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| Type: | Resource | |
| Storage: | The size of this resource is 3.0 MB | |
| Created: | Nov 20, 2019 at 6:28 p.m. | |
| Last updated: | Dec 03, 2019 at 8:29 p.m. | |
| DOI: | 10.4211/hs.94a5baa2cc774cd48ba3174bc9a71f22 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 2553 |
| Downloads: | 372 |
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Abstract
This site provides the Python code we used to modify SWAT model input files for the Spatial Factor Substitution (SFS) method of Jepsen and Harmon (in press). In the SFS method, categorical factors are transferred from a "source" subbasin to a "target" subbasin of a watershed according to a prescribed space-for-time substitution scenario. Categorical factors handled in this Python code are precipitation time series, air temperature time series, land cover, soil type, and slope. We developed the SFS method to resolve landscape contributions to an elevational gradient in long-term ET, and the influence of the "covariance-stationarity" assumption of a typical space-for-time substitution model of climate warming (Jepsen and Harmon, in press).
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Related Resources
| This resource is referenced by | Jepsen, S. M., & Harmon, T. C. (in press). Resolving drivers of a spatial gradient in evapotranspiration through the simulated translocation of landscape factors. Water Resources Research. https://doi.org/10.1029/2019WR025811 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| Inter-American Institute for Global Change Research (IAI) | CRN3038 | |
| National Science Foundation | CBET 1336839 | |
| University of California, Merced |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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