An analytical model for estimation of land surface net water flux from near-surface soil moisture observations

Sadeghi, Morteza; Tuller, Markus; Warrick, A. W.; Babaeian, Ebrahim; Parajuli, Kshitij; Gohardoust, Mohammad R.; Jones, Scott B.

doi:10.1016/j.jhydrol.2018.12.038

Cited by 30 publications

(28 citation statements)

References 41 publications

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“…Although the derivation of this model was based on many simplifying assumptions, the analytical model still could achieve satisfactory results compared with the GLF and soil water balance methods (Figure 9). Sadeghi et al (2019) pointed out that the analytical solution reasonably captures net water flux variations in natural settings (e.g., layered and vegetated soils) for four vastly sites in Arizona, California, Idaho and Indiana. The most noticeable convenience is that the analytical model just requires measured soil moisture close to the ground surface.…”

Section: Discussionmentioning

confidence: 99%

“…The analytical model underestimated groundwater recharge after June. Sadeghi et al (2019) found that the model may lower the prediction accuracy under extreme dry or wet conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Sadeghi et al (2019) proposed an analytical model to estimate net flux using measured surface soil moisture.

\frac{\partial θ}{\partial t} = \frac{\partial}{\partial z} ((), D \frac{\partial θ}{\partial z} - K),

where θ (cm 3 cm −3 ) denotes the volumetric soil moisture content, D (cm −2 day −1 ) is the soil water diffusivity, K (cm day −1 ) is the unsaturated soil hydraulic conductivity, t (day) is time, and z (cm) indicates soil depth. Assuming D is a constant and K is a linear function of θ :

K = italickθ

the linearized Richards' equation can be expressed as:

\frac{\partial θ}{\partial t} = D \frac{\partial^{2} θ}{\partial z^{2}} - k \frac{\partial θ}{\partial z} .

…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, Sadeghi et al (2019) obtained a calibration‐free model to calculate net water flux (cm day −1 ) at any time from measured soil moisture data.

\frac{θ_{\max} - θ_{\infty}}{θ_{\infty}} \approx \frac{f_{\max} - k θ_{\infty}}{k θ_{\infty}} \sqrt{\frac{k^{2} t_{italicmax}}{D}},

…”

Section: Methodsmentioning

confidence: 99%

“…However, configurations conducive to practical application to real hydrogeological situations are normally intractable. Recently, Sadeghi et al (2019) derived a more practical analytical solution, which does not need a calibration process; it acts as a useful tool to estimate groundwater recharge in practical hydrogeological situations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A comparison of methods to estimate groundwater recharge from bare soil based on data observed by a large‐scale lysimeter

Zhang

Wang

Gong

et al. 2020

Hydrological Processes

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Effectively estimating groundwater recharge is critical to manage water resources, especially in arid and semi‐arid regions as impacted by intensive human activities and climate changes. Rare insights have been gained into groundwater recharge since direct observation is hard to carry out. Although several methods are currently available to estimate groundwater recharge, the estimated results may cover noticeable bias. The behaviours of different methods based on different conceptual frameworks and exhibiting different levels of complexity should be examined to estimate actual groundwater recharge. This study aims to assess the performance of four common methods to estimate groundwater recharge. For this end, large‐scale lysimeters equipped with soil water content sensors and water table sensors were set up at a research site established in Guanzhong Basin of China. The data achieved by 1‐year observation were employed to compare four estimation methods. As revealed from the results, the following findings are drawn. (a) Groundwater level fluctuation (GLF) method is simple, whereas its accuracy is determined by specific yield, and adopting a water balance method to estimate specific yield can considerably enhance the accuracy of GLF. (b) The calibrated numerical model can obtain the optimal result compared with the other methods, whereas long‐term observation data are required for parameter calibration. (c) In the water balance method, the maximum entropy production (MEP) model and a practical method (estimating evaporation between two rainfall events) were used to calculate evaporation. As indicated by the results, water balance method combined with MEP is capable of obtaining more reliable results of groundwater recharge compared with the practical method. (d) With an analytical model based on linearized Richards' equation, accurate results can be achieved. What is more, the analytical model only needs the measurement of soil moisture near the surface. The limitation of this method is that it is difficult to determine the maximal water flux. The mentioned findings are of critical implications to the management and sustainable development of groundwater.

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Section: Discussionmentioning

confidence: 99%

“…The analytical model underestimated groundwater recharge after June. Sadeghi et al (2019) found that the model may lower the prediction accuracy under extreme dry or wet conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Sadeghi et al (2019) proposed an analytical model to estimate net flux using measured surface soil moisture.

\frac{\partial θ}{\partial t} = \frac{\partial}{\partial z} ((), D \frac{\partial θ}{\partial z} - K),

K = italickθ

the linearized Richards' equation can be expressed as:

\frac{\partial θ}{\partial t} = D \frac{\partial^{2} θ}{\partial z^{2}} - k \frac{\partial θ}{\partial z} .

…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, Sadeghi et al (2019) obtained a calibration‐free model to calculate net water flux (cm day −1 ) at any time from measured soil moisture data.

\frac{θ_{\max} - θ_{\infty}}{θ_{\infty}} \approx \frac{f_{\max} - k θ_{\infty}}{k θ_{\infty}} \sqrt{\frac{k^{2} t_{italicmax}}{D}},

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A comparison of methods to estimate groundwater recharge from bare soil based on data observed by a large‐scale lysimeter

Zhang

Wang

Gong

et al. 2020

Hydrological Processes

View full text Add to dashboard Cite

show abstract

Retrieving Heterogeneous Surface Soil Moisture at 100 m across the Globe via Synergistic Fusion of Remote Sensing and Land Surface Parameters

Huang

Desai

Zhu

et al. 2020

Preprint

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Towards new soil water flow equations using physics‐constrained machine learning

Ghorbani

Sadeghi

Jones

2021

Vadose Zone Journal

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The Richardson-Richards equation (RRE) is a widely used partial differential equation (PDE) for modeling moisture dynamics in unsaturated soil. However, field soil moisture observations do not always satisfy RRE. In this paper, we introduce a new physically constrained machine learning (PCML) approach to derive governing soil water flow PDE directly from moisture observations. This paper is viewed as a feasibility study and reports results of our first attempt in developing the PCML approach.Here, we rely on noisy synthetic soil moisture data obtained from the linear RRE subject to real flux boundary conditions. The linear RRE was used as a reference PDE to check the PCML-derived PDEs, where the PCML performance was shown to be highly dependent upon the sample size in time and space. Results presented here confirm the feasibility of deriving soil water flow governing PDEs directly from soil moisture observations using PCML.

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An analytical model for estimation of land surface net water flux from near-surface soil moisture observations

Cited by 30 publications

References 41 publications

A comparison of methods to estimate groundwater recharge from bare soil based on data observed by a large‐scale lysimeter

A comparison of methods to estimate groundwater recharge from bare soil based on data observed by a large‐scale lysimeter

Retrieving Heterogeneous Surface Soil Moisture at 100 m across the Globe via Synergistic Fusion of Remote Sensing and Land Surface Parameters

Towards new soil water flow equations using physics‐constrained machine learning

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