Estimation of surface depression storage capacity from random roughness and slope

  • Mohamed AM Abd Elbasit 1. Agricultural Research Council – Soil Climate and Water, Pretoria 0001, South Africa; 2. School of Geography, Archaeology, and Environmental Studies, University of the Witwatersrand, Johannesburg 2000, South Africa
  • Majed M Abu-Zreig 1. Civil Engineering Department, Jordan University of Science and Technology, Irbid, Jordan; 2. International Platform for Dryland Research and Education, Tottori University, Tottori, Japan
  • Chandra SP Ojha Department of Civil Engineering, Indian Institute of Technology, Roorkee, India
  • Hiroshi Yasuda Organization for Educational Support and International Affairs, Tottori University, Koyama Minami 4-101, Tottori 680-8550, Japan
  • Liu Gang State 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
Keywords: modelling, depression storage, surface roughness, arid regions, runoff

Abstract

Depression storage capacity (DSC) 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 micro-catchments and bare soil in arid regions with only one fitting parameter. Laboratory impermeable surfaces of 30 x 30 cm2 were constructed with 4 sizes of gravel and mortar resulting in random roughness values ranging 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 was estimated by the mass balance approach.  Analysis of experimental results indicated that the developed linear model between DSC and the square root of the ratio of random roughness (RR) to slope was significant at p < 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. However, the model is based on small-scale laboratory plots and further testing in the field will provide more insight for practical applications.

Views
  • Abstract 24
  • PDF 14
Views and downloads are with effect from 11 January 2018
Published
2020-07-28
Section
Research paper