Skip to main content

Advertisement

SpringerLink
Log in

Evaluation and Comparison of the Effectiveness Rate of the Various Meteorological Parameters on UNEP Aridity Index Using Backward Multiple Ridge Regression

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Climate changes and its undesirable impacts (such as melting glaciers, the occurrence of floods, hurricanes and droughts, etc.) are among the most important disasters that human beings have faced in recent decades. Considering the important role of the meteorological parameters on climate change, in this research the effectiveness rate of various meteorological parameters including the mean minimum and maximum annual temperature (Max-Temp and Min-Temp), the mean annual temperature (M-Temp), the mean annual sunshine (Sunshine), the mean annual relative humidity (Humidity), the mean annual wind speed (Wind) and the mean of annual precipitation (Rainfall) on United Nations Environmental Programme (UNEP) aridity index was assessed and prioritized using the Backward Multiple Ridge Regression (BMRR). In this study, the meteorological data series of 25 synoptic stations in Iran with different climate conditions during 1967–2017 was used. The results indicated that the BMRR method had a nice capability to predict the UNEP index using the above-mentioned meteorological parameters (the linear regression between observed and predicted the UNEP index had no difference with perfect reliable line (Y = X) at 0.05 significant levels and the R2 between two mentioned data series were significant at 0.01 levels at all stations). According to the results, Rainfall, Wind and Max-Temp parameters were the most effective parameters on the UNEP aridity index, respectively and the Min-Temp, M-Temp and Sunshine parameters were the least effective parameters on the UNEP index, respectively. Therefore, it is suggested that all human activities that have direct or indirect effects in the increasing temperature and reducing the rainfall must be revised and optimized based on the principles of sustainable development.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Ahmed AU, Appadurai AN, Neelormi S (2019) Status of climate change adaptation in South Asia region. In status of climate change adaptation in Asia and the Pacific (pp. 125-152). Springer, Cham

  • Amraoui N, Sbai MA, Stollsteiner P (2019) Assessment of climate change impacts on water resources in the Somme River basin (France). Water Resour Manag 33(6):2073–2092

    Article  Google Scholar 

  • Bahrami M, Bazrkar S, Zarei AR (2019) Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index. Journal of Water and Climate Change 10(1):181–196

    Article  Google Scholar 

  • Behera B, Tandi S (2019) Impact of climate change on agriculture: a study of western Odisha. Asian Journal of Multidimensional Research (AJMR) 8(2):145–156

    Article  Google Scholar 

  • Cattaneo C, Beine M, Fröhlich CJ, Kniveton D, Martinez-Zarzoso I, Mastrorillo M, Millock K, Piguet E, Schraven B (2019) Human migration in the era of climate change. Rev Environ Econ Policy 13(2):189–206

    Article  Google Scholar 

  • Chu H, Venevsky S, Wu C, Wang M (2019) NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River basin from 1982 to 2015. Sci Total Environ 650:2051–2062

    Article  Google Scholar 

  • Dehghan Z, Fathian F, Eslamian S (2019) Climate change impact on agriculture and irrigation network. In climate change-resilient agriculture and agroforestry (pp. 333-354). Springer, Cham. https://doi.org/10.1007/978-3-319-75004-0_19

  • Farsani IF, Farzaneh MR, Besalatpour AA, Salehi MH, Faramarzi M (2019) Assessment of the impact of climate change on spatiotemporal variability of blue and green water resources under CMIP3 and CMIP5 models in a highly mountainous watershed. Theor Appl Climatol 136(1–2):169–184

    Article  Google Scholar 

  • Galavi H, Mirzaei M (2020) Analyzing uncertainty drivers of climate change impact studies in tropical and arid climates. Water Resour Manag 34(6):2097–2109

    Article  Google Scholar 

  • Guermazi E, Milano M, Reynard E, Zairi M (2019) Impact of climate change and anthropogenic pressure on the groundwater resources in arid environment. Mitig Adapt Strateg Glob Chang 24(1):73–92

    Article  Google Scholar 

  • Guilkey DK, Murphy JL (1975) Directed ridge regression techniques in cases of multicollinearity. J Am Stat Assoc 70(352):769–775

    Article  Google Scholar 

  • Han Z, Long D, Fang Y, Hou A, Hong Y (2019) Impacts of climate change and human activities on the flow regime of the dammed Lancang River in Southwest China. J Hydrol 570:96–105

    Article  Google Scholar 

  • Hemmerle WJ (1975) An explicit solution for generalized ridge regression. Technometrics 17(3):309–314

    Article  Google Scholar 

  • Hoerl AE, Kennard RW (1970a) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1):55–67

    Article  Google Scholar 

  • Hoerl AE, Kennard RW (1970b) Ridge regression: applications to nonorthogonal problems. Technometrics 12(1):69–82

    Article  Google Scholar 

  • Huang J, Zhou L, Zhang F, Hu Z, Li Y (2019) Regional climate–yield relationship for winter oilseed rape in Jiangsu Province, Southeast China. International Journal of Plant Production 13(2):93–102

    Article  Google Scholar 

  • Hwang HT, Park YJ, Sudicky EA, Berg SJ, McLaughlin R, Jones JP (2018) Understanding the water balance paradox in the Athabasca River basin, Canada. Hydrol Process 32(6):729–746

    Article  Google Scholar 

  • Imdad MU, Aslam M (2018) lmridge: Linear Ridge Regression with Ridge Penalty and Ridge Statistics, b. URL https://cran.r-project.org/package=lmridge. R package version 1.2: p 326

  • Karmalkar AV, Thibeault JM, Bryan AM, Seth A (2019) Identifying credible and diverse GCMs for regional climate change studies—case study: northeastern United States. Clim Chang 154(3–4):367–386

    Article  Google Scholar 

  • Karim F, Mainuddin M, Hasan M, Kirby M (2020) Assessing the potential impacts of climate changes on rainfall and evapotranspiration in the northwest region of Bangladesh. Climate 8(8):94. https://doi.org/10.3390/cli8080094

    Article  Google Scholar 

  • Katrantsiotis C, Norström E, Smittenberg RH, Finne M, Weiberg E, Hättestrand M, Avramidis P, Wastegård S (2019) Climate changes in the eastern Mediterranean over the last 5000 years and their links to the high-latitude atmospheric patterns and Asian monsoons. Glob Planet Chang 175:36–51

    Article  Google Scholar 

  • Kunkel KE, Karl TR, Easterling DR, Redmond K, Young J, Yin X, Hennon P (2013) Probable maximum precipitation and climate change. Geophys Res Lett 40(7):1402–1408

    Article  Google Scholar 

  • Liu J, Cheng H, Jiang D, Huang L (2019) Impact of climate-related changes to the timing of autumn foliage colouration on tourism in Japan. Tour Manag 70:262–272

    Article  Google Scholar 

  • Mahdavi M (2002) Applied hydrology. Tehran university press 2:40

    Google Scholar 

  • Malone AG, Doughty AM, Macayeal DR (2019) Interannual climate variability helps define the mean state of glaciers. J Glaciol 65(251):508–517

    Article  Google Scholar 

  • Şen Z (2020) Water structures and climate change impact: a review. Water Resour Manag 34(13):4197–4216

    Article  Google Scholar 

  • Sellers S, Ebi KL, Hess J (2019) Climate change, human health, and social stability: addressing Interlinkages. Environ Health Perspect 127(04):045002

    Article  Google Scholar 

  • Song Y, Linderholm HW, Wang C, Tian J, Huo Z, Gao P, Song Y, Guo A (2019) The influence of excess precipitation on winter wheat under climate change in China from 1961 to 2017. Sci Total Environ 690:189–196

    Article  Google Scholar 

  • Tashilova AA, Ashabokov BA, Kesheva LA, Teunova NV (2019) Analysis of climate change in the caucasus region: end of the 20th–beginning of the 21st century. Climate 7(1):11

    Article  Google Scholar 

  • United Nations Environmental Programme (UNEP) (1992) World Atlas of Desertification

  • Wei D, González-Sampériz P, Gil-Romera G, Harrison SP, Prentice IC (2019) Climate changes in interior semi-arid Spain from the last interglacial to the late Holocene. Clim Past Discuss. https://doi.org/10.5194/cp-2019-16

  • Zellner A (1971) An introduction to Bayesian inference in econometrics (No. 519.54 Z4)

  • Zarei AR, Mahmoudi MR, Shabani A (2020) Investigating of the climatic parameters effectiveness rate on barley water requirement using the random forest algorithm, Bayesian multiple linear regression and cross-correlation function Paddy and Water Environment https://doi.org/10.1007/s10333-020-00825-4

  • Zarei AR, Masoudi M (2019) Trend assessment of climate changes in Iran. EQA-International Journal of Environmental Quality 34:1–16

    Google Scholar 

  • Zarei AR, Shabani A, Mahmoudi MR (2019) Comparison of the climate indices based on the relationship between yield loss of rain-fed winter wheat and changes of climate indices using GEE model. Sci Total Environ 661:711–722

    Article  Google Scholar 

  • Zhai ZJ, Helman JM (2019) Implications of climate changes to building energy and design. Sustain Cities Soc 44:511–519

Download references

Acknowledgments

The authors thank the Iranian Meteorological Organization (IMO) for providing the necessary meteorological data.

Availability of Data and Materials

The was used data in this research will be available (by the corresponding author), upon reasonable request.

Author information

Authors and Affiliations

  1. Department of Range and Watershed Management (Nature engineering), Faculty of Agriculture, Fasa University, Fasa, Iran

    Abdol Rassoul Zarei

  2. Department of Statistics, Faculty of Science, Fasa University, Fasa, Iran

    Mohammad Reza Mahmoudi

Authors
  1. Abdol Rassoul Zarei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Reza Mahmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The participation of Abdol Rassoul Zarei in the article includes data collection, data evaluation, assistance in analyzing the results and writing the article, and the participation of Mohammad Reza Mahmoudi includes Programming and implementation of statistical models and assistance in analyzing the results.

Corresponding author

Correspondence to Abdol Rassoul Zarei.

Ethics declarations

Ethics Approval

The authors confirm that this article is original research and has not been published or presented previously in any journal or conference in any language (in whole or in part).

Competing Interests

The authors have no conflict of interest and are completely satisfied with the publication of their article in water resources management journal.

Consent to Participate and Consent to Publish

The authors declare that have consent to participate and consent to publish.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(DOCX 58 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zarei, A.R., Mahmoudi, M.R. Evaluation and Comparison of the Effectiveness Rate of the Various Meteorological Parameters on UNEP Aridity Index Using Backward Multiple Ridge Regression. Water Resour Manage 35, 159–177 (2021). https://doi.org/10.1007/s11269-020-02716-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-020-02716-z

Keywords

Search

Navigation