Investigating reservoir sediment and watershed erosion using a geographical information system

Abstract

Abstract:Most economic developments may have been at the expense of soil conservation. Soil erosion results in impacts on reservoir sediment, crop yield, and water quality. This study introduces the procedure of using a geographical information system for investigating reservoir sediments and soil erosion of the corresponding watershed. First, the bathymetric surface of a reservoir is surveyed using two real-time kinematic global position systems and a sonar-based depth gauge. Using the on-board computer, receivers, and sonar, bathymetric readings are recorded three times a second to establish actual coordinates and depth measurements. Data are then converted using a digital terrain model to obtain the current image bathymetric surface. The image of original bathymetric surfaces when constructed can be obtained by digitizing the existing map. The subtraction of these two images is used to estimate the amount of sediment deposits. Next, parameters of rainfall-runoff erosivity factor, soil erodibility factor, slope length factor, slope steepness factor, cover-management factor, and support practice factor are evaluated for each grid of the corresponding watershed. The average annual soil loss from each grid is estimated by the revised universal soil loss equation to obtain the total amount of the soil loss for the watershed. Using the example of Charles Mill Lake, Ohio, USA, the estimated average amount of sediment deposits in the lake was about 2Ð44 ð 10 4 m 3 year 1 . On the other hand, the estimated soil erosion from the corresponding watershed was about 4Ð22 ð 10 8 kg year 1 or 1Ð56 ð 10 5 m 3 year 1 . Hence, it can be concluded that about 15Ð6% of the soils eroded from the watershed have been deposited in the lake.

Soil loss, water ponding and sediment deposition variations as a consequence of rainfall intensity and land use: a multi‐criteria analysis

“…For the role of vegetation cover in reducing the risk of soil loss, a relatively high value was given. This determination by both (A) and (B) is in line with the literature (Chang et al, 2003;Bhattarai and Dutta, 2007).…”

Investigation of the temporal relation of remotely sensed coastal water quality with GIS modelled upstream soil erosion

“…In small‐scale river basins, various models have been introduced for the relational assessment of water quality to upstream hydrological processes (Ritchie et al ., ). A geographical information system (GIS) and 3D geographic analysis was used to link the accumulated sediment deposited in a reservoir over 64 years with the annual estimation of upstream soil erosion estimated by the Revised Universal Soil Loss Equation (RUSLE) (Chang et al ., ). Similarly, the AGNPS model coupled with a GIS was used to estimate sediment and total phosphorus yield from upstream soil erosion (Rode and Suhr, ).…”

The effect of cultivation method on erosion in agricultural catchments: integrating AHP in GIS environments

“…Clay rich soils are prone to erosion due to low hydraulic conductivitydespite their high cohesion, which decreases with wetting (Evans, 1980). A second factor is topography, with soil erosion increasing as the slope gradient and length grow due to the increased potential of runoff acceleration and the increase of contributing area (Chang et al, 2003). Moreover, soil exposure to the raindrop impact effect can cause the formation of a structural seal layer which may reduce the infiltration rate.…”