Abstract
The widespread availability of high-fidelity topography combined with advances in geospatial analysis offer the opportunity to reimagine approaches to the difficult problem of predicting sediment delivery from watersheds. Here we present a model that uses high-resolution topography to filter sediment sources to quantify sediment delivery to the watershed outlet. It is a reduced-complexity, top-down model that defines transfer functions—topographic filters—between spatially distributed sediment sources and spatially integrated sediment delivery. The goal of the model is to forecast changes in watershed suspended sediment delivery in response to spatially distributed changes in sediment source magnitude or delivery, whether a result of watershed drivers or intentional management actions. Such an application requires the context of a watershed model that accounts for all sediment sources, enforces sediment mass balance throughout the spatial domain, and accommodates sediment storage and delivery over time. The model is developed for a HUC-8 watershed with a flat upland dominated by corn-soybean agriculture and deeply incised valleys near the watershed outlet with large sediment contributions from near-channel sources. Topofilter computes delivery and storage of field-derived sediment according to its spatial and structural connectivity to the stream channel network; subsequently, delivery of both field- and near-channel-derived sediment along with floodplain storage are computed in the stream channel network to the watershed outlet. The model outputs provide a spatially rich representation of sediment delivery and storage on field and along the stream that is consistent with available independent information on sediment accumulations and fluxes. Rather than a single best-calibrated solution, Topofilter uses the Generalized Likelihood Uncertainty Estimate (GLUE) approach to develop many possible solutions with sediment delivery rates expressed as probability distributions across the watershed. The ensemble of simulation outputs provides a useful basis for estimating uncertainty in sediment delivery and the effectiveness of different landscape management allocation across a watershed.
| Original language | English (US) |
|---|---|
| Article number | 155701 |
| Journal | Science of the Total Environment |
| Volume | 836 |
| DOIs | |
| State | Published - Aug 25 2022 |
Bibliographical note
Funding Information:This work was funded by the Minnesota Agricultural Water Resources Center , a 319 Grant from the U.S. Environmental Protection Agency via the Minnesota Pollution Control Agency, and by Minnesota Clean Water Land and Legacy Amendment funds provided through the Minnesota Pollution Control Agency and the Minnesota Department of Agriculture . This work was supported by the US Department of Agriculture NRCS ( 69-3A75-14-269 ) and Utah Agricultural Experiment Station . This work would not have been possible without the participation of the stakeholders who invested many days over many years, teaching us about local conditions and agricultural practice and maintaining their commitment to cleaner water and collaboration through development and application of the simulation model. We thank Dr. Patrick Belmont for research support and providing the river survey data, Dr. Karthik Kumarasamy for providing calibrated SWAT outputs, and Keelin Schaffrath for providing DEM differencing results. We would also like to thank the support of the U.S. Geological Survey, and manuscript review by Joel Groten. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Topofilter input files and model are available for download at: https://github.com/scho-GitHub/Topofilter . Management Option Simulation Model (MOSM) that uses Topofilter as an internal engine to estimate management effectiveness on reducing sediment loading can be found in the University of Minnesota Digital Conservancy ( http://hdl.handle.net/11299/191082 ).
Funding Information:
This work was funded by the Minnesota Agricultural Water Resources Center, a 319 Grant from the U.S. Environmental Protection Agency via the Minnesota Pollution Control Agency, and by Minnesota Clean Water Land and Legacy Amendment funds provided through the Minnesota Pollution Control Agency and the Minnesota Department of Agriculture. This work was supported by the US Department of Agriculture NRCS (69-3A75-14-269) and Utah Agricultural Experiment Station. This work would not have been possible without the participation of the stakeholders who invested many days over many years, teaching us about local conditions and agricultural practice and maintaining their commitment to cleaner water and collaboration through development and application of the simulation model. We thank Dr. Patrick Belmont for research support and providing the river survey data, Dr. Karthik Kumarasamy for providing calibrated SWAT outputs, and Keelin Schaffrath for providing DEM differencing results. We would also like to thank the support of the U.S. Geological Survey, and manuscript review by Joel Groten. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Topofilter input files and model are available for download at: https://github.com/scho-GitHub/Topofilter. Management Option Simulation Model (MOSM) that uses Topofilter as an internal engine to estimate management effectiveness on reducing sediment loading can be found in the University of Minnesota Digital Conservancy (http://hdl.handle.net/11299/191082).
Publisher Copyright:
© 2022
Keywords
- Generalized Likelihood Uncertainty Estimate
- Monte Carlo simulation
- Sediment delivery ratio
- Suspended sediment loading
- Watershed model
PubMed: MeSH publication types
- Journal Article