Estimation of potential soil erosion rate using RUSLE and E30 model

Abstract

This research established an empirical methodology to estimate potential soil erosion rate based on revised universal soil loss equation (RUSLE) and E 30 model. The study was conducted on a highly precipitated, rugged, tropical forested with steep slope watershed during 1992 to 2009. The fourth (4th) largest river of Papua New Guinea, and its catchment area was considered for this research. Lots of commercial mining and logging activities are the ongoing processes in the upper catchment area without proper conservation measures. Digital elevation model (DEM), landsat satellite images, average annual rainfall, soil texture data base were used to derived mandatory input factors into the RUSLE and E 30 model. Raster calculator of ArcGIS spatial analyst was used to generate all input factors and final pixel-by-pixel based computation of soil loss pattern. The average potential soil erosion rate were calculated in the range of 20.34 mm/year to 23.70 mm/year through RSULE model and in the other hand the rate varies from 21.07 mm/year to 26.78 mm/year through E 30 model during 1992 to 2009 respectively. The erosion rate through both model indicates extremely severe rate of erosion in the upper catchment area are required immediate attention of soil conservation practices.

Flood susceptibility analysis through remote sensing, GIS and frequency ratio model

“…Within the soil drainage factor, poorly to very poorly drained class had the highest FR value of 2.66, followed by waterlogged (swampy) area with the FR value of 2.21. Finally, the soil texture map was generated for the study area where ten (10) soil texture classes could be found (Samanta et al 2016b), namely sandy clay loam, sandy loam, silty clay loam, peat, silty loam, sandy clay, river course-gravel, sand, lake and silty clay (Fig. 2h).…”

“…1a) with huge bed load (Tilley et al 2006). This is the fourth longest river in PNG born at Finisterre range (approximately 457 m altitude) and flows into the Huon Gulf near to the downtown of Lae, which is the second largest city of PNG after 180 km of checkered path (Samanta et al 2016b). The upper basin is dominated by natural forests, steep slopes and rugged terrain Solin 2012).…”

Analysis of sediment accessibility and availability concepts based on sediment connectivity throughout a watershed

“…The LS factor values range from 0.03 to 88.8 for respectively the flat areas to higher ones. The C factor layer was made using LANDSAT images from 2014 based on the NDVI and developed method by Karaburun (2010) because of some similarities between the condition (climate and land use pattern) of the study areas (Jamshidi, Dragovich, & Webb, 2014). It needs to be noted that regarding the conducted protective measures and management strategies since 1996 in the study area and based on the available information, controlled livestock number, and analysis of temporospatial variation of C-factor values (Najafi et al, 2017), the calculated C-factor using images in 2014 was assumed as representative of vegetation in the Taham watershed over the long term.…”

Quantifying the water soil erosion hazard using RUSLE, GIS, and RS approach: A case study of Al-Qshish River Basin, Lattakia, Syria.

“…Maintenance practice factor (P) is the proportion of soil erosion after a selective support exercise to the corresponding soil loss after up and down farming (Samanta et al 2016). However, P factor fundamentally affects soil loss by modifying the streaming pattern, degree or orientation of overland ow, and decreasing the runoff potentials (Ozsoy and Aksoy, 2015).…”