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| Type: | Resource | |
| Storage: | The size of this resource is 17.5 MB | |
| Created: | May 22, 2020 at 7:12 p.m. (UTC) | |
| Last updated: | Nov 11, 2020 at 8:37 p.m. (UTC) | |
| Citation: | See how to cite this resource | |
| Content types: | Multidimensional Content |
| Sharing Status: | Public |
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| Views: | 3030 |
| Downloads: | 58 |
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Abstract
Irrigation representation in land surface models has been advanced over the past decade, but the soil moisture (SM) data from SMAP satellite have not yet been utilized in large-scale irrigation modeling. Here we investigate the potential of improving irrigation representation in the Community Land Model version-4.5 (CLM4.5) by assimilating SMAP data. Simulations are conducted over the heavily irrigated central U.S. region. We find that constraining the target SM in CLM4.5 using SMAP data assimilation with 1-D Kalman filter reduces the root-mean-square error of simulated irrigation water requirement by 50% on average (for Nebraska, Kansas, and Texas) and significantly improves irrigation simulations by reducing the bias in irrigation water requirement by up to 60%. An a priori bias correction of SMAP data further improves these results in some regions but incrementally. Data assimilation also enhances SM simulations in CLM4.5. These results could provide a basis for improved modeling of irrigation and land-atmosphere interactions.
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Related Resources
| The content of this resource is derived from | https://waterdata.usgs.gov/nwis/water_use/ |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| The National Science Foundation | None | 1752729 |
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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