期刊
FRESHWATER SCIENCE
卷 36, 期 1, 页码 195-217出版社
UNIV CHICAGO PRESS
DOI: 10.1086/690444
关键词
solute transport; inverse modeling; tracer; transient storage; hyporheic zone; parameter estimation; OTIS; MCAT; OTIS-MCAT
资金
- National Science Foundation (NSF) [EAR 1331906]
- NSF [EAR 1505309]
- US Department of Agriculture (USDA) [2013-67019-21365]
- Lilly Endowment, Inc. through Indiana University (IU) Pervasive Technology Institute
- Indiana METACyt Initiative
- USGS Toxic Substances Hydrology Program
- Directorate For Geosciences
- Division Of Earth Sciences [1505309] Funding Source: National Science Foundation
Researchers and practitioners alike often need to understand and characterize how water and solutes move through a stream in terms of the relative importance of in-stream and near-stream storage and transport processes. In-channel and subsurface storage processes are highly variable in space and time and difficult to measure. Storage estimates are commonly obtained using transient-storage models (TSMs) of the experimentally obtained solute-tracer test data. The TSM equations represent key transport and storage processes with a suite of numerical parameters. Parameter values are estimated via inverse modeling, in which parameter values are iteratively changed until model simulations closely match observed solute-tracer data. Several investigators have shown that TSM parameter estimates can be highly uncertain. When this is the case, parameter values cannot be used reliably to interpret stream-reach functioning. However, authors of most TSM studies do not evaluate or report parameter certainty. Here, we present a software tool linked to the One-dimensional Transport with Inflow and Storage (OTIS) model that enables researchers to conduct uncertainty analyses via Monte-Carlo parameter sampling and to visualize uncertainty and sensitivity results. We demonstrate application of our tool to 2 case studies and compare our results to output obtained from more traditional implementation of the OTIS model. We conclude by suggesting best practices for transient-storage modeling and recommend that future applications of TSMs include assessments of parameter certainty to support comparisons and more reliable interpretations of transport processes.
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