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\begin{document}

\title{Utilizing SMAP Soil Moisture Data to Improve Irrigation
Parameterizations in Land Surface Models}



\author[1]{Farshid Felfelani}%
\author[1]{Yadu Pokhrel}%
\author[2]{Kaiyu Guan}%
\author[3]{David Lawrence}%
\affil[1]{Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI, USA}%
\affil[2]{Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA}%
\affil[3]{National Center for Atmospheric Research, Boulder, CO, USA}%


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  \date{June 10, 2024}


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\begin{abstract}
Irrigation parameterizations in land surface models have been advanced
over the past decade, but the newly available data from the Soil
Moisture Active Passive (SMAP) satellite has seldom been used to improve
irrigation modeling. Here, we investigate the potential of assimilating
SMAP soil moisture (SM) data into the Community Land Model (CLM) to
improve irrigation representation. Simulations are conducted at 3
arc-minute resolution over the highly irrigated region in the central
US, fully enclosing the upstream areas of the river basins draining over
the High Plains Aquifer (i.e., the Missouri and Arkansas), and Colorado
River basins. We test the original CLM4.5 irrigation scheme and two new
irrigation parameterizations using SMAP data assimilation by: (1)
directly integrating raw SMAP data, and (2) integrating SMAP data using
1-D Kalman Filter (KF) smoother. An a priori scaling approach is also
used to account for bias correction of the shortly-recorded SMAP data
based on the ground observations, enabling us to use SMAP for
out-of-sample tests (i.e., assessment of the new parameterizations
during a non-SMAP period). The ground-based SM observations from three
monitoring networks, namely Soil Climate Analysis Network (SCAN), US
Climate Reference Network (USCRN), and SNOwpack TELemetry (SNOTEL) are
employed for bias correcting SMAP data and validating SM simulations.
Results show that SMAP data assimilation using 1-D KF significantly
improves irrigation simulations. Bias correction of SMAP data further
improves results from KF assimilation in some regions. However, the
improvements are small compared to those achieved from 1-D KF
application alone, indicating the robustness of using SMAP data and KF
globally even for the regions where ground-based data are not available
for bias correction. The data assimilation also improves the accuracy of
the temporal dynamics and vertical profile of simulated SM. These
results are expected to provide a basis for improved modeling of
irrigation water use and land-atmosphere interactions.%
\end{abstract}%



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