Preprints
https://doi.org/10.5194/egusphere-2026-2437
https://doi.org/10.5194/egusphere-2026-2437
22 Jun 2026
 | 22 Jun 2026
Status: this preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).

Structural limits and ill-posedness of soil water storage balance method for diagnosing root water uptake

Han Fu, Bingcheng Si, and Wenxiu Zou

Abstract. Depth-resolved root water uptake (RWU) can be inferred through soil water storage balance (SWSB) from soil water content observations, yet their fundamental identifiability and robustness remain unclear. Using controlled numerical experiments with prescribed contrasting RWU profiles, we systematically evaluate the performance of SWSB-based RWU inversion under varying spatiotemporal aggregation and upper boundary conditions. Our results show that while accurate estimates can be obtained under idealized, error-free datasets, even modest uncertainties in soil hydraulic parameters (±10 %) and soil water content measurements (±1 %) lead to error amplification in RWU estimates by around 60-fold. Furthermore, the sensitivity analysis shows that SWSB-based RWU inversion is highly sensitive to soil water content, as it directly influences storage change (ΔV) and regulates the relationships between soil water content (θ) and pressure head (h), as well as saturated (ks) and unsaturated (k) hydraulic conductivities. These findings highlight SWSB-based RWU inversion is fundamentally ill-posed and becomes non-identifiable under realistic uncertainties. Overall, this study delineates the conditions under which SWSB-based RWU inversion can be reliably applied and emphasizes the need for additional independent constraints.

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Han Fu, Bingcheng Si, and Wenxiu Zou

Status: open (until 03 Aug 2026)

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Han Fu, Bingcheng Si, and Wenxiu Zou
Han Fu, Bingcheng Si, and Wenxiu Zou
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Short summary
This study evaluates the identifiability and robustness of root water uptake (RWU) estimates derived from soil water storage balance (SWSB). Results show that SWSB-based inversion is highly sensitive to realistic uncertainties, leading to strong error amplification and loss of identifiability, and provide guidance on its reliable application.
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