Interpreting effective hydrologic depth estimates derived from soil moisture remote sensing: A Bayesian non-linear...

Area of Science:

• Hydrology and Earth System Science
• Remote Sensing Applications
• Environmental Modeling

Background:

• The water balance equation (WBE) is crucial for understanding water supply and the terrestrial water cycle.
• Recent studies focus on fitting WBE with remote sensing data to estimate parameters like ΔZ, which links water fluxes to soil moisture changes.
• Physically interpreting WBE parameters can be challenging due to model assumptions, data limitations, and estimation uncertainties.

Purpose of the Study:

• To investigate the challenges in obtaining physically interpretable water balance parameters (ΔZ) using remotely-sensed data.
• To demonstrate how limitations in satellite data and WBE implementation can bias ΔZ estimates.
• To propose a Bayesian approach for analyzing these biases and improving parameter estimation.

Main Methods:

• Utilized a Bayesian non-linear modeling approach to analyze the water balance equation.
• Incorporated remotely-sensed precipitation (P) and soil moisture (SM) data.
• Assessed the impact of temporal resolution, accuracy of satellite-based SM, and neglected hydrological components on ΔZ estimates.

Main Results:

• Found that ΔZ estimates are likely to be spuriously biased due to limitations in satellite data (temporal resolution, accuracy) and omitted hydrological components.
• Bayesian modeling revealed biases in ΔZ, with maps showing the bounds of the 94% Highest Density Interval (HDI).
• Demonstrated that ΔZ estimates are effective parameters reflecting the fitted remote sensing data characteristics rather than direct physical quantities.

Conclusions:

• Physically interpreting ΔZ derived from current WBE and remote sensing data requires caution due to inherent biases.
• ΔZ should be considered effective parameters tied to the specific remote sensing datasets used.
• Advancements in remote sensing techniques and precise WBE are needed for more accurate ΔZ estimates and improved understanding of the terrestrial water cycle.