Location: Agroclimate and Hydraulics Research UnitTitle: GIS-based RUSLE reservoir sedimentation estimates: temporally variable C-factors, SDR, and adjustment for stream channel and bank sediment sources
Submitted to: Land
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/3/2023
Publication Date: 10/12/2023
Citation: Starks, P.J., Moriasi, D.N., Fortuna, A. 2023. GIS-based RUSLE reservoir sedimentation estimates: temporally variable C-factors, SDR, and adjustment for stream channel and bank sediment sources. Land. 12(10).1913.
Interpretive Summary: Modeling is a good choice for conducting preliminary investigations of how much eroded soil has entered a reservoir (sedimentation) and for prioritization of reservoirs for follow up investigations for public safety and performance standards. The Revised Universal Soil Loss Equation (RUSLE) is a mathematical model originally designed to estimate soil erosion for a single field and for a single point in time. In recent years, RUSLE has been adapted to work in computer software programs to assess, simultaneously, potential soil erosion of multiple fields within an area (for example, fields within a watershed). Some of these large-area versions of RULSE have been linked to other software which allows RUSLE’s soil erosion estimates to be routed to receiving water bodies, such as reservoirs. The linkage of a large-area RUSLE with such routing software enables study of factors that impact a reservoir’s rate of sedimentation. Contradictory results have been reported in the literature concerning RUSLE’s ability to assess reservoir sedimentation. These contradictory results may be related to RUSLE’s inability to account for soils eroded from stream channels and stream banks, and the fact that land cover may change several times over the course of a long study period, which affects the choice of values to represent RUSLE’s land cover/land management (C-) factors. We used a computer-based, large-area version of RUSLE that incorporates sediment routing software to assess sedimentation for 12 reservoirs located in central Oklahoma. Considerable variability in estimated sedimentation, for some reservoirs, was associated with changes in land cover (i.e., the C-factors). Thus, for reservoir sedimentation studies it is advisable to run several RUSLE computer simulations to better account for variations in land cover as it greatly affects contributions to reservoir sedimentation. Using data from a related study it was shown that an adjustment for erosion from stream channels and stream banks brought estimated sedimentation to within about 6% of measured values for five of the watersheds but only to within 65 % for the remaining seven. In the absence of erosion estimates for stream channel and stream bank contributions, results revealed that certain combinations of soil and topographic measurements could be used to predict the degree of sediment underestimation with a high level of correlation. The findings of this study suggest that the level of agreement between computer-based RUSLE estimates of reservoir sedimentation and measured values is a function of watershed characteristics such as high (= 21degrees) slopes and the amount of moderate to highly erosive soils within the fields and stream channels. These characteristics are easily obtained from publicly available digital elevation models and soils data and may be useful in prioritizing reservoirs for assessments of function and safety.
Technical Abstract: The literature presents contradictory results as to the efficacy of using RUSLE in a GIS context for quantifying reservoir sedimentation. The contradictory results may be a function of the RUSLE’s inability to account for sediments derived from gullies, stream channels, or stream banks; the temporal variability of some of RUSLE’s empirically based factors that are used to numerically adjust erosion of a given site against a baseline condition; and the fact that RUSLE cannot route sediments through a watershed to the receiving reservoir. In the absence of or in combination with a routing routine in a GIS-based RUSLE, the sediment delivery ratio (SDR) is used to reduce the amount of sediment generated by RUSLE before delivery to the receiving reservoir. There is a need for further evaluation and validation of GIS-based RUSLE approach to estimate reservoir sedimentation, specifically regarding the impact of temporally variable land cover data sets, the impact of channel and stream bank contributions to reservoir sedimentation, and the routing of sediments through the watershed to the receiving reservoir. To this end we use a GIS-based RUSLE model that incorporates an SDR within a routing (i.e., sedimentation) routine applied to data acquired from 12 watersheds, and their reservoirs, in Oklahoma. Considerable variability in estimated sedimentation, for some reservoirs, was associated with date of satellite coverage used to the develop the RUSLE C-factors. Thus, for reservoir sedimentation studies it is advisable to run several simulations to capture the impact of temporally variable C-factors to better account for variations in overland sediment contributions to reservoir sedimentation. Using a priori information, this study showed that a first-order adjustment for sediment contributions from gullies, stream channels, and stream banks brought estimated sedimentation to within 6.0 ± 11.9% of measured values for five of the watersheds but only to within 65.0 ± 9.3% for the remaining seven. Setting SDR = 1 implies that all sediment generated by RUSLE is deposited into the reservoir, which led to large overestimation of reservoir sedimentation for all reservoirs. Setting the SDR < 1 in the model invoked routing of the RUSLE sediments through the watershed which were reduced by the watershed specific SDR value and by a proportionality value, calculated by the sedimentation module, based on the length of shared boundary of two adjacent cells across which the sediment is passed. These two reductions resulted in large underestimations of sedimentation. In the absence of a priori estimates of gully, stream channel, and stream bank sediment contributions, statistical analysis revealed that certain combinations of soil and topographic variables could be used to predict the degree of sediment underestimation with a high level of correlation (> 0.72 R2 = 0.99). The findings of this study suggest that the level of agreement (or disagreement) between GIS-based RUSLE estimates of reservoir sedimentation and measured values is a function of watershed characteristics; namely, the area weighted K-factor of the soils within the watershed and stream channels, the stream entrenchment ratio and bank full depth, the percentage of the stream corridor having slopes = 21 degrees, and the width of the stream flood way as a percentage of the watershed area. Within the context of GIS, these metrics are easily obtained from digital elevation models and publicly available soils data and may be useful in prioritizing reservoirs assessments of function and safety.