Crop growth depends on the root-zone soil
moisture (RZSM) (~top 1m). Accurate estimation of RZSM is
vital to optimize irrigation management for saving water and
energy while sustaining crop yield. The High-Resolution Land
Assimilation System (HRLDAS) from NCAR can generate
RZSM at field scales for irrigation management. The soil
moisture data from various agriculture sites in the AmeriFlux
network, U.S. Climate Reference Network (USCRN), and Soil
Climate Analysis Network (SCAN) are used to verify the soil
moisture products generated by HRLDAS. Although the
HRLDAS products is not location specific and could be
applied nationwide, this study will focus on Nebraska for
evaluation, validation, and further calibration. We also
compared NASA’s SMAP surface soil moisture products to
HRLDAS surface layer soil moisture. Since the accuracy of
the SMAP product is known, this comparison directly
validates the HRLDAS surface soil moisture product and
indirectly validate its RZSM products. Results from these two
validation methods show a good accuracy of HRLDAS soil
moisture products. The conspicuous differences between
HRLDAS and SMAP products indicate that HRLDAS omits
the irrigation activities as its simulation is based on weather
variables and energy balance. It’s hard for HRLDAS to
consider and include the irrigation actions in its results, while
as SMAP products remotely sense the soil moisture as it is, the
changes caused by irrigation are clearly reflected. Therefore,
a simple calibration is applied to the HRLDAS products by
including irrigation amount as its variables.
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Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model
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.
more » « less- Award ID(s):
- 1752729
- NSF-PAR ID:
- 10452709
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 45
- Issue:
- 23
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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