Felfelani_GRL2018_Utilizing_SMAP_to_Improve_Irrigation_in_CLM_Fig_2
Authors: | |
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Owners: | Farshid FelfelaniYadu Pokhrel |
Type: | Resource |
Storage: | The size of this resource is 5.8 MB |
Created: | May 22, 2020 at 7:55 p.m. |
Last updated: | Nov 11, 2020 at 8:37 p.m. |
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Content types: | Multidimensional Content |
Sharing Status: | Public |
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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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The content of this resource is derived from | https://nsidc.org/data/smap/smap-data.html |
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This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
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The National Science Foundation | 1752729 |
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This resource is shared under the Creative Commons Attribution CC BY.
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