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Use of Global Reanalysis Data in the Study of the Aridity Index in the Magdalena-Cauca Macro-Basin, Colombia

8 pagesPublished: September 20, 2018

Abstract

The Magdalena-Cauca macro-basin (MCMB) in Colombia, by its tropical location, annually experiences the effects of movement of the Intertropical Convergence Zone, and it is highly affected by interannual macro-climatic phenomena, such as El Niño– Southern Oscillation (ENSO). With the aim of increasing the use of global reanalysis and remote sensing data for supporting water management decisions at the watershed scale and within the framework of the eartH2Observe research project, the aridity index (AI) was calculated with three different data sources. Precipitation products and AI results were compared with their corresponding in-situ national official data. The comparison shows high correlations between the AI derived from observed data and AI obtained from the reanalysis, with Pearson correlation coefficients above 0.8 for two of the products investigated. This shows the importance of using global reanalysis data in water availability studies on a regional scale for the MCMB and the potential of this information in others macrobasins in Colombia including the Orinoquia and Amazon regions, where in-situ data is scarce.

Keyphrases: Aridity index, Colombia, Global reanalysis, Magdalena-Cauca macro-basin

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 2162--2169

Links:
BibTeX entry
@inproceedings{HIC2018:Use_of_Global_Reanalysis,
  author    = {Carolina Vega-Viviescas and David A. Zamora and Erasmo A. Rodr\textbackslash{}'iguez},
  title     = {Use of Global Reanalysis Data in the Study of the Aridity Index in the Magdalena-Cauca Macro-Basin, Colombia},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  pages     = {2162--2169},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {https://easychair.org/publications/paper/tFNX},
  doi       = {10.29007/92l9}}
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