IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i23p15797-d985897.html
   My bibliography  Save this article

Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland

Author

Listed:
  • Joanna Wicher-Dysarz

    (Department of Hydraulic and Sanitary Engineering, Faculty of Environmental Engineering and Mechanical Engineering, Poznan’ University of Life Sciences, Piątkowska St. 94A, 60-649 Poznan, Poland)

  • Tomasz Dysarz

    (Department of Hydraulic and Sanitary Engineering, Faculty of Environmental Engineering and Mechanical Engineering, Poznan’ University of Life Sciences, Piątkowska St. 94A, 60-649 Poznan, Poland)

  • Joanna Jaskuła

    (Department of Land Improvement, Environmental Development and Spatial Management, Faculty of Environmental Engineering and Mechanical Engineering, Poznan’ University of Life Sciences, Piątkowska St. 94E, 60-649 Poznan, Poland)

Abstract

The primary aim of this work is to assess the accuracy of the methods for spatial interpolation applied for the reconstruction of the spatial distribution of the Standardized Precipitation Index (SPI). The one-month version called SPI-1 is chosen for this purpose due to the known greatest variability of this index in comparison with its other versions. The analysis has been made for the territory of the entire country of Poland. At the same time the uncertainty related to the application of such computational procedures is determined based on qualitative and quantitative measures. The public data of two kinds are applied: (1) measurements of precipitation and (2) the locations of the meteorological stations in Poland. The analysis has been made for the period 1990–2020. However, all available observations since 1950 have been implemented. The number of available meteorological stations has decreased over the analyzed period. In January 1990 there were over one thousand stations making observations. In the end of the period of the study, the number of stations was below six hundred. Obviously, the temporal scarcity of data had an impact on the obtained results. The main tools applied were ArcGIS supported with Python scripting, including generally used modules and procedures dedicated to geoprocessing. Such an approach appeared crucial for the effective processing of the large number of data available. It also guaranteed the accuracy of the produced results and brought about drought maps based on SPI-1. The methods tested included: Inverse Distance Weighted, Natural Neighbor, Linear, Kriging, and Spline. The presented results prove that all the procedures are inaccurate and uncertain, but some of them provide satisfactory results. The worst method seems to be the interpolation based on Spline functions. The practical aspects related to the implementation of the methods led to removal of the Linear and Kriging interpolations from further use. Hence, Inverse Distance Weighted, as well as Natural Neighbor, seem to be well suited for this problem.

Suggested Citation

  • Joanna Wicher-Dysarz & Tomasz Dysarz & Joanna Jaskuła, 2022. "Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland," IJERPH, MDPI, vol. 19(23), pages 1-18, November.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15797-:d:985897
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/23/15797/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/23/15797/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hongpeng Guo & Jia Chen & Chulin Pan, 2021. "Assessment on Agricultural Drought Vulnerability and Spatial Heterogeneity Study in China," IJERPH, MDPI, vol. 18(9), pages 1-17, April.
    2. Saeed Azimi & Erfan Hassannayebi & Morteza Boroun & Mohammad Tahmoures, 2020. "Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4703-4724, December.
    3. Xuezhen Zhang & Miao He & Mengxin Bai & Quansheng Ge, 2021. "Meteorological drought and its large-scale climate patterns in each season in Central Asia from 1901 to 2015," Climatic Change, Springer, vol. 166(3), pages 1-18, June.
    4. Yang, Yueting & Li, Kaiwei & Wei, Sicheng & Guga, Suri & Zhang, Jiquan & Wang, Chunyi, 2022. "Spatial-temporal distribution characteristics and hazard assessment of millet drought disaster in Northern China under climate change," Agricultural Water Management, Elsevier, vol. 272(C).
    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. Mikio Ishiwatari & Daisuke Sasaki, 2023. "Special Issue “Disaster Risk Reduction and Climate Change Adaptation: An Interdisciplinary Approach”," IJERPH, MDPI, vol. 20(3), pages 1-4, February.
    2. Cai, Siyang & Zuo, Depeng & Wang, Huixiao & Xu, Zongxue & Wang, GuoQing & Yang, Hong, 2023. "Assessment of agricultural drought based on multi-source remote sensing data in a major grain producing area of Northwest China," Agricultural Water Management, Elsevier, vol. 278(C).
    3. Huili He & Rafiq Hamdi & Geping Luo & Peng Cai & Xiuliang Yuan & Miao Zhang & Piet Termonia & Philippe Maeyer & Alishir Kurban, 2022. "The summer cooling effect under the projected restoration of Aral Sea in Central Asia," Climatic Change, Springer, vol. 174(1), pages 1-21, September.
    4. Wentong Yang & Liyuan Zhang & Chunlei Liang, 2023. "Agricultural drought disaster risk assessment in Shandong Province, 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. 118(2), pages 1515-1534, September.
    5. Min Zhou & Liu Yang & Dan Ye, 2023. "Spatiotemporal Variation of Rural Vulnerability and Its Clustering Model in Guizhou Province," Land, MDPI, vol. 12(7), pages 1-25, July.
    6. Chenle Xue & Dan Qiao & Noshaba Aziz, 2022. "Influence of Natural Disaster Shock and Collective Action on Farmland Transferees’ No-Tillage Technology Adoption in China," Land, MDPI, vol. 11(9), pages 1-23, September.
    7. Marzieh Mokarram & Tam Minh Pham, 2023. "Prediction of drought-driven land use/land cover changes in the Bakhtegan Lake watershed of Iran using Markov chain cellular automata model and remote sensing data," 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. 116(1), pages 1291-1314, March.
    8. Guga, Suri & Ma, Yining & Riao, Dao & Zhi, Feng & Xu, Jie & Zhang, Jiquan, 2023. "Drought monitoring of sugarcane and dynamic variation characteristics under global warming: A case study of Guangxi, China," Agricultural Water Management, Elsevier, vol. 275(C).
    9. Yining Ma & Suri Guga & Jie Xu & Jiquan Zhang & Zhijun Tong & Xingpeng Liu, 2021. "Comprehensive Risk Assessment of High Temperature Disaster to Kiwifruit in Shaanxi Province, China," IJERPH, MDPI, vol. 18(19), pages 1-22, October.
    10. Zhenya Li & Zulfiqar Ali & Tong Cui & Sadia Qamar & Muhammad Ismail & Amna Nazeer & Muhammad Faisal, 2022. "A comparative analysis of pre- and post-industrial spatiotemporal drought trends and patterns of Tibet Plateau using Sen slope estimator and steady-state probabilities of Markov Chain," 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. 113(1), pages 547-576, August.

    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:gam:jijerp:v:19:y:2022:i:23:p:15797-:d:985897. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.