IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v111y2022i3d10.1007_s11069-021-05147-0.html
   My bibliography  Save this article

Aridity indices to assess desertification susceptibility: a methodological approach using gridded climate data and cartographic modeling

Author

Listed:
  • Janaína Cassiano Santos

    (Federal Fluminense University (UFF))

  • Gustavo Bastos Lyra

    (Federal Rural University of Rio de Janeiro (UFRRJ))

  • Marcel Carvalho Abreu

    (Federal Rural University of Rio de Janeiro (UFRRJ))

  • José Francisco Oliveira-Júnior

    (Federal University of Alagoas (UFAL))

  • Leonardo Bohn

    (Federal University of Rio Grande do Sul (UFRGS))

  • Gisleine Cunha-Zeri

    (National Institute for Space Research (INPE))

  • Marcelo Zeri

    (National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN))

Abstract

Desertification is a land degradation phenomenon with dire and irreversible consequences, affecting different regions of the world. Assessment of spatial climate susceptibility to desertification requires long-term averages of precipitation (P) and potential evapotranspiration (PET). An alternative to desertification susceptibility analysis is the use of spatially gridded climate data. The aim of this study was to assess an approach based on gridded climate data and cartographic modeling to characterize climate susceptibility to desertification over Southeast Brazil. Two indices were used to identify climate desertification susceptibility: the aridity index Ia (P/PET) and D (PET/P). Precipitation gridded data from the Global Precipitation Climatology Centre (GPCC), and air temperature from the Global Historical Climatology Network (GHCN) were used. The PET was estimated by the Thornthwaite’s method using air temperature data. The assessment of these gridded climate series, PET and indices was performed using independent observed climate series (1961–2010) from the National Institute of Meteorology (INMET) of Brazil—(68 weather stations). Determination coefficient (r2) and the Willmott’s coefficient (d) between gridded and observed data revealed satisfactory precision and agreement for grids of precipitation (r2 > 0.93, d > 0.90), air temperature (r2 > 0.94, d > 0.53) and PET (r2 > 0.93, d > 0.63). Overall, the aridity indices based on climate gridded presented good performance when used to identify areas susceptible to desertification. Susceptible areas to desertification were identified by the index Ia over the Northern regions of Minas Gerais and Rio de Janeiro states. No susceptible areas to desertification were identified using the index D. However, both indices indicated large areas of sub-humid climate, which can be strongly affected by desertification in the future.

Suggested Citation

  • Janaína Cassiano Santos & Gustavo Bastos Lyra & Marcel Carvalho Abreu & José Francisco Oliveira-Júnior & Leonardo Bohn & Gisleine Cunha-Zeri & Marcelo Zeri, 2022. "Aridity indices to assess desertification susceptibility: a methodological approach using gridded climate data and cartographic modeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(3), pages 2531-2558, April.
  • Handle: RePEc:spr:nathaz:v:111:y:2022:i:3:d:10.1007_s11069-021-05147-0
    DOI: 10.1007/s11069-021-05147-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-05147-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-021-05147-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rosmeri Rocha & Michelle Reboita & Lívia Dutra & Marta Llopart & Erika Coppola, 2014. "Interannual variability associated with ENSO: present and future climate projections of RegCM4 for South America-CORDEX domain," Climatic Change, Springer, vol. 125(1), pages 95-109, July.
    2. Gueymard, Christian A., 2014. "A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1024-1034.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Starke, Allan R. & Lemos, Leonardo F.L. & Boland, John & Cardemil, José M. & Colle, Sergio, 2018. "Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction," Renewable Energy, Elsevier, vol. 125(C), pages 472-484.
    2. Purohit, Ishan & Purohit, Pallav, 2018. "Performance assessment of grid-interactive solar photovoltaic projects under India’s national solar mission," Applied Energy, Elsevier, vol. 222(C), pages 25-41.
    3. Hussain, C.M. Iftekhar & Norton, Brian & Duffy, Aidan, 2017. "Technological assessment of different solar-biomass systems for hybrid power generation in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1115-1129.
    4. Nonnenmacher, Lukas & Kaur, Amanpreet & Coimbra, Carlos F.M., 2016. "Day-ahead resource forecasting for concentrated solar power integration," Renewable Energy, Elsevier, vol. 86(C), pages 866-876.
    5. Benkaciali, Saïd & Haddadi, Mourad & Khellaf, Abdellah, 2018. "Evaluation of direct solar irradiance from 18 broadband parametric models: Case of Algeria," Renewable Energy, Elsevier, vol. 125(C), pages 694-711.
    6. Amani, Madjid & Ghenaiet, Adel, 2020. "Novel hybridization of solar central receiver system with combined cycle power plant," Energy, Elsevier, vol. 201(C).
    7. Sun, Xixi & Bright, Jamie M. & Gueymard, Christian A. & Bai, Xinyu & Acord, Brendan & Wang, Peng, 2021. "Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Voyant, Cyril & Soubdhan, Ted & Lauret, Philippe & David, Mathieu & Muselli, Marc, 2015. "Statistical parameters as a means to a priori assess the accuracy of solar forecasting models," Energy, Elsevier, vol. 90(P1), pages 671-679.
    9. Marzo, A. & Trigo-Gonzalez, M. & Alonso-Montesinos, J. & Martínez-Durbán, M. & López, G. & Ferrada, P. & Fuentealba, E. & Cortés, M. & Batlles, F.J., 2017. "Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation," Renewable Energy, Elsevier, vol. 113(C), pages 303-311.
    10. Mattia Manni & Alessandro Nocente & Martin Bellmann & Gabriele Lobaccaro, 2023. "Multi-Stage Validation of a Solar Irradiance Model Chain: An Application at High Latitudes," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    11. Behar, O. & Sbarbaro, D. & Marzo, A. & Gonzalez, M. Trigo & Vidal, E. Fuentealba & Moran, L., 2020. "Critical analysis and performance comparison of thirty-eight (38) clear-sky direct irradiance models under the climate of Chilean Atacama Desert," Renewable Energy, Elsevier, vol. 153(C), pages 49-60.
    12. Victor Hugo Wentz & Joylan Nunes Maciel & Jorge Javier Gimenez Ledesma & Oswaldo Hideo Ando Junior, 2022. "Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models," Energies, MDPI, vol. 15(7), pages 1-23, March.
    13. Polo, Jesús & Alonso-Abella, Miguel & Martín-Chivelet, Nuria & Alonso-Montesinos, Joaquín & López, Gabriel & Marzo, Aitor & Nofuentes, Gustavo & Vela-Barrionuevo, Nieves, 2020. "Typical Meteorological Year methodologies applied to solar spectral irradiance for PV applications," Energy, Elsevier, vol. 190(C).
    14. Laudari, R. & Sapkota, B. & Banskota, K., 2018. "Validation of wind resource in 14 locations of Nepal," Renewable Energy, Elsevier, vol. 119(C), pages 777-786.
    15. Del Hoyo, Mirko & Rondanelli, Roberto & Escobar, Rodrigo, 2020. "Significant decrease of photovoltaic power production by aerosols. The case of Santiago de Chile," Renewable Energy, Elsevier, vol. 148(C), pages 1137-1149.
    16. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    17. Kulesza, Kinga, 2017. "Comparison of typical meteorological year and multi-year time series of solar conditions for Belsk, central Poland," Renewable Energy, Elsevier, vol. 113(C), pages 1135-1140.
    18. Vamvakas, Ioannis & Salamalikis, Vasileios & Benitez, Daniel & Al-Salaymeh, Ahmed & Bouaichaoui, Sofiane & Yassaa, Noureddine & Guizani, AmenAllah & Kazantzidis, Andreas, 2020. "Estimation of global horizontal irradiance using satellite-derived data across Middle East-North Africa: The role of aerosol optical properties and site-adaptation methodologies," Renewable Energy, Elsevier, vol. 157(C), pages 312-331.
    19. Fernández-Peruchena, Carlos M. & Gastón, Martín, 2016. "A simple and efficient procedure for increasing the temporal resolution of global horizontal solar irradiance series," Renewable Energy, Elsevier, vol. 86(C), pages 375-383.
    20. Every, Jeremy P. & Li, Li & Dorrell, David G., 2020. "Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations," Renewable Energy, Elsevier, vol. 147(P1), pages 2453-2469.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:111:y:2022:i:3:d:10.1007_s11069-021-05147-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.