GEP 3750 Data Acquisition and Integration Methods for GIS Analysis

The techniques and science of data acquisition and creation for spatial analysis in a geographic information system (GIS); includes field data collection. Students will be instructed in the use GPS devices, mobile GIS, workstation GIS, as well as data from other sources including remotely sensed data. The full course site is available at https://gep3750.commons.gc.cuny.edu/

Syllabus GEO101

Syllabus for courses Dynamic Earth (undergraduate course): GEO 101-81 and Earth Processes (graduate course): GEO 501-81. Open textbook used in both courses linked in the syllabus and available at the following link: https://pressbooks.cuny.edu/gorokhovich

An Overland Flood Model for Geographical Information Systems

A variety of flood models and commercial flood simulation software are provided in the literature, with different accuracies and precisions changing from coarse to fine, depending on model structure and detailed descriptions of basin and hydrologic properties. These models generally focus on river processes, taking overland processes as inputs of 1D or 2D hydrodynamic or hydrologic river flow models. Due to the discrete structure of overland flow and unknown-dynamic boundary conditions, such classical approaches are not cable of fast and reliable spatio–temporal estimations for overland flows, and require detailed and well-organized spatial data that cannot be immediately obtained during an emergency. A spatially-distributed Geographical Information Systems (GIS) based flood model is developed in this study to simulate overland floods, using cellular automata principles. GIS raster cells are considered hydrologic homogeneous areas throughout which hydrologic properties remain constant. Hydrodynamic flow principles, conservations of mass, momentum and energy are applied at pixel level to simulate floodwaters. The proposed GIS model is capable of directly manipulating spatio–temporal pixel level data (e.g., topography, precipitation, infiltration, surface roughness etc.) for modeling of rainfall-induced overland floods; therefore, it can provide fast, temporal and spatial flood depth estimations as well as maximum flood depths and times of concentration for all pixels throughout a study area. The model is quite simple and easy to apply via easily creatable GIS input layers, and is thus very convenient for preliminary engineering applications that need quick and fast response. Its main advantage is that it does not need a predefined flood boundary and boundary conditions. This advantage is especially valuable for coastal plains where delineation of a basin is generally too difficult. Floodwaters of Cyclone Nargis/Myanmar were simulated to test the model. Sensitivity analyses were applied to evaluate the effects of the model parameters (i.e., surface roughness and infiltration rates) on simulation results. The study shows that the proposed GIS model can be readily applied for the fast and inexpensive modeling of rainfall caused floods in areas where flood boundaries and boundary conditions cannot be clearly identified

Tsunami Mortality Estimates and Vulnerability Mapping in Aceh, Indonesia

Objectives. We aimed to quantify tsunami mortality and compare approaches to mortality assessment in the emergency context in Aceh, Indonesia, where the impact of the 2004 tsunami was greatest. Methods. Mortality was estimated using geographic information systems–based vulnerability models and demographic methods from surveys of tsunami-displaced populations. Results. Tsunami mortality in Aceh as estimated by demographic models was 131066 and was similar to official figures of 128063; however, it was a conservative estimate of actual mortality and is substantially less than official estimates of 168561 presumed dead, which included those classified as missing. Tsunami impact was greatest in the district of Aceh Jaya, where an estimated 27.0% (n=23862) of the population perished; Aceh Besar and Banda Aceh were also severely affected, with mortality at 21.0% (n = 61 650) and 11.5% (n = 25 903), respectively. Mortality was estimated at 23.7% for the population at risk and 5.6% overall. Conclusions. Mortality estimates were derived using methodologies that can be applied in future disasters when predisaster demographic data are not available. Models could be useful in the early stages of disaster response by facilitating geographic targeting and management of humanitarian assistance