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There is a need for computational tools in water science to integrate large spatially distributed datasets to provide rapid insight in the spatial and temporal domains of data while providing visualization, rapid spatiotemporal analyses, data metrics, and pattern recognition into one tool. High spatial and temporal resolution datasets better represent the variability of hydrological processes and the associated fate and transport of solutes and sediments. However, integration of the temporal and spatial domains of data (e.g., dynamics) commonly results in copious computational tasks and extensive analysis requiring the integration of multiple computational platform, tools and data formats.

SPELLmap evolved from SPELL-stat (Guzman and Chu, 2003; see Legacy TAB) and provides a graphic user interface to integrate raster (e.g., grids) and time series associated to networks for data collection. Also, SPELLmap serves as a framework for hydrologic and environmental data preparation, analysis, interpretation, visualization, data modeling, and for conducting data quality assurance of large spatially distributed datasets.

SPELLmap gives modelers and researchers the ability to explore data patterns at finer spatial and temporal scales, prepare time series or raster data, and conduct data aggregation/segregation and interpolation while evaluating the impacts of these data processes in the phenomena representation. SPELLmap is more than an application, it is a scripted computational language to perform analysis in the spatio-temporal domains of data.

What should I do to start working with SPELLmap?

1. Go to the download TAB, get the latest version and install it
2. Copy the "examples" folder in c:\Program File (x86)\SPELLmap to an unprotected folder
3. Understand the SPELLmap GUI, notation and data representation. Check the documentation TAB
4. Be playful with the datasets provided, SPELLmap API calls, and scripts


Guzman, J.A., Moriasi, D.N., Chu, M.L., Starks, P.J., Steiner, J.L., Gowda, P.H., 2013. A tool for mapping and spatio-temporal analysis of hydrological data. Environ. Modell. Softw. 48: 163-170.

Jorge A. GUZMAN a        Ma. Librada CHU b
Daniel N. MORIASI a       Jean L. STEINER a
Patrick J. STARKS a       Prassana H. GOWDA c

a USDA-ARS Grazinglands Research Laboratory, El Reno, Oklahoma.
b Saint Louis University, Saint Louis, Missouri.
c USDA-ARS, Conservation and Production Research Laboratory, Bushland, Texas


© 2013 Jorge A. GUZMAN web manager • updated Jan 22-2013 DownloadDocumentationExamplesSPELLsScreenshotsHome