Large-scale exploratory analysis of the spatiotemporal distribution of climate projections: applying the STRIVIng toolbox

Book Chapter (2019)
Authors

Vitali Diaz Mercado (TU Delft - Water Resources, IHE Delft Institute for Water Education)

Gerald A. Corzo (IHE Delft Institute for Water Education)

José R. Pérez (Instituto Nacional de Recursos Hidráulicos)

Research Group
Water Resources
Copyright
© 2019 Vitali Diaz, Gerald A. Corzo, José R. Pérez
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Vitali Diaz, Gerald A. Corzo, José R. Pérez
Research Group
Water Resources
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
59-76
ISBN (print)
9780128117316
ISBN (electronic)
9780128116890
DOI:
https://doi.org/10.1016/B978-0-12-811689-0.00003-3
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Extreme hydrological events (EHEs), such as droughts and floods, vary spatially and temporally in nature. The increase in the number of events in the last few decades has motivated the research of the spatiotemporal variability of the future extreme precipitation and temperature. To study the consequences on the EHEs due to the uncertainty of projected climate changes, the analysis in more detail of precipitation and temperature, in space and time, is vital. In addition, for proper planning and decision-making process to address EHEs, understanding such climate changes requires more information. In this chapter we present a summarized assessment of the spatiotemporal variations of climate projections. A simplified way to aggregate global data is used for the spatiotemporal analysis of precipitation and temperature. To carry out this analysis, the Spatio-TempoRal distribution and Interannual VarIability of projections (STRIVIng) toolbox is proposed for statistical exploratory analysis of climate projections. Three large-scale applications were carried out for illustration: Dominican Republic (48,670 km2), Mexico (1,972,550 km2), and Amazon basin (6,171,148.7 km2). The methodology and toolbox presented here allow regions to be identified where the changes are expected to be more severe on precipitation and temperature, as well as months in which those changes are likely to occur. The STRIVIng toolbox is open source and helps to provide basic information to increase the interpretations and research in the space–time analysis of extremes.

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