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

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.

Original languageEnglish (US)
Pages (from-to)12,892-12,902
JournalGeophysical Research Letters
Volume45
Issue number23
DOIs
StatePublished - Dec 16 2018

Keywords

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

ASJC Scopus subject areas

  • Geophysics
  • Earth and Planetary Sciences(all)

Fingerprint Dive into the research topics of 'Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model'. Together they form a unique fingerprint.

Cite this