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Vegetation and water resource variability within the Köppen-Geiger global climate classification scheme: a probabilistic interpretation

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Abstract

Worldwide, climate classification schemes are useful for characterizing the biological potentials of terrestrial ecosystems. Among these schemes, the Köppen-Geiger global climate classification has increasingly been used in a wide range of environmental fields. However, the resulting climate information is insufficient to fully portend the dynamics of ecosystem components such as vegetation gradients and water resources. To enhance the interpretability of this climate information, research-driven frameworks are needed to connect terrestrial vegetation and water resource signals to the Köppen-Geiger climate classes. Hence, this study developed a probabilistic framework for characterizing vegetation and water resources variability within the Köppen-Geiger climate classification system. The framework combines an application of entropy theory with multivariate logistic models, and it uses variables including half-degree gridded precipitation, surface temperature, leaf area index, and liquid water equivalence anomalies. Explicitly, entropy-based disorder index (DI) values are quantified for individual variables and thresholds of DI percentiles are used to discretize vegetation and water resources variability zones at the global scale. Multivariate logistic models are later applied to grid-level DI zone attributes and long-term average values to predict Köppen-Geiger climate classes. The statistical evaluation sustained variable models’ likelihoods (0.04≤McFadden’s pseudo-R2≤0.92) but consistent estimates (0.87≤Count R2≤0.99) within the global climate classes. The developed framework could be an avenue to improve the interpretability of Köppen-Geiger climate classes by providing a probabilistic insight into vegetation and water resources change at regional, continental, or global levels.

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Data availability

Sources of all data are mentioned in the data and method session. These data are accessible to the public. The Köppen-Geiger classification data based on Kottek et al. (2006) are accessible at https://koeppen-geiger.vu-wien.ac.at; the Gravity Recovery and Climate Experiment (GRACE) liquid water equivalence (LWE) data are available at https://grace.jpl.nasa.gov/mission/grace; the Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) data are assessable at https://modis.gsfc.nasa.gov/data; and the University of East Anglia’s Climatic Research Unit (CRU) precipitation and temperature data are available at https://crudata.uea.ac.uk/cru/data/hrg. However, the processed data reported in the analyses are available from the corresponding author on reasonable requests.

References

  • Arguez A, Vose RS (2011) The definition of the standard WMO climate normal: the key to deriving alternative climate normals. Bull Am Meteorol Soc 92(6):699–704

    Article  Google Scholar 

  • Baltas E (2007) Spatial distribution of climatic indices in northern Greece. Meteorol Appl 14(1):69–78

    Article  Google Scholar 

  • Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF (2018) Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5(1):1–12

    Article  Google Scholar 

  • Berg A, McMurry T, Politis DN (2012) Testing time series linearity: traditional and bootstrap methods. Handbook of Statistics 30:27–42

    Article  Google Scholar 

  • Bravi A, Longtin A, Seely AJ (2011) Review and classification of variability analysis techniques with clinical applications. Biomed Eng Online 10:1–27

    Article  Google Scholar 

  • Cooley SS, Landerer FW (2019) Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) Level-3 data product user handbook. Jet Propulsion Laboratory, California Institute of Technology: Pasadena, CA, USA, pp 1–57.

  • De Martonne E (1926) L’indice d’aridité. Bulletin de l’Association de géographes français 3(9):3–5

    Google Scholar 

  • Deniz A, Toros H, Incecik S (2011) Spatial variations of climate indices in Turkey. Int J Climatol 31(3):394–403

    Article  Google Scholar 

  • Famiglietti JS, Rodell M (2013) Water in the balance. Science 340(6138):1300–1301

    Article  Google Scholar 

  • Feddema JJ (2005) A revised Thornthwaite-type global climate classification. Physical Geography 26(6):442–466

    Article  Google Scholar 

  • Garcia RA, Cabeza M, Rahbek C, Araújo MB (2014) Multiple dimensions of climate change and their implications for biodiversity. Science 344(6183):1247579

    Article  Google Scholar 

  • Gao F, Morisette JT, Wolfe RE, Ederer G, Pedelty J, Masuoka E, Myneni R, Tan B, Nightingale J (2008) An algorithm to produce temporally and spatially continuous MODIS-LAI time series. IEEE Geosci Remote Sens Lett 5(1):60–64. https://doi.org/10.1109/LGRS.2007.907971

    Article  Google Scholar 

  • Glonek GF, McCullagh P (1995) Multivariate logistic models. J R Stat Soc B Methodol 57(3):533–546

    Google Scholar 

  • Goldscheider N, Chen Z, Auler AS, Bakalowicz M, Broda S, Drew D, Hartmann J, Jiang G, Moosdorf N, Stevanovic Z, Veni G (2020) Global distribution of carbonate rocks and karst water resources. Hydrgeol J 28(5):1661–1677

    Article  Google Scholar 

  • Gunasekara NK, Kazama S, Yamazaki D, Oki T (2014) Water conflict risk due to water resource availability and unequal distribution. Water Resour Manag 28(1):169–184

    Article  Google Scholar 

  • Harris I, Osborn TJ, Jones P, Lister D (2020) Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7(1):1–18

    Article  Google Scholar 

  • Harris IPDJ, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. Int J Climatol 34(3):623–642

    Article  Google Scholar 

  • Köppen W (1900) Versuch einer Klassifikation der Klimate, Vorzugsweise nachihren Beziehungen zur Pflanzenwelt [Attempted climate classification in relation to plant distributions]. Geographische Zeitschrift 6(593–611):657–679

    Google Scholar 

  • Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of the Köppen-Geiger climate classification updated. Meteorol Z 15(3):259–263

    Article  Google Scholar 

  • Landerer FW, Swenson SC (2012) Accuracy of scaled GRACE terrestrial water storage estimates. Water Resour Manag 48:W04531. https://doi.org/10.1029/2011WR011453

    Article  Google Scholar 

  • Maestre FT, Salguero-Gómez R, Quero JL (2012) It is getting hotter in here: determining and projecting the impacts of global environmental change on drylands. Philosophical Transactions of the Royal Society B: Biological Sciences 367(1606):3062–3075. https://doi.org/10.1098/rstb.2011.0323

    Article  Google Scholar 

  • Mishra AK, Özger M, Singh VP (2009) An entropy-based investigation into the variability of precipitation. J Hydrol 370(1-4):139–154

    Article  Google Scholar 

  • Murray SJ, Foster PN, Prentice IC (2012) Future global water resources with respect to climate change and water withdrawals as estimated by a dynamic global vegetation model. J Hydrol 448:14–29

    Article  Google Scholar 

  • Netzel P, Stepinski T (2016) On using a clustering approach for global climate classification. J Climate 29(9):3387–3401

    Article  Google Scholar 

  • Oki T, Kanae S (2006) Global hydrological cycles and world water resources. Science 313(5790):1068–1072

    Article  Google Scholar 

  • Passarella G, Bruno D, Lay-Ekuakille A, Maggi S, Masciale R, Zaccaria D (2020) Spatial and temporal classification of coastal regions using bioclimatic indices in a Mediterranean environment. Sci Total Environ 700:134415

    Article  Google Scholar 

  • Pellicone G, Caloiero T, Guagliardi I (2019) The De Martonne aridity index in Calabria (Southern Italy). J Maps 15(2):788–796

    Article  Google Scholar 

  • Pickett ST, Cadenasso ML (2005) Vegetation dynamics. E. van der Maarel (Ed.), Vegetation ecology, Blackwell Publishing, Oxford 172-198.

    Google Scholar 

  • Qaqish BF, Ivanova A (2006) Multivariate logistic models. Biometrika 93(4):1011–1017

    Article  Google Scholar 

  • Scanlon BR, Fakhreddine S, Rateb A, de Graaf I, Famiglietti J, Gleeson T, Grafton RQ, Jobbagy E, Kebede S, Kolusu SR, Konikow LF (2023) Global water resources and the role of groundwater in a resilient water future. Nat Rev Earth Environ 4(2):87–101

    Article  Google Scholar 

  • Singh KR, Dutta R, Kalamdhad AS, Kumar B (2019) An investigation on water quality variability and identification of ideal monitoring locations by using entropy-based disorder indices. Sci Total Environ 647:1444–1455

    Article  Google Scholar 

  • Singh VP (2016) Introduction to Tsallis entropy theory in water engineering. CRC Press/Taylor & Francis Group, Boca Rtaon, Florida, p 434

    Book  Google Scholar 

  • Singh VP (2013) Entropy theory and its application in environmental and water engineering. Wiley, New York, p 666

    Book  Google Scholar 

  • Singh VP (1998) Entropy-based parameter estimation in hydrology, Water Science and Technology Library. Springer, Netherlands, Dordrecht. https://doi.org/10.1007/978-94-017-1431-0

    Book  Google Scholar 

  • Spinoni J, Vogt J, Naumann G, Carrao H, Barbosa P (2015) Towards identifying areas at climatological risk of desertification using the Köppen-Geiger classification and FAO aridity index. Int J Climatol 35(9):2210–2222

  • Sohoulande CDD, Awoye H, Nouwakpo KS, Dogan S, Szogi AA, Stone KC, Martin JH (2022) A global-scale assessment of water resources and vegetation cover dynamics in relation with the earth climate gradient. Remote Sens Earth Syst Sci 5:193–206. https://doi.org/10.1007/s41976-021-00063-0

  • Sohoulande CD, Martin J, Szogi A, Stone K (2020) Climate-driven prediction of land water storage anomalies: an outlook for water resources monitoring across the conterminous United States. J Hydrol 588:125053

    Article  Google Scholar 

  • Sohoulande DCD (2018) Toward an integrated watershed zoning framework based on the spatio-temporal variability of land-cover and climate: application in the Volta River basin. Environ Dev 28:55–66

    Article  Google Scholar 

  • Sohoulande DC, Singh VP (2016) Entropy-based index for spatiotemporal analysis of streamflow, precipitation, and land-cover. J Hydrol Eng 21(11):05016024

    Article  Google Scholar 

  • Sohoulande DCD, Singh VP, Frauenfeld OW (2014) Analysis of watershed topography effects on summer precipitation variability in the southwestern United States. J Hydrol 511:838–849

    Article  Google Scholar 

  • Sturges HA (1926) The choice of a class interval. J Am Stat Assoc 21(153):65–66

    Article  Google Scholar 

  • Swenson SC (2012) GRACE monthly land water mass grids NETCDF RELEASE 5.0. Ver. 5.0. PO.DAAC, CA, USA. Dataset accessed [2019-10-11] at https://doi.org/10.5067/TELND-NC005.

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Funding

This research was part of USDA-ARS National Programs 211 Water Availability and Watershed Management, Project 6082-13000-010-00D.

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Authors and Affiliations

  1. USDA-ARS Coastal Plain Soil, Water and Plant Conservation Research Center, 2611 west Lucas Street, Florence, SC, 29501, USA

    Clement D.D. Sohoulande

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  1. Clement D.D. Sohoulande
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Dr. Clement Sohoulande conceived and completed this study. He prepared the data and developed the method reported in the paper. He completed all the data analyses and wrote the manuscript.

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Correspondence to Clement D.D. Sohoulande.

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Sohoulande, C.D. Vegetation and water resource variability within the Köppen-Geiger global climate classification scheme: a probabilistic interpretation. Theor Appl Climatol 155, 1081–1092 (2024). https://doi.org/10.1007/s00704-023-04682-z

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