Process-based Modeling Approach Research Articles

Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change

Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change. In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management. Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso‑ and eutrophic), both of which drain into the largest drinking water reservoir in Germany. Due to the prolonged droughts in 2015–2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone). We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust. Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart. Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.

A hierarchical stochastic modeling approach for representing point bar geometries and petrophysical property variations

The flow of fluids in point bars is affected by the existence of heterogeneities like shale drapes that are found on the surfaces of inclined heterolithic stratifications. In fact, these shale drapes can act as fluid flow baffles; therefore, developing a framework for modeling point bars and their associated heterogeneities is vital. In this study, a stochastic process-based modeling approach is presented for capturing the main point bar heterogeneities: accretion surfaces (i.e., the aerial heterogeneity) and inclined heterolithic stratifications (i.e., the vertical heterogeneity). The former was modeled using a sine-generation function and the latter, with a sigmoidal function after which they were combined into a 3D point bar model. To ensure proper modeling of petrophysical properties, we developed a more representative gridding scheme which generates curvilinear grids representative of the point bar geometry. This grid was then transformed into a rectilinear grid to allow for geostatistical simulation after which all petrophysical properties were mapped back into the original curvilinear grid. An essential element of this modeling approach is the stochastic representation of shale drapes at the interface between successive accretion surfaces. The workflow was tested using a real field dataset for the Cranfield field, Mississippi. The constructed model was then subjected to a flow simulation study mimicking a CO2 storage scenario. Various sensitivities were simulated to evaluate the effect of heterogeneities on CO2 flow within the point-bar. Results demonstrate the importance of representing point-bar related heterogeneity and the spatial distribution of shale drapes on CO2 plume migration and storage.

Emerging weed resistance increases tillage intensity and greenhouse gas emissions in the US corn-soybean cropping system.

Tillage is a common agricultural practice that helps prepare the soil and remove weeds. However, it remains unknown how tillage intensity has evolved and its effect on net greenhouse gas (GHG) emissions. Here, using a process-based modelling approach with a multi-source database, we examined the change in tillage intensity across the US corn-soybean cropping systems during 1998-2016 and the impact of tillage intensity on soil GHG emissions. We found that tillage intensity first decreased and then, after 2008, increased, a trend that is strongly correlated with the adoption of herbicide-tolerant crops and emerging weed resistance. The GHG mitigation benefit (-5.5 ± 4.8 TgCO2e yr-1) of decreasing tillage intensity before 2008 has been more than offset by increased GHG emissions (13.8 ± 5.6 TgCO2e yr-1) due to tillage reintensification under growing pressure of weed resistance. As weed resistance persists or grows, tillage intensity is anticipated to continue rising, probably increasing GHG emissions. Our results imply that farmers' choices in managing herbicide resistance may help mitigate agricultural GHG emissions, underscoring the importance of an alternative strategy to control weeds.

Modeling the freshwater ecological response to changes in flow and thermal regimes influenced by reservoir dynamics

Capturing the ecological effects of climate-induced shifts in hydrologic and thermal regimes in regulated river systems remains a challenge in regional-scale studies. In this study, we used a well-established species distribution model to analyze the results of a process-based hydrologic modeling approach that accounts explicitly for regulation impacts on river flow and temperatures. We also accounted for the stream network fragmentation resulting from dam blockage. This combined model framework can be used to predict historical environmental suitability of river reaches for specific fish species and estimate changes in suitability in response to changes in climate and reservoir operations. As a case study in the highly regulated and fragmented Tennessee River system, we examined the environmental suitability for nonnative rainbow trout (Oncorhynchus mykiss), a species of high recreational value, and native blackfin darter (Etheostoma nigripinne), an endemic species of unique ecological importance. To quantify the impacts of reservoir regulation, we examined the historical environmental suitability, climate change signals, and the environmental drivers of change between the regulated and unregulated model setups. By the end of the 21st century (2080s), historically suitable streams for rainbow trout will disappear due to higher river temperatures. Only 8% of historically suitable streams for blackfin darter will remain but over half of them will be unreachable by current populations due to dam blockage. For river reaches influenced by reservoir regulations, the regulated model projects decreasing environmental suitability for blackfin darter while the unregulated model projects the opposite because of higher river temperatures. The contradictory climate signals between regulated and unregulated models highlight the necessity of considering reservoir regulation.

Event-based quickflow simulation with OpenLISEM in a burned Mediterranean forest catchment

Recently burnt areas typically reveal strong to extreme hydrological responses, as a consequence of loss of protective soil cover and heating-induced changes in topsoil properties. Soil water repellency (SWR) has frequently been referred to as one of the explanatory variables for fire-enhanced surface runoff generation but this has been poorly demonstrated, especially at the catchment scale. This study employs a process-based modelling approach to better understand the relevance of SWR in the hydrological response of a small, entirely burnt catchment in central Portugal, in particular by comparing hydrological events under contrasting initial conditions of dry vs wet soils. The OpenLISEM model was applied to a selection of 16 major rainfall runoff events that occurred during the first 2 post-fire years. The automatic calibration procedure resulted in good model performance, but it worsened for validation events. Furthermore, uncertainty analysis revealed an elevated sensitivity of OpenLISEM to event-specific conditions, especially for predicting the events’ total and peak flows. Also, predicted spatial patterns in runoff poorly agreed with the runoff observed in microplots. Model performance improved when events were separated by dry and wet initial moisture conditions, particularly for wet conditions, suggesting the role of variables other than initial soil moisture.

Comparing statistical and mechanistic models to identify the drivers of mortality within a rear-edge beech population

Since several studies have been reporting an increase in the decline of forests, a major issue in ecology is to better understand and predict tree mortality. The interactions between the different factors and the physiological processes giving rise tree mortality, as well as the inter-individual variability in mortality risk, still need to be better assessed. This study investigates mortality in a rear-edge population of European beech (Fagus sylvatica L.) using a combination of statistical and process-based modelling approaches. Based on a survey of 4323 adult beeches since 2002 within a natural reserve, we first used statistical models to quantify the effects of competition, tree growth, size, defoliation and fungi presence on mortality. Secondly, we used an ecophysiological process-based model (PBM) to separate out the different mechanisms giving rise to temporal and inter-individual variations in mortality by simulating depletion of carbon stocks, loss of hydraulic conductance and damage due to late frosts in response to climate. The combination of all these simulated processes was associated with the temporal variations in the population mortality rate. The individual probability of mortality decreased with increasing mean growth, and increased with increasing crown defoliation, earliness of budburst, fungi presence and increasing competition, in the statistical model. Moreover, the interaction between tree size and defoliation was significant, indicating a stronger increase in mortality associated to defoliation in smaller than larger trees. Finally, the PBM predicted a higher conductance loss together with a higher level of carbon reserves for trees with earlier budburst, while the ability to defoliate the crown was found to limit the impact of hydraulic stress at the expense of the accumulation of carbon reserves. We discuss the convergences and divergences obtained between statistical and process-based approaches and we highlight the importance of combining them to characterize the different processes underlying mortality, and the factors modulating individual vulnerability to mortality.

A hybrid data–model approach to map soil thickness in mountain hillslopes

Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

The environmental performance of greenhouse versus open-field cherry production systems in China

Greenhouse farming increasingly has been adopted in China to provide off-season products because fruit and vegetable crops cannot be grown year-round. Compared with conventional open-field cultivation, however, greenhouse cultivation usually requires higher energy and material inputs for facility construction and interior heating, increasing the environmental burdens associated with product provision. Thus, the cradle-to-gate “time-environmental” trade-off of off-season products should be completely quantified to guide green consumption. In this study we employed the process-based life-cycle assessment modeling approach to estimate the environmental costs of greenhouse cherry cultivation and compared these costs with the impacts of open-field cultivation. We examined 10 impact categories, including the abiotic depletion of fossil fuel potential, global warming potential, acidification potential, eutrophication potential, ozone layer depletion, photochemical oxidation potential, fresh water aquatic ecotoxicity potential, marine aquatic ecotoxicity potential, terrestrial ecotoxicity potential, and human toxicity potential. The results show that the environmental impacts of greenhouse cherry cultivation are considerably higher (112.7% to 1672.5%) than those of open-field production, indicating significant environmental burdens associated with producing fresh cherries one to two months early. For greenhouse cherry production, interior heating was the hotspot in all impact categories, and fertilizer dominated the impacts of open-field cultivation. For greenhouse cherries, impact mitigation opportunities mainly were found in the adoption of cleaner heating fuel and animal manure use.

Drivers of extreme water levels in a large, urban, high-energy coastal estuary – A case study of the San Francisco Bay

Reliable and long-term hindcast data of water levels are essential in quantifying return period and values of extreme water levels. In order to inform design decisions on a local flood control district level, process-based numerical modeling has proven an essential tool to provide the needed temporal and spatial coverage for different extreme value analysis methods. To determine the importance of different physical processes to the extreme water levels we developed a process-based numerical model (Delft3D Flexible Mesh) and applied it to simulate a large, urban, high-energy coastal estuary (the San Francisco Bay). The unstructured grid with 1D/2DH model elements, allows for efficient model simulations and therefore it was possible to simulate over 70 years between 1950 and 2019. Results show significant skill in reproducing observations for the entire modeled time period with an average root-mean-square error of 8.0 cm. A process-based modeling approach allows for the explicit in- and exclusion of different physical processes to quantify their importance to the extremes. For the 100-year still water level (SWL), tide (70%) and non-tidal residual (NTR) (25%) explain the majority of the simulated high water levels in the Bay relative to Mean Higher High Water (MHHW). However, closer to the Delta, local fluvial inflow increases in importance. For longer return periods, the importance of tide decreases and the importance of remote NTRs and fluvial inflow increases.

Forecasting impacts of biological control under future climates: mechanistic modelling of an aphid pest and a parasitic wasp

Climate change impacts agricultural pests in complicated ways, and accounting for these responses should form an integral part of pest management programmes. Process-based models are often used in agriculture to forecast population dynamics of pests within a growing season, however these are often constrained to assessing the impacts of temperature in isolation of other factors. These models are therefore unable to fully explore species’ responses to climate change, which may be driven by multiple abiotic and biological stressors. Here, we build a mechanistic model of a globally distributed agricultural aphid pest (Myzus persicae (Sulzer)), and an associated parasitic wasp (Diaeretiella rapae (M'Intosh)) that is known to biologically control the aphid. We simulate temporal dynamics of the crop with a well-established canola growing degree-day model (APSIM) and incorporate the impacts of temperature and rainfall on insect survival. The model was parameterised with laboratory-measured datasets from around the globe, and we have calibrated and validated the model to Australian broadacre cropping systems using regional observation records. We then ran the validated models with future temperature and rainfall scenarios to reveal that suppression of aphid populations by the wasp is enhanced under stressful abiotic conditions, which are predicted to occur more frequently in the future. The process-based modelling approach affords valuable and novel insights into physiological traits that influence population dynamics of both species and highlights gaps in our current understanding of the system. In the future, farmers could have greater confidence in the bio-control potential of D. rapae under different conditions, and be in a position to adjust their M. persicae management programmes accordingly. This is the first model to explore the interaction of these two cosmopolitan species in the field, which is applicable across broad geographic regions, while also providing insights as to how both species could be better managed on a local scale.

A Gaussian process approach for monitoring autocorrelated batch production processes

AbstractIn statistical process monitoring, statistical control tools are used to identify deviations from normal operating conditions. In many industrial processes, such as batch production processes, multiple process variables must be monitored as they play a key role in the quality of the final product. Several monitoring tools are found in the literature that deal with the case of multiple process variables, but a few of them deal with the case of autocorrelated data. Existing tools that are used to monitor autocorrelated process variables have two main drawbacks. First, they are run offline. That is, monitoring is performed at the end of the production cycle and hence no corrective actions can be made. Second, these tools utilize dimensionality reduction techniques to solve for computational complexity issues. A Gaussian Process–based modeling approach is proposed in this work to monitor batch production processes online. The proposed monitoring approach takes into consideration of both the correlation between process variables and the within variable autocorrelations. A simulated and two real data sets were used to evaluate the performance of the proposed modeling approach. Model performance metrics showed that the proposed modeling approach has similar performance to the optimal case of Shewhart type control charts. Process status classification accuracy of about 92–98% were achieved for the cases considered.

Economies of scale in battery cell manufacturing: The impact of material and process innovations

One key lever to reduce high battery cost, a main hurdle to comply with CO2 emission targets by overcoming generation variability from renewable energy sources and widespread electric vehicle adoption, is to exploit economies of scale in battery production. In an industry growth currently supported by subsidies, cost-efficient battery plant sizes are vital for the establishment of a self-sustaining industry and a transition into a long-term climate-neutral society. For optimal plant sizing, no consensus has yet been achieved in the battery literature and a detailed analysis of economies of scale is unavailable. To close this gap, a process-based cost modeling approach is taken that reflects the determinants of economies of scale. In state-of-the-art, minimum viable plant sizes are demonstrated to be below 2 GWh year−1 but may exceed 15 GWh year−1 in the future. This study finds that economies of scale are related to the capacity of the roll-to-roll processes in electrode manufacturing and can be maximized if the respective equipment operates at its capacity limit. This capacity depends on materials, cell design and roll-to-roll process parameters. Since these parameters improve over time, increased plant sizes will become necessary to achieve cost-efficient production levels. Required plant investments are found to decrease on a per GWh basis, whereas significantly increased funds will become necessary to reach efficient plant sizes in the future. Finally, implications are presented that support future battery cost reductions and a self-sustaining market breakthrough of battery-powered products.

Understanding How Fuel Treatments Interact With Climate and Biophysical Setting to Affect Fire, Water, and Forest Health: A Process-Based Modeling Approach

Fuel treatments are a key forest management practice used to reduce fire severity, increase water yield, and mitigate drought vulnerability. Climate change exacerbates the need for fuel treatments, with larger and more frequent wildfires, increasing water demand, and more severe drought. The effects of fuel treatments can be inconsistent and uncertain and can be altered by a variety of factors including the type of treatment, the biophysical features of the landscape, and climate. Variation in fuel treatment effects can occur even within forest stands and small watershed management units. Quantifying the likely magnitude of variation in treatment effects and identifying the dominant controls on those effects is needed to support fuel treatment planning directed at achieving specific fire, water, and forest health goals. This research aims to quantify and better understand how local differences in treatment, landscape features, and climate alter those fuel treatment effects. We address these questions using a mechanistic coupled ecohydrologic model—the Regional Hydro-Ecological Simulation System (RHESSys). We ran 13,500 scenarios covering a range of fuel treatment, biophysical, and climate conditions, for the Southern Sierra Nevada of California. Across fuel treatment type, biophysical, and climate parameters, we find nontrivial variation in fuel treatment effects on stand carbon, net primary productivity, evapotranspiration, and fire-related canopy structure variables. Response variable estimates range substantially, from increases (1–48%) to decreases (−13 to −175%) compared to untreated scenarios. The relative importance of parameters differs by response variable; however, fuel treatment method and intensity, plant accessible water storage capacity (PAWSC), and vegetation type consistently demonstrate a large influence across response variables. These parameters interact to produce non-linear effects. Results show that projections of fuel treatment effects based on singular mean parameter values (such as mean PAWSC) provide a limited picture of potential responses. Our findings emphasize the need for a more complete perspective when assessing expected fuel treatment outcomes, both in their effects and in the interacting biophysical and climatic parameters that drive them. This research also serves as a demonstration of methodology to assess the likely variation in potential effects of fuel treatments for a given planning unit.

DDRP: Real-time phenology and climatic suitability modeling of invasive insects.

Rapidly detecting and responding to new invasive species and the spread of those that are already established is essential for reducing their potential threat to food production, the economy, and the environment. We describe a new spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance for stakeholders needing to know both where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool to help detect new invasive threats, schedule monitoring and management actions, optimize biological control, and predict potential impacts on agricultural production. DDRP uses a process-based modeling approach in which degree-days and temperature stress are calculated daily and accumulate over time to model phenology and climatic suitability, respectively. Outputs include predictions of the number of completed generations, life stages present, dates of phenological events, and climatically suitable areas based on two levels of climate stress. Species parameter values can be derived from laboratory and field studies or estimated through an additional modeling step. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. We illustrate the DDRP modeling platform and the process of model parameterization using two invasive insect species as example threats to United States agriculture: the light brown apple moth, Epiphyas postvittana, and the small tomato borer, Neoleucinodes elegantalis. We then discuss example applications of DDRP as a decision support tool, review its potential limitations and sources of model error, and outline some ideas for future improvements to the platform.

Life cycle bioenergetics of the gray seal (Halichoerus grypus) in the Baltic Sea: Population response to environmental stress

Wildlife population dynamics are shaped by multiple natural and anthropogenic factors, including predation, competition, stressful life history events, and external environmental stressors such as diseases and pollution. Marine mammals such as gray seals rely on extensive blubber layers for insulation and energy storage, making this tissue critical for survival and reproduction. This lipid rich blubber layer also accumulates hazardous fat soluble pollutants, such as polychlorinated biphenyls (PCBs), that can directly impact adipose function or be mobilized during periods of negative energy balance or transferred to offspring to exert further impacts on target tissues or vulnerable life stages. To predict how marine mammals will respond to ecological and anthropogenic stressors, it is necessary to use process-based modelling approaches that integrate environmental inputs, full species life history, and stressor impacts with individual dynamics of energy intake, storage, and utilization. The purpose of this study was to develop a full lifecycle dynamic energy budget and individual based model (DEB-IBM) that captured Baltic gray seal physiology and life history, and showcase potential applications of the model to predict population responses to select stressors known to threaten gray seals and other marine mammals around the world. We explore variations of three ecologically important stressors using phenomenological simulations: food limitation, endocrine disrupting chemicals that reduce fertility, and infectious disease. Using our calibrated DEB-IBM for Baltic gray seals, we found that continuous incremental food limitation can be more detrimental to population size than short random events of starvation, and further, that the effect of endocrine disruptors on population growth and structure is delayed due to bioaccumulation, and that communicable diseases significantly decrease population growth even when spillover events are relatively less frequent. One important finding is the delayed effect on population growth rate from some stressors, several years after the exposure period, resulting from a decline in somatic growth, increased age at maturation and decreased fecundity. Such delayed responses are ignored in current models of population viability and can be important in the correct assessment of population extinction risks. The model presented here provides a test bed on which effects of new hazardous substances and different scenarios of future environmental change affecting food availability and/or seal energetic demands can be investigated. Thus, the framework provides a tool for better understanding how diverse environmental stressors affect marine mammal populations and can be used to guide scientifically based management.

Large-scale modelling and forecasting of ambulance calls in northern Sweden using spatio-temporal log-Gaussian Cox processes

Although ambulance call data typically come in the form of spatio-temporal point patterns, point process-based modelling approaches presented in the literature are scarce. In this paper, we study a unique set of Swedish spatio-temporal ambulance call data, which consist of the spatial (GPS) locations of the calls (within the four northernmost regions of Sweden) and the associated days of occurrence of the calls (January 1, 2014–December 31, 2018). Motivated by the nature of the data, we here employ log-Gaussian Cox processes (LGCPs) for the spatio-temporal modelling and forecasting of the calls. To this end, we propose a K-means clustering based bandwidth selection method for the kernel estimation of the spatial component of the separable spatio-temporal intensity function. The temporal component of the intensity function is modelled by means of Poisson regression, using different calendar covariates, and the spatio-temporal random field component of the random intensity of the LGCP is fitted using the Metropolis-adjusted Langevin algorithm. Spatial hot-spots have been found in the south-eastern part of the study region, where most people in the region live and our fitted model/forecasts manage to capture this behaviour quite well. Also, there is a significant association between the expected number of calls and the day-of-the-week, and the season-of-the-year. A non-parametric second-order analysis indicates that LGCPs seem to be reasonable models for the data. Finally, we find that the fitted forecasts generate simulated future spatial event patterns which quite well resemble the actual future data.

Estimating stratal completeness of carbonate deposition via process-based stratigraphic forward modeling

Hiatuses are ubiquitous in stratigraphic records at various temporal scales, but they cannot be easily identified and quantified owing to the lack of adequate methods in determining the duration of hiatuses or stratal completeness. Here a process-based stratigraphic forward modeling (SFM) approach was used to effectively estimate the completeness of carbonate strata in three dimensions and at basin-scale. By using information derived from both spatial and temporal domains in the SFM outputs for five grid locations (pseudo wells) under different depositional settings, we were able to delineate basin-wide hiatuses of various temporal scales and determine their durations and stratal completeness quantitatively. The stratal completeness appears to be controlled by sea level changes, depositional environments, carbonate growth rates and tectonic subsidence patterns in various ways.

Tectono-sedimentary evolution of the Late Ediacaran to early Cambrian trough in central Sichuan Basin, China: New insights from 3D stratigraphic forward modelling

Several conceptual models have been proposed on the tectono-sedimentary evolution of the Late Ediacaran to early Cambrian trough in the central Sichuan Basin, China. However, none of them has adequately accounted for the regional stratigraphic architecture, palaeogeography and sedimentary facies revealed by recent petroleum exploration in a quantitative manner. This study presents the first attempt to simulate the tectono-sedimentary evolution of the trough in three dimensions using a process-based stratigraphic forward modelling approach. The model simulates the tectono-sedimentary evolution and indicates that: (1) the trough started from a prototype of small sag during the Early Ediacaran Doushantuo stage; (2) it began to expand progressively during the Late Ediacaran Dengying stage, and (3) the trough was eventually filled up in the early Cambrian. The tectono-sedimentary evolution differs in the southern and central-northern parts of the study area, and also inside and outside of the trough. The developing mechanism of the trough is due primarily to a combination of regional extension, differential subsidence and especially differential carbonate growth or deposition. Differential subsidence and sea level changes resulted in relatively deep water in the central and western parts of the study area during the Deng-2 and Deng-3 deposition. Slope to basinal facies were deposited with relatively slow rates. The depositional rate of the deep-water sediments in the area could not catch up with the rate of tectonic subsidence. This had led the central and western parts of the study area to finally become “starved” and developed into a deep trough with steep slopes developed on both flanks of the trough. In contrast, in the eastern part of the study area shallow water carbonates were persistently developed with relatively high growth or depositional rates, maintaining a shallow-water carbonate depositional setting. Platform facies were well developed throughout the Dengying stage, although the area experienced a similar tectonic evolution history as the central and western parts with a slower subsidence rate. The new tectono-sedimentary model may offer some new insights for future hydrocarbon exploration in the Sichuan Basin.

Nitrate-leaching and groundwater vulnerability mapping in North Bihar, India

Aquifer vulnerability assessment is crucial for studying the impact of increasing pollution load scenarios on the quantification of contaminant concentration with movement of plume for protecting groundwater resources. Only a few recent studies have focused on the performance evaluation of vulnerability assessment methods using process-based modeling approach. However, the moisture flow and pollutant transport through partially saturated zone plays a crucial role under varying hydrogeological conditions, which are generally ignored in index-based methods. Thus, the objective of this research is to evaluate the vulnerability of groundwater resources to nitrate in Samastipur, Darbhanga and Madhubani districts of Bihar State, India, using soil moisture flow and solute transport modeling. Richard’s equation integrated with the classical advection dispersion equation is simulated using HYDRUS 1D by incorporating a constant head and atmospheric boundary conditions. The time taken to reach the nitrate peak concentration at groundwater table is considered to estimate vulnerability index (VI). Results have shown that high risk in terms of nitrate-leaching vulnerability in southern part of study area is dominated by Gangetic kankar in subsurface. Further, high pollution risk was reported in eastern north part of study area having alluvial deposition in subsurface. The main causes of high risk were due to the short depth of water table, little discharge and more hydraulic conductivity presence in the subsurface media. Moreover, comparatively low vulnerability was observed in area having clay capping of 2–4 m from surface. This research may help in better implementation of agricultural, soil–water conservation practices and urban/industrial infrastructure development in and around the study area.

A Gaussian Process-Based Crack Pattern Modeling Approach for Battery Anode Materials Design

Abstract Silicon-based anodes are one of the promising candidates for the next generation high-power/energy density lithium ion batteries (LIBs). However, a major drawback limiting the practical application of the Si anode is that Si experiences a significant volume change during lithiation/delithiation, which induces high stresses causing degradation and pulverization of the anode. This study focuses on crack initiation within a Si anode during the delithiation process. A multi-physics-based finite element (FE) model is built to simulate the electrochemical process and crack generation during delithiation. On top of that, a Gaussian process (GP)-based surrogate model is developed to assist the exploration of the crack patterns within the anode design space. It is found that the thickness of the Si coating layer, TSi, the yield strength of the Si material, σFc, the cohesive strength between Si and the substrate, σFs, and the curvature of the substrate, ρ, have large impacts on the cracking behavior of Si. This coupled FE simulation-GP surrogate model framework is also applicable to other types of LIB electrodes and provides fundamental insights as building blocks to investigate more complex internal geometries.