California State University, Long Beach

About: California State University, Long Beach is a education organization based out in Long Beach, California, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 10036 authors who have published 13933 publications receiving 377394 citations. The organization is also known as: Cal State Long Beach & Long Beach State.

Geomorphic and geologic controls of geohazards induced by Nepal’s 2015 Gorkha earthquake

Abstract: The Gorkha earthquake (M 7.8) on 25 April 2015 and later aftershocks struck South Asia, killing ~9,000 and damaging a large region. Supported by a large campaign of responsive satellite data acquisitions over the earthquake disaster zone, our team undertook a satellite image survey of the earthquakes’ induced geohazards in Nepal and China and an assessment of the geomorphic, tectonic, and lithologic controls on quake-induced landslides. Timely analysis and communication aided response and recovery and informed decision makers. We mapped 4,312 co-seismic and post-seismic landslides. We also surveyed 491 glacier lakes for earthquake damage, but found only 9 landslide-impacted lakes and no visible satellite evidence of outbursts. Landslide densities correlate with slope, peak ground acceleration, surface downdrop, and specific metamorphic lithologies and large plutonic intrusions.

School Reform and Standards-Based Education: A Model for English-Language Learners.

Abstract: The authors examined a model of instruction for English-language learners (ELLs) who were learning academic English while they tried to meet content standards required by the nation's education reform movement. In previous work (J. Echevarria, M. E. Vogt, & D. Short, 2000), the authors developed and validated a model of instruction (Sheltered Instruction Observation Protocol; SIOP model) for ELLs. In this study, the authors tested the model for its effects on student achievement. Findings revealed that students whose teachers implemented the SIOP model performed slightly better than did a comparison group on an expository essay writing task, which closely approximated academic assignments that ELLs must perform in standards-based classrooms.

Uncertainties associated with digital elevation models for hydrologic applications: a review

Abstract: . Digital elevation models (DEMs) represent the topography that drives surface flow and are arguably one of the more important data sources for deriving variables used by numerous hydrologic models. A considerable amount of research has been conducted to address uncertainty associated with error in digital elevation models (DEMs) and the propagation of error to derived terrain parameters. This review brings together a discussion of research in fundamental topical areas related to DEM uncertainty that affect the use of DEMs for hydrologic applications. These areas include: (a) DEM error; (b) topographic parameters frequently derived from DEMs and the associated algorithms used to derive these parameters; (c) the influence of DEM scale as imposed by grid cell resolution; (d) DEM interpolation; and (e) terrain surface modification used to generate hydrologically-viable DEM surfaces. Each of these topical areas contributes to DEM uncertainty and may potentially influence results of distributed parameter hydrologic models that rely on DEMs for the derivation of input parameters. The current state of research on methods developed to quantify DEM uncertainty is reviewed. Based on this review, implications of DEM uncertainty and suggestions for the GIS research and user communities are offered.