Subsurface Transport Research Articles

Parameterization of the Vertical Mixing for the Luzon Undercurrent in the Northern Western Pacific Ocean

AbstractThe Luzon Undercurrent (LUC) is one of the most significant western boundary undercurrents in the northern western Pacific Ocean (WPO), essential for subsurface water transport and connecting subtropical–equatorial circulations. Over the last three decades, abundant observational progress has been made in revealing the basic features of the LUC. However, the dynamics and thus successful modeling of the LUC remain unresolved. In this work, we conducted a high‐resolution (3 km, 60 levels) numerical investigation of the WPO circulation using the China sea multi‐scale ocean modeling system. We paid particular attention to the vertical mixing, aiming to reasonably resolve the LUC by modifying the vertical mixing parameterization. Based on physics reasoning and experiments of physically based modeling, we designed an adaptive mixing scheme (AMS), which used a Munk‐like function in the thermocline for enhanced vertical mixing in the areas of small Richardson numbers. Using the AMS, we reproduced the two‐layer WPO circulations well and captured the inshore component of the LUC consistent with observations. Furthermore, we studied the dynamics of the LUC by analyzing the momentum balance and found that the LUC is primarily maintained by baroclinic pressure gradient force due to strong thermocline tilting near the western boundary. Enhanced vertical mixing in this highly sheared region crucially provides sufficient geostrophic support to sustain the LUC. Also, nonlinearity and submesoscale motions contribute positively to the LUC. This work advances physical understanding of the current‐undercurrent system and improves numerical capability in capturing the LUC and WPO circulations.

Use of Deep-Learning-Accelerated Gradient Approximation for Reservoir Geological Parameter Estimation

The estimation of space-varying geological parameters is often not computationally affordable for high-dimensional subsurface reservoir modeling systems. The adjoint method is generally regarded as an efficient approach for obtaining analytical gradient and, thus, proceeding with the gradient-based iteration algorithm; however, the infeasible memory requirement and computational demands strictly prohibit its generic implementation, especially for high-dimensional problems. The autoregressive neural network (aNN) model, as a nonlinear surrogate approximation, has gradually received increasing popularity due to significant reduction of computational cost, but one prominent limitation is that the generic application of aNN to large-scale reservoir models inevitably poses challenges in the training procedure, which remains unresolved. To address this issue, model-order reduction could be a promising strategy, which enables us to train the neural network in a very efficient manner. A very popular projection-based linear reduction method, i.e., propel orthogonal decomposition (POD), is adopted to achieve dimensionality reduction. This paper presents an architecture of a projection-based autoregressive neural network that efficiently derives an easy-to-use adjoint model by the use of an auto-differentiation module inside the popular deep learning frameworks. This hybrid neural network proxy, referred to as POD-aNN, is capable of speeding up derivation of reduced-order adjoint models. The performance of POD-aNN is validated through a synthetic 2D subsurface transport model. The use of POD-aNN significantly reduces the computation cost while the accuracy remains. In addition, our proposed POD-aNN can easily obtain multiple posterior realizations for uncertainty evaluation. The developed POD-aNN emulator is a data-driven approach for reduced-order modeling of nonlinear dynamic systems and, thus, should be a very efficient modeling tool to address many engineering applications related to intensive simulation-based optimization.

An easy-to-use analytical model for standing column wells operating with bleed

Accurate assessment of subsurface heat transport is vital for the design of standing column well systems. When bleed is utilized, the mathematical problem is governed by coupled heat transfer and groundwater flow within a radially convergent flow field, making the development of models challenging. While numerical models exist to simulate these transient processes, the absence of simple and accessible analytical solutions limits their broader application. This study addresses this gap by developing a novel easy-to-use analytical model to accurately represent the heat advection–diffusion problem in standing column wells operating with bleed. The proposed model combines the well-known infinite line source model with an innovative scaling function, inspired from the field of solute transport, through a simple convolution product. Notably, the developed model depends on only three dimensionless parameters: dimensionless advection time, Péclet number, and bleed ratio. Rigorous validation against two distinct sets of reference numerical solutions demonstrated the model’s efficiency, accuracy, and reliability across a broad spectrum of nine physical parameters. Key results include relative root mean square errors on the order of 10−3 across 200 reference solutions, confirming the model’s robustness. These findings highlight the model’s potential to significantly advance both research and practical applications in the design and optimization of standing column wells.

A novel mathematical model for modeling viscosity and temperature relationship for dead oils

Viscosity is crucial in subsurface and surface transport, used in engineering domains like heat transfer and pipeline design. However, measurements are limited, necessitating predictive viscosity relationships. Existing models lack precision or pertain to limited fluids, and accurately forecasting dead oil viscosity remains challenging due to errors. The study presents a mathematical algorithm to accurately estimate viscosity values in hydrocarbon fluids. It uses a robust non-linear regression technique to establish a reliable relationship between fluid viscosity and temperature within a specific temperature range. The algorithm is applied to extra-heavy to light crude oil samples from Iranian oilfields, revealing viscosity values ranging from 0.29 cp to 5328.74 cp within a dataset of 243 viscosity data points. After modeling each of these five fluids, the highest values obtained for the maximum absolute error and relative error are related to the fluid with an API gravity of 12.92. The maximum absolute error and relative error for this fluid sample are 1.25 cp and 6.04%, respectively. The algorithm offers acceptable precision in outcome models, even with limited training data, demonstrating its effectiveness in training models with less than 30% of available data. Moreover, these models end up with a near-unity coefficient of determination in testing data, reaffirming their proficiency at reflecting empirical data with remarkable accuracy.

Isotopic fractionation of methane on Mars via diffusive separation in the subsurface

Many processes have been identified in the Martian subsurface which may produce or release methane that eventually can be emitted into the atmosphere. Given the wide range of isotopic values for source carbon reported on Mars and the importance of atmospheric methane isotopologues as a tracer for subsurface processes, it is critical to quantify the level of isotopic fractionation that can occur during subsurface transport. On Earth, isotopic fractionation occurs when methane transport is dominated by Knudsen diffusion through small pores. However, unlike the Earth, on Mars the low atmospheric pressure and commensurate longer mean free path suggest that most subsurface transport of methane occurs in the Knudsen regime, amplifying this effect. Here, we report on simulations of diffusion through the martian subsurface and report on the level of fractionation that would be expected under two end-member scenarios. For Interplanetary Dust Particles (IDPs) incorporated in near-surface sediments in which methane is released quickly upon generation, atmospheric emissions of methane are expected to be representative of the reservoir isotopic ratio. However, for deeper sources in which methane accumulates as trapped gas, subsurface transport will result in depletions of 13CH4 compared to reservoir concentrations by approximately −31‰. Over time, both the reservoir and the emitted gas will evolve to become isotopically enriched in 13CH4 compared to a standard of constant isotopic ratio. This necessitates temporal measurements of emitted methane to understand the δ13C of the reservoir and depth of the release, preferably with hourly or better frequency. Finally, a seasonal cycle in δ13C with an amplitude of 5.3‰ is expected with adsorption acting to create small temporary reservoirs that are filled and emptied over the year by the subsurface thermal wave. This effect may provide a way to probe near-surface thermophysical properties.

Experimental and modeling insights into mixing-limited reactive transport in heterogeneous porous media: Role of stagnant zones

The understanding of mixing-controlled reactive dynamics in heterogeneous porous media remains limited, presenting significant challenges for modeling subsurface contaminant transport processes and for designing cost-effective environmental remedial efforts. The complexity of accurately observing, measuring, and modeling mixing-limited reactive transport has led to inadequate exploration of these critical processes. This study investigates the mixing and reaction kinetics affected by stagnant zones, which are commonly found in alluvial aquifers-aquitards and fracture-matrix systems. By conducting experiments involving conservative and bimolecular reactive transport through porous media within translucent chambers filled with two sizes of glass beads and under varying flow rates, we explored the effects of grain size and hydrodynamic conditions. Using a high-resolution camera, we monitored the concentration changes of conservative and reactive tracers, with subsequent interpretation through three-dimensional numerical simulations. The outcomes revealed the emergence of distinct mixing interfaces within both mobile and stagnant zones, culminating in a bi-peaked plume formation. Notably, the mixing and reaction times in media containing stagnant zones were found to be approximately 10 times longer than in homogeneous media. These findings, through experimental and modeling efforts, advance our understanding of mixing-limited reactive transport phenomena within heterogeneous media, underscoring the significant role of stagnant zones—a topic previously underexplored.

Circumpolar Transport and Overturning Strength Inferred From Satellite Observables Using Deep Learning in an Eddying Southern Ocean Channel Model

AbstractThe Southern Ocean connects the ocean's major basins via the Antarctic Circumpolar Current (ACC), and closes the global meridional overturning circulation (MOC). Observing these transports is challenging because three‐dimensional mesoscale‐resolving measurements of currents, temperature, and salinity are required to calculate transport in density coordinates. Previous studies have proposed to circumvent these limitations by inferring subsurface transports from satellite measurements using data‐driven methods. However, it is unclear whether these approaches can identify the signatures of subsurface transport in the Southern Ocean, which exhibits an energetic mesoscale eddy field superposed on a highly heterogeneous mean stratification and circulation. This study employs Deep Learning techniques to link the transports of the ACC and the upper and lower branches of the MOC to sea surface height (SSH) and ocean bottom pressure (OBP), using an idealized channel model of the Southern Ocean as a test bed. A key result is that a convolutional neural network produces skillful predictions of the ACC transport and MOC strength (skill score of 0.74 and 0.44, respectively). The skill of these predictions is similar across timescales ranging from daily to decadal but decreases substantially if SSH or OBP is omitted as a predictor. Using a fully connected or linear neural network yields similarly accurate predictions of the ACC transport but substantially less skillful predictions of the MOC strength. Our results suggest that Deep Learning offers a route to linking the Southern Ocean's zonal transport and overturning circulation to remote measurements, even in the presence of pronounced mesoscale variability.

Modeling underground climate change across a city based on data about a building block

Subsurface heat islands induce an underground climate change in urban areas, which can threaten public comfort and health, subsurface ecosystems, transportation infrastructure, and civil infrastructure. Meanwhile subsurface heat islands harbor a marked energy recovery potential. Despite increasing investigations, the understanding of subsurface heat islands remains limited and suffers from the lack of expedient and accurate simulation approaches. Here we explore the use of machine learning to accurately and expediently simulate subsurface heat islands in terms of ground temperature and deformation anomalies. Using the Chicago Loop district as a case study, we identify a series of physical features to establish a relationship between central drivers and effects of subsurface heat islands. We incorporate these features into a random forest model to simulate underground climate change with variable training datasets. The results indicate that ground temperature and deformation anomalies across an entire city district can be predicted based on data extracted solely from a handful of buildings. The proposed approach achieves comparable accuracy to current simulation methods but boasts a calculation speed that can be over a hundred times faster, promising to advance fundamental science while effectively informing engineering and decision-making in the mitigation of underground climate change.

Transport and fate of Fukushima-derived 137Cs and 134Cs in the seawater of theNorthwest Pacific in 2015.

To understand the influence of the Fukushima accident on the Northwest Pacific, the distributions and transportations of 134Cs and 137Cs in the seawater in the Northwest Pacific in May and September 2015 were studied. The data showed that the Fukushima-derived 134Cs and 137Cs at some stations can still be distinguished from background level ~ 4years later. On the whole, the activities of 137Cs and 134Cs in seawater were decreasing from May to Sep 2015. But the increased inventories and the surface activities of 137Cs imply that there has ever been an extra 137Cs from offshore water transported to this study area (from 31° N to 27° N, 145° E to 152.5° E) in May 2015. The average activities of 137Cs in subtropical gyre area in south of KE were the highest and the least were to the east of Luzon Strait in 2015. In vertical direction, 137Cs in subtropical gyre area were mainly distributed at 100 ~ 500m layer and 137Cs only at 500m layer in this area showed an increasing trend from May to Sep 2015 which reflects more 137Cs were still penetrating to deeper layer of 500m from upper water. But they were almost not found below 1000m layer. It was associated with the subsurface transport of radiocesiums by Northwest Pacific Mode Water (NPMW) and the diffusion of mesoscale eddy. Different distribution characteristics of 137Cs existed between north of KE and south of KE. The low-temperature-low-salinity water mass likely to be the first Oyashio Intrusion was the main factor that resulted in higher 137Cs appearing at the upper 100m layers in north of KE.

On the transferability of residence time distributions in two 10-km long river sections with similar hydromorphic units

Quantifying hydrologic exchange fluxes (HEFs) at the stream-groundwater interface and their residence time distributions (RTDs) in the subsurface are important for managing the water quality and ecosystem health in dynamic river corridors. However, field measurements and directly simulating high-spatial resolution HEFs and RTDs can be time-consuming, particularly at watershed-scale. Recent research has proposed the potential application of using hydromorphic units (HUs), which cluster the bathymetry and surface water hydrodynamics attributes, to aid in RTD estimation. However, more pioneering studies have demonstrated that underground structure and large-scale river channel geomorphology are the primary factors controlling the HEFs and RTDs. To address this contradiction, this work evaluates the HEFs and resulting HU-RTD relationships for two 10-km long river sections along the Columbia River, leveraging a one-way coupled three-dimensional transient surface–subsurface water transport modeling framework. Applying such a framework at the two river sections with similar HUs allows for quantitative comparisons of HEFs and RTDs using both statistical tests and machine learning classification models. The comparison reveals that the similarity and transferability of the HU-RTD relationship is very low for the two investigated river sections. This suggests that, creating a general algorithm to estimate RTDs in large-scale river reaches based solely on surface water hydrodynamics and short-distance bathymetry topography data may be nearly impossible.

Multiscale pore‐network reconstruction of a fine‐textured heterogeneous soil

AbstractDigital samples offer many opportunities to study subsurface fluid flow and contaminant transport processes. The pore size distribution of especially fine‐textured porous media often covers many orders of magnitude in the length scale, which makes accurate microCT scanning and modeling of the underlying processes difficult. When a single‐resolution image is not capable of capturing all relevant details of a sample, one should scan the sample, or selected parts of it, at different resolutions. Combining multiple resolutions into one single sample for subsequent pore‐scale modeling is generally not possible due to limitations in computer memory and speed, thus making it necessary to create a simpler sample containing relevant information from the parent networks. We imaged four samples using different resolutions to capture the multiscale heterogeneity of a fine‐textured soil and combined them into one overall digital sample based on the original pore networks. The parent networks were characterized using their geometrical properties, correlations between these properties, and connectivity functions describing the network topologies. Our approach creates stochastic networks of arbitrary size with the same flow properties as the parent network. The method, implemented using the PoreStudio pore network model, repeatedly integrates information at two subsequent scales, with the resulting digital sample having the same hydraulic properties as the original samples. The procedure leads to more useful three‐dimensional digital models, facilitating basic analyses of underlying pore size distributions. Porosity calculations were compared with direct measurements, while those for the hydraulic conductivity were compared with estimates based on the particle size distribution and nearby field data.

Contaminant Transport Modeling and Source Attribution With Attention‐Based Graph Neural Network

AbstractGroundwater contamination induced by anthropogenic activities has long been a global issue. Characterizing and modeling contaminant transport processes is crucial to groundwater protection and management. However, challenges still exist in process complexity, data constraint, and computational cost. In the era of big data, the growth of machine learning has led to new opportunities in studying contaminant transport in groundwater systems. In this work, we introduce a new attention‐based graph neural network (aGNN) for modeling contaminant transport with limited monitoring data and quantifying causal connections between contaminant sources (drivers) and their spreading (outcomes). In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). It also demonstrates a high level of applicability in inferring observations for unmonitored sites (i.e., inductive learning). Furthermore, an explanatory analysis based on aGNN quantifies the influence of each contaminant source, which has been validated by a physics‐based model with consistent outcomes with an R2 value exceeding 92%. The major advantage of aGNN is that it not only has a high level of predictive power in multiple scenario evaluations but also substantially reduces computational cost. Overall, this study shows that aGNN is efficient and robust for highly nonlinear spatiotemporal learning in subsurface contaminant transport, and provides a promising tool for groundwater management involving contaminant source attribution.

A spatio-temporal analysis of environmental fate and transport processes of pesticides and their transformation products in agricultural landscapes dominated by subsurface drainage with SWAT+

Pesticides are detected in surface water and groundwater, endangering the environment. In lowland regions with subsurface drainage systems, drained depressions become hotspots for transport of pesticides and their transformation products (TPs). This study focuses on detailed modelling of the degradation and transport of pesticides with different physico-chemical properties. The objective is to analyse complex hydrological transport processes, to understand the temporal and spatial dynamics of the degradation and transport of pesticides. The ecohydrological model SWAT+ simulates hydrological processes as well as agricultural management and pesticide degradation, and can therefore be used to develop pesticide loss reduction strategies. This study focuses on modelling of three pesticides (pendimethalin, diflufenican, and flufenacet), and two TPs, flufenacet-oxalic acid (FOA) and flufenacet sulfonic acid (FESA). The study area is a 100-hectare farmland in the northern German lowlands of Schleswig-Holstein that is characterized by an spacious drainage network of 6.3 km and managed according to common conventional agricultural practice.SWAT+ modelled streamflow with very good agreement between observed and simulated data during calibration and validation. Regarding pesticides, the model performance for highly mobile substances is better than for non-mobile pesticides. While the transport of the moderately to very mobile substances via tile drains played an important role in both wet and dry conditions, no transport via tile drains was modelled for the highly sorptive and non-mobile pendimethalin.In conclusion, the model can reliably represent the degradation of moderately to very mobile pesticides in small-scale tile drainage-dominated catchments, as well as surface runoff-induced peak loads. However, it has weaknesses in accounting for the subsurface transport of non-mobile substances, which can lead to an underestimation of the subsequent delivery after precipitation events and thus underestimates the total load.

Subsurface Transport of Plutonium in Organic and Aqueous Acidic Processing Wastes at the Hanford Site, USA.

Over 4 million liters of mixed acidic (∼pH 2.5), high ionic strength (∼5 M nitrate) plutonium (Pu) processing waste were released into the 216-Z-9 (Z-9) trench at the Hanford Site, USA, and trace Pu has migrated 37 m below the trench. In this study, we used flowthrough columns to investigate Pu transport in simplified processing waste through uncontaminated Hanford sediments to determine the conditions that led to Pu migration. In low pH aqueous fluids, some Pu breakthrough is observed at pH < 4, and increased Pu transport (14% total Pu breakthrough) is observed at pH < 2. However, Pu migrates in organic processing solvents through low pH sediments virtually uninhibited with approximately 94 and 86% total Pu breakthrough observed at pH 1 and pH 3, respectively. This study demonstrates that Pu migration can occur both with and without organic solvents at pH < 4, but significantly more Pu can be transported when partitioned into organic processing solvents. Our data suggest that under acidic conditions (pH < 4) in the vadose zone beneath the Z-9 trench, Pu present in organic processing solvents moved relatively unhindered and may explain the historical downward migration of Pu tens of meters below the Z-9 trench.

Enhanced subsurface chloride transport resistance of cement pastes via optimizing CO2 curing time

The chloride erosion of CO2 cured cementitious materials could be a concern for concrete structure application. This study aims to clarify the chloride transport and binding behavior by elucidating the alteration of free and bound chloride content from the outer towards the core (5 layers with 2 mm intervals). The carbonation depth and chloride binding ratio at the carbonation zone were also determined. The results showed that dense carbonated surface significantly reduced the penetration of chloride, especially in the carbonation zone, resulting in a decrease in total chloride content (48 %–83 %) and a slight increase in the chloride binding ratio (up to 22 %). It is found that the benefits of minimizing surface porosity to impede chloride penetration significantly outweigh the drawbacks associated with the consumption of AFm phase and C-S-H gel. However, as prolonged the CO2 curing time to 7 days, the surface areas with the highest carbonation degree exhibited more chloride content than the inward partially carbonated zone. This is because excessive carbonation at early age could increase the surface pore structures, resulting in the promotion of chloride transport. It was found that the content of chloride and CaCO3 are strongly correlated with CO2 curing time, and CO2 at first 24 h curing can achieve greater benefits in reducing chloride penetration. Moreover, amorphous calcium carbonate gradually transformed into crystalline calcium carbonate during the process of chloride attack, conducive to the further improvement of the compactness of the sample.

Engineering properties of microcapsules self-healing geopolymer cutoff wall backfill under dry-wet cycles

Vertical cutoff wall has been employed for decades to control groundwater flow and subsurface contaminant transport. Environmental stress fluctuations induced by alternating dry and wet conditions impair the permeability and durability of the cutoff wall, leading to the dispersion of contaminants. The objective of this study is to incorporate self-healing microcapsules into existing geopolymer cutoff wall backfill (GCWB) to form self-healing geopolymer cutoff wall backfill (SHGCWB) that hold promise for better durability and performance. The in-situ polymerization method was used to develop single-walled and double-walled microcapsules. The microcapsules use sodium silicate as the healing agent encapsulated in single-walled polyurethane (PU) and double-walled polyurethane/melamine-formaldehyde (PU/MF) microcapsules. The effect of microcapsules on the unconfined compressive strength (UCS) and hydraulic conductivity of SHGCWB were elaborated. The durability and hydraulic conductivity variation of SHGCWB in dry-wet cycle was thoroughly investigated by Scanning Electron Microscopy (SEM) test for understanding the self-healing mechanism. The overall performance demonstrated the significant potential of the use microcapsules as a self-healing approach for cutoff walls.

Heat transport process associated with the 2021 eruption of Aso volcano revealed by thermal and gas monitoring

The thermal activity of a magmatic–hydrothermal system commonly changes at various stages of volcanic activity. Few studies have provided an entire picture of the thermal activity of such a system over an eruptive cycle, which is essential for understanding the subsurface heat transport process that culminates in an eruption. This study quantitatively evaluated a sequence of thermal activity associated with two phreatic eruptions in 2021 at Aso volcano. We estimated plume-laden heat discharge rates and corresponding H2O flux during 2020–2022 by using two simple methods. We then validated the estimated H2O flux by comparison with volcanic gas monitoring results. Our results showed that the heat discharge rate varied substantially throughout the eruptive cycle. During the pre-eruptive quiescent period (June 2020–May 2021), anomalously large heat discharge (300–800 MW) were observed that were likely due to enhanced magma convection degassing. During the run-up period (June–October 2021), there was no evident change in heat discharge (300–500 MW), but this was accompanied by simultaneous pressurization and heating of an underlying hydrothermal system. These signals imply progress of partial sealing of the hydrothermal system. In the co-eruptive period, the subsequent heat supply from a magmatic region resulted in additional pressurization, which led to the first eruption (October 14, 2021). The heat discharge rates peaked (2000–4000 MW) the day before the second eruption (October 19, 2021), which was accompanied by sustained pressurization of the magma chamber that eventually resulted in a more explosive eruption. In the post-eruptive period, enhanced heat discharge (~ 1000 MW) continued for four months, and finally returned to the background level of the quiescent period (< 300 MW) in early March 2022. Despite using simple models, we quantitatively tracked transient thermal activity and revealed the underlying heat transport processes throughout the Aso 2021 eruptive activity.Graphical abstract

Preferential flow of phosphorus and nitrogen under steady‐state saturated conditions

AbstractRepeated broiler litter application on agricultural lands can cause nutrient enrichment of subsurface effluent, especially with the existence of preferential flow through soil macropores. Previous studies quantifying soil macropores have not attempted to establish a connection of soil macropore characteristics with the subsurface nutrient (nitrogen [N] and phosphorus [P]) losses, across different topographical locations in the field. This study investigated the effect of broiler litter application and preferential flow on subsurface nutrient transport (N and P) at different topographical positions (upslope, midslope, and downslope) in a no‐till pasture field located in Alabama, USA. Twelve intact soil columns (150 mm id and 500 mm length) were used, and the nutrient leaching measurements from laboratory experiments were linked to soil macropore characteristics quantified using X‐ray computed tomography image analysis and solute transport modeling. Treatments included surface broadcast broiler litter (5 Mg ha−1, on dry basis) and unamended control. Leachates were analyzed for dissolved reactive P (DRP), total P (TP), and nitrate + nitrite‐N (NO3− + NO2−–N). The bromide breakthrough curves provided evidence of preferential flow in all columns. Litter application significantly increased leachate P concentrations, and average TP and DRP concentrations were significantly higher in the leachate from upslope columns compared to those at downslope location. The NO3−–N concentrations in leachate exceeded the US EPA drinking water standard of 10 mg L−1 in all the treatment columns. The highest flow‐weighted mean concentrations of TP and DRP, at 2.7 and 2.5 mg L−1, respectively, were recorded in the upslope columns. Soil physicochemical properties and nutrient leaching losses varied substantially across topographical positions, indicating a need for variable litter application rates to reduce P build‐up and subsequent leaching in vulnerable locations within the field. The relevance of the effect of topographic position on nutrient leaching found in this study should be further tested by investigating a wider range of slopes and soil types in pastures.

Hydrological responses to permafrost degradation on Tibetan Plateau under changing climate

The Tibetan Plateau (TP) has undergone significant warming and humidification in recent years, resulting in rapid permafrost degradation and spatiotemporal variations in hydrological processes, such as subsurface water transport, hydrothermal conversion, and runoff generation. Understanding the mechanisms of hydrological processes in permafrost areas under changing climate is crucial for accurately evaluating hydrological responses on the TP. This study comprehensively discusses the permafrost hydrological processes of the TP under changing climate. Topics include climate conditions and permafrost states, subsurface water transport under freeze–thaw conditions, development of thermokarst lakes and hydrothermal processes, and runoff response during permafrost degradation. This study offers a comprehensive understanding of permafrost changes and their hydrological responses, contributing significantly to water security and sustainable development on the TP.

Transport of nanoscale zero-valent iron in the presence of rhamnolipid

Nanoscale zero-valent iron (nZVI) particles have gained widespread use for in-situ treatment of various chlorinated hydrocarbons. Their non-toxic nature, affordability, and minimal maintenance requirements have made them a favored material for nanoremediation. The treatment typically involves the injection of nZVI particles into contaminated sites using direct-push well injection systems. However, their small size leads to high surface energy, causing aggregation that alters their physiochemical properties, reactivity, and transport behavior. To counteract aggregation, nZVI suspension can be stabilized with different surfactants, reducing the surface energy during subsurface soil transport. This study investigates the impact of rhamnolipid, a biosurfactant produced by Pseudomonas aeruginosa during the late growth phase, on the aggregation and mobility of nZVI particles. The retardation factor of nZVI in the model media of zeolite, ZK406H, decreased from 1.66 in the absence of rhamnolipid to 1.03, 0.98, 0.93, and 0.87, corresponding to the presence of rhamnolipid at concentrations of 20, 50, 80, and 100 mg/L. The deposition coefficient also decreased from 2.39 in the absence of rhamnolipid to 0.459, 0.279, 0.217, and 0.0966, corresponding to the presence of rhamnolipid at concentrations of 20, 50, 80, and 100 mg/L. The transport parameters of nZVI in ZK406H were linked to the interactions of nZVI particles with ZK406H by the DLVO theory.