Sediment delivery modeling in practice: Comparing the effects of watershed characteristics and data resolution across hydroclimatic regions

Hamel, Perrine; Falinski, Kim; Sharp, Richard; Auerbach, Daniel A.; Sanchez-Canales, Maria; Dennedy-Frank, P. James
Publisher N/A
SourceWeb of Science
Volume / Issue580
Pages1381 - 1388
Total Pages N/A
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TagsInVEST, Sediment delivery model, Sensitivity analyses, DEM, Uncertainty
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Publication DateFEB 15 2017
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AbstractGeospatial models are commonly used to quantify sediment contributions at the watershed scale. However, the sensitivity of these models to variation in hydrological and geomorphological features, in particular to land use and topography data, remains uncertain. Here, we assessed the performance of one such model, the InVEST sediment delivery model, for six sites comprising a total of 28 watersheds varying in area (6Ð13,500 km2), climate (tropical, subtropical, mediterranean), topography, and land use/land cover. For each site, we compared uncalibrated and calibrated model predictions with observations and alternative models. We then performed correlation analyses between model outputs and watershed characteristics, followed by sensitivity analyses on the digital elevation model (DEM) resolution. Model performance varied across sites (overall r2 = 0.47), but estimates of the magnitude of specific sediment export were as or more accurate than global models. We found significant correlations between metrics of sediment delivery and watershed characteristics, including erosivity, suggesting that empirical relationships may ultimately be developed for ungauged watersheds. Model sensitivity to DEM resolution varied across and within sites, but did not correlate with other observed watershed variables. These results were corroborated by sensitivity analyses performed on synthetic watersheds ranging in mean slope and DEM resolution. Our study provides modelers using InVEST or similar geospatial sediment models with practical insights into model behavior and structural uncertainty: first, comparison of model predictions across regions is possible when environmental conditions differ significantly; second, local knowledge on the sediment budget is needed for calibration; and third, model outputs often show significant sensitivity to DEM resolution.
Created: 12/14/2017 10:30 AM (ET)
Modified: 12/14/2017 10:30 AM (ET)
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