|Politecnico di Torino|
|Anno Accademico 2015/16|
Spatial analysis for water resources modeling and management (didattica di eccellenza)
Dottorato di ricerca in Ingegneria Civile E Ambientale - Torino
PERIODO: LUGLIO 2016
Il corso sarà tenuto dal Prof. Ramesh Teegavarapu della Florida Atlantic University
Instructor: Prof. Ramesh S. V. Teegavarapu, Ph.D., P.E.
Associate Professor, Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, Florida, 33431. Personal Web Site: http://faculty.eng.fau.edu/ramesh
Director, Hydrosystems Research Laboratory (HRL), Lab Web Site: http://hrl.fau.edu
This course presents a comprehensive review of spatial analysis methods and tools that are critical for analysis, modeling and management of hydrologic, environmental and water resource systems. The course emphasizes on three issues: (1) analysis, (2) methodologies and (3) techniques. The course provides an exhaustive background and in-depth review of spatial point patterns, statistics, and geographical information systems and discusses methods to evaluate spatial information, clusters, interpolation methods, generation of spatially varying inputs to distributed hydrologic models, and approaches for hydrometeorological monitoring network design. Spatial analysis methods and their applications for watershed and water resources management will be discussed and elaborated in this course.
Location: Politecnico di Torino
Introduction to Spatial Analysis
Introductive overview of spatial analysis. Observations in space and development of sampling schemes. Modifiable areal unit problem (MAUP), spatial autocorrelation, scales of measurement, extents. Local and global models: regression, moving window regression, geographically weighted regression.
Spatial Patterns and Statistics
Point pattern analysis, descriptive statistics, quadrat analysis, identification of nearest neighbors, nearest neighbor statistics, testing for higher-order neighbor statistics, clusters, K-function analysis, measures of spatial autocorrelation: Moran’s I Index and Geary’s Ratio. Line and polygon pattern analysis. Spatial filters (median and mean) for data smoothing.
Spatial Data Analysis with Geographical Information Systems
Introduction to geographical information. Types of data, database and attributes, spatial queries, data models, maps and projections, coordinate systems, representations of Earth, raster and vector data, processing of raster and vector data, spatial estimation, developing spatial data. Digital elevation models, terrain analysis. Concepts of digital elevation model processing and geoprocessing of hydrometeorology datasets.
Deterministic and Stochastic Spatial Interpolation
Basics of spatial interpolation. Point patterns, spatial autocorrelation, spatial statistics, Voronoi polygons. Deterministic interpolation methods. Stochastic interpolation methods. Geostatistics, ordinary kriging, co-kriging and other variants of kriging, optimal spatial interpolation and spatial interpolation for general of surfaces of hydroclimatic variable surfaces, missing data estimation, and uncertainty in spatial analysis estimates. Thin plate splines (with or without tension), locally adaptive interpolation, trend surface and local polynomial models. Objective selection of points in space for interpolation using optimization methods.
Public domain spatial interpolation software: With over 40 interpolation methods in Rainfall Analysis and INterpolation (RAIN) software developed by the Dr. Ramesh Teegavarapu
Spatial-Analysis Assisted Hydrologic and Environmental Modeling and Water Resources Management
Spatial inputs to hydrologic and environmental modeling efforts. Inputs to lumped and distributed hydrologic models. Generation of gridded precipitation data, interpolation (examples of radar-based precipitation datasets), derivation of watershed-specific properties for hydrologic and environmental modeling (pollutant load), watershed delineation using D-8 algorithm, processing of land use and land cover data sets. Several examples of spatial analysis-based hydrologic and environmental modeling. Linking of geographic information systems and water management models.
Public domain software: BASINS (Better Assessment Science Integrating point and Non-point Sources), Source US EPA (United States Environmental Protection Agency).
Hydometeorological Network Design
Evaluation of point-based spatial observations, heterogeneity of the hydroclimatic processes represented by continuous surfaces, concepts of optimal spatial sampling schemes and variance-based methods for sampling network design. Geostatistical approaches for monitoring network design. Examples and applications of spatial analysis methods for optimal precipitation and solar radiation monitoring network designs.
DATES: 11/07/2016 - 14/07/2016
SPEAKER: Prof. Ramesh S. V. Teegavarapu, Florida Atlantic University (USA)
Monday July 11, 2016, 9:00-12:30 Aula Bibolini, 14.30-16.30 LGI1 DIATI
Tuesday July 12, 2016, 9:00-12:30 Aula Bibolini, 14.30-16.30 LGI1 DIATI
Wednesday July 13, 2016, 9:00-12:30 Aula Bibolini, 14.30-16.30 LGI1 DIATI
Thursday July 14, 2016, 9:00-12:30, 14.30-16.30 LGI1 DIATI
Both Aula Bibolini and Computer Lab LG1 can be found at the ground floor of DIATI department, entrance #3.
For further information: firstname.lastname@example.org