January 2022

Water Security (WSIM-GLDAS) Monthly Grids, v1 (1948-2014)


PURPOSE

To provide data characterizing 67 years (1948-2014) of anomalous freshwater surpluses, deficits, and the parameters determining them, across the global terrestrial surface.


DESCRIPTION

The Water Security Indicator Model - Global Land Data Assimilation System (WSIM-GLDAS) Monthly Grids data set identifies and characterizes surpluses and deficits of freshwater, and the parameters determining these anomalies, at monthly intervals over the period January 1948 to December 2014. The data set uses the Noah land surface model outputs from NASA's Global Land Data Assimilation System, covering the global extent, to generate anomaly values for the following parameters at a gridded resolution of 0.25 degrees: temperature, precipitation, soil moisture, potential minus actual evapotranspiration (petme), runoff, total blue water (flow-accumulated runoff), composite index of water surplus, and composite index of water deficits. These data are provided in terms of return periods, scientific units, and standardized (normalized) anomalies, and are computed over 1-month, 3-month, 6-month, and 12-month temporal periods of accumulation, referred to as integration periods. Anomaly values are present in terms of return periods with respect to a fitted Generalized Extreme Value (GEV) probability distribution function over a historical baseline period of January 1950 to December 2009, at a global spatial resolution of 0.25 degrees over the monthly, 3-month, 6-month, and 12-month periods of integration. Composite surpluses and deficit indices are derived from composite anomaly values in order to meaningfully summarize overall hydrological conditions at a resolution of 0.25 degrees. Parameter values (location, scale, shape) of the fitted GEV probability distribution, which are fit separately for each calendar month, are distributed per parameter for each integration period.


ACCESSING THE DATA

The data may be downloaded at https://sedac.ciesin.columbia.edu/data/set/water-wsim-gldas-v1/data-download


DATA FORMAT

This archive contains gridded data in netCDF format.

The data files are compressed zipfiles. Downloaded files need to be uncompressed in a single folder using either WinZip (Windows file compression utility) or similar application. Users should expect an increase in the size of downloaded data after decompression. 


DATA VALUES

For more information on the Water Security (WSIM-GLDAS) Monthly Grids, v1 (1948-2014), please review the Documentation for the Water Security Indicator Model - Global Land Data Assimilation System (WSIM-GLDAS) Monthly Grids, Version 1. This documentation can be downloaded at https://sedac.ciesin.columbia.edu/data/set/water-wsim-gldas-v1/docs.


SPATIAL EXTENT

180.0, -60.0, 180.0, 90.0 


DISCLAIMER

CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN.

   
USE CONSTRAINTS

This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.


RECOMMENDED CITATION(s)

Data Set:

ISciences, and Center for International Earth Science Information Network (CIESIN), Columbia University. 2022. Water Security Indicator Model - Global Land Data Assimilation System (WSIM-GLDAS) Monthly Grids, Version 1. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/z1fn-kf73. Accessed DAY MONTH YEAR.

Scientific Publication:

ISciences, L.L.C. 20 August 2019. Water Security Indicator Model: https://wsim.isciences.com/.


REFERENCES

Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control (AC-19), 716-723.

Baston, D. 2018. GitHub repository: https://github.com/isciences/exactextract.

Beaudoing, H., M. Rodell, and NASA/GSFC/HSL. 2015. GLDAS Noah Land Surface Model L4 monthly 0.25 x 0.25 degree V2.0. Greenbelt, Maryland, U.S.A.: Goddard Earth Sciences Data and Information Services Center (GES DISC). https://doi.org/10.5067/9SQ1B3ZXP2C5.

Du, T., L. Xiong, C.-Y Xu, C. J. Gippel, S. Guo, S., and P Liu. 2015. Return period and risk analysis of nonstationary low-flow series under climate change. Journal of Hydrology, 234-250.

GDAL/OGR contributors. 2019. GDAL/OGR Geospatial Data Abstraction software Library. Open Source Geospatial Foundation. https://gdal.org.

Hosking, J. R. 2017. L-Moments. CRAN. https://cran.r-project.org/web/packages/lmom/.

Hosking, J. R., and J. R. Wallis. 1997. Regional frequency analysis: An approach based on L-moments. Cambridge, U. K.: Cambridge University Press.

Hyman, R. J., and Y. Fan. November 1996. Sample Quantiles in Statistical Packages. The American Statistician, 361-365. https://www.jstor.org/stable/2684934.

O'Callaghan J. F., and D. M. Mark. 1984. The extraction of drainage networks from digital elevation data. Computer Vision, Graphics, and Image Processing. 28, 323-344. https://doi.org/10.1016/S0734-189X(84)80011-0.

R Core Team. 2019. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C. Meng, and D. Toll. 2004. The Global Land Data Assimilation System. Bulletin of the American Meteorological Society, 381-394. https://doi.org/10.1175/BAMS-85-3-381.

Stagge, J. H., L. M. Tallaksen, L. Gudmundsson, A. F. Van Loon, and K. Stahl. 19 February 2015. Candidate Distributions for Climatological Drought Indices (SPI and SPEI). International Journal of Climatology, 4027-4040. https://doi.org/10.1002/joc.4267.

Wilks, D. S. 2006. Statistical Methods in the Atmospheric Sciences. Burlington, MA: Academic Press.