A comprehensive assessment of soil moisture data assimilation and its potential to increase agricultural forecasting capacity

Kivi, Marissa Suzanne

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https://hdl.handle.net/2142/115471 Copy

Description

Title

A comprehensive assessment of soil moisture data assimilation and its potential to increase agricultural forecasting capacity

Author(s)

Kivi, Marissa Suzanne

Issue Date

2022-04-27

Director of Research (if dissertation) or Advisor (if thesis)

Dokoohaki, Hamze

Committee Member(s)

• Martin, Nicolas
• Bhattarai, Rabin
• Li, Bo

Department of Study

Crop Sciences

Discipline

Crop Sciences

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Agricultural forecasting
• soil moisture
• data assimilation
• remote sensing
• Bayesian
• Ensemble Kalman filter

Abstract

In the face of today’s large-scale agricultural issues, the need for robust methods of agricultural forecasting has never been clearer. Yet, the accuracy and precision of recent forecasts remains limited by current tools and methods. Past studies have proposed and tested soil moisture data assimilation as a method to merge soil moisture observations with process-based crop models, thereby accounting for spatial heterogeneity in soil water dynamics and improving crop model estimates. Building on these previous studies, the following work systematically and comprehensively explored the potential for soil moisture data assimilation to serve as a powerful and generalizable tool for improving agricultural predictions in the U.S. Midwest. First, a scalable, flexible, and robust data-assimilation system was developed. The system (1) utilizes ensemble-based filtering approaches to constrain model states and update model parameters at observed time steps, (2) propagates uncertainties, and (3) incorporates an algorithm that improves system performance through the joint estimation of system error matrices. After assimilating in situ soil moisture observations into the APSIM crop model for an experimental site in central Illinois over two growing seasons, the system demonstrated strong constraint of soil moisture forecasts, improving soil moisture estimates in the two assimilation layers by 42% and 48%. Such constraint propagated into improved accuracy in estimates of lower layer soil moisture, annual tile flow, and annual nitrate loads, but did not have strong impacts on aboveground measures of crop productivity due to a lack of water stress at the site. To further evaluate the developed system, the constraint of in situ soil moisture data assimilation was evaluated for 5 experimental sites across the U.S. Midwest using observations spanning 19 site-years. The system’s impact on estimates of soil moisture, yield, NDVI, tile drainage, and nitrate leaching was assessed across all simulated growing seasons. For all site-years, the accuracy of soil moisture forecasts in the assimilation layers was improved. These changes also led to improved simulation of soil moisture in deeper parts of the soil profile in most cases. Although crop yield was improved for most site-years, the greatest improvement in yield accuracy was demonstrated in site-years with higher water stress, where assimilation served to increase available soil water for crop uptake. Alternatively, estimates of annual tile drainage and nitrate leaching were not well constrained across the study sites. Trends in drainage constraint suggest the importance of evapotranspiration observations as a next point for constraint. Finally, to test the full generalizability of the developed system, the application of remote sensing surface soil moisture observations was investigated. Four different data products were assimilated within the developed data-assimilation system for the same 5 study sites. The assimilation of surface soil moisture showed weaker constraint of downstream model state variables when compared to the assimilation of root-zone soil moisture values from the previous analysis. The median reduction in soil moisture RMSE for observed soil layers was lower, on average, by a factor of 4. However, crop yield estimates were still improved overall with a median RMSE reduction of 17.2%, and there is strong evidence that yield improvement was higher when under water-stressed conditions. Comparisons of system performance across different combinations of remote sensing data products indicated the importance of high temporal resolution and accurate observation uncertainty estimates when assimilating surface soil moisture observations. This study highlighted many opportunities and challenges of soil moisture data assimilation as an agricultural forecasting tool and laid a strong foundation for future innovation and application of the approach.

Graduation Semester

2022-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Marissa Kivi

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