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Calculator of non-parametric standardized drought indices.

Project description

pySDI

Thanks for your interest in pySDI. This is a free, open source code to compute univariated and multivariated nonparametric Standardized Drought Indices (SDI) following the methodology proposed by McKee et al. (1993), and extended by Farahmand & AghaKouchak (2015), using raster maps as data source. It has been designed to use the land surface diagnosis data files of the NASA's atmospheric reanalysis product MERRA-2 (Modern-Era Reanalysis for Research and Applications, version 2; Gelaro et al., 2017) but future versions will allow to use other datasources (such as GLDAS-2; Rodell et al., 2004).

This project was originally developed as part of the Master in Engineering (Hydraulics) final project Monitoreo de sequías en México a través de índices multivariados [Drought monitoring in Mexico by mean of multivariated indices], developed in the Institute of Engineering of the National Autonomous University of Mexico (II-UNAM).

Currently, the documentation is still in development. Please, contact Roberto A. Real-Rangel (rrealr@iingen.unam.mx) for more information or support. This is an ongoing work. Any comments, suggestions or bugs reports will be appreciated.

Main source of the project

The project repository is available at https://bitbucket.org/pysdi/pysdi.

Installation

Write the following line in a terminal: pip install [repository local path]

Additionally, you'll need to install the GDAL library through: pip install GDAL

Python dependencies

Required Python packages:

  • gdal
  • numpy
  • pathlib2
  • scipy
  • sys
  • toml
  • warnings
  • xarray

Features in development

  • Drought forecasting using a multivariated linear regression approach.

References

  • Farahmand, A., & AghaKouchak, A. (2015). A generalized framework for deriving nonparametric standardized drought indicators. Advances in Water Resources, 76, 140–145. https://doi.org/10.1016/j.advwatres.2014.11.012
  • Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., … Zhao, B. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1
  • McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Eighth Conference on Applied Climatology (pp. 179–184). American Meteorological Society.
  • Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K. E., Meng, C.-J., … Toll, D. (2004). The Global Land Data Assimilation System. Bulletin of the American Meteorological Society, 85(3), 381–394. https://doi.org/10.1175/BAMS-85-3-381

Publications

  • Real-Rangel, Roberto Alejandro. (2016). Monitoreo de sequías en México a través de índices multivariados [Master Thesis, Universidad Nacional Autónoma de México]. http://oreon.dgbiblio.unam.mx/F/26RGSMDMG66D3MCT844UJ8B7PNM9TDC8UVYB4S9N7ND1HBQ9TQ-27197?func=full-set-set&set_number=006474&set_entry=000001&format=999.

  • Real-Rangel, Roberto A., Pedrozo-Acuña, A., Breña Naranjo, J. A., & Alcocer-Yamanaka, V. H. (2017, March). Monitorización de sequías en México a través del Índice Estandarizado Multivariado de Sequía. XXIV Congreso Nacional de Hidráulica, Acapulco, México.

  • Real-Rangel, Roberto A., Pedrozo-Acuña, A., Breña-Naranjo, J. A., & Alcocer-Yamanaka, V. H. (2017). An extended multivariate framework for drought monitoring in Mexico. European Geophysics Union General Assembly 2017, Vienna, Austria.

  • Real-Rangel, Roberto Alejandro, Pedrozo-Acuña, A., Breña-Naranjo, J. A., Alcocer-Yamanaka, V. H., & Ocón-Gutiérrez, A. R. (2017, December 11). An improvement of drought monitoring through the use of a multivariate magnitude index. AGU Fall Meeting 2017, New Orleans, LA.

  • Real-Rangel, Roberto A., Pedrozo-Acuña, A., Breña-Naranjo, J. A., & Alcocer-Yamanaka, V. H. (2018). Novel Drought Hazard Monitoring Framework for Decision Support Under Data Scarcity. In G. La Loggia, G. Freni, V. Puleo, & M. D. Marchis (Eds.), HIC 2018. 13th International Conference on Hydroinformatics (Vol. 3, pp. 1744–1751). EasyChair. https://doi.org/10.29007/1l5w

  • Real-Rangel, Roberto A., Pedrozo-Acuña, A., Breña-Naranjo, J. A., & Alcocer-Yamanaka, V. H. (2020). A drought monitoring framework for data-scarce regions. Journal of Hydroinformatics, 22(1), 170–185. https://doi.org/10.2166/hydro.2019.020

Author

Roberto A. Real-Rangel. Institute of Engineering of the National Autonomous University of Mexico (II-UNAM). rrealr@iingen.unam.mx.

Project details


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