30-m Digital Elevation Model Research Articles

Vadose zone lag time and potential 21st century climate change effects on spatially distributed groundwater recharge in the semi-arid Nebraska Sand Hills

Summary Deep drainage of water below plant root zones (potential groundwater recharge) will become groundwater recharge (GR) after a delay (or lag time) in which soil moisture traverses the vadose zone before reaching the water table. Depending on the thickness of the vadose zone, the magnitude of deep drainage, and soil hydraulic properties, lag times will vary broadly, exceeding decades to centuries in semi-arid and arid environments. Yet, studies of future climate change impacts to GR have typically avoided focusing on impacts beyond 100 years and often neglect to consider lag effects caused by the vadose zone. We investigate the effects of vadose zone lag time and potential 21st century climate change on the spatial distribution and timing of GR throughout the semi-arid Nebraska Sand Hills (NSH) region (∼50,000 km2). We propose a simple and rapid quantitative approach for assessment of the groundwater system response time to changes in climate. Understanding of such effects is needed for groundwater modeling, analysis of climate change impacts on groundwater, and the effective management and sustainability of future water resources. Lag time estimates are made using the pressure-based vertical velocity of soil moisture changes, equivalent to a kinematic wave approximation of Richards’ equation. The underlying assumptions (unit hydraulic gradient and relatively slow changes in climate) are supported by observations in the High Plains aquifer region, encompassing the NSH. The analysis relies on four sources of input data, including: Spatially distributed high resolution (1.1-km) GR rates (as the difference in 2000–2009 mean precipitation and evapotranspiration—estimated using a novel MODIS-based approach); thickness of the vadose zone—based on 30-m digital elevation models of the land surface and water table elevations; statistically downscaled estimates of future (2010–2099) potential GR rates from two hydroclimate model projections (WRCP CMIP3) with opposing GR trends; and estimates of hydraulic conductivity and vertical velocity of the vadose zone pressure changes using the van Genuchten–Mualem equation. We found that vadose zone lag times (with a mean GR rate of 78 mm/yr for 2000–2009) have an estimated spatial mean of 5.28 yrs (vertical velocity of 5.68 m/yr). End-of-century lag times (with mean potential GR rates of 128 and 61 mm/yr for 2090–2099) have estimated spatial means of 3.78 and 6.55 yrs (vertical velocities of 8.13 and 4.69 m/yr) for wet and dry scenarios, respectively. Findings suggest that the highly conductive materials (primarily sand deposits) and high potential GR rates in the NSH lead to relatively quick drainage of vadose zone water on the order of several years (less than a decade) for the entire 21st century. However, considerable spatial variations of lag times exist due to gradients in potential GR rates and the highly variable vadose zone thickness in the NSH.

A Technique for Low Cost Soil Mapping and Validation Using Expert Knowledge on a Watershed in Minas Gerais, Brazil

Understanding the soil attributes and types occurring within a region is critical for providing the best land-use decisions. Soils vary in their ability to clean and store water, provide water for plant growth, and many other ecosystem services. Soil variability is dependent on climate, parent material, organisms, time, and topography. When only topography varies within an area, the topography and redistribution of water should be the main drivers for soils differentiation. Digital soil mapping (DSM) has advantages due to computational tools and easily accessible digital elevation models (DEMs) at multiple resolutions. Terrain attributes (e.g., slope, wetness index, and profile curvature) are derived from the DEM and, in association with a soil expert, knowledge-based models can be applied to predict soil variability. The objective of this study was to create and validate a predicted Cambisol (Inceptisol) solum depth map for Lavrinha Creek Watershed (LCW) in Minas Gerais, Brazil, by applying DSM techniques for the Brazilian soil landscapes. The best available 30-m DEM was used to derive the terrain derivatives. A set of rules were formulated according to the terrain attributes, limited data, and expert knowledge to predict the solum depth behavior throughout the watershed. Conditioned Latin hypercube sampling scheme was used for allocating the validation points. In this study, 20 out of the 25 validating samples were correctly classified yielding a Kappa index of 0.616. Soil expert knowledge and Digital Soil Mapping techniques can be employed for mapping areas, especially in countries where there is limited data available, which will provide a useful soil map for planning while saving time and investments.

Comparing methods for estimating β points for use in statistical snow avalanche runout models

Snow avalanche runout estimates are core to risk assessment and mitigation for infrastructure development and transportation corridors in mountain regions. Two statistical models, the Runout Ratio model and the alpha-beta model, estimate the extreme runout position using the point where the slope of the avalanche path centerline first reduces to ten degrees (β point). In North America, the β point has traditionally been determined through a field survey of the avalanche path runout zone; however, as they become more accessible, digital elevation models (DEM) are increasingly being used to determine β as part of a preliminary review. While DEM requirements have been identified in avalanche literature, more focus is required on reviewing field error and relating the two methods.We surveyed 53 paths in western Canada, and estimated a field error distribution for the β point with an interquartile range of ±2% of path length and a maximum range of ±6.5% of path length. Five DEMs were sourced with spatial resolutions ranging from 1m to 90m. Of these, a 10m DEM generated the most similar β point estimates to the field survey.

Determination of potential runoff coefficient for Al-Baha Region, Saudi Arabia using GIS

The curve number (CN) is a hydrologic parameter used to describe the stormwater runoff potential for drainage areas, and it is a function of land use, soil type, and soil moisture. This study was conducted to estimate the potential runoff coefficient (PRC) using geographic information system (GIS) based on the area’s hydrologic soil group, land use, and slope and to determine the runoff volume. The soil map for the study area was developed using GPS data carried on to identify the soil texture to be used in building a soil hydrological groups map. Unsupervised and supervised classifications were done to Landsat 5/7 TM/ETM image to generate land-use and land-cover map. This map was reclassified into four main classes (forest, grass and shrub, cropland, and bare soil). Slope map for Al-Baha was generated from a 30-m digital elevation model. The GIS technique was used to combine the previous three maps into one map to generate PRC map. Annual runoff depth is derived based on the annual rainfall surplus and runoff coefficient per pixel using raster calculator tool in ArcGIS. An indication that in the absence of reliable ground measurements of rainfall product, it can satisfactorily be applied to estimate the spatial rainfall distribution based on values of R and R 2 (0.9998) obtained. Annual runoff generation from the study area ranged from 0 to 82 % of the total rainfall. Rainfall distribution in the study area shows the wise use of identifying suitable sites for rainwater harvesting, where most of the constructed dams are located in the higher rainfall areas.

Sediment delivery estimates in water quality models altered by resolution and source of topographic data.

Moderate-resolution (30-m) digital elevation models (DEMs) are normally used to estimate slope for the parameterization of non-point source, process-based water quality models. These models, such as the Soil and Water Assessment Tool (SWAT), use the Universal Soil Loss Equation (USLE) and Modified USLE to estimate sediment loss. The slope length and steepness factor, a critical parameter in USLE, significantly affects sediment loss estimates. Depending on slope range, a twofold difference in slope estimation potentially results in as little as 50% change or as much as 250% change in the LS factor and subsequent sediment estimation. Recently, the availability of much finer-resolution (∼3 m) DEMs derived from Light Detection and Ranging (LiDAR) data has increased. However, the use of these data may not always be appropriate because slope values derived from fine spatial resolution DEMs are usually significantly higher than slopes derived from coarser DEMs. This increased slope results in considerable variability in modeled sediment output. This paper addresses the implications of parameterizing models using slope values calculated from DEMs with different spatial resolutions (90, 30, 10, and 3 m) and sources. Overall, we observed over a 2.5-fold increase in slope when using a 3-m instead of a 90-m DEM, which increased modeled soil loss using the USLE calculation by 130%. Care should be taken when using LiDAR-derived DEMs to parameterize water quality models because doing so can result in significantly higher slopes, which considerably alter modeled sediment loss.

Glacially conditioned specific stream powers in low-relief river catchments of the southern Laurentian Great Lakes

Fluvial systems of the southern Laurentian Great Lakes region are carved into a complex glacial landscape shaped by continental ice and meltwater of the late Pleistocene. These glacially conditioned river catchments are typically small with drainage areas <104km2. A 10-m digital elevation model (DEM) is used to map the spatial distribution of stream gradient for 22 major river catchments of peninsular southern Ontario, which drain to base levels in the lower Great Lakes (Huron, St. Clair, Erie, and Ontario). Raw data from the DEM show stream gradients that exhibit multiscale variance from real and from artifact sources. Based on a vertical slice and multiple-pass moving-window averaging approach, slope data are generalised to the river reach scale (1–2km) as a representative spatial scale for fluvial processes operating over Holocene timescales. Models of specific stream power are then compared with glacial landform and surface geology mapping. Inherited glacial signatures in river slope appear as deviations in a stream length-gradient index (SL/K index), where river reaches are frequently oversteepened or understeepened. Based on a slope–area analysis, and complementary to theories of channel pattern discrimination, constant stream power curves (with power-law exponent of −0.4) provide a first-order approach to stratify river reaches in terms of glacial conditioning and expected planform morphologies. However, multiple-channel planform types are rare and localised in southern Ontario, indicating that oversteepened reaches with high stream powers may often be moderated by (1) sediment calibre, with cobble-beds from inherited glacial sediments; and/or (2) relative bank strength, with limited channel widening particularly in gravel and sand-bed channels. Further discrimination of glacially conditioned fluvial process domains will ultimately require consideration of alluvial floodplain characteristics in addition to general observations of river morphology and channel pattern.

Quantifying local flow direction uncertainty

Absolute elevation error in digital elevation models (DEMs) can be within acceptable National Map Accuracy standards, but still have dramatic impacts on field-level estimates of surface water flow direction, particularly in level regions. We introduce and evaluate a new method for quantifying uncertainty in flow direction rasters derived from DEMs. The method utilizes flow direction values derived from finer resolution digital elevation data to estimate uncertainty, on a cell-by-cell basis, in flow directions derived from coarser digital elevation data. The result is a quantification and spatial distribution of flow direction uncertainty at both local and regional scales. We present an implementation of the method using a 10-m DEM and a reference 1-m lidar DEM. The method contributes to scientific understanding of DEM uncertainty propagation and modeling and can inform hydrological analyses in engineering, agriculture, and other disciplines that rely on simulations of surface water flow.

Derivation of 30-m-resolution water maps from TERRA/MODIS and SRTM

The high temporal resolution and large coverage of coarse- to moderate-resolution satellite imagery, such as NOAA/AVHRR and EOS-TERRA/MODIS, are very advantageous for flood monitoring, but their coarse spatial resolution hinders their wider application. Overcoming this limitation is an interesting scientific challenge with substantial application potential.When both the total water area and topography are known, it is possible to locate regional water distribution in small regions, e.g., less than 1km2. The water fraction derived from TERRA/MODIS not only provides the water area in 500-m pixels, but it also contains geo-location information in each pixel, providing a potential way to derive high-resolution water maps with the use of SRTM digital elevation model (DEM) data. In this study we introduce an integration method to downscale MODIS 500-m water fraction maps to 30-m water maps with SRTM 30-m DEM data. The method uses an iteration cycle to compare water fraction by increasing water surface level based on the minimal elevation (searched from the 30-m SRTM cells within a 500-m pixel) to water fraction retrieved with dynamic nearest neighboring searching (DNNS) method from TERRA/MODIS multi-channel data, and iterations are terminated when the two fractions are equal to each other. The final water surface level is viewed as the maximal water surface level and smoothed by a uniformity process to reduce the impact from TERRA/MODIS water fraction retrieval errors. Then, cells in SRTM 30-m DEM with elevation between the minimal and maximal water surface levels are assigned as water to generate 30-m water maps.The presented method was applied to the Mississippi River flood of May 2011 and showed promising results. A validation analysis was performed using simultaneous Landsat TM 30-m data, Worldview-2 2-m data and river gauge observations and showed high consistency between integrated MODIS 30-m water maps and high resolution real-time satellite water maps, and the matched rate with TM is above 93%. The calculated maximal water surface levels used for integration are also close to the water levels observed at river gauges along Mississippi River. The validation result indicates the feasibility of this method to derive high-resolution water maps from coarse- or moderate-resolution satellite data, which can significantly improve the capability of these satellite data in flood analysis for decision-makers and downstream users.

Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils

Detailed knowledge on the spatial distribution of soils is crucial for environmental monitoring, management, and modeling. However soil maps with a finite number of discrete soil map units are often the only available information about soils. Depending on the map scale or the detailing of the map legend this information could be too imprecise. We present a method for the spatial disaggregation of map units, namely the refinement of complex soil map units in which two or more soil types are aggregated. Our aim is to draw new boundaries inside the map polygons to represent a single soil type and no longer a mixture of several soil types. The basic idea for our method is the functional relationship between soil types and topographic position as formulated in the concept of the catena. We use a comprehensive soil profile database and topographic attributes derived from a 10m digital elevation model as input data for the classification of soil types with random forest models. We grouped all complex map units which have the same combination of soil types. Each group of map units is modeled separately. For prediction of the soil types we stratified the soil map into these groups and apply a specific random forest model only to the associated map units. In order to get reliable results we define a threshold for the predicted probabilities at 0.7 to assign a specific soil type. In areas where the probability is below 0.7 for every possible soil type we assign a new class “indifferent” because the model only makes unspecific classification there. Our results show a significant spatial refinement of the original soil polygons. Validation of our predictions was estimated on 1812 independent soil profiles which were collected subsequent to prediction in the field. Field validation gave an overall accuracy of 70%. Map units, in which shallow soils were grouped together with deep soils could be separated best. Also histosols could be predicted successful. Highest error rate were found in map units, in which Gleysoils were grouped together with deep soils or Anthrosols. To check for validity of our results we open the black box random forest model by calculating the variable importance for each predictor variable and plotting response surfaces. We found good confirmations of our hypotheses, that topography has a significant influence on the spatial arrangement of soil types and that these relationships can be used for disaggregation.

Scale effects on the estimation of erosion thresholds through a distributed and physically-based hydrological model

Slope incision and subsequent development of rills, gullies and channels are responsible for significant soil losses and are often irreversible with very high restoration costs particularly in semiarid environments. The location of potential areas of erosion where these processes occur is vital to land management and conservation. The study of the relationship between the local slope (S) and the drainage area (A) through the geomorphological relationship S=αAb, combined with hydrologic simulation models, has proved to be appropriate for the identification and characterisation of potential areas of incision, especially when it takes into account the spatial distribution of soil properties and the evolution of hydrological processes. However, physical properties of soil, digital elevation models (DEMs) and flow algorithms used may affect the results. This study employs a distributed, physically-based hydrological model to evaluate the infiltration–runoff relationships and their influence on selecting critical area from three DEMs with different resolutions. The results show a significant scale effect on flow distribution and the location of threshold points on slopes. The results obtained from a 30-m DEM significantly differ from those obtained from 10 and 5-m DEMs because the former was unable to capture the spatial variability of geomorphic processes. The selected set of critical points shows high S–A correlations for different values of critical shear stress. The physical model confirmed the dominance of surface runoff in the study site and was validated from field identification of erosion risk areas, although for incision areas <2ha, an appreciable error remains in relation to the calculation algorithm used for the drainage network and DEM resolution.

Comparing Methods for Interpolation to Improve Raster Digital Elevation Models

Digital elevation models (DEMs) are available as raster files at 100m, 30m, and 10m resolutions for the contiguous United States and are used in a variety of geographic analyses. Some projects may require a finer resolution. GIS software offers many options for interpolating data to higher resolutions. We compared ten interpolation methods using 10m sample data from the Ouachita Mountains in central Arkansas. We interpolated the 10m DEM to 5m, 2.5m, and 1m resolutions and compared the absolute mean difference (AMD) for each using surveyed control points. Overall, there was little difference in the accuracy between interpolation methods at the resolutions tested and minimal departure from the original 10m raster.

A comparison of LiDAR-based DEMs and USGS-sourced DEMs in terrain analysis for knowledge-based digital soil mapping

While LiDAR-based digital elevation models (DEM) are more accurate and precise than the USGS-sourced DEM that are widely used in soil mapping in the US, their high cost and other problems prohibit an easy decision of adopting them in service-oriented soil mapping conducted by a government agency like USDA-NRCS. This study compares the performances of LiDAR-based DEM and the USGS-sourced DEM in calculating slope gradient as an input for knowledge-based digital soil mapping (KBDSM), aiming to provide scientific evidence and more importantly, propose a scientific approach to evaluating the two types of DEM for KBDSM. We conducted the comparison by evaluating how closely the DEM-based slope gradient values match the field-measured values. For a small watershed in northern Vermont, US, we prepared three DEM, including a 10-m DEM interpolated from the 7.5-minute USGS topographic map, a 1-m DEM based on LiDAR points, and a 5-m DEM resampled from the 1-m DEM. When calculating slope gradient, we applied two neighborhood sizes (10m and 30m), two neighborhood shapes (square and circular), and three slope gradient algorithms (Evans–Young, Horn, and modified Zevenbergen–Thorne) to the three DEM. We then compared the calculated slope gradient values with the values measured by soil scientists at 159 sample locations in the study area. Statistics show that across all the tested settings, the LiDAR-based DEM perform significantly better than the USGS-sourced DEM. We conclude that LiDAR-based DEM may considerably improve the quality of inputs for KBDSM. We also find that the results from the 1-m LiDAR-based DEM and the resampled 5-m DEM do not show considerable and consistent differences.

Spatial Relationships between Landslides and Topographical Factors at the Liukuei Experimental Forest, Southwestern Taiwan after Typhoon Morakot

Typhoon Morakot produced copious amounts of rainfall and triggered enormous landslides in southern Taiwan when it hit Taiwan on 8~9 August 2009. The Liukuei Experimental Forest (LEF) managed by the Taiwan Forestry Research Institute was located in the peak rainfall region and inescapably incurred severe landslides and road destruction. This paper presents a statistical approach to study spatial relationships between landslides and their geographic factors in the LEF after typhoon Morakot. The normalized difference vegetation index (NDVI) of satellite images SPOT2 and SPOT4 photographed before and after typhoon Morakot were compared to extract landslide patches. Those extracted patches, consisting of 10 x 10-m digital elevation model (DEM) data and the related coverage of vegetation of LEF, were examined using the WinGrid computer software for the topographic analysis. Results indicated that the extremely heavy rainfall was the main factor that triggered landslides during the typhoon. There were 204 locations which experienced identifiable landslides, and their total area was about 804.49 ha. The elevation and types of vegetation cover were not factors related to the failure of the slopes. Steepness, aspect, and distances to roads and streams were factors that contributed to instability of the slopes. About 70.2% of the total area of landslides occurred on slopes with steepness in the range of 60~80%, and this showed that steep slopes are prone to collapse. Landslides with distance of the lower edge to a stream of ＜ 10 m occurred in 49 locations, and their areas accounted for 33.6% of the total landslide areas. In addition, 34 landslides (24.7% of all landslides areas) occurred within 50 m of a forest road, and the average area of those landslides was 5.79 ha. The deeply concentrated surface flows resulting from the extreme rainfall in streams and on road surfaces striking against the foot of hillslopes that contained saturated soil water were the main factors causing slides of large area. This also indicated that landslides in the LEF were closely related to stream systems, and forest road construction. The aspect of slope was not the main factor related to landslides in most cases. However, slopes with southerly and southeasterly aspects directly faced the strong winds and heavy rainfall brought by typhoon Morakot, and more landslides occurred on those slopes than on slopes with other aspects.

Effect of Topographic Data Accuracy on Water Storage Environmental Service and Associated Hydrological Attributes in South Florida

AbstractThe effects of digital elevation model (DEM) accuracy on terrain attributes and water storage were evaluated by comparing a standard 10-m U.S. Geological Survey DEM and a 10-m DEM derived from Light Detection and Ranging (LIDAR) data, on a ranchland that participated in a water storage environmental services program in South Florida. Elevation differences between the two DEMs ranged from -0.73 m to 1.6 m with a low (31%) correlation. Frequency analysis showed that 64% of the grids in the study area had elevation differences of 0.8 m or greater. Spatial distribution of wetlands and ditches were better represented by the LIDAR than the USGS DEM. Surface water storage in the study area and an isolated wetland was 49% and 21% more, respectively, for the USGS DEM than LIDAR DEM in wet seasons. In dry seasons, the LIDAR DEM resulted in 28% higher storage than the USGS DEM. Annually, the USGS DEM resulted in an average 44% more storage than LIDAR DEM. For a major rainstorm in 2008, the USGS showed 92% i...

Assessing resolution and source effects of digital elevation models on automated floodplain delineation: A case study from the Camp Creek Watershed, Missouri

Digital elevation models (DEMs) have been widely used in automated floodplain modeling to determine floodplain boundaries. However, the effects of DEM resolution and data source on floodplain delineation are not well quantified. This paper presents a case study to assess these effects from the Camp Creek Watershed, Missouri, using two sets of DEMs. One is the Light Detection and Ranging (LiDAR) DEMs re-sampled from 1-m to 3, 5, 10, 15, and 30-m resolutions. The other is 5, 10, and 30-m DEMs obtained from the U.S. Geological Survey (USGS). Floodplain boundaries are delineated using a combination of hydrological, hydraulic and floodplain delineation models under the Federal Emergency Management Agency’s (FEMA) guideline. Model outputs including stream network, watershed and floodplain boundaries are compared to 1-m LiDAR DEM outputs (as the reference) to assess the uncertainty. Results indicate that re-sampled 3 or 5-m LiDAR DEMs produce similar streams and floodplain boundaries within 10% difference of the reference. In contrast, coarser LiDAR DEMs (such as the 10-m resolution) are more appropriate for watershed boundary delineation because higher DEM resolutions are likely more sensitive to minor topographic changes and may introduce erroneous boundaries. For different data sources, uncertainties introduced by USGS DEMs are much higher than LiDAR DEMs with a distinct relationship between uncertainties and DEM resolutions. Uncertainties of LiDAR DEMs consistently increase with decreasing resolutions, whereas similar levels of uncertainty are observed for different USGS DEM resolutions. This difference is probably due to the inherited difference in their original data source resolutions to make these two types of DEMs.

The importance of digital elevation model resolution on granular flow simulations: a test case for Colima volcano using TITAN2D computational routine

The mobility of gravity-driven granular flows such as debris flows or pyroclastic density currents are extremely sensitive to topographic changes, such as break in slopes, obstacles, or ravine deviations. In hazard assessment, computer codes can reproduce past events and evaluate hazard zonation based on inundation limits of simulated flows over a natural terrain. Digital Elevation Model (DEM) is a common input for the simulation algorithm and its accuracy to reproduce past flows is crucial. In this work, we use TITAN2D code to reproduce past block-and-ash flows at Colima volcano (Mexico) over DEMs with different cell size (5, 10, 30, 50, and 90 m) in order to illustrate the influences of the resolution on the numeric simulations. Our results show that topographic resolution significantly affects the flow path and runout. Also, we found that simulations of past flows with the same input parameters (such as the basal friction angle) over topography with different resolutions resulted in different flow paths, areas, and thickness of the simulated flows. In particular, the simulations performed with the 5- and 10-m DEMs produced similar results. Also, we obtained consistent simulation results for the 30- and 50-m DEMs. However, for the coarser 90-m DEM results are largely different and inaccurate. We recommend generating a benchmark table in order to acquire characteristic values for the basal friction angle of studied events. In case of rugged topographies, a DEM with high resolution should be used for more confident results.

Exploring Cloud-to-Ground Lightning Earth Highpoint Attachment Geography by Peak Current

Abstract This study applied remotely sensed cloud-to-ground (CG) lightning strike location data, a digital elevation model (DEM), and a geographic information system (GIS) to characterize negative polarity peak current CG lightning Earth attachment behavior. It explored the propensity for (i) flashes to favor topographic highpoint attachment and (ii) striking distance (a near-Earth attachment force) to increase with peak current. On a 16 000 km2 10-m DEM covering a section of southeast and south-central Colorado, a GIS extraction method identified approximately 5000 hilltop and outcrop highpoints containing at least 15 m of vertical gain in a 300-m radius neighborhood with a minimum horizontal separation of 600 m. Flashes with peak currents ranging from −20 to −119 kiloamps (kA), collected between February 2005 and May 2009, were subdivided into 10 kA classes and mapped on this modified DEM. Buffers of 100-, 200-, and 300-m radii created around each highpoint were used to assess the hypothesis that striking distance increases with higher negative peak current. Point-in-polygon counts compared actual CG strike totals to random point totals received inside buffers. CG strikes favored topographic highpoints by as much as 5.0% when compared to random points. Chi-square goodness-of-fit tests further corroborated that actual CG strikes at highpoints were generated by a more nonrandom process. A positive trend between striking distance and peak current was also observed. Although this correlation has been characterized in controlled settings, this study is the first to document this physical process at real-world landscape scales over multiple years.

Structural geometry of Raplee Ridge monocline and thrust fault imaged using inverse Boundary Element Modeling and ALSM data

We model the Raplee Ridge monocline in southwest Utah, where Airborne Laser Swath Mapping (ALSM) topographic data define the geometry of exposed marker layers within this fold. The spatial extent of five surfaces were mapped using the ALSM data, elevations were extracted from the topography, and points on these surfaces were used to infer the underlying fault geometry and remote strain conditions. First, we compare elevations extracted from the ALSM data to the publicly available National Elevation Dataset 10-m DEM (Digital Elevation Model; NED-10) and 30-m DEM (NED-30). While the spatial resolution of the NED datasets was too coarse to locate the surfaces accurately, the elevations extracted at points spaced ∼50 m apart from each mapped surface yield similar values to the ALSM data. Next, we used a Boundary Element Model (BEM) to infer the geometry of the underlying fault and the remote strain tensor that is most consistent with the deformation recorded by strata exposed within the fold. Using a Bayesian sampling method, we assess the uncertainties within, and covariation between, the fault geometric parameters and remote strain tensor inferred using the model. We apply these methods to the Raplee Ridge monocline, and find that the resolution and precision of the ALSM data are unnecessary for inferring the fault geometry and remote strain tensor using our approach. However, the ALSM data were necessary for the mapping of the spatial distribution of surface outcrops. Our models considered two scenarios: one in which fault geometry and remote strains were inferred using a single deformed stratum, and another in which all mapped strata were used in the inversion. Modeled elevations match those observed to within a root-mean-squared error of 16–18 m, and show little bias with position along the fold. Both single- and multilayer inversions image a fault that is broadly constrained to be ∼4.5–14 km in down-dip height, 13–30 km in along-strike width, with a tip-line 2.0–9.5 km below the surface at the time of deformation. Poisson's ratio was not well resolved by the inversion. The idealized elastic model is oversimplified when considering the complicated layered nature of this fold, however, it provides a good fit to the observations. Thus, comparable surface displacements may be produced with a variety of rheological models, so independent constraints on factors such as the fault geometry may be required to ascertain the appropriate rheology of the fold.

Susceptibility assessment of shallow landslides on Oahu, Hawaii, under extreme-rainfall events

The deterministic Stability INdex MAPping (SINMAP) model, which integrates a mechanistic infinite-slope stability model and a hydrological model, was applied to assess susceptibility of slopes in 32 shallow-landslide-prone watersheds of the eastern to southern areas of Oahu, Hawaii, USA. Input to the model includes a 10-m Digital Elevation Model (DEM), an inventory of storm-induced landslides that occurred from 1949 to 2006, and listings of soil-strength and hydrological parameters including transmissivity and steady-state recharge. The study area of ca. 384 km 2 was divided into four calibration regions with different geotechnical and hydrological characteristics. All parameter values were separately calibrated using observed landslides as references. The study used a quasi-dynamic scenario of soil wetness resulting from extreme daily rainfall events with a return period of 50 years. The return period was based on almost-90-year-long (1919–2007) daily rainfall records from 26 raingauge stations in the study area. Output of the SINMAP model includes slope-stability-index-distribution maps, slope-versus-specific-catchment-area charts, and statistical summaries for each region. The SINMAP model assessed susceptibility at the locations of all 226 observed shallow landslides and classified these susceptible areas as unstable. About 55% of the study area was predicted as highly unstable, highlighting a critical island problem. The SINMAP predictions were compared to an existing debris-flow-hazard map. Areas classified as unstable in the current study were classified as low-to-moderate and moderate-to-high debris-flow hazard risks by the prior mapping. The slope-stability maps provided by this study will aid in explaining the causes of known landslides, making emergency decisions, and, ultimately mitigating future landslide risks. The maps may be further improved by incorporating heterogeneous and anisotropic soil properties and spatial and temporal variation of rainfalls as well as by improving the accuracy of the DEM and the locations of shallow landslide initiation.

Potential impacts of sea-level rise on the Mid- and Upper-Atlantic Region of the United States

We made projections of relative sea-level rise, horizontal inundation, and the associated impacts on people and infrastructure in the coastal portion of the Mid- and Upper-Atlantic Region (MUAR) of the United States. The output of five global climate models (GCMs) run under two greenhouse gas scenarios was used in combination with tide gauge observations to project sea-level increases ranging from 200 to 900 mm by 2100, depending on location, GCM and scenario. The range mainly reflects equal contributions of spatial variability (due to subsidence) and GCM uncertainty, with a smaller fraction of the range due to scenario uncertainty. We evaluated 30-m Digital Elevation Models (DEMs) using 10-m DEMs and LIDAR data at five locations in the MUAR. We found average RMS differences of 0.3 m with the 10-m DEMs and 1.2 m with the LIDAR data, much lower than the reported mean RMS errors of 7 m for the 30-m DEMs. Using the 30-m DEMs, the GCM- and scenario-means of projected sea-level rise, and local subsidence estimates, we estimated a total inundation of 2,600 km2 for the MUAR by 2100. Inundation area increases to 3,800 km2 at high tide if we incorporate local tidal ranges in the analysis. About 510,000 people and 1,000 km of road lie within this area. Inundation area per length of coastline generally increases to south, where relative sea-level rise is greater and relief is smaller. More economically developed states, such as New York and New Jersey, have the largest number of people and infrastructure exposed to risk of inundation due to sea-level rise.