Uncertainty In Topography Research Articles

Sediment Transport Modeling Based on Geological Data for Holocene Coastal Evolution: Wave Source Estimation of Sandy Layers on the Coast of Hidaka, Hokkaido, Japan

AbstractSediment transport modeling (STM) is a potentially effective tool for estimating the magnitude of tsunamis and earthquakes without historical records. However, the application of STM to prehistorical tsunamis is challenging because of multiple uncertainties in topography and roughness. Along the coast of Hidaka, Hokkaido, Japan, there is potential to conduct STM even in the absence of historical records because comprehensive geological data for the coastal evolution during the Holocene are available here. The tsunami deposits in Hokkaido indicate the presence of events on a larger scale than historical tsunamis; in particular, the seventeenth‐century tsunami had multiple potential wave sources other than a Kuril Trench earthquake, inhibiting its magnitude estimation. In this study, we applied STM to paleotsunamis for the coast of Hidaka, where comprehensive geological data are available, although the wave source is unknown. The modeling parameters—paleotopography, roughness, grain size, initial sand source, sea level, and beach ridge height—were estimated using data obtained from geological surveys and sensitivity tests. The modeling of a tsunami induced by a Kuril Trench earthquake reproduced the sediment distributions and sedimentary structures of the observed sand layers better than that of extreme storms and volcanic tsunamis. Because the conventional fault model is reasonably suitable in the western area from the Kuril Trench, a much wider rupture zone is less likely. Parameters such as sand source area, roughness, ridge height, and paleo sea level are important for geologists and modelers when applying STM to paleotsunamis.

Improving Channel Hydrological Connectivity in Coastal Hydrodynamic Models With Remotely Sensed Channel Networks

AbstractCoastal wetlands are nourished by rivers and periodical tidal flows through complex, interconnected channels. However, in hydrodynamic models, channel dimensions with respect to model grid size and uncertainties in topography preclude the correct propagation of tidal and riverine signals. It is therefore crucial to enhance channel geomorphic connectivity and simplify sub‐channel features based on remotely sensed networks for practical computational applications. Here, we utilize channel networks derived from diverse remote sensing imagery as a baseline to build a ∼10 m resolution hydrodynamic model that covers the Wax Lake Delta and adjacent wetlands (∼360 km2) in coastal Louisiana, USA. In this richly gauged system, intensive calibrations are conducted with 18 synchronous field‐observations of water levels taken in 2016, and discharge data taken in 2021. We modify channel geometry, targeting realism in channel connectivity. The results show that a minimum channel depth of 2 m and a width of four grid elements (approximatively 40 m) are required to enable a realistic tidal propagation in wetland channels. The optimal depth for tidal propagation can be determined by a simplified cost function method that evaluates the competition between flow travel time and alteration of the volume of the channels. The integration of high spatial‐resolution models and remote sensing imagery provides a general framework to improve models performance in salt marshes, mangroves, deltaic wetlands, and tidal flats.

Improving the accuracy of barotropic and internal tides embedded in a high-resolution global ocean circulation model of MITgcm

In recent years, high-resolution global ocean circulation models have begun to include astronomical tidal forcing to concurrently simulate ocean circulation, barotropic and internal tides. The inclusion of tides is of great significance for understanding three-dimensional (3-D) ocean mixing and developing internal tide prediction. However, due to uncertainty in topography and damping terms, improper representation of the self-attraction and loading term, barotropic tidal estimate cannot match that in the data-constrained barotropic tidal model. In this study, the TPXO8 data are assimilated to optimize the tides embedded in a global 9-km 3-D general circulation model of MITgcm as follows. For each time step of the MITgcm, the previous 25-hour-averaged sea surface height (SSH) is subtracted from the instantaneous SSH to obtain the MITgcm surface tidal elevation. Then, the difference of surface tidal elevation between the MITgcm and TPXO8 together with a damping coefficient acts as an additional term for the right-hand side of the SSH equation in order to correct the SSH field. Results show that the global M2 (K1) error of the corrected surface tidal elevation relative to the TPXO8 reaches 2.12 cm (0.89 cm). Compared with tide gauge data, the accuracy of surface tidal elevation after using the correction scheme is almost the same as that of TPXO8 for all areas except for some continental shelf and coastal areas. The internal tidal signals are subsequently improved compared to the along-track altimetry and TAO observations. Compared to previous tidal assimilation scheme, the implementation of the correction scheme used in this study is simple and more suitable for realistic applications.

Probabilistic Godunov-type hydrodynamic modelling under multiple uncertainties: robust wavelet-based formulations

Intrusive stochastic Galerkin methods propagate uncertainties in a single model run, eliminating repeated sampling required by conventional Monte Carlo methods. However, an intrusive formulation has yet to be developed for probabilistic hydrodynamic modelling incorporating robust wetting-and-drying and stable friction integration under joint uncertainties in topography, roughness, and inflow. Robustness measures are well-developed in deterministic models, but rely on local, nonlinear operations that can introduce additional stochastic errors that destabilise an intrusive model. This paper formulates an intrusive hydrodynamic model using a multidimensional tensor product of Haar wavelets to capture fine-scale variations in joint probability distributions and extend the validity of robustness measures from the underlying deterministic discretisation. Probabilistic numerical tests are designed to verify intrusive model robustness, and compare accuracy and efficiency against a conventional Monte Carlo approach and two other alternatives: a nonintrusive stochastic collocation formulation sharing the same tensor product wavelet basis, and an intrusive formulation that truncates the basis to gain efficiency under multiple uncertainties. Tests reveal that: (i) A full tensor product basis is required to preserve intrusive model robustness, while the nonintrusive counterpart achieves identically accurate results at a reduced computational cost; and, (ii) the Haar wavelet basis requires at least three levels of refinements per uncertainty dimension to reliably capture complex probability distributions. Accompanying model software and simulation data are openly available online.

Functional derivatives applied to error propagation of uncertainties in topography to large-aperture scintillometer-derived heat fluxes

Abstract. Scintillometer measurements allow for estimations of the refractive index structure parameter Cn2 over large areas in the atmospheric surface layer. Turbulent fluxes of heat and momentum are inferred through coupled sets of equations derived from the Monin–Obukhov similarity hypothesis. One-dimensional sensitivity functions have been produced that relate the sensitivity of heat fluxes to uncertainties in single values of beam height over flat terrain. However, real field sites include variable topography. We develop here, using functional derivatives, the first analysis of the sensitivity of scintillometer-derived sensible heat fluxes to uncertainties in spatially distributed topographic measurements. Sensitivity is shown to be concentrated in areas near the center of the beam path and where the underlying topography is closest to the beam height. Relative uncertainty contributions to the sensible heat flux from uncertainties in topography can reach 20% of the heat flux in some cases. Uncertainty may be greatly reduced by focusing accurate topographic measurements in these specific areas. A new two-dimensional variable terrain sensitivity function is developed for quantitative error analysis. This function is compared with the previous one-dimensional sensitivity function for the same measurement strategy over flat terrain. Additionally, a new method of solution to the set of coupled equations is produced that eliminates computational error.

Uncertainty Quantification in Flood Inundation Mapping Using Generalized Likelihood Uncertainty Estimate and Sensitivity Analysis

The process of creating flood inundation maps is affected by uncertainties in data, modeling approaches, parameters, and geoprocessing tools. Generalized likelihood uncertainty estimation (GLUE) is one of the popular techniques used to represent uncertainty in model predictions through Monte Carlo analysis coupled with Bayesian estimation. The objectives of this study are to (1) compare the uncertainty arising from multiple variables in flood inundation mapping using Monte Carlo simulations and GLUE and (2) investigate the role of subjective selection of the GLUE likelihood measure in quantifying uncertainty in flood inundation mapping. The role of the flow, topography, and roughness coefficient is investigated on the output of a one-dimensional Hydrologic Engineering Center–River Analysis System (HEC–RAS) model and flood inundation map for an observed flood event on East Fork White River near Seymour, Indiana (Seymour reach) and Strouds Creek in Orange County, North Carolina. Performance of GLUE is assessed by selecting three likelihood functions including the sum of absolute error (SAE) in water surface elevation and inundation width, sum of squared error (SSE) in water surface elevation and inundation width, and a statistic (F-statistic) on the basis of the area of observed and simulated flood inundation map. Results show that the uncertainty in topography, roughness and flow information created an uncertainty bound in the inundation area that ranged from 1.4 to 4.6% for Seymour reach and 4 to 29% for Strouds Creek of the base inundation areas. Additionally, flood inundation maps produced by applying GLUE have different uncertainty bounds depending on the selection of the likelihood functions. However, the overall difference in the flood inundation maps on the basis different likelihood functions is less than 2%, suggesting that the subjectivity involved in selecting the likelihood measure in GLUE did not create a significant effect on the overall uncertainty quantification in flood inundation mapping of the selected study areas.

Effect of DEM Uncertainty on the Positional Accuracy of Airborne Imagery

The geometric and atmospheric processing of airborne imagery is a complex task that involves many correction steps. Geometric correction is particularly challenging because slight movements of the aircraft and small changes in topography can have a great impact on the geographic positioning of the processed imagery. This paper focused on how uncertainty in topography, represented by a digital elevation model (DEM), propagates through the geometric correction process. We used a Monte Carlo analysis, in which, first, a geostatistical uncertainty model of the DEM was developed to simulate a large number of DEM realizations. Next, geometric correction was run for each of the simulated DEMs. The analysis of the corrected images and their variability provided valuable information about the positional accuracy of the corrected image. The method was applied to a hyperspectral image of a mountainous area in Calabria, Italy, by using the Shuttle Radar Topography Mission-DEM as the topographic information source. We found out that the uncertainty varies greatly over the whole terrain and is substantial at large off-nadir viewing angles in the across-track direction. Also, positional uncertainty is larger in rugged terrains. We conclude that Monte Carlo uncertainty propagation analysis is a valuable technique in deriving quality layers that inform end users about the positional accuracy of airborne imagery, and we recommend that it is integrated in the operational processing steps of the Processing and Archiving Facilities.

An evaluation of snowline data across New Guinea during the last major glaciation, and area-based glacier snowlines in the Mt. Jaya region of Papua, Indonesia, during the Last Glacial Maximum

Geomorphological mapping and lake-core data from Mt. Jaya, western New Guinea, show that Last Glacial Maximum (LGM) glaciation was less extensive than previously thought. Average equilibrium line altitudes (ELAs), calculated using the area–altitude-balance ratio method, for minimum and maximum ice configurations were 4050±49 and 4000±56 m a.s.l., respectively. This is about 600 m below the ELA of the Mt. Jaya glaciers in 1971–73 and ca 400 m higher than values previously quoted for LGM ELAs in this area. A reappraisal of the evidence used to reconstruct the ELA of glaciers across New Guinea suggests that published chronologies are not sufficient to demonstrate that reported ELAs fall within the LGM window of 21,000±2 yr BP. Furthermore, the published information only constrains the altitude of the ELAs between 3400 and 3800 m a.s.l., not including uncertainty in topography. A simple mass and energy-balance model indicates that an ELA depression of 500 m (i.e., the observed change at Mt. Jaya after adjustment for sea-level change) could be accomplished with 2.5–3 °C of cooling provided precipitation was reduced by 35% and lapse rate changed. This cooling is less than the 6–8 °C cooling inferred from LGM pollen.