Hybrid Modeling Approaches for Solving Multi-Scale Problems in Engineering

Hybrid approach to modeling spatial dynamics of systems with generalist predators

Currently used models in occupational exposure assessment are mostly mechanistic-based and their performance varies. The usage of more advanced modeling approaches such as machine learning (ML) or hybrid modeling approaches such as Bayesian Networks could improve accuracy of exposure predictions. Within this study, a case study was conducted where 5 different ML or hybrid modeling approaches were used to redevelop the “asbestos removal exposure assessment tool,” an existing mechanistic exposure model, and their performance was compared. Multiple Linear Regression, Random Forest, Gradient Boosting Machines, Bayesian Network, and Neural Network models were developed using the same dataset and model determinants used to develop the mechanistic model. Random Forest and Gradient Boosting Machines performed best with regards to accuracy, followed by Bayesian Network, Multiple Linear Regression, the original model, and Neural Network. Such models show a promise for the development of more accurate models, but their limitations need to be considered before they can be implemented in a regulatory context. For these models, a trade-off exists where transparency in decision-making is traded against accuracy. Hybrid models such as Bayesian Networks might be a solution for this trade-off as expert knowledge and data-driven approaches are combined in a transparent model.

Relationships between land use and water quality of rivers and lakes vary spatially and temporally. These variations were analyzed using spatial analysis and mathematical statistical methods for the Suzhou Creek in Shanghai. Based on the data of water quality and land use in 2001， 2005， 2010， 2015， and 2020， five spatial scales （200， 500， 1 000， 2 000， and 5 000 m reach buffer） of the landscape pattern were extracted using correlation and redundancy analysis to explore the impact of land use composition and spatial pattern on water quality at different spatial and temporal scales. The results showed that： ① the water quality of Suzhou Creek has gradually improved in the past 20 years； other indicators were between Class II to Class IV in 2020 except TN， and TN was the main pollutant. ② The main land use type of the buffer zone was construction land， and the proportion of greenland and woodland showed a small growth trend. ③ The water quality was closely related to landscape pattern， showing temporal and spatial scale effects. On the time scale， indicators such as construction land， agricultural land， landscape dominance， aggregation， and diversity had significant correlations with various water quality parameters， and there was an inverse correlation in 2010 compared with that in other years for NH4+-N， TP， and TN. The landscape pattern in 2001 had the greatest explanation for water quality， with an explanation rate of 93.65%. The impact of greenland and woodland on water quality has begun to emerge in the past 10 years. ④ On the spatial scale， there were significant correlations between greenland and woodland， patch number， landscape shape index， diversity index， and water quality. There was a strong positive regulatory effect of greenland and woodland on NH4+-N， TP， and TN at the scale of 2 000 m. The patch number and landscape shape index had relatively strong regulatory effects on water quality on a larger spatial scale， whereas the Shannon diversity index had a better positive regulatory effect on water quality on a small scale. The landscape pattern within a buffer of 2 000 m had the highest interpretation degree for all factors， with an explanation rate of 68.47%. The study showed that rationally planning the proportion of greenland and woodland within the 2 000 m buffer zone and optimizing its landscape configuration is an important measure to purify the surface water quality of Suzhou Creek.

近年来国内森林生态系统服务功能研究较多,但应用价值不高,其科学性受到诸多质疑。从森林水源涵养功能的多尺度内涵、过程以及其计量方法出发,对国内外研究动态和发展趋势进行了总结分析,重新审视森林水源涵养功能的研究意义,探讨森林水源涵养功能的多尺度特征。从森林水源涵养功能作用的空间尺度上看,其拦蓄洪水削减洪峰的功能仅在较小尺度上有效,而调节径流的功能只有当森林土壤的入渗量超过蒸散量时,才可能有更多地下水补给河道径流,进而增加旱季河道流量。同时森林水源涵养功能也具有明显的时间尺度特征,具体表现为在次降水事件中,由于蒸散发量较小,森林水源涵养功能的物质量等于森林不同层次的截留量,在功能上表现为拦蓄降水;在长时间尺度上,由于林地蒸散要耗去大量水分,森林水源涵养功能的物质量等于森林不同层次的截留量减去林地蒸散发量,在功能上表现为净化水质和调节径流。大多数研究仅对单一林分的个别层次蓄水功能进行研究,缺乏流域尺度或者更大空间尺度的森林水源涵养功能研究。建议从区域降尺度到流域或将坡面尺度上推到流域,集中在流域尺度解决森林水源涵养空间异质性将是解决森林水源涵养功能尺度外推的有效办法。就目前国内流行的森林水源涵养功能计量方法而言,其与尺度及研究目的有较大相关性,在研究中应根据研究目的、研究尺度和可获取的数据情况选择合适的计量方法。研究突出了不同尺度作用下森林水文过程的复杂性及重要性,并结合森林与水关系的争论问题,分析目前国内对森林水源涵养功能研究的一些误区,提出森林水源涵养功能研究的一些关键科学问题及未来可能的发展方向,主要包括:1)明确界定森林水源涵养功能的边界,探索森林水源涵养功能计量的新方法;2)加强不同时空尺度关联的森林水源涵养功能研究,包括正确评价森林水源涵养功能的时空变异规律,森林生态系统水源涵养功能的尺度效应,森林水源涵养与下游水生态安全,森林水源涵养研究范式转变等核心问题。;Forest water conservation as an important ecosystem function has caused a rising interests in the scientific community in recent years. However, there remain some arguments in several aspects regarding the forest water conservation function. For the concept of the forest water conservation, there is no clear definition about its connotation. Another concern is the scaling issue. It is a challenge to scale up the estimation for the forest water conservation from the plot scale to larger scale, e.g., watershed scale or regional scale. Many quantification methods have been developed, but their suitability, performance, advantages and disadvantages are unknown. Many of such argument issues are significant limits of the forest water conservation in science and management. On the basis of the literature review, we clarify the important role of the water conservation in forest ecosystem function. We also summarize and analyze the concepts of the forest water conservation and its multi-scale connotation, processes and quantification methods. On the spatial scale, function of the forest in mitigating peak flow is effective at a smaller scale but not necessarily at a larger scale. More groundwater recharges into stream flow when forest soil infiltration exceeds evapotranspiration, thus increasing stream flow in the dry season. During a rainfall event, forest water conservation has different features at different temporal scales. For example, the amount of the forest conservation during a rainfall event dominates by the forest canopy interception and most water is stored in the forest due to less evapotranspiration in the rainfall event scale. At the longer temporal scale, the forest water conservation can be determined by the forest canopy interception subtracting evapotranspiration. The functional roles of the forest water conservation at longer temporal scale dominate by purifying water from natural rainfall and mitigating seasonal flow peaks of the river discharges. Most previous studies of the forest water conservation focused on water storage function at the plot scale, but lack better understanding in the forest water conservation function at the watershed or regional scales. We suggest the approach that combines downscaling from the regional to watershed scales and upscaling from the hillside to the watershed scales can an effective way of the scale extrapolation. This idea can be used to address the heterogeneity issue of the forest water conservation function at different spatio-temporal scales. Among the existing quantification approaches of the forest water conservation function, each one highly relies on the temporal and spatial scales and its application goal. An appropriate calculation method should be carefully evaluated and considered based on the objectives, data availability, temporal and spatial scales of each study case. The suitability difference in different calculation methods of the forest water conservation demonstrates the complexity and importance of the multi-scale consideration in forest hydrological processes. In the context of the long-term debates about the relationship between the forest and water, we figure out a few confusing points in the current studies of the forest water conservation function. We also propose the critical scientific questions that should be addressed and a few directions in future studies of forest water conservation function including: 1) to clearly define the connotation and boundary of the forest water conservation function, and develop innovatory methods for determining the forest water conservation function; 2) to pay more attentions on the forest water conservation function studies associated with the different spatial and temporal scales, including evaluation of the spatio-temporal variations in forest water conservation function, the scaling effects on estimation of the water conservation function, the trade-off relationship between the upstream forest water conservation and the downstream ecological water need, and the development of the innovatory ways of forest water conservation practices.

Defining the appropriate scale over which to conduct a study in the marine environment is critical to achieving appropriate scientific, management, mitigation and conservation objectives. This paper focuses on applications of passive acoustic technologies over a range of spatial and temporal scales. It is divided into sections dealing with archival and real-time passive acoustic sensor applications. Each section assesses the principles behind using the respective technology and pro- vides recent examples of research and management applications for marine mammals and fish. The section on archival sensors highlights the need for continued development of automated acoustic detectors to assess large data sets. Case studies are presented of detectors developed for determining seasonal occurrence and distribution of haddock sounds and humpback whale vocalizations. Also presented are studies of other applications using archival sensors: tracking singing humpback whales in Brazil, using vocalizations to assess the reproductive strategies of Arctic bearded seals and assessing regional variability in call patterns for North Atlantic right whales. The section on real-time passive acoustic sensors focuses on real-time buoys and towed arrays. Case studies presented include a real-time buoy system used for monitoring endangered North Atlantic right whales and a station- ary autonomous array providing real-time access to Antarctic acoustic data. The value of using towed arrays for real-time applications is also assessed, and a case study is provided on the use of towed arrays to improve abundance estimates of North Pacific cetaceans and to better understand vocaliza- tion behaviors.

This study focused on the structure of the aquatic insect community in spatial and temporal scales in the Esparza River. The river was sampled for one full year throughout 2007. During the dry season low flow months, five sampling points were selected in two different habitats (currents and pools), with five replicates per sample site. During the wet season with peak rain, only the data in the "current habitat" were sampled at each site. Specimens present in the different substrates were collected and preserved in situ. A nested ANOVA was then applied to the data to determine richness and density as the response variables. The variations in temporal and spatial scales were analyzed using width, depth and discharge of the river, and then analyzed using a nested ANOVA. Only a correlation of 51% similarity in richness was found, while in spatial scale, richness showed significant variation between sampling sites, but not between habitats. However, the temporal scale showed significant differences between habitats. Density showed differences between sites and habitats during the dry season in the spatial scale, while in the temporal scale significant variation was found between sampling sites. Width varied between habitats during the dry season, but not between sampling points. Depth showed differences between sampling sites and season. This work studies the importance of community structure of aquatic insects in rivers, and its relevance for the quality of water in rivers and streams.

Healthy ecosystems such as forests and wetlands have a great potential to support adaptation to climate change and are the foundation of sustainable livelihoods. Ecosystem-based adaptation (EbA) can help to protect and maintain healthy ecosystems providing resilience against the impacts of climate change. This paper explores the role of EbA in reconciling socio-economic development with the conservation and restoration of nature in Lake Victoria Basin, Kenya, East Africa. Using selected ecosystems in the Lake region, the paper identifies key EbA approaches and explores trade-offs and synergies at spatial and temporal scales and between different stakeholders. The research methods used for this study include site visits, key informant interviews, focus group discussions, participatory workshops and literature reviews. An analytical framework is applied to advance the understanding of EbA approaches and how they lead to synergies and trade-offs between ecosystem services provision at spatial and temporal scales and multiple stakeholders. Our results show that EbA approaches such as ecosystem restoration have the potential to generate multiple adaptation benefits as well as synergies and trade-offs occurring at different temporal and spatial scales and affecting various stakeholder groups. Our paper underscores the need to identify EbA trade-offs and synergies and to explore the ways in which they are distributed in space and time and between different stakeholders to design better environmental and development programmes.

A whole-lake acoustic telemetry array was utilized to monitor the three-dimensional position of 20 largemouth bass (Micropterus salmoides). Code division multiple access (CDMA) technology enabled the simultaneous monitoring of the 20 transmitters (equipped with pressure and temperature sensors) at 15 s intervals with sub-meter accuracy. Fish were monitored between November 2003 and April 2004 to evaluate the behaviour of fish across different temporal and spatial scales. The distance moved by largemouth bass, assessed both on a daily and hourly basis, varied by season and was positively correlated with water temperature. For example, daily movement rates were 2.69 ± 1.45 km/day in mid November (average daily water temperature 5.9°C), 2.24 ± 0.73 km/day in early January (5.1°C), and 7.28 ± 2.62 km/day in mid April (7.7°C). Interestingly, daily movement rates varied by as much as 25 fold among individual fish. Visualization of fish swimming paths revealed that whereas some fish occupied discrete areas and made only localized movements, other individuals made lengthier journeys covering much of the lake in periods of as little as one day. Analysis of fish behaviour at a finer temporal scale revealed that during the winter, fish spend more than 95% of their time swimming at speeds less than 0.1 m/s (0.07 ± 0.24 m/s). During late fall, and especially in spring, swimming speeds were higher with mean swimming speeds of 0.11 ± 0.27 m/s and 0.19 ± 0.29 m/s, respectively. When the telemetry dataset was queried to simulate 24 h manual tracking intervals, it was clear that manual tracking data would not have been representative of actual daily movement rates, underestimating daily movement and swimming speeds by at least 75 fold. This study identifies the importance of evaluating fish activity at multiple spatial (whole lake to sub-meter position) and temporal (seasonal to seconds) scales and illustrates the potential of CDMA telemetry to yield such data.

The combination of different modeling approaches has been applied to create so-called Grey-Box-Models for a few decades. But due to increasing data availability provided by IoT-devices, new possibilities regarding data-driven modeling arise. The purpose of this article is to develop a concept which is able to combine data-driven models created during operation with such respective theoretical models created during the design phase. The model combination should result in an added value for the operator at shop floor in the shape of real-time-capable simulation models. Initially, the different modeling methods as well as the way they can be combined are introduced. Afterwards, a concept for training and operation will be presented. The article ends with a first example demonstrating the potential of the approach.

• Spatiotemporal modelling of SOC stocks and pools at 25 m resolution was conducted. • Spatially explicit carbon inputs from crop residues and manure were incorporated. • Comprehensive assessment of land use and manure effects on SOC stocks and pools. • SOC stocks in the Netherlands increased by 13.2% in grassland from 1986 to 2022. • SOC stocks declined by 10.4% in cropland and by 3.9% in conversion areas. Soils are the largest terrestrial carbon reservoir, with soil organic carbon (SOC) playing a critical role in maintaining soil quality and associated ecosystem services. Accurately estimating SOC stocks at high spatial and temporal resolution over large scales remains challenging, particularly in agricultural systems where carbon inputs are often uncertain or unavailable. In this study, we used the RothC model to simulate SOC stocks in Dutch agricultural mineral soils from 1986 to 2022, at 25 m × 25 m resolution. We examined the temporal and spatial variation of the total SOC stock and its distribution over RothC carbon pools and unravelled how livestock manure inputs and land use affect the observed trends. Averaged SOC stocks in the topsoil (0 – 30 cm) increased by 13.2% under grassland, decreased by 10.4% under cropland, and decreased by 3.9% in areas with changing land use. Carbon gains in grassland were linked to systematically higher manure inputs and accumulation in stable pools, whereas lower manure inputs and more intensive management led to declining labile SOC pools. Independent validation on three spatial datasets showed the highest model performance for point-based field data (model efficiency coefficient MEC = 0.32 in 1986 and 0.37 in 2022). Observed changes in SOC over time could be less well reproduced (MEC ≈ 0) across all datasets, but simulated spatiotemporal patterns were consistent with previous observational studies. The study illustrates the potential of RothC for national-scale SOC stock assessment and monitoring, while highlighting the need for improved input data and temporal validation data. Importantly, this modelling approach effectively captures SOC stock dynamics, which remains challenging for purely empirical, statistical models. Future work could benefit from hybrid modelling approaches that integrate RothC with machine learning, enhancing the ability to capture currently unexplained variability and improve simulation performance.

<p>Multi-hazard events have the potential to cause damages to infrastructures and people that may differ greatly from the associated risks posed by singular hazards. Interrelations between natural hazards also operate on different spatial and temporal scales than single natural hazards. Therefore, the measure of spatial and temporal scales of natural hazard interrelations still remain challenging. The objective of this study is to refine and measure temporal and spatial scales of natural hazards and their interrelations by using a spatiotemporal clustering technique. To do so, spatiotemporal information about natural hazards are extracted from the ERA5 climate reanalysis. We focus here on the interrelation between two natural hazards (extreme precipitation and extreme wind gust) during the period 1969-2019 within a region including Great Britain and North-West France. The characteristics of our input data (i.e. important size, high noise level) and the absence of assumption about the shape of our hazard clusters guided the choice of a clustering algorithm toward the DBSCAN clustering algorithm. To create hazard clusters, we retain only extreme values (above the 99% quantile) of precipitation and wind gust. We analyse the characteristics (eg., size, duration, season, intensity) of single and compound events of rain and wind impacting our study area. We then measure the impact of the spatial and temporal scales defined in this study on the nature of the interrelation between extreme rainfall and extreme wind in the UK. We therefore demonstrate how this methodology can be applied to a different set of natural hazards.</p>

The transformation of industry towards improved sustainability results from a strategic complex setting of factors and through a transformation process. However, such process requires methodologies and tools that support and facilitate the communication and cooperation of different players and their interactions. Hybrid modelling approaches have gained prominence exceeding limitations of traditional methodologies and tools with respect to complexity arisen of industrial sustainability strategies. This paper proposes a hybrid modelling approach based on Agent Based and System Dynamics supporting the analysis and redesign of industrial areas based on sustainable strategy with industrial symbiosis. The results of this work suggest that the hybrid modelling approach can provide useful information about the effectiveness of sustainable issues. Additionally, the model can be applied as a generic framework to different economic sectors including manufacturing, energy system and agriculture.

Ecosystem coupling: A unifying framework to understand the functioning and recovery of ecosystems

Connectivity among populations influences resilience, genetic diversity , adaptation and speciation, so understanding this process is fundamental for conservation and management. This chapter summarises the main mechanisms of gene flow within and among seagrass meadows, and what we know about the spatial patterns of gene flow around Australia’s coastline. Today a significant body of research on the demographic and genetic connectivity of Australian seagrass meadows has developed. Most studies have focused on the genera Posidonia, Zostera, Heterozostera and Thalassia, in tropical and temperate systems across a range of habitats. These studies have shown overwhelmingly, that sexual reproduction is important for meadow persistence, as in most cases Australian seagrass meadows are genotypically diverse, with moderate to high levels of genotypic diversity. This high diversity could be generated through demographic connectivity, recruitment of individuals sourced from within a meadow, or from dispersal between meadows. Attempts to understand the relative significance of these processes are limited, highlighting a major gap in our understanding. Genetic structure is apparent across a range of spatial scales, from m’s to 100’s to 1000’s km. At local and regional scales, particularly in confined systems such as estuaries and bays, it is not necessarily the dominant oceanographic currents influencing patterns of genetic connectivity, but local eddies, winds and tides. Over larger spatial scales, isolation by distance is consistently significant, with unique genetic clusters spreading over 100s of kilometres. This indicates that regional structure occurs at the limits of long distance dispersal for the species and this is particularly evident where meadows are highly fragmented. The number of genetic studies on Australian seagrasses has increased dramatically recently; however, there are still many opportunities to improve our understanding through focusing on species with different dispersal potentials, more detailed sampling across a range of spatial and temporal scales and combining ecological and modelling approaches.

Surface albedo has been documented as one of the Essential Climate Variables (ECV) of the Global Climate Observing System (GCOS) that governs the Earth's Radiation Budget. The availability of surface albedo data is necessary for a comprehensive environmental modelling study. Thus, both temporal and spatial scale issues need to be rectified. This study reports about the availability of surface albedo data through in-situ and remote sensing satellite observations. In this paper, we reviewed the existing models for surface albedo derivation and various initiatives taken by related environmental agencies in order to understand the issues of climate with respect to surface albedo. This investigation evaluated the major activities on albedo-related research specifically for the retrieval methods used to derive the albedo values. Two main existing albedo measurement methods are derived through in-situ measurement and remotely sensed observations. In-situ measurement supported with number of instruments and techniques such aspyrheliometers, pyranometers and Baseline Surface Radiation Network (BSRN) and remotely sensed observations using angularly integrated Bi-directional Reflectance Distribution Function (BRDF) by both geostationary and polar orbit satellites. The investigation results reveals that the temporal and spatial scaling is the major issues when the albedo values are needed for microclimatic study, i.e. high-resolution time-series analyses and at heterogeneity and impervious surface. Thus, an improved technique of albedo retrieval at better spatial and temporal scale is required to fulfil the need for such kind of studies. Amongst many others, there are two downscaling methods that have been identified to be used in resolving the spatial scaling biased issues: Smoothing Filter-based Intensity Modulation (SFIM) and Pixel Block Intensity Modulation (PBIM). The temporal issues can be resolved using the multiple regression techniques of land surface temperature, selected air quality parameters, aerosol and daily skylight.