Resource type: Composite Resource
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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 
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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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Resource Level Coverage


Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Central United States
North Latitude
East Longitude
South Latitude
West Longitude


Data Services

The following web services are available for data contained in this resource. Geospatial Feature and Raster data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to support additional data types.



Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
The National Science Foundation 1752729

How to Cite

Felfelani, F., Y. Pokhrel (2020). Felfelani_GRL2018_Utilizing_SMAP_to_Improve_Irrigation_in_CLM_Fig_2, HydroShare, http://www.hydroshare.org/resource/900d074cac464614b524bd4a75346b8f

This resource is shared under the Creative Commons Attribution CC BY.



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