Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model

Farshid Felfelani, Yadu Pokhrel, Kaiyu Guan, David M. Lawrence

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)12,892-12,902
JournalGeophysical Research Letters
Issue number23
StatePublished - Dec 16 2018


  • CLM
  • SMAP
  • bias correction
  • data assimilation
  • irrigation

ASJC Scopus subject areas

  • Geophysics
  • General Earth and Planetary Sciences


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