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 language | English (US) |
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Pages (from-to) | 12,892-12,902 |
Journal | Geophysical Research Letters |
Volume | 45 |
Issue number | 23 |
DOIs | |
State | Published - Dec 16 2018 |
Keywords
- CLM
- SMAP
- bias correction
- data assimilation
- irrigation
ASJC Scopus subject areas
- Geophysics
- General Earth and Planetary Sciences