalayan Hydropower

Schwanghart

Ryan

Korup

2018

Geophysical Research Letters

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We compiled damages to hydropower projects (HPPs) in the wake of Nepal's 2015 Gorkha earthquake and its main aftershock. Based on 41 records of HPP losses, we identify the main mechanisms determining the degree of loss. We show that, among these mechanisms, earthquake‐triggered landslides had the largest share. Landslides clustered where peak ground acceleration and river steepness Mχ, a metric frequently used in tectonic geomorphology, attain high values. A Bayesian logistic regression reveals that both metrics are credible predictors for HPP damage states. Our study underscores the vulnerability of Himalayan hydropower to landsliding. We estimate that ~25% of existing, currently constructed, and planned HPPs and >10% of potential HPP sites along Himalayan rivers have high probabilities of moderate to severe damage during future earthquake. Our study highlights that HPP hazard assessments need to consider both seismic and the geomorphic setting of HPP sites conjunctively.

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“…There are many published methods to make noisy DEM surfaces more suitable for hydrologic research, including filling (Jenson and Domingue, 1988), carving (Soille et al, 2003), spline regression (Harbor et al, 2005), and slope-constrained quantile regression (Schwanghart and Scherler, 2017). One reach definition method being considered for SWOT operations uses a Gaussian smoothing filter to reduce noise on SWOT profiles.…”

Section: Profile Smoothingmentioning

confidence: 99%

Anticipated Improvements to River Surface Elevation Profiles From the Surface Water and Ocean Topography Mission

et al. 2019

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Existing publicly available digital elevation models (DEMs) provide global-scale data but are often not precise enough for studying processes that depend on small-scale topographic features in rivers. For example, slope breaks and knickpoints in rivers can be important in understanding tectonic processes, and riffle-pool structures are important drivers of riverine ecology. More precise data (e.g., lidar) are available in some areas, but their spatial extent limits large-scale research. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission is planned to launch in 2021 and will provide measurements of elevation and inundation extent of surface waters between 78 • north and south latitude on average twice every 21 days. We present a novel noise reduction method for multitemporal river water surface elevation (WSE) profiles from SWOT that combines a truncated singular value decomposition and a slope-constrained least-squares estimator. We use simulated SWOT data of 85-145 km sections of the Po, Sacramento, and Tanana Rivers to show that 3-12 months of simulated SWOT data can produce elevation profiles with mean absolute errors (MAEs) of 5.38-12.55 cm at 100-200 m along-stream resolution. MAEs can be reduced further to 4-11 cm by averaging all observations. The average profiles have errors much lower than existing DEMs, allowing new advances in riverine research globally. We consider two case studies in geomorphology and ecology that highlight the scientific value of the more accurate in-river DEMs expected from SWOT. Simulated SWOT elevation profiles for the Po reveal convexities in the river longitudinal profile that are spatially coincident with the upward projection of blind thrust faults that are buried beneath the Po Plain at the northern termination of the Apennine Mountains. Meanwhile, simulated SWOT data for the Sacramento River reveals locally steep sections of the river profile that represent important habitat for benthic invertebrates at a spatial scale previously unrecognizable in large-scale DEMs presently available for this river.

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Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques

Cited by 143 publications

References 65 publications

Computing water flow through complex landscapes – Part 1: Incorporating depressions in flow routing using FlowFill

Computing water flow through complex landscapes – Part 1: Incorporating depressions in flow routing using FlowFill

Topographic and Seismic Constraints on the Vulnerability of Himalayan Hydropower

Anticipated Improvements to River Surface Elevation Profiles From the Surface Water and Ocean Topography Mission

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