Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model
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 Communi...
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| Format: | Article |
| Language: | English |
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Wiley
2018-12-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2018GL080870 |
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| author | Farshid Felfelani Yadu Pokhrel Kaiyu Guan David M. Lawrence |
| author_facet | Farshid Felfelani Yadu Pokhrel Kaiyu Guan David M. Lawrence |
| author_sort | Farshid Felfelani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-387c46e26c8e42978dc286dc256b6393 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2018-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-387c46e26c8e42978dc286dc256b63932025-08-20T02:31:37ZengWileyGeophysical Research Letters0094-82761944-80072018-12-01452312,89212,90210.1029/2018GL080870Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land ModelFarshid Felfelani0Yadu Pokhrel1Kaiyu Guan2David M. Lawrence3Department of Civil and Environmental Engineering Michigan State University East Lansing MI USADepartment of Civil and Environmental Engineering Michigan State University East Lansing MI USADepartment of Natural Resources and Environmental Sciences and National Center for Supercomputing Applications University of Illinois at Urbana‐Champaign Urbana IL USANational Center for Atmospheric Research Boulder CO USAAbstract 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.https://doi.org/10.1029/2018GL080870SMAPCLMirrigationdata assimilationbias correction |
| spellingShingle | Farshid Felfelani Yadu Pokhrel Kaiyu Guan David M. Lawrence Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model Geophysical Research Letters SMAP CLM irrigation data assimilation bias correction |
| title | Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model |
| title_full | Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model |
| title_fullStr | Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model |
| title_full_unstemmed | Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model |
| title_short | Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model |
| title_sort | utilizing smap soil moisture data to constrain irrigation in the community land model |
| topic | SMAP CLM irrigation data assimilation bias correction |
| url | https://doi.org/10.1029/2018GL080870 |
| work_keys_str_mv | AT farshidfelfelani utilizingsmapsoilmoisturedatatoconstrainirrigationinthecommunitylandmodel AT yadupokhrel utilizingsmapsoilmoisturedatatoconstrainirrigationinthecommunitylandmodel AT kaiyuguan utilizingsmapsoilmoisturedatatoconstrainirrigationinthecommunitylandmodel AT davidmlawrence utilizingsmapsoilmoisturedatatoconstrainirrigationinthecommunitylandmodel |