The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data
| Autor: | Gustavious P. Williams, J. Enoch Jones, Norman Jones, Everett James Nelson, Daniel P. Ames, Riley Chad Hales |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Geographic information system
010504 meteorology & atmospheric sciences Computer science Interface (computing) Geography Planning and Development 0207 environmental engineering 02 engineering and technology Aquatic Science grids computer.software_genre 01 natural sciences Biochemistry Raster data Software Multiple time dimensions 020701 environmental engineering Spatial analysis TD201-500 0105 earth and related environmental sciences Water Science and Technology computer.programming_language Water supply for domestic and industrial purposes business.industry gridded data Hydraulic engineering Python (programming language) File format raster data time series data Data mining business TC1-978 computer multidimensional data |
| Zdroj: | Water, Vol 13, Iss 2066, p 2066 (2021) Water Volume 13 Issue 15 |
| ISSN: | 2073-4441 |
| Popis: | Scientific datasets from global-scale earth science models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. Water data are frequently stored as multidimensional arrays, also called gridded or raster data, and span two or three spatial dimensions, the time dimension, and other dimensions which vary by the specific dataset. Water engineers and scientists need these data as inputs for models and generate data in these formats as results. A myriad of file formats and organizational conventions exist for storing these array datasets. The variety does not make the data unusable but does add considerable difficulty in using them because the structure can vary. These storage formats are largely incompatible with common geographic information system (GIS) software. This introduces additional complexity in extracting values, analyzing results, and otherwise working with multidimensional data since they are often spatial data. We present a Python package which provides a central interface for efficient access to multidimensional water data regardless of the file format. This research builds on and unifies existing file formats and software rather than suggesting entirely new alternatives. We present a summary of the code design and validate the results using common water-related datasets and software. |
| Databáze: | OpenAIRE |
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