|
|
The study of mass and energy transfer across landscapes has recently evolved to comprehensive considerations acknowledging the role of biota and humans as geomorphic agents, as well as the importance of small-scale landscape features. A contributing and supporting factor to this evolution is the emergence over the last two decades of technologies able to acquire high resolution topography (HRT) (meter and sub-meter resolution) data. Landscape features can now be captured at an appropriately fine spatial resolution at which surface processes operate; this has revolutionized the way we study Earth-surface processes. The wealth of information contained in HRT also presents considerable challenges. For example, selection...
|
One of the grand challenges of Earth Surface Science and Natural Resource Management lies in the prediction of mass and energy transfer for large watersheds and landscapes. High resolution topography (lidar) datasets show potential to significantly advance our understanding of hydrologic and geomorphic processes controlling mass and energy transfer because they represent features at the appropriate fine scale on which surface processes operate. While lidar datasets have become readily available across the United States, challenges remain in extracting accurate and objective information relevant for hydrologic and geomorphic research, modeling, and prediction, as well as watershed management. We primarily focus our...
Categories: Data,
Project;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: All Working Groups,
Completed,
Core Science Systems,
LIDAR,
Landscapes, All tags...
Natural Hazards,
Water Resources, Fewer tags
|
ABSTRACT Probabilistic forecasting of earthquake‐producing fault ruptures informs all major decisions aimed at reducing seismic risk and improving earthquake resilience. Earthquake forecasting models rely on two scales of hazard evolution: long‐term (decades to centuries) probabilities of fault rupture, constrained by stress renewal statistics, and short‐term (hours to years) probabilities of distributed seismicity, constrained by earthquake‐clustering statistics. Comprehensive datasets on both hazard scales have been integrated into the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3). UCERF3 is the first model to provide self‐consistent rupture probabilities over forecasting intervals from less...
|
|
