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Coupled dynamics in soil : experimental and numerical studies of energy, momentum and mass transfer
In arid and semi-arid areas, the main contributions to land surface processes are precipitation, surface evaporation and surface energy balancing. In the close-to-surface layer and root-zone layer, vapor flux is the dominant flux controlling these processes - process which, in turn, influence the local climate pattern and the local ecosystem. The work reported in this thesis attempts to understand how the soil airflow affects the vapor transport during evaporation processes, by using a two-phase heat and mass transfer model. The necessity of including the airflow mechanism in land surface process studies is discussed and highlighted.
Book
Forward and inverse modeling of water flow in unsaturated soils with discontinuous hydraulic conductivities using physics-informed neural networks with domain decomposition
Modeling water flow in unsaturated soils is vital for describing various hydrological and ecological phenomena. Soil water dynamics is described by well-established physical laws (Richardson–Richards equation – RRE). Solving the RRE is difficult due to the inherent nonlinearity of the processes, and various numerical methods have been proposed to solve the issue. However, applying the methods to practical situations is very challenging because they require well-defined initial and boundary conditions. Recent advances in machine learning and the growing availability of soil moisture data provide new opportunities for addressing the lingering challenges. Specifically, physics-informed machine learning allows both the known physics and data-driven modeling to be taken advantage of. Here, we present a physics-informed neural network (PINN) method that approximates the solution to the RRE using neural networks while concurrently matching available soil moisture data. Although the ability of PINNs to solve partial differential equations, including the RRE, has been demonstrated previously, its potential applications and limitations are not fully known. This study conducted a comprehensive analysis of PINNs and carefully tested the accuracy of the solutions by comparing them with analytical solutions and accepted traditional numerical solutions. We demonstrated that the solutions by PINNs with adaptive activation functions are comparable with those by traditional methods. Furthermore, while a single neural network (NN) is adequate to represent a homogeneous soil, we showed that soil moisture dynamics in layered soils with discontinuous hydraulic conductivities are correctly simulated by PINNs with domain decomposition (using separate NNs for each unique layer). A key advantage of PINNs is the absence of the strict requirement for precisely prescribed initial and boundary conditions. In addition, unlike traditional numerical methods, PINNs provide an inverse solution without repeatedly solving the forward problem. We demonstrated the application of these advantages by successfully simulating infiltration and redistribution constrained by sparse soil moisture measurements. As a free by-product, we gain knowledge of the water flux over the entire flow domain, including the unspecified upper and bottom boundary conditions. Nevertheless, there remain challenges that require further development. Chiefly, PINNs are sensitive to the initialization of NNs and are significantly slower than traditional numerical methods.
Journal Article
A comprehensive study of deep learning for soil moisture prediction
Soil moisture plays a crucial role in the hydrological cycle, but accurately predicting soil moisture presents challenges due to the nonlinearity of soil water transport and the variability of boundary conditions. Deep learning has emerged as a promising approach for simulating soil moisture dynamics. In this study, we explore 10 different network structures to uncover their data utilization mechanisms and to maximize the potential of deep learning for soil moisture prediction, including three basic feature extractors and seven diverse hybrid structures, six of which are applied to soil moisture prediction for the first time. We compare the predictive abilities and computational costs of the models across different soil textures and depths systematically. Furthermore, we exploit the interpretability of the models to gain insights into their workings and attempt to advance our understanding of deep learning in soil moisture dynamics. For soil moisture forecasting, our results demonstrate that the temporal modeling capability of long short-term memory (LSTM) is well suited. Furthermore, the improved accuracy achieved by feature attention LSTM (FA-LSTM) and the generative-adversarial-network-based LSTM (GAN-LSTM), along with the Shapley (SHAP) additive explanations analysis, help us discover the effectiveness of attention mechanisms and the benefits of adversarial training in feature extraction. These findings provide effective network design principles. The Shapley values also reveal varying data leveraging approaches among different models. The t-distributed stochastic neighbor embedding (t-SNE) visualization illustrates differences in encoded features across models. In summary, our comprehensive study provides insights into soil moisture prediction and highlights the importance of the appropriate model design for specific soil moisture prediction tasks. We also hope this work serves as a reference for deep learning studies in other hydrology problems. The codes of 3 machine learning and 10 deep learning models are open source.
Journal Article
Impact of Soil Moisture–Atmosphere Interactions on Surface Temperature Distribution
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.
Journal Article
Nutrient cycling drives plant community trait assembly and ecosystem functioning in a tropical mountain biodiversity hotspot
• Community trait assembly in highly diverse tropical rainforests is still poorly understood. Based on more than a decade of field measurements in a biodiversity hotspot of southern Ecuador, we implemented plant trait variation and improved soil organic matter dynamics in a widely used dynamic vegetation model (the Lund-Potsdam-Jena General Ecosystem Simulator, LPJ-GUESS) to explore the main drivers of community assembly along an elevational gradient.
• In the model used here (LPJ-GUESS-NTD, where NTD stands for nutrient-trait dynamics), each plant individual can possess different trait combinations, and the community trait composition emerges via ecological sorting. Further model developments include plant growth limitation by phosphorous (P) and mycorrhizal nutrient uptake.
• The new model version reproduced the main observed community trait shift and related vegetation processes along the elevational gradient, but only if nutrient limitations to plant growth were activated. In turn, when traits were fixed, low productivity communities emerged due to reduced nutrient-use efficiency. Mycorrhizal nutrient uptake, when deactivated, reduced net primary production (NPP) by 61–72% along the gradient.
• Our results strongly suggest that the elevational temperature gradient drives community assembly and ecosystem functioning indirectly through its effect on soil nutrient dynamics and vegetation traits. This illustrates the importance of considering these processes to yield realistic model predictions.
Journal Article
Effect of intercropping maize and sunn hemp at different times and stand densities on soil properties and crop yield under in-field rainwater harvesting (IRWH) tillage in semi-arid South Africa
Background
Evidence suggests that manipulating intercropping timing and stand density within intercropping systems could enhance crop yields. However, our current understanding of the effects of intercropping a cover crop on soil chemical properties and moisture still needs to be improved. This study investigates the effects of intercropping sunn hemp with maize at different timings and stand densities on selected soil properties and crop yield.
Materials and methods
A split-plot experiment was conducted under the in-field rainwater harvesting (IRWH) tillage. The trial had three intercropping times (simultaneously with maize planting, at V15 maize growth stage, and R1 maize growth stage) as the main plot factors and three stand densities (16, 32, and 48 plants m
−2
) as the subplot factors, with three replicates for both the 2019/20 and 2020/21 seasons. Changes in soil properties were assessed within the uppermost layer (0-30 cm). Soil moisture content was continuously monitored throughout the growing season and specific soil chemical properties were analyzed at harvest.
Results
The results showed that the interaction of sunn hemp intercropping period and stand densities did not significantly influence most of the measured soil properties. The early planting of sunn hemp had significantly 32.4% higher soil organic matter (SOM) than the last planting date at low stand density. After two growing seasons SOM, nitrogen, potassium, and manganese were significantly enhanced by 39.7%, 19.0%, 21% and 60.6% respectively. However, during the same period calcium, sodium and iron were significantly reduced by 13.4%, 46.1% and 78.0% respectively. The management of sunn hemp crop had significant effect on maize grain yield across the two seasons. The maize yields in the medium and high stand densities in the first season were significantly 15.3% and 34.3% higher than in the second season, respectively.
Conclusion
Due to the intercropping treatments, the retention of sunn hemp residues with varying quantities and qualities may have influenced the soil nutrient dynamics in the short-term. Significant changes in soil chemical properties and yield may need more time, and future research should be conducted out in agricultural regions with different soil mineral matrices.
Journal Article
Rootzone Soil Moisture Dynamics Using Terrestrial Water‐Energy Coupling
A lack of high‐density rootzone soil moisture (θRZ) observations limits the estimation of continental‐scale, space‐time contiguous θRZ dynamics. We derive a proxy of daily θRZ dynamics — active rootzone degree of saturation (SRZ) — by recursive low‐pass (LP) filtering of surface soil moisture (θS) within a terrestrial water‐energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise‐linear coupling between SRZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θS from the Soil Moisture Active‐Passive (SMAP) satellite and 218 in‐situ stations, with EF from the Moderate Resolution Imaging Spectroradiometer. The estimated SRZ compares well against SMAP Level‐4 estimates and in‐situ θRZ, at the corresponding scale. The instantaneous hydrologic state (SRZ) vis‐à‐vis the WEC thresholds is proposed as a rootzone soil moisture stress index (SMSRZ) for near‐real‐time operational agricultural drought monitoring and agrees well with established drought metrics. Plain Language Summary Rootzone soil moisture plays a vital role in agricultural, hydrological, and ecosystem processes. The available spaceborne satellites for monitoring soil moisture can only capture variability in a shallow soil layer at the surface, typically limited to the top 5 cm. Hence, spatiotemporally continuous estimation of rootzone soil moisture dynamics typically relies on soil moisture estimates from land‐surface models, which are subject to errors in the surface meteorological forcing data, process formulations, and model parameters. Some studies suggest that the rootzone soil moisture dynamics can be estimated by filtering the high‐frequency variability in the surface soil moisture. However, such “filters” require observed rootzone data (often unavailable at high spatial density) for calibration. This study uses the relationship between surface soil moisture and evaporative fraction derived using spaceborne observations from the Soil Moisture Active Passive mission and the Moderate Resolution Imaging Spectroradiometer to estimate rootzone soil moisture dynamics for the Contiguous U.S. at 9 km grid resolution. We further demonstrate that this approach can be extended into a near‐real‐time agricultural drought monitor to assess drought impacts on vegetation using surface soil moisture observations. Key Points Terrestrial water‐energy coupling is used to parameterize low‐pass filter to estimate rootzone dynamics from surface soil moisture Rootzone degree of saturation and water‐energy coupling thresholds are estimated using evaporative fraction and surface soil moisture SMAP‐based rootzone degree of saturation can used for operational, near‐real‐time agricultural drought monitoring over Contiguous U.S
Journal Article
Anaerobic Soil Disinfestation (ASD) Combined with Soil Solarization as a Methyl Bromide Alternative: Vegetable Crop Performance and Soil Nutrient Dynamics
BACKGROUND AND AIMS: Soil treatment by anaerobic soil disinfestation (ASD) combined with soil solarization can effectively control soilborne plant pathogens and plant-parasitic nematodes in specialty crop production systems. At the same time, research is limited on the impact of soil treatment by ASD + solarization on soil fertility, crop performance and plant nutrition. Our objectives were to evaluate the response of 1) soil nutrients and 2) vegetable crop performance to ASD + solarization with differing levels of irrigation, molasses amendment, and partially-composted poultry litter amendment (CPL) compared to an untreated control and a methyl bromide (MeBr) + chloropicrin-fumigated control. METHODS: A 2-year field study was established in 2008 at the USDA-ARS U.S. Horticultural Research Lab in Fort Pierce, Florida, USA to determine the effectiveness of ASD as an alternative to MeBr fumigation for a bell pepper (Capsicum annum L.)-eggplant (Solanum melongena L.) double crop system. A complete factorial combination of treatments in a split-split plot was established to evaluate three levels of initial irrigation [10, 5, or 0 cm], two levels of CPL (amended or unamended), and two levels of molasses (amended or unamended) in combination with solarization. Untreated and MeBr controls were established for comparison to ASD treatments. CONCLUSIONS: Results suggest that ASD treatment using molasses as the carbon source paired with solarization can be an effective strategy to maintain crop yields in the absence of soil fumigants. For both bell pepper and eggplant crops, ASD treatments with molasses as the carbon source had equivalent or greater marketable yields than the MeBr control. The application of organic amendments in ASD treatment (molasses or molasses + CPL) caused differences in soil nutrients and plant nutrition compared to the MeBr control that must be effectively managed in order to implement ASD on a commercial scale as a MeBr replacement.
Journal Article
Simulation of layered soil water transport in the semi-arid region based on Hydrus-3D
This study addresses the unclear water transport patterns in reconstructed layered soils in the arid and semi-arid climate zones of northwestern China by utilizing the Hydrus-3D model to simulate the rainfall infiltration process. Simulation experiments were designed to investigate different configurations of layered soils, with changes in soil moisture profiles monitored throughout. The water transport characteristics of these soils were comprehensively analyzed from four perspectives: soil moisture, water potential, water flux, and lateral flow within the soil. In order to further explore the influence of interlayer properties on shallow soil moisture dynamics, scenario simulations and global sensitivity analysis were conducted based on optimized models. The results demonstrated that interlayers significantly influence soil water distribution and transport patterns. During the rainy season, soil water content and lateral flow decreased with increasing soil depth, whereas these values increased during the dry season, suggesting that deeper soil layers exhibit strong water storage capacities. Both loess and sandy interlayers impeded water infiltration, albeit through different mechanisms. The loess interlayer retained water due to its low permeability, while the sandy interlayer caused water retention in the overlying clay soil as a result of its low matric potential. Based on the simulation outcomes, it is recommended that a 10 cm thick loess interlayer at a depth of 40 cm in sandy soil enhances upper soil moisture availability for vegetation, whereas a 10 cm thick sandy interlayer at the same depth in loess soil improves soil permeability. This study not only advances understanding of the impact of loess infill on soil moisture dynamics in sandy soil regions but also provides critical guidance for soil reconstruction practices in northwestern China, where sandy soils and loess are predominant.
Journal Article