scholarly journals Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

2017 ◽  
Author(s):  
Maoya Bassiouni ◽  
Chad W. Higgins ◽  
Christopher J. Still ◽  
Stephen P. Good
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Dynamics ◽  
Control Soil ◽  
Soil Water Dynamics ◽  
Water Losses ◽  
Near Surface ◽  
Soil Moisture Dynamics ◽  
Soil Saturation ◽  
Moisture Dynamics

Abstract. Ecohydrological parameters that describe vegetation controls on soil moisture dynamics are not easy to measure at hydrologically meaningful scales and site-specific values are rarely available. We hypothesize that sufficient information required to determine these ecohydrological parameters is encoded in empirical probability density functions (pdfs) of soil saturation, and that this information can be extracted through inverse modeling of the commonly used stochastic soil water balance. We developed a generalizable Bayesian inference approach to estimate soil saturation thresholds at which plants control soil water losses, based only on soil texture, rainfall and soil moisture data at point, footprint, and satellite scales. The optimal analytical soil saturation pdfs were statistically consistent with empirical pdfs and parameter uncertainties were on average under 10 %. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. The algorithm convergence was most successful and the best goodness-of-fit statistics were obtained at the satellite scale. Robust and accurate results were obtained with as little as 75 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series. A sensitivity analysis showed that estimates of soil saturation thresholds at which plants control soil water losses were not sensitive to soil depth and near-surface observations are valuable to characterize ecohydrological factors driving soil water dynamics at ecosystem scales. This work combined modeling and empirical approaches in ecohydrology and provided a simple framework to obtain analytical descriptions of soil moisture dynamics at a range of spatial scales that are consistent with soil moisture observations.

scholarly journals Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

2018 ◽  
Vol 22 (6) ◽  
pp. 3229-3243 ◽  
Author(s):  
Maoya Bassiouni ◽  
Chad W. Higgins ◽  
Christopher J. Still ◽  
Stephen P. Good
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Dry Season ◽  
Soil Water Balance ◽  
Rainfall Pattern ◽  
Site Specific ◽  
Soil Moisture Dynamics ◽  
Soil Saturation ◽  
Moisture Dynamics

Abstract. Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash–Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < 1 to 15 %. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations.

A laboratory study of the effect of soil-water dynamics on the migration of gases from subsurface sources

2021 ◽  
Author(s):  
Ana M. C. Ilie ◽  
Tissa H. Illangasekare ◽  
Kenichi Soga ◽  
William R. Whalley
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Sandy Soil ◽  
Land Surface ◽  
Soil Gas ◽  
Water Dynamics ◽  
Gas Migration ◽  
Soil Water Dynamics ◽  
Scale Test ◽  
Properties Of Soil

&lt;p&gt;Understanding the soil-gas migration in unsaturated soil is important in a number of problems that include carbon loading to the atmosphere from the bio-geochemical activity and leakage of gases from subsurface sources from carbon storage unconventional energy development. The soil water dynamics in the vadose zone control the soil-gas pathway development and, hence, the gas flux's spatial and temporal distribution at the soil surface. The spatial distribution of soil-water content depends on soil water characteristics. The dynamics are controlled by the water flux at the land surface and water table fluctuations. Physical properties of soil give a better understanding of the soil gas dynamics and migration from greater soil depths. The fundamental process of soil gas migration under dynamic water content was investigated in the laboratory using an intermediate-scale test system under controlled conditions that is not possible in the field. The experiments focus on observing the methane gas migration in relation to the physical properties of soil and the soil moisture patterns. A 2D soil tank with dimensions of 60 cm &amp;#215; 90 cm &amp;#215; 5.6 cm (height &amp;#215; length &amp;#215; width) was used.&amp;#160; The tank was heterogeneously packed with sandy soil along with a distributed network of soil moisture, temperature, and electrical conductivity sensors. The heterogeneous soil configuration was designed using nine uniform silica sands with the effective sieve numbers #16, #70, #8, #40/50, #110, #30/40, #50, and #20/30 (Accusands, Unimin Corp., Ottawa, MN), and a porosity ranging in values from 0.31 to 0.42. Four methane infrared gas sensors and a Flame Ionization detector (HFR400 Fast FID) were used for the soil gas sampling at different depths within the soil profiles and at the land surface.&amp;#160; A complex transient soil moisture distribution and soil gas migration patterns were observed in the 2D tank. These processes were successfully captured by the sensors. These preliminary experiments helped us to understand the mechanism of soil moisture sensor response and methane gas migration into a heterogeneous sandy soil with a view to developing a large-scale test in a 3D tank (4.87 m &amp;#215; 2.44 m &amp;#215; 0.40 m) and finally transition to field deployment.&lt;/p&gt;

scholarly journals Parameterization of Soil Hydraulic Parameters for HYDRUS-3D Simulation of Soil Water Dynamics in a Drip-Irrigated Orchard

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1858 ◽  
Author(s):  
Jesús María Domínguez-Niño ◽  
Gerard Arbat ◽  
Iael Raij-Hoffman ◽  
Isaya Kisekka ◽  
Joan Girona ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Drip Irrigation ◽  
Water Dynamics ◽  
Hydraulic Parameters ◽  
Soil Water Dynamics ◽  
Heterogeneous Distribution ◽  
Neutron Probe ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic

Although surface drip irrigation allows an efficient use of water in agriculture, the heterogeneous distribution of soil water complicates its optimal usage. Mathematical models can be used to simulate the dynamics of water in the soil below a dripper and promote: a better understanding, and optimization, of the design of drip irrigation systems, their improved management and their monitoring with soil moisture sensors. The aim of this paper was to find the most appropriate configuration of HYDRUS-3D for simulating the soil water dynamics in a drip-irrigated orchard. Special emphasis was placed on the source of the soil hydraulic parameters. Simulations parameterized using the Rosetta approach were therefore compared with others parameterized using that of HYPROP + WP4C. The simulations were validated on a seasonal scale, against measurements made using a neutron probe, and on the time course of several days, against tensiometers. The results showed that the best agreement with soil moisture measurements was achieved with simulations parameterized from HYPROP + WP4C. It further improved when the shape parameter n was empirically calibrated from a subset of neutron probe measurements. The fit of the simulations with measurements was best at positions near the dripper and worsened at positions outside its wetting pattern and at depths of 80 cm or more.

scholarly journals A particle based model for soil water dynamics: how to match and step beyond Richards' equation?

2016 ◽  
Author(s):  
E. Zehe ◽  
C. Jackisch
Keyword(s):  
Soil Moisture ◽  
Random Walk ◽  
Soil Water ◽  
Drift Velocity ◽  
Maximum Velocity ◽  
Water Dynamics ◽  
Richards Equation ◽  
Particle Model ◽  
Soil Water Dynamics ◽  
Non Linear

Abstract. Within this study we propose a stochastic approach to simulate soil water dynamics in the unsaturated zone by using a non-linear, space domain random walk of water particles. Soil water is represented by particles of constant mass, which travel according to the Itô form of the Fokker Planck equation. The model concept builds on established soil physics by estimating the drift velocity and the diffusion term based on the soil water characteristics. A naive random walk, which assumes all water particles to move at the same drift velocity and diffusivity, overestimated depletion of soil moisture gradients compared to a Richards' solver. This is because soil water and hence the corresponding water particles in smaller pore size fractions, are, due to the non-linear decrease of soil hydraulic conductivity with decreasing soil moisture, much less mobile. After accounting for this subscale variability of particle mobility, the particle model and a Richards' solver performed similarly during simulated wetting and drying circles in three distinctly different soils. The particle model typically produced slightly smaller top soil water contents during wetting and was faster in depleting soil moisture gradients during subsequent drainage phases. Within a real world benchmark the particle model matched observed soil moisture response to a moderated rainfall event even slightly better than the Richards' solver. The proposed approach is hence a promising, easy to implement alternative to the Richards equation. This is particularly also because it allows one to step beyond the assumption of local equilibrium during rainfall driven conditions. This is demonstrated by treating infiltrating event water particles as different type of particle which travel initially, mainly gravity driven, in the largest pore fraction at maximum velocity, and yet experience a slow diffusive mixing with the pre-event water particles within a characteristic mixing time.

scholarly journals Hydrotropic Root Behavior in Water-Saving Cultivation: A High-Resolution Monitoring Study of Soil Water Dynamics

2021 ◽  
Author(s):  
Qichen Li ◽  
Toshiaki Sugihara ◽  
Sakae Shibusawa ◽  
Minzan Li
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Water Content ◽  
Root System ◽  
Soil Water ◽  
Water Saving ◽  
Water Dynamics ◽  
Soil Water Dynamics ◽  
Soil Moisture Sensors ◽  
Wet Zone

Abstract BackgroundSubsurface irrigation has been confirmed to have high water use efficiency due to it irrigating only the crop root zone. Hydrotropism allows roots to grow towards higher water content areas for drought avoidance, which has research interests in recent years. However, most hydrotropism studies focused on a single root and were conducted in air or agar systems. The performance of hydrotropism in subsurface irrigation is not clear. ResultsWe developed a method to observe and analyze hydrotropism in soil under water-saving cultivation. A wet zone was produced around the whole root system based on using subsurface irrigation method and micro soil water dynamics were observed using high-resolution soil moisture sensors. This method enabled the observation and analysis of plant water absorption activities and the hydrotropic response of the root system. In the analysis, we first applied a high-pass filter and fast Fourier transform to the soil water dynamics data. The results indicated that the plant’s biological rhythm of photosynthetic activities can be identified from the soil moisture data. We then observed root growth in response to the dynamics of soil water content in the wet zone. We quantified root distribution inside and outside the wet zone and observed the shape of the root system from the cross-section of the wet zone. The results showed that the root hydrotropic response is not uniform for all roots of an individual plant. ConclusionsThis study verified the feasibility of using high-resolution soil moisture sensors to study root hydrotropic responses in soil during water-saving cultivation. To further evaluate a plant’s hydrotropic ability, it is necessary to use statistical analysis and/or a non-deterministic approach. Future studies may also explore developing an automated experimental system and robotic manipulations for getting steady repeatable observation of hydrotropism in water-saving cultivation.

Estimating soil hydraulic properties from saturation to complete dryness

2021 ◽  
Author(s):  
Budiman Minasny ◽  
Rudiyanto Rudiyanto ◽  
Federico Maggi
Keyword(s):  
Soil Moisture ◽  
Hydraulic Conductivity ◽  
Soil Water ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Pedotransfer Functions ◽  
Soil Hydraulic Properties ◽  
Soil Moisture Dynamics ◽  
Soil Hydraulic ◽  
Moisture Dynamics

&lt;p&gt;To study the effect of drought on soil water dynamics, we need an accurate description of water retention and hydraulic conductivity from saturation to complete dryness. Recent studies have demonstrated the inaccuracy of conventional soil hydraulic models, especially in the dry end. Likewise, current pedotransfer functions (PTFs) for soil hydraulic properties are based on the classical Mualem-van Genuchten functions.&lt;/p&gt;&lt;p&gt;This study will evaluate models that estimate soil water retention and unsaturated hydraulic conductivity curves in full soil moisture ranges. An example is the Fredlund-Xing scaling model coupled with the hydraulic conductivity model of Wang et al. We will develop pedotransfer functions that can estimate parameters of the model. We will compare it with existing PTFs in predicting water retention and hydraulic conductivity.&lt;/p&gt;&lt;p&gt;The results show that a new suite of PTFs that used sand, silt, clay, and bulk density can be used successfully to predict water retention and hydraulic conductivity over a range of moisture content. The prediction of hydraulic properties is used in a soil water flow model to simulate soil moisture dynamics under drought. This study demonstrates the importance of accurate hydraulic model prediction for a better description of soil moisture dynamics.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Impact of Soil Moisture–Atmosphere Interactions on Surface Temperature Distribution

2014 ◽  
Vol 27 (21) ◽  
pp. 7976-7993 ◽  
Author(s):  
Alexis Berg ◽  
Benjamin R. Lintner ◽  
Kirsten L. Findell ◽  
Sergey Malyshev ◽  
Paul C. Loikith ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temperature Distribution ◽  
Surface Temperature ◽  
Extreme Temperature ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Near Surface ◽  
Soil Moisture Dynamics ◽  
Available Energy ◽  
Mean And Variance ◽  
Moisture Dynamics

Abstract Understanding how different physical processes can shape the probability distribution function (PDF) of surface temperature, in particular the tails of the distribution, is essential for the attribution and projection of future extreme temperature events. In this study, the contribution of soil moisture–atmosphere interactions to surface temperature PDFs is investigated. Soil moisture represents a key variable in the coupling of the land and atmosphere, since it controls the partitioning of available energy between sensible and latent heat flux at the surface. Consequently, soil moisture variability driven by the atmosphere may feed back onto the near-surface climate—in particular, temperature. In this study, two simulations of the current-generation Geophysical Fluid Dynamics Laboratory (GFDL) Earth System Model, with and without interactive soil moisture, are analyzed in order to assess how soil moisture dynamics impact the simulated climate. Comparison of these simulations shows that soil moisture dynamics enhance both temperature mean and variance over regional “hotspots” of land–atmosphere coupling. Moreover, higher-order distribution moments, such as skewness and kurtosis, are also significantly impacted, suggesting an asymmetric impact on the positive and negative extremes of the temperature PDF. Such changes are interpreted in the context of altered distributions of the surface turbulent and radiative fluxes. That the moments of the temperature distribution may respond differentially to soil moisture dynamics underscores the importance of analyzing moments beyond the mean and variance to characterize fully the interplay of soil moisture and near-surface temperature. In addition, it is shown that soil moisture dynamics impacts daily temperature variability at different time scales over different regions in the model.

scholarly journals A Lagrangian model for soil water dynamics during rainfall-driven conditions

2016 ◽  
Vol 20 (9) ◽  
pp. 3511-3526 ◽  
Author(s):  
Erwin Zehe ◽  
Conrad Jackisch
Keyword(s):  
Soil Moisture ◽  
Random Walk ◽  
Soil Water ◽  
Drift Velocity ◽  
Water Dynamics ◽  
Richards Equation ◽  
Particle Model ◽  
Soil Water Dynamics ◽  
Non Linear ◽  
Non Equilibrium

Abstract. Within this study we propose a stochastic approach to simulate soil water dynamics in the unsaturated zone by using a non-linear, space domain random walk of water particles. Soil water is represented by particles of constant mass, which travel according to the Itô form of the Fokker–Planck equation. The model concept builds on established soil physics by estimating the drift velocity and the diffusion term based on the soil water characteristics. A naive random walk, which assumes all water particles to move at the same drift velocity and diffusivity, overestimated depletion of soil moisture gradients compared to a Richards solver. This is because soil water and hence the corresponding water particles in smaller pore size fractions are, due to the non-linear decrease in soil hydraulic conductivity with decreasing soil moisture, much less mobile. After accounting for this subscale variability in particle mobility, the particle model and a Richards solver performed highly similarly during simulated wetting and drying circles in three distinctly different soils. Both models were in very good accordance during rainfall-driven conditions, regardless of the intensity and type of the rainfall forcing and the shape of the initial state. Within subsequent drying cycles the particle model was typically slightly slower in depleting soil moisture gradients than the Richards model. Within a real-world benchmark, the particle model and the Richards solver showed the same deficiencies in matching observed reactions of topsoil moisture to a natural rainfall event. The particle model performance, however, clearly improved after a straightforward implementation of rapid non-equilibrium infiltration, which treats event water as different types of particles, which travel initially in the largest pore fraction at maximum velocity and experience a slow diffusive mixing with the pre-event water particles. The proposed Lagrangian approach is hence a promising, easy-to-implement alternative to the Richards equation for simulating rainfall-driven soil moisture dynamics, which offers straightforward opportunities to account for preferential, non-equilibrium flow.

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