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  "description": "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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        "name": "Michigan State University"
      },
      "name": "Felfelani, Farshid"
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        "name": "Michigan State University"
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      "name": "Pokhrel, Yadu"
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  ],
  "dateCreated": "2020-05-22 19:12:44.317123+00:00",
  "keywords": [
    "SMAP",
    "Irrigation Water Use",
    "USGS",
    "CLM",
    "Irrigation Water Requirement",
    "USGS Irrigation Data",
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  "dateModified": "2020-11-11 20:37:21.481077+00:00",
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      },
      "name": "The National Science Foundation"
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  "citation": [
    "Felfelani, F., Y. Pokhrel (2020). Felfelani_GRL2018_Utilizing_SMAP_to_Improve_Irrigation_in_CLM_Fig_3, HydroShare, http://www.hydroshare.org/resource/cc7365bbc2794028be5e9db9219259d5"
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