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https://doi.org/10.5194/hess-2019-74
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-74
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  11 Mar 2019

11 Mar 2019

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This preprint has been withdrawn by the authors.

Estimation of surface depression storage capacity from surface roughness

Mohamed A. M. Abd Elbasit1,2, Chandra S. P. Ojha3, Majed M. Abu-Zerig4,5, Hiroshi Yasuda4, Liu Gang6, and Fethi Ahmed2 Mohamed A. M. Abd Elbasit et al.
  • 1Agricultural Research Council-Soil Climate and Water, Private Bag X79, Pretoria 0001, South Africa
  • 2School of Geography, Archaeology, and Environmental Studies, University of the Witwatersrand, Johannesburg 2000, South Africa
  • 3Dept. of Civil Engineering, Indian Institute of Technology, Roorkee, India
  • 4International Platform for Dryland Research and Education, Tottori University, Tottori, Japan
  • 5Civil Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
  • 6State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, People's Republic of China

Abstract. Depression storage models found in the literature were developed using statistical regression for relatively large soil surface roughness and slope values resulting in several fitting parameters. In this research, we developed and tested a conceptual model to estimate surface depression storage having small roughness values usually encountered in rainwater harvesting microcatchments in arid regions with only one fitting parameter. Laboratory impermeable surfaces of 30 × 30 cm2 were constructed with four sizes of gravel and mortar resulting in random roughness values ranged from 0.9 to 6.3 mm. A series of laboratory experiments were conducted under 9 slope values using simulated rain. Depression storage for each combination of relative roughness and slope were estimated by mass balance approach. Analysis of experimental results indicated that the developed linear model between DSC and the square root of the ration of random roughness (RR) to slope was significant at probability value of 0.001 and coefficient of determination R2 = 0.90. The developed model predicted depression storage of small relief at higher accuracy compared to other models found in the literature.

This preprint has been withdrawn.

Mohamed A. M. Abd Elbasit et al.

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Mohamed A. M. Abd Elbasit et al.

Mohamed A. M. Abd Elbasit et al.

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Short summary
We developed and tested a conceptual model to estimate depression storage capacity (DSC) having small roughness values usually encountered in rainwater harvesting microcatchments in arid regions with only one fitting parameter. Results indicated that the developed linear model between DSC and the square root of the ration of random roughness (RR) to slope was significant. The model predicts depression storage of small relief at higher accuracy compared to other models found in the literature.
We developed and tested a conceptual model to estimate depression storage capacity (DSC) having...
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