Geology topics

William Kleiber

Publications and source records attributed to William Kleiber.

3 recordsLinked to original sources

USGS2019

Topographic change detection at Chalk Cliffs, Colorado, USA, using Airborne LiDAR and UAS-based Structure-from-Motion photogrammetry

The Chalk Cliffs debris-flow site is a small headwater catchment incised into highly fractured and hydrothermally altered quartz monzonite in a semi-arid climate. Over half of the extremely steep basin is exposed bedrock. Debris flows occur multiple times per year in response to rainstorm events, typically during the summer monsoon season. The frequency of debris flows, and the uniformity of the underlying rock, make Chalk Cliffs an ideal study catchment for translating mechanistic understanding of natural debris flows to other sites. A 2008 National Center for Airborne Laser Mapping (NCALM) Airborne LiDAR survey provides baseline topography for the site; however, heretofore there has been no systematic effort to collect repeat topography of the entire site. Starting in May 2018, we made repeat surveys of the basin with an unmanned aircraft system (UAS). The UAS-based imagery was processed into (x, y, z) point clouds using Structure-from-Motion (SfM) photogrammetry. We geo-registered the point clouds using 12 ground control points placed within and around the study basin. In this study we compare the LiDAR with one SfM point cloud to assess topographic change over a 10-year time period. The difference map provides observational data relevant to understanding sediment provenance and transport at the Chalk Cliffs. The difference image indicates erosion of colluvial surfaces, with limited deposition in the survey area. Some colluvial hillslopes show spatially uniform erosion while others experienced concentrated erosion of up to three meters depth over a ten-year period.

Colorado↗

USGS2016

Kriging and local polynomial methods for blending satellite-derived and gauge precipitation estimates to support hydrologic early warning systems

Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2015

A Bayesian kriging approach for blending satellite and ground precipitation observations

Drought and flood management practices require accurate estimates of precipitation. Gauge observations, however, are often sparse in regions with complicated terrain, clustered in valleys, and of poor quality. Consequently, the spatial extent of wet events is poorly represented. Satellite-derived precipitation data are an attractive alternative, though they tend to underestimate the magnitude of wet events due to their dependency on retrieval algorithms and the indirect relationship between satellite infrared observations and precipitation intensities. Here we offer a Bayesian kriging approach for blending precipitation gauge data and the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates for Central America, Colombia, and Venezuela. First, the gauge observations are modeled as a linear function of satellite-derived estimates and any number of other variables—for this research we include elevation. Prior distributions are defined for all model parameters and the posterior distributions are obtained simultaneously via Markov chain Monte Carlo sampling. The posterior distributions of these parameters are required for spatial estimation, and thus are obtained prior to implementing the spatial kriging model. This functional framework is applied to model parameters obtained by sampling from the posterior distributions, and the residuals of the linear model are subject to a spatial kriging model. Consequently, the posterior distributions and uncertainties of the blended precipitation estimates are obtained. We demonstrate this method by applying it to pentadal and monthly total precipitation fields during 2009. The model's performance and its inherent ability to capture wet events are investigated. We show that this blending method significantly improves upon the satellite-derived estimates and is also competitive in its ability to represent wet events. This procedure also provides a means to estimate a full conditional distribution of the “true” observed precipitation value at each grid cell.

Water Resources Research↗