The grids python tool for querying spatiotemporal multidimensional water data

Abstract

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 diffi-culty 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.