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The Innovative Polygon Trend Analysis (IPTA) as a Simple Qualitative Method to Detect Changes in Environment—Example Detecting Trends of the Total Monthly Precipitation in Semiarid Area

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  • Mohammed Achite

    (Laboratory of Water & Environment, Faculty of Nature and Life Sciences, University Hassiba Benbouali of Chlef, Ouled Fares, Chlef 02180, Algeria
    National Higher School of Agronomy, ENSA, Hassan Badi, El Harrach, Algiers 16200, Algeria)

  • Gokmen Ceribasi

    (Department of Civil Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya 54050, Turkey)

  • Ahmet Iyad Ceyhunlu

    (Department of Civil Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya 54050, Turkey)

  • Andrzej Wałęga

    (Department of Sanitary Engineering and Water Management, University of Agriculture in Krakow, Mickiewicza 24/28 Street, 30-059 Krakow, Poland)

  • Tommaso Caloiero

    (National Research Council of Italy, Institute for Agriculture and Forest Systems in the Mediterranean (CNR-ISAFOM), Via Cavour 4/6, 87036 Rende, CS, Italy)

Abstract

Precipitation is a crucial component of the water cycle, and its unpredictability may dramatically influence agriculture, ecosystems, and water resource management. On the other hand, climate variability has caused water scarcity in many countries in recent years. Therefore, it is extremely important to analyze future changes of precipitation data in countries facing climate change. In this study, the Innovative Polygon Trend Analysis (IPTA) method was applied for precipitation trend detection at seven stations located in the Wadi Sly basin, in Algeria, during a 50-year period (1968–2018). In particular, the IPTA method was applied separately for both arithmetic mean and standard deviation. Additionally, results from the IPTA method were compared to the results of trend analysis based on the Mann–Kendall test and the Sen’s slope estimator. For the different stations, the first results showed that there is no regular polygon in the IPTA graphics, thus indicating that precipitation data varies by years. As an example, IPTA result plots of both the arithmetic mean and standard deviation data for the Saadia station consist of many polygons. This result means that the monthly total precipitation data is not constant and the data is unstable. In any case, the application of the IPTA method showed different trend behaviors, with a precipitation increase in some stations and decrease in others. This increasing and decreasing variability emerges from climate change. IPTA results point to a greater focus on flood risk management in severe seasons and drought risk management in transitional seasons across the Wadi Sly basin. When comparing the results of trend analysis from the IPTA method and the rest of the analyzed tests, good agreement was shown between all methods. This shows that the IPTA method can be used for preliminary analysis trends of monthly precipitation.

Suggested Citation

  • Mohammed Achite & Gokmen Ceribasi & Ahmet Iyad Ceyhunlu & Andrzej Wałęga & Tommaso Caloiero, 2021. "The Innovative Polygon Trend Analysis (IPTA) as a Simple Qualitative Method to Detect Changes in Environment—Example Detecting Trends of the Total Monthly Precipitation in Semiarid Area," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12674-:d:680466
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    References listed on IDEAS

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    1. Mohamed Meddi & Ali Assani & Hind Meddi, 2010. "Temporal Variability of Annual Rainfall in the Macta and Tafna Catchments, Northwestern Algeria," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 3817-3833, November.
    2. Michela Biasutti, 2019. "Rainfall trends in the African Sahel: Characteristics, processes, and causes," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 10(4), July.
    3. Sheng Yue & ChunYuan Wang, 2004. "The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(3), pages 201-218, June.
    4. T. Caloiero & R. Coscarelli & E. Ferrari, 2018. "Application of the Innovative Trend Analysis Method for the Trend Analysis of Rainfall Anomalies in Southern Italy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4971-4983, December.
    5. G. Buttafuoco & T. Caloiero & R. Coscarelli, 2015. "Analyses of Drought Events in Calabria (Southern Italy) Using Standardized Precipitation Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 557-573, January.
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    1. Gokmen Ceribasi & Ahmet Iyad Ceyhunlu & Andrzej Wałęga & Dariusz Młyński, 2022. "Investigation of the Effect of Climate Change on Energy Produced by Hydroelectric Power Plants (HEPPs) by Trend Analysis Method: A Case Study for Dogancay I–II HEPPs," Energies, MDPI, vol. 15(7), pages 1-17, March.

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