Elevation Uncertainty Research Articles

Three years of weekly DEMs, aerial orthomosaics and surveyed shoreline positions at Waikīkī Beach, Hawai‘i

In this dataset, we present 128 coastal surveys conducted between 2018 and 2021 at Kahaloa Beach, also known as the Royal Hawaiian Beach, in Waikīkī, Hawai‘i. Surveys were conducted on a near-weekly basis, providing a 0.5 m digital elevation model, an orthorectified image mosaic with 0.03 m resolution, and shoreline vectors at MHHW and MSL, along with a surveyed shoreline position for each survey. We captured overlapping images using a small Unoccupied Aerial System (sUAS), processing the imagery with photogrammetric software to produce orthomosaics and Digital Terrain Models (DTM). Simultaneously, the shoreline position and reference points for sUAS-derived products were surveyed using total station and rod-mounted surveying prism. A quality assessment of 424 randomly sampled points across two surveys showed normally distributed errors of DTM elevations (µ1 = 0.0060 m; σ1 = 0.0998 m; µ2 = 0.0035 m; σ2 = 0.0680). Elevation uncertainties were quantified as 95% confidence intervals (±0.0130 m and ±0.0095 m). These data are intended to encourage research on reef-fringed beaches and provide a dataset for evaluating the accuracy of satellite-derived shorelines at reef-fringed beaches.

Characterization of uncertainty in maximum tidal elevation near Bangladesh coastline due to uncertain sea level rise

AbstractThe Ganges‐Brahmaputra‐Meghna delta is vulnerable to sea level rise from global warming. Based on sea level rise predictions for the year 2100 given in the 6th Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), we examine the effect of uncertainty in sea level rise on maximum tidal elevation statistics at several locations along the Bangladesh coastline using a discretized derived distribution approach. For five IPCC scenarios, the standard deviation in maximum tidal elevation including sea level rise is predicted to increase by between 3% and 61% at three different locations for a 41% increase in the standard deviation of mean sea level rise. By excluding the linear effect of sea level rise, the increase in the standard deviation of maximum tidal elevation is found to vary spatially from 2% to 68% with a 41% increase in the standard deviation of sea level rise.

Lightning VHF Radiation Mapping Method for an Irregular Short‐Baseline Array

AbstractLightning very high frequency interferometry is a crucial tool for studying the development of lightning leaders within thunderclouds, and the conventional antenna array is composed of two coplanar orthogonal baselines. In this study, we develop two new location methods for an irregular array based on the optimization algorithm, using all baselines in the calculation. Besides, we have analyzed the effects of two kinds of time uncertainty on the location result and provided a calibration method. The first type—the systematic time drift of antennas—can induce an offset on the location result, leading to a worse accuracy. We can calibrate the time drift of each antenna efficiently during the experiment. The second type—the waveform distortion between different antennas—can introduce the noise on time, leading to worse precision. The location precision improves when more baselines are used, whether they are independent or redundant. Our result shows that the non‐coplanar configuration can significantly improve the elevation uncertainty near the horizon and the azimuth uncertainty near the zenith after considering the angular uncertainty's theoretical distribution.

Elevation-based probabilistic mapping of irregularly flooded wetlands along the northern Gulf of Mexico coast

Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation (r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.

Automated Processing of Declassified KH-9 Hexagon Satellite Images for Global Elevation Change Analysis Since the 1970s

Observing changes in Earth surface topography is crucial for many Earth science disciplines. Documenting these changes over several decades at regional to global scale remains a challenge due to the limited availability of suitable satellite data before the year 2000. Declassified analog satellite images from the American reconnaissance program Hexagon (KH-9), which surveyed nearly all land surfaces from 1972 to 1986 at metric resolution, provide a unique opportunity to fill the gap in observations. However, large-scale processing of analog imagery remains challenging. We developed an automated workflow to generate Digital Elevation Models (DEMs) and orthophotos from scanned KH-9 mapping camera stereo images. The workflow includes a preprocessing step to correct for film and scanning distortions and crop the scanned images, and a stereo reconstruction step using the open-source NASA Ames Stereo Pipeline. The processing of several hundreds of image pairs enabled us to estimate reliable camera parameters for each KH-9 mission, thereby correcting elevation biases of several tens of meters. The resulting DEMs were validated against various reference elevation data, including snow-covered glaciers with limited image texture. Pixel-scale elevation uncertainty was estimated as 5 m at the 68% confidence level, and less than 15 m at the 95% level. We evaluated the uncertainty of spatially averaged elevation change and volume change, both from an empirical and analytical approach, and we raise particular attention to large-scale correlated biases that may impact volume change estimates from such DEMs. Finally, we present a case study of long-term glacier elevation change in the European Alps. Our results show the suitability of these historical images to quantitatively study global surface change over the past 40-50 years.

Airborne observations of arctic-boreal water surface elevations from AirSWOT Ka-Band InSAR and LVIS LiDAR

AirSWOT is an experimental airborne Ka-band radar interferometer developed by NASA-JPL as a validation instrument for the forthcoming NASA Surface Water and Ocean Topography (SWOT) satellite mission. In 2017, AirSWOT was deployed as part of the NASA Arctic Boreal Vulnerability Experiment (ABoVE) to map surface water elevations across Alaska and western Canada. The result is the most extensive known collection of near-nadir airborne Ka-band interferometric synthetic aperture radar (InSAR) data and derivative high-resolution (3.6 m pixel) digital elevation models to produce water surface elevation (WSE) maps. This research provides a synoptic assessment of the 2017 AirSWOT ABoVE dataset to quantify regional WSE errors relative to coincident in situ field surveys and LiDAR data acquired from the NASA Land, Vegetation, and Ice Sensor (LVIS) airborne platform. Results show that AirSWOT WSE data can penetrate cloud cover and have nearly twice the swath-width of LVIS as flown for ABoVE (3.2 km vs. 1.8 km nominal swath-width). Despite noise and biases, spatially averaged AirSWOT WSEs can be used to estimate sub-seasonal hydrologic variability, as confirmed with field GPS surveys and in situ pressure transducers. This analysis informs AirSWOT ABoVE data users of known sources of measurement error in the WSEs as influenced by radar parameters including incidence angle, magnitude, coherence, and elevation uncertainty. The analysis also provides recommended best practices for extracting information from the dataset by using filters for these four parameters. Improvements to data handing would significantly increase the accuracy and spatial coverage of future AirSWOT WSE data collections, aiding scientific surface water studies, and improving the platform’s capability as an airborne validation instrument for SWOT.

Spatially distributed sensitivity of simulated global groundwater heads and flows to hydraulic conductivity, groundwater recharge, and surface water body parameterization

Abstract. In global hydrological models, groundwater storages and flows are generally simulated by linear reservoir models. Recently, the first global gradient-based groundwater models were developed in order to improve the representation of groundwater–surface-water interactions, capillary rise, lateral flows, and human water use impacts. However, the reliability of model outputs is limited by a lack of data and by uncertain model assumptions that are necessary due to the coarse spatial resolution. The impact of data quality is presented in this study by showing the sensitivity of a groundwater model to changes in the only available global hydraulic conductivity dataset. To better understand the sensitivity of model output to uncertain spatially distributed parameters, we present the first application of a global sensitivity method for a global-scale groundwater model using nearly 2000 steady-state model runs of the global gradient-based groundwater model G3M. By applying the Morris method in a novel domain decomposition approach that identifies global hydrological response units, spatially distributed parameter sensitivities are determined for a computationally expensive model. Results indicate that globally simulated hydraulic heads are equally sensitive to hydraulic conductivity, groundwater recharge, and surface water body elevation, though parameter sensitivities vary regionally. For large areas of the globe, rivers are simulated to be either losing or gaining, depending on the parameter combination, indicating a high uncertainty in simulating the direction of flow between the two compartments. Mountainous and dry regions show a high variance in simulated head due to numerical instabilities of the model, limiting the reliability of computed sensitivities in these regions. This is likely caused by the uncertainty in surface water body elevation. We conclude that maps of spatially distributed sensitivities can help to understand the complex behavior of models that incorporate data with varying spatial uncertainties. The findings support the selection of possible calibration parameters and help to anticipate challenges for a transient coupling of the model.

RESULTS OF POSITIONAL AND PHOTOMETRIC MEASURE- MENTS OF METEOR TRAJECTORIES OBSERVED IN MYKOLAIV 2017-2018

Regular meteor observation using TV CCD unintensified techniques was started in 2011 in RI «Mykolaiv astronomical observatory» (RI MAO). The method of meteor registration is based on using “track- and-stack” technique for obtaining frames with reference stars and online meteor detection software developed at RI NAO. The main accent of the research is made on precise astrometry and meteoroid orbits calculation. New observa- tional campaign with baselines 11.7 and 100 km was start- ed in 2017-2018 in collaboration with Astronomical Ob- servatory of the I.I. Mechnikov Odessa National Universi- ty. Eight telescopes with narrow field lens (f=50 mm, f/1.2) were installed in Mykolaiv and Odessa. More than 3000 single station meteors and 221 double station mete- ors were detected during 2017-2018. Uncertainties of me- teor trajectory and orbital parameters are calculated using Monte Carlo method. Catalog of meteor trajectory posi- tions and photometric parameters has created. Standard deviation of approximation of a meteoric trajectory in a large circle is (15-20). Kinematic parameters of atmos- pheric meteoric trajectories and elements of heliocentric orbits for 3 meteors were calculated based on the results of television observations with cameras with a field of view <10° at a baseline distance 100 km. The average uncertainty of visible radiant estimation is in (0.3-0.7)o with baseline 100 km. The geocentric velocity estimation uncertainty is 0.5 km/s, elevation uncertainty is 50-150 m.

Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset

Abstract. Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect. Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies. Using lidar data with point density of ∼10 pts m−2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions.

Advancing barrier island habitat mapping using landscape position information

Barrier islands are dynamic ecosystems that change gradually from coastal processes, including currents and tides, and rapidly from episodic events, such as storms. These islands provide many important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism. Habitat maps, developed by scientists, provide a critical tool for monitoring changes to these dynamic ecosystems. Barrier island monitoring often requires custom habitat maps due to several factors, including island size and the classification of unique geomorphology-based habitats, such as beach, dune, and barrier flats. In this study, we reviewed barrier-island-specific habitat mapping efforts and highlighted common habitat class types, source data, and mapping approaches. We also developed a framework for mapping geomorphology-based barrier island habitats using a rule-based, geographic object-based image analysis approach, which included the use of field data, tide data, high-resolution orthophotography, and lidar data. This framework integrates several barrier island mapping advancements with regard to the use of landscape position information for automated dune extraction and the use of Monte Carlo analyses for the treatment of elevation uncertainty for elevation-dependent habitats. Specifically, we used the uncertainty analyses to refine automated dune delineation based on elevation relative to extreme storm water levels and to increase the accuracy of intertidal and supratidal/upland habitat delineation. We found that dune extraction results were enhanced when elevation relative to storm water levels and visual interpretation were also applied. This framework could also be applied to beach–dune systems found along a mainland.

Sea-level change in southern Africa since the Last Glacial Maximum

Sea-level change around southern Africa (southern Namibia, South Africa, southern Mozambique) since Termination I has been quantified using a variety of indicators. Existing and new data are reviewed to provide a baseline for future studies and identify key research needs and opportunities in the region. While the southern African records broadly agree with other far-field records, detailed Holocene records present as-yet unresolved discrepancies with glacial isostatic adjustment (GIA) model predictions. Two domains, the west coast and east coast are considered. Radiocarbon dated saltmarsh facies and marine shells in life position provide the basis for the west coast sea-level curve back to 9 ka BP. Given the age and elevation uncertainties, a Mid-Holocene highstand of +2 to +4 m is suggested between 7.3 and 6 ka BP, as are several Late Holocene oscillations of <1 m amplitude. On the east coast, fewer data are available for the Mid to Late Holocene (post 7 ka BP) compared to the west, but many submerged indicators are available back to 13 ka BP. Reappraisal of existing data suggests a sea-level curve similar to that of the west coast. In both instances, the resolution of existing sea-level index points is neither sufficient to accurately constrain the magnitude and timing of the peak highstand nor the existence of minor inferred subsequent oscillations. Between 13 and 7 cal ka BP chronological and geomorphological evidence (submerged shoreline complexes) suggest several alternating periods of slow and rapid sea-level change. Despite abundant data, the indicator resolution to quantify these changes remains elusive.

Linking Water Infrastructure, Public Health, and Sea Level Rise: Integrated Assessment of Flood Resilience in Coastal Cities

Coastal community water infrastructure is increasingly vulnerable to climate-sensitive coastal hazards. Tides, storm surges, rainfall, and salt intrusion affect infrastructure and human health. In case studies of Charleston, South Carolina, and Morehead City, North Carolina, USA, this project sought to advance risk assessment of urban water and wastewater infrastructure and identify linkages to human health impacts as risk evolves with sea level rise. The methodology integrates community infrastructure, health care, emergency resources, geospatial simulation, and a tabletop exercise with planners, emergency managers, public utilities, and health care providers. Resilience is assessed by community participants using interactive online maps, susceptibility indices, and a resilience matrix. Results highlight differential vulnerability, population susceptibility, and elevation uncertainty. We observe similar trends of increasing magnitude, frequency, and impact of flood events on water infrastructure and public health as sea level rises. Implications for tackling challenges across sectors are highlighted for improving coastal resilience.

Impact of Uncertainty of Floodplain Digital Terrain Model on 1D Hydrodynamic Flow Calculation

This study investigates the effect of the Digital Terrain Model (DTM) uncertainty effect on the output of a 1D flow model. The analysis is performed for the lowland river Biebrza, covered with dense wetland vegetation, with a high uncertainty of terrain elevations. The DTM uncertainty is modeled in two ways: (1) accounting for the uncertainty spatial dependency on the basis of the correlogram function and (2) neglecting the correlation of the elevation points. The model explanation of water levels improves when elevation uncertainty is being included. Without the elevation uncertainty, the model provided a good fit only for peak flows, with uncertainty also representation of lower flows is better. It was shown that the correlation of the elevation uncertainty had a noticeable effect on the modeling outcomes, especially for near bankfull flows, where for the uncorrelated case water levels were underestimated by 5 cm, comparing to the correlated case. The effect was also present for inundation extents, obtained by an interpolation of computed water levels. The correlation of the elevation uncertainty strongly affects estimates of standard deviations of computed water levels, which were almost twice smaller when correlation was neglected. In the result, only when the correlation of the elevation uncertainty was included, it was possible to obtain confidence bands that enclosed observation points.

Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV)

This study reports on a low-cost unmanned aerial vehicle (UAV)-borne light detection and ranging (LiDAR) system called LasUAV, from hardware selection and integration to the generation of three-dimensional point clouds, and an assessment of its performance. Measurement uncertainties were estimated in angular static, angular dynamic, and real flight conditions. The results of these experiments indicate that the point cloud elevation accuracy in the case of angular static acquisition was 3.8 cm, and increased to 3.9 cm in angular dynamic acquisition. In-flight data were acquired over a target surveyed by nine single passages in different flight directions and platform orientations. In this case, the uncertainty of elevation ranged between 5.1 cm and 9.8 cm for each single passage. The combined elevation uncertainty in the case of multiple passages (i.e., the combination of one to nine passages from the set of nine passages) ranged between 5 cm (one passage) and 16 cm (nine passages). The study demonstrates that the positioning device, i.e., the Global Navigation Satellite System real-time kinematic (GNSS RTK) receiver, is the sensor that mostly influences the system performance, followed by the attitude measurement device and the laser sensor. Consequently, strong efforts and greater economic investment should be devoted to GNSS RTK receivers in low-cost custom integrated systems.

Evaluation of geomorphic changes and retreat rates of a coastal pyroclastic cliff in the Campi Flegrei volcanic district, southern Italy

Geomorphic evolution of sea cliffs has significant impact on coastal settlements worldwide, so that evaluation of cliff instability processes, failure factors, and retreat rates involves a growing number of scientists for coastal risk and management purposes. Aerial photogrammetry and lidar are among the most used techniques for topographic characterization and geomorphic monitoring of coastal environments. A crucial role in the combined use of such techniques is the evaluation of different spatial accuracy and co-registration between derived terrain models, so that a robust error analysis is required. In this study, we present a change detection analysis of the Torrefumo coastal cliff, in the Campi Flegrei volcanic area (Southern Italy). Aerial images taken in 1956 and 1974, processed through digital photogrammetry, and an airborne lidar point cloud acquired in 2008 were used to produce Digital Elevation Models of the cliff. The analysis was based on the multi-temporal comparison of these models and included the calculation of volumetric changes and average retreat rates of the cliff face with reference to the 1956–1974 and 1974–2008 time intervals. The spatially variable elevation uncertainty of each Digital Elevation Model was evaluated with a probabilistic approach based on the fuzzy set theory. The results of this study showed significant eroded volumes during the period 1956–1974 and relatively smaller volumes in the 1974–2008 time span, with mean annual retreat rates of 1.2 m/year and 0.17 m/year, respectively. We infer that the significant decrease of erosion characterizing the second period was induced by the sheltering effect at the base of the cliff produced by the construction of a seawall in the early 80’s.

Elevation Change Derived from SARAL/ALtiKa Altimetric Mission: Quality Assessment and Performance of the Ka-Band

The waveform retracking algorithm is a key factor that affects the accuracy of elevation change from satellite altimetry over an ice sheet. The elevation change results from four waveform retracker algorithms (ICE1/ICE2/Sea Ice/OCEAN) provided by the Satellite with ARgos and ALtiKa (SARAL/ALtiKa) data were compared using repeated SARAL data between March 2013 and April 2016 to determine the optimal retracker in the crossovers of descending and ascending orbits over a Greenland ice sheet (GrIS). The ICE1 provided slightly better results than the three other algorithms with the lowest standard deviation (SD) of 0.30 m year−1. Further comparison was also conducted between the Satellite with ARgos and ALtiKa (SARAL) and Operation ICEBridge laser data, thereby indicating that ICE1 was the best retracker with an Root Mean Square Error (RMSE) of 0.43 m year−1. The distribution of elevation change rate and uncertainties over Greenland from SARAL were presented using the selected ICE1 retracker with a volume loss of 40 ± 12 km3 year−1. This volume loss did not include the fast-changing coastal areas of the GrIS. A large thinning was observed in Jakobshavn Isbræ, and a trend that extended far inland was also found from 2013–2016. Furthermore, a melting ice sheet was observed in the large areas northwest over the GrIS.

Simulating and quantifying legacy topographic data uncertainty: an initial step to advancing topographic change analyses

Rapid technological advances, sustained funding, and a greater recognition of the value of topographic data have helped develop an increasing archive of topographic data sources. Advances in basic and applied research related to Earth surface changes require researchers to integrate recent high-resolution topography (HRT) data with the legacy datasets. Several technical challenges and data uncertainty issues persist to date when integrating legacy datasets with more recent HRT data. The disparate data sources required to extend the topographic record back in time are often stored in formats that are not readily compatible with more recent HRT data. Legacy data may also contain unknown error or unreported error that make accounting for data uncertainty difficult. There are also cases of known deficiencies in legacy datasets, which can significantly bias results. Finally, scientists are faced with the daunting challenge of definitively deriving the extent to which a landform or landscape has or will continue to change in response natural and/or anthropogenic processes. Here, we examine the question: how do we evaluate and portray data uncertainty from the varied topographic legacy sources and combine this uncertainty with current spatial data collection techniques to detect meaningful topographic changes? We view topographic uncertainty as a stochastic process that takes into consideration spatial and temporal variations from a numerical simulation and physical modeling experiment. The numerical simulation incorporates numerous topographic data sources typically found across a range of legacy data to present high-resolution data, while the physical model focuses on more recent HRT data acquisition techniques. Elevation uncertainties observed from anchor points in the digital terrain models are modeled using “states” in a stochastic estimator. Stochastic estimators trace the temporal evolution of the uncertainties and are natively capable of incorporating sensor measurements observed at various times in history. The geometric relationship between the anchor point and the sensor measurement can be approximated via spatial correlation even when a sensor does not directly observe an anchor point. Findings from a numerical simulation indicate the estimated error coincides with the actual error using certain sensors (Kinematic GNSS, ALS, TLS, and SfM-MVS). Data from 2D imagery and static GNSS did not perform as well at the time the sensor is integrated into estimator largely as a result of the low density of data added from these sources. The estimator provides a history of DEM estimation as well as the uncertainties and cross correlations observed on anchor points. Our work provides preliminary evidence that our approach is valid for integrating legacy data with HRT and warrants further exploration and field validation.

Effect of uncertainty in Digital Surface Models on the extent of inundated areas

The impact of uncertainty in ground elevation on the extent of areas that are inundated due to flooding is investigated. Land surface is represented through a Digital Surface Model (DSM). The effect of uncertainty in DSM is compared to that of the uncertainty due to rainfall. The Monte Carlo method is used to quantify the uncertainty. A typical photogrammetric procedure and conventional maps are used to obtain a reference DSM, later altered to provide DSMs of lower accuracy. Also, data from the Shuttle Radar Topography Mission (SRTM) are used. Floods are simulated in two stages. In the first stage flood hydrographs for typical return periods are synthesized using generated storm hyetographs, the SCS-CN method for effective rainfall and the SCS synthetic Unit Hydrograph. In the second stage hydrographs are routed via a 1D hydraulic model. Uncertainty in DSM is considered only in the second stage. Data from two real-world basins in Greece are used.. To characterize the inundated area we employ the 90% quantile of the inundation extent and inundation topwidth for peak water level at specific river cross-sections. For topwidths, apart from point estimates, also interval estimates are acquired using the bootstrap method. The effect of DSM uncertainty is compared to that for rainfall. Low uncertainty in DSM is found to widen the inundated area, whereas the opposite occurred with high uncertainty. SRTM data proved unsuitable for our test basins and modelling context.

Accounting digital elevation uncertainty for flood consequence assessment

A digital elevation model (DEM) is commonly used as a substitute for surveyed topographic data. Selection of suitable DEM and optimum spatial resolution is, thus, a key for achieving expected accuracy within sufficient simulation time. This study compared DEMs from different sources (i.e. Shuttle Radar Topography Mission, Advanced Spaceborne Thermal Emission and Reflection Radiometer, National Elevation Dataset, and Light Detection and Ranging) with various spatial resolutions for a 35 km long stretch of the American River downstream of Folsom Dam in California. The study period was the 1997 ‘New Year's Flood’ used to estimate downstream flood consequences, especially in urban areas near Sacramento. The objective of this study was to quantify the comparative deviation of model accuracy for each specific set of topographic data. This study also looked into developing correlations between consequences and flood magnitude. The hydrodynamic model furnished input for flood damage assessment. The Hydrologic Engineering Center's Flood Impact Analysis (HEC‐FIA) was employed to estimate flood losses for each scenario. This analysis will assist decision‐makers in selecting the appropriate DEM for flood consequence assessment to get reasonable results within a convenient amount of time. It is also expected that this study will be useful for estimating consequences in absence of high‐quality terrain data, which will be especially helpful in remote study areas.

Snow depth from ICESat laser altimetry — A test study in southern Norway

Direct snow depth measurements are sparse, especially in remote areas. In this study, we assess the potential of ICESat laser altimetry for providing snow depths for its operational period 2003–2009 on the example of the Scandinavian Mountains in southern Norway. Snow cover during ICESat campaigns typically results in positive elevation differences (dh) between ICESat GLAH14 elevations and reference elevations from Digital Elevation Models (DEMs). Three DEMs are used: the Norwegian national DEM for the entire study area, and the SRTM DEM and a high-resolution airborne lidar DEM for a spatial subset on the Hardangervidda mountain plateau. To account for uncertainty in elevation data, ICESat samples are grouped into spatial subsets, elevation bands, and over time (e.g. all winter campaigns together). We find that ICESat has the potential to provide regional-scale snow depths for the years 2003–2009 for its winter (March) and late spring (June) campaigns. ICESat-derived snow depth time series for different elevation bands agree well with measured (RMSE 0.47m) and modelled (RMSE 0.61m) snow depths in the study area. Annual differences in snow amounts and the increase of snow depths with elevation and coastal proximity over the study area are correctly reproduced. Uncertainties in reference elevations exceed ICESat elevation uncertainty and good control over errors and biases in reference DEMs turn out essential. Spatially varying vertical offsets between ICESat and the reference DEMs make it necessary to bias-correct March/June snow depths with autumn dh per spatial unit or elevation band. Best results are achieved when samples are summarised per season over the entire observation period. After correction of local DEM biases, the spatial pattern of ICESat 2003–2009 March dh matches spatially distributed modelled snow depths in southern Norway with decimeter-scale accuracy. In the western part of Hardangervidda, ICESat-based March snow depths agree better with measurements (RMSE ≤0.15m for all DEMs) than modelled snow depths do (RMSE 0.61m). In eastern Hardangervidda, the coarse resolution SRTM DEM (RMSE 0.41m) performs better than the 10m Norwegian DEM (RMSE 0.64m) which is based on a less consistent mosaic of elevation data. Using the high-resolution lidar DEM, even single footprints show good agreement (R2 0.59, RMSE 0.94m) with measured snow depths from the same year. Snow depth estimates could be further improved by using full waveform ICESat data or elevation measurements from ICESat-2 once this satellite is operational. Good quality reference DEMs may still be acquired in the future even in areas where no such data exists today.