IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v76y2015i1p515-541.html
   My bibliography  Save this article

Identification of long-term annual pattern of meteorological drought based on spatiotemporal methods: evaluation of different geostatistical approaches

Author

Listed:
  • Bardia Bayat
  • Mohsen Nasseri
  • Banafsheh Zahraie

Abstract

Estimation and identification of long-term meteorological drought pattern play an important role in regional water management and dry land agricultural practices in arid and semiarid climates. In this work, Standardized Precipitation Index (SPI) has been selected as the main criterion for evaluating the severity of meteorological drought events. The purpose of this paper was to produce meteorological drought occurrence probability maps for different SPI classes by spatiotemporal analysis. Several statistical methods known as non-geostatistical approaches (such as Thiessen polygons, inverse distance-weighted, and spline-based) and geostatistical approaches (such as different types of kriging and Bayesian maximum entropy (BME)) are available, which can be used for the purpose of this study. In this study, ordinary kriging (OK) as a classical geostatistical method and BME as a modern geostatistical method have been used. The case study of this research has been the Namak Lake Watershed located in the central part of Iran with an area of approximately 90,000 km 2 . This basin includes regions with significantly different climatic conditions ranging from very dry to very wet. The results of the case study include spatial distribution of SPI for dry SPI classes (moderately, severely, and extremely dry classes) and wet SPI classes (moderately, severely, and extremely wet classes) which can be used to locate vulnerable areas against drought. The selected geostatistical methods have been compared based on leave-one-out cross-validation procedure and spatiotemporal distribution of SPI values. The results of cross-validation have shown the superiority of BME over OK. BME maps of probability of occurrence have also been more realistic than OK maps. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Bardia Bayat & Mohsen Nasseri & Banafsheh Zahraie, 2015. "Identification of long-term annual pattern of meteorological drought based on spatiotemporal methods: evaluation of different geostatistical approaches," 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. 76(1), pages 515-541, March.
  • Handle: RePEc:spr:nathaz:v:76:y:2015:i:1:p:515-541
    DOI: 10.1007/s11069-014-1499-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-014-1499-3
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11069-014-1499-3?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. Ashoke Basistha & D. Arya & N. Goel, 2008. "Spatial Distribution of Rainfall in Indian Himalayas – A Case Study of Uttarakhand Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(10), pages 1325-1346, October.
    2. F. Sönmez & Ali Kömüscü & Ayhan Erkan & Ertan Turgu, 2005. "An Analysis of Spatial and Temporal Dimension of Drought Vulnerability in Turkey Using the Standardized Precipitation Index," 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. 35(2), pages 243-264, June.
    3. D. Pai & Latha Sridhar & Pulak Guhathakurta & H. Hatwar, 2011. "District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI)," 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. 59(3), pages 1797-1813, December.
    4. De Iaco, S. & Myers, D. E. & Posa, D., 2002. "Space-time variograms and a functional form for total air pollution measurements," Computational Statistics & Data Analysis, Elsevier, vol. 41(2), pages 311-328, December.
    5. Sergio Vicente-Serrano, 2007. "Evaluating the Impact of Drought Using Remote Sensing in a Mediterranean, Semi-arid Region," 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. 40(1), pages 173-208, January.
    6. Cesare, L. De & Myers, D. E. & Posa, D., 2001. "Estimating and modeling space-time correlation structures," Statistics & Probability Letters, Elsevier, vol. 51(1), pages 9-14, January.
    7. Iaco, S. De & Myers, D. E. & Posa, D., 2001. "Space-time analysis using a general product-sum model," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 21-28, March.
    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. Sandra De Iaco, 2010. "Space-time correlation analysis: a comparative study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 1027-1041.
    2. Sandra De Iaco, 2011. "A new space--time multivariate approach for environmental data analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2471-2483, January.
    3. P. Gregori & E. Porcu & J. Mateu & Z. Sasvári, 2008. "On potentially negative space time covariances obtained as sum of products of marginal ones," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 865-882, December.
    4. Bruno Scarpa, 2005. "Non parametric space-time modeling of SO2 in presence of many missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(1), pages 67-82, February.
    5. José-María Montero & Gema Fernández-Avilés & Tiziana Laureti, 2021. "A Local Spatial STIRPAT Model for Outdoor NO x Concentrations in the Community of Madrid, Spain," Mathematics, MDPI, vol. 9(6), pages 1-33, March.
    6. Omvir Singh & Divya Saini & Pankaj Bhardwaj, 2021. "Characterization of meteorological drought over a dryland ecosystem in north western India," 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. 109(1), pages 785-826, October.
    7. Watinee Thavorntam & Netnapid Tantemsapya & Leisa Armstrong, 2015. "A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand," 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. 77(3), pages 1453-1474, July.
    8. Rui Jiang & Chunxue Liu & Xiaowei Liu & Shuai Zhang, 2022. "Space–Time Effect of Green Total Factor Productivity in Mineral Resources Industry in China: Based on Space–Time Semivariogram and SPVAR Model," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    9. Lina Eklund & Jonathan Seaquist, 2015. "Meteorological, agricultural and socioeconomic drought in the Duhok Governorate, Iraqi Kurdistan," 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. 76(1), pages 421-441, March.
    10. Montero, José-María, 2018. "Geostatistics: Unde venis et quo vadis? /Geoestadística:¿De dónde vienes y a dónde vas?," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 36, pages 81-106, Enero.
    11. Liu, Jinfu & Ren, Guorui & Wan, Jie & Guo, Yufeng & Yu, Daren, 2016. "Variogram time-series analysis of wind speed," Renewable Energy, Elsevier, vol. 99(C), pages 483-491.
    12. P. Vijaya Kumar & Mohammed Osman & P. K. Mishra, 2019. "Development and application of a new drought severity index for categorizing drought-prone areas: a case study of undivided Andhra Pradesh state, India," 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. 97(2), pages 793-812, June.
    13. Alexandre Rodrigues & Peter J. Diggle, 2010. "A Class of Convolution‐Based Models for Spatio‐Temporal Processes with Non‐Separable Covariance Structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 553-567, December.
    14. Firoozeh Rivaz & Mohsen Mohammadzadeh & Majid Jafari Khaledi, 2011. "Spatio-temporal modeling and prediction of CO concentrations in Tehran city," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1995-2007, November.
    15. Rui Li & Jing’ai Wang & Tianjie Zhao & Jiancheng Shi, 2016. "Index-based evaluation of vegetation response to meteorological drought in Northern China," 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. 84(3), pages 2179-2193, December.
    16. Javad Bazrafshan & Somayeh Hejabi & Jaber Rahimi, 2014. "Drought Monitoring Using the Multivariate Standardized Precipitation Index (MSPI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1045-1060, March.
    17. Shamsuddin Shahid & Houshang Behrawan, 2008. "Drought risk assessment in the western part of Bangladesh," 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. 46(3), pages 391-413, September.
    18. Jin‐Feng Wang & Lian‐Fa Li, 2008. "Improving Tsunami Warning Systems with Remote Sensing and Geographical Information System Input," Risk Analysis, John Wiley & Sons, vol. 28(6), pages 1653-1668, December.
    19. Jet-chau Wen & Yen-jen Lee & Shin-jen Cheng & Ju-huang Lee, 2014. "Changes of rural to urban areas in hydrograph characteristics on watershed divisions," 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. 74(2), pages 887-909, November.
    20. D. Chiru Naik & Sagar Rohidas Chavan & P. Sonali, 2023. "Incorporating the climate oscillations in the computation of meteorological drought over India," 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. 117(3), pages 2617-2646, July.

    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:76:y:2015:i:1:p:515-541. 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.