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Article

The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data

Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USA
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Author to whom correspondence should be addressed.
Academic Editor: Joaquín Izquierdo
Water 2021, 13(15), 2066; https://doi.org/10.3390/w13152066
Received: 20 May 2021 / Revised: 21 July 2021 / Accepted: 27 July 2021 / Published: 29 July 2021
(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)
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. View Full-Text
Keywords: multidimensional data; time series data; raster data; gridded data; grids multidimensional data; time series data; raster data; gridded data; grids
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MDPI and ACS Style

Hales, R.C.; Nelson, E.J.; Williams, G.P.; Jones, N.; Ames, D.P.; Jones, J.E. The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data. Water 2021, 13, 2066. https://doi.org/10.3390/w13152066

AMA Style

Hales RC, Nelson EJ, Williams GP, Jones N, Ames DP, Jones JE. The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data. Water. 2021; 13(15):2066. https://doi.org/10.3390/w13152066

Chicago/Turabian Style

Hales, Riley C., Everett J. Nelson, Gustavious P. Williams, Norman Jones, Daniel P. Ames, and J. E. Jones 2021. "The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data" Water 13, no. 15: 2066. https://doi.org/10.3390/w13152066

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