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Morphometry of AFs in upstream and downstream of floods in Gribayegan, Iran

Author

Listed:
  • Marzieh Mokarram

    (Shiraz University)

  • Hamid Reza Pourghasemi

    (Shiraz University)

  • John P. Tiefenbacher

    (Texas State University)

Abstract

This study aims to determine the effect of a flood-spreading system on the morphometric characteristics of alluvial fans (AFs) in Gribayegan Fasa, Iran, and its relationship with erosion, age, texture, and type of formations. After determining the AFs using the semiautomatic method and determining their recharged watersheds, 25 morphometric characteristics were investigated. The most important morphometric characteristics were identified using principal component analysis (PCA). The group method of data handling (GMDH) neural network is used to predict erosion, soil texture, age, and formation material based on the morphometric characteristics of the fan. The results demonstrate that the semiautomatic method can effectively extract AF from the landscape. The results of AF morphometry before and after flood spreading show that the fan area, drainage basin circularity (Cirb), fan perimeter, relief ratio of the fan, fan length, minimum fan height, and maximum fan height were higher before flood spreading, and basin shape, St (soil texture), upper fan slope, fan volume, and sweep angle had higher values after the flood. In addition, the results of PCA show that fan area, fan perimeter, fan length, fan radius, fan volume, basin area, basin perimeter, main channel length, basin length, and drainage density are important factors. The results of the GMDH algorithm reveal that this method can accurately predict the formation, age, soil texture, and erosion rate using morphometric characteristics. Therefore, the R2 is 0.92 for the formation age and R2 is 0.86 for the erosion rates, formation types, and soil texture, respectively. Therefore, the most important morphometric parameters can be determined using PCA, and the conditions and processes in the basin, such as formation material, age, soil texture, and erosion rate, can be predicted with high accuracy using the GMDH algorithm.

Suggested Citation

  • Marzieh Mokarram & Hamid Reza Pourghasemi & John P. Tiefenbacher, 2021. "Morphometry of AFs in upstream and downstream of floods in Gribayegan, Iran," 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. 108(1), pages 425-450, August.
    • Handle: RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04690-0
    DOI: 10.1007/s11069-021-04690-0
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    References listed on IDEAS

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    1. Gerardo Grelle & Antonietta Rossi & Paola Revellino & Luigi Guerriero & Francesco Maria Guadagno & Giuseppe Sappa, 2019. "Assessment of Debris-Flow Erosion and Deposit Areas by Morphometric Analysis and a GIS-Based Simplified Procedure: A Case Study of Paupisi in the Southern Apennines," Sustainability, MDPI, vol. 11(8), pages 1-20, April.
    2. Henry Kaiser, 1974. "An index of factorial simplicity," Psychometrika, Springer;The Psychometric Society, vol. 39(1), pages 31-36, March.
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    1. Hector Diaz & Bruno Mazzorana & Bernhard Gems & Ivan Rojas & Nicole Santibañez & Pablo Iribarren & Mario Pino & Andrés Iroumé, 2022. "What do biphasic flow experiments reveal on the variability of exposure on alluvial fans and which implications for risk assessment result from this?," 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 3099-3120, April.

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