Jochen E. Schubert

An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope

The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results.

Structural Damage Prediction in a High-Velocity Urban Dam-Break Flood: Field-Scale Assessment of Predictive Skill

AbstractDam safety and flood risk management programs are dependent on damage predictions that are difficult to validate and subject to considerable uncertainty. The 1963 Baldwin Hills dam-break flood caused high-velocity flows exceeding 5 m/s and structural failure of 41 wood-framed residences built in the mid-1940s, 16 of which were completely washed out. The flood is revisited here to examine the predictive skill and variability of established structural damage models when coupled with a hydraulic flood model that predicts parcel-scale depths and velocities. Two-way coupling is introduced so that predictions of structural failure affect localized flood predictions, which in turn affects damage predictions, in contrast to one-way coupling where structural failure has no impact on flood predictions. Two damage states defined by structural failure (Level 2) and washout (Level 3) are considered, along with 10 different structural damage models. One damage model considers flood depth alone, while the remain...

Terrestrial Laser Scanning of Anthropogenic Beach Berm Erosion and Overtopping

ABSTRACT Schubert, J.E.; Gallien, T.W.; Majd, M.S., and Sanders, B.F., 2015. Terrestrial laser scanning of anthropogenic beach berm erosion and overtopping. Anthropogenic berms are widely deployed to manage coastal flooding. The dynamic erosion of scraped berms exposed to waves and a rising tide in southern California was monitored with a terrestrial laser scanner (TLS) on three occasions in February and March of 2012. An improved characterization of initial berm geometry and the dynamics of berm erosion was pursued to accurately predict the onset and impact of coastal flooding associated with berm erosion and overtopping. TLS is shown to yield a digital terrain model (DTM) with a vertical accuracy of ca. 3 cm, indicating it is an excellent source of data for initializing mechanistic and/or empirical models that could be used to predict the onset and rate of wave overtopping. Minimum scan point spacings required to achieve this level of accuracy are investigated and reported. Additionally, a dimensionless water level representing the fractional submergence of the berm is identified as a good predictor of cumulative berm erosion under the test conditions.

Metric-Resolution 2D River Modeling at the Macroscale: Computational Methods and Applications in a Braided River

Metric resolution digital terrain models (DTMs) of rivers now make it possible for multi-dimensional fluid mechanics models to be applied to characterize flow at fine scales that are relevant to studies of river morphology and ecological habitat, or microscales. These developments are important for managing rivers because of the potential to better understand system dynamics, anthropogenic impacts, and the consequences of proposed interventions. However, the data volumes and computational demands of microscale river modeling have largely constrained applications to small multiples of the channel width, or the mesoscale. This report presents computational methods to extend a microscale river model beyond the mesoscale to the macroscale, defined as large multiples of the channel width. A method of automated unstructured grid generation is presented that automatically clusters fine resolution cells in areas of curvature (e.g., channel banks), and places relatively coarse cells in areas lacking topographic variability. This overcomes the need to manually generate breaklines to constrain the grid, which is painstaking at the mesoscale and virtually impossible at the macroscale. The method is applied to a braided river with an extremely complex channel network configuration and shown to yield an efficient fine resolution model. The sensitivity of model output to grid design and resistance parameters is also examined as it relates to analysis of hydrology, hydraulic geometry and river habitats and the findings reiterate the importance of model calibration and validation.

Lifting GIS maps into strong geometric context for scene understanding

Contextual information can have a substantial impact on the performance of visual tasks such as semantic segmentation, object detection, and geometric estimation. Data stored in Geographic Information Systems (GIS) offers a rich source of contextual information that has been largely untapped by computer vision. We propose to leverage such information for scene understanding by combining GIS resources with large sets of unorganized photographs using Structure from Motion (SfM) techniques. We present a pipeline to quickly generate strong 3D geometric priors from 2D GIS data using SfM models aligned with minimal user input. Given an image resectioned against this model, we generate robust predictions of depth, surface normals, and semantic labels. Despite the lack of detail in the model, we show that the precision of the predicted geometry is substantially more accurate than other single-image depth estimation methods. We then demonstrate the utility of these contextual constraints for re-scoring pedestrian detections, and use these GIS contextual features alongside object detection score maps to improve a CRF-based semantic segmentation framework, boosting accuracy over baseline models.

The Influence of Hazard Maps and Trust of Flood Controls on Coastal Flood Spatial Awareness and Risk Perception

Understanding the impact of digital, interactive flood hazard maps and flood control systems on public flood risk perception could enhance risk communication and management. This study analyzed a survey of residents living near California’s Newport Bay Estuary and found that self-rated nonspatial perceptions of dread or concern over future flood impacts were positively associated with spatial awareness of flood-prone areas. Trust in flood control systems was associated with greater spatial flood hazard awareness but weaker nonspatial dread or concern, suggesting residents who witnessed and trust flood control systems developed a confident sense of flood-prone areas and that this confidence reduced the overall nonspatial sense of flood dread and concern. Viewing a flood hazard map eliminated differences in spatial hazard awareness between subgroups that existed prior to viewing a map, and viewing a map with estimated flood depth and greater spatial differentiation was associated with higher levels of postmap spatial awareness.