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When does artificial intelligence replace process-based models in ecological modelling?

ECOLOGICAL MODELLING 2025年 499卷

作者： Alexandrov, Georgii A. Russian Acad Sci AM Obukhov Inst Atmospher Phys Pyzhevsky 3 Moscow 119017 Russia

Sixteen years ago, Sven J & oslash;rgensen, a founder of Ecological Modelling, wrote that artificial neural networks could be very useful in most cases but cannot replace biogeochemical models based on conservation principles. The literature study shows no sign that artificial neural networks are replacing any process-based models in ecological modelling, although the recent efforts in developing the methods for solving differential equations by using neural networks enable turning the process-based models into neural networks without loss of their theoretical rigor. It seems that a well-orchestrated research program is needed to promote turning existing process-based models into neural networks aimed at taking advantage of opportunities offered by the big data revolution.

关键词： process-based models Neural networks AI techniques AI tools Modelling Ecology Forestry

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Monitor the energy and carbon emissions of process-based models: processC

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RESOURCES CONSERVATION AND RECYCLING 2025年 215卷

作者： Li, Ziwei Qi, Zhiming Li, Birk Xu, Junzeng Wu, Ruiqi Liu, Yuchen Smith, Ward McGill Univ Dept Bioresource Engn Ste Anne De Bellevue PQ H9X 3V9 Canada Agr & Agri Food Canada Ottawa Res & Dev Ctr Ottawa ON K1A 0C6 Canada Hohai Univ Coll Agr Sci & Engn Nanjing 210024 Peoples R China Hohai Univ Coll Hydraul Engn Nanjing 210024 Peoples R China Indiana Univ Bloomington Dept Comp Sci Bloomington IN 47408 USA

Sustainable modelling to reduce carbon emissions from heavy computations is being adopted in machine learning communities. However, this concept has not been considered in process-based model communities, with carbon emissions rarely monitored and reported. This study developed processC, a multi-platform program to monitor energy usage and carbon emissions in process-based models' simulations. processC was tested through a case study involving a process-based model, RZ-SHAW, for winter artificial drainage simulation. Results indicated that the RZ-SHAW model consumed 115 times more energy and released 29,397 times more carbon emissions compared to machine learning (ML) models for the winter artificial drainage simulation. The study suggests deploying computing systems in regions with low grid carbon intensity, choosing energy-efficient systems, and reducing simulation time as potential solutions for a more carbon-sustainable modelling. The findings from the current study urge the process-based community to commence considering and reporting carbon emissions in future modelling studies.

关键词： Modeling Carbon emissions RZWQM2 Energy usage process-based models

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Groundwater nitrate vulnerability assessment in alluvial aquifer using process-based models and weights-of-evidence method: Lower Savinja Valley case study (Slovenia)

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ENVIRONMENTAL EARTH SCIENCES 2011年第1期64卷 97-105页

作者： Uhan, Joze Vizintin, Goran Pezdic, Joze Environm Agcy Republ Slovenia Ljubljana 1000 Slovenia Univ Ljubljana Fac Nat Sci & Engn Ljubljana 1000 Slovenia RO GEORIS Radovljica 4240 Slovenia

This paper describes the implementation of process-based models reflecting relative groundwater nitrate vulnerability of the shallow alluvial Lower Savinja Valley (LSV) aquifer in Slovenia. A spatially explicit identification of the potentially vulnerable priority areas within groundwater bodies at risk from a chemical point of view is being required for cost-effective measures and monitoring planning. The shallow LSV unconfined aquifer system consists of high-permeable Holocene and middle- to low-permeable Pleistocene gravel and sand, with a maximum thickness of about 30 m, mainly covered by shallow eutric fluvisoils or variously deep eutric cambisoil. The hydrogeological parameters, e.g. the depth to the groundwater, hydrological role of the topographic slope, etc. usually used in different point count schemes are, in the case of the lowland aquifer and shallow groundwater, spatially very uniform with low variability. Furthermore, the parametric point count methods are generally not able to illustrate and analyze important physical processes, and validation of the results is difficult and expensive. Instead of a parametric point count scheme, we experimentally used the Arc-WofE extension for weights-of-evidence (WofE) modelling. All measurement locations with a concentration higher than the value of 20 mg NO(3) (-) per litre of groundwater have been considered as training points (173), and the three process-based models generalized output layers of groundwater recharge (GROWA), nitrate leached from the soil profile (SWAT) and groundwater flow velocity (FEFLOW), served as evidential themes. The technique is based on the Bayesian idea of phenomena occurrences probability before (prior probability) and after consideration of any evidential themes (posterior probability), which were measured by positive and negative weights as an indication of the association between a phenomena and a prediction pattern. The response theme values describe the relative probability that

关键词： Groundwater nitrate vulnerability process-based models Weights-of-evidence Lower Savinja Valley (Slovenia)

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Predicting coastal erosion trends using non-stationary statistics and process-based models

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COASTAL ENGINEERING 2012年 70卷 40-49页

作者： Corbella, Stefano Stretch, Derek D. Univ KwaZulu Natal Sch Civil Engn Surveying & Construct Ctr Res Environm Coastal & Hydrol Engn ZA-4041 Durban South Africa eThekwini Municipal Coastal Engn & Drainage Unit ZA-4001 Durban South Africa

Storms and water levels are subject to seasonal variations but may also have decadal or longer trends that need to be included when estimating risks in the coastal zone. We propose a non-stationary multivariate generalised extreme value model for wave height, wave period, storm duration and water levels that is constructed using Archimedean copulas. The statistical model was applied to a South African case study to test the impacts of decadal trends on beach erosion. Erosion was estimated using three process-based models - SBEACH, XBEACH, and the Time Convolution model. The XBEACH model provided the best calibration results and was used to simulate potential future long-term trends in beach erosion. based on the simulated erosion results of 5 beach profiles for storms with 25.50 and 100 year return periods, it is estimated that the erosion rate could increase by 0.20%/year/storm and should therefore be a significant factor in long-term planning. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Coastal erosion Climate trends Multivariate statistics Copulas process-based models

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Functional Approach to Simulating Short-Rotation Woody Crops in process-based models

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BIOENERGY RESEARCH 2015年第4期8卷 1598-1613页

作者： Guo, Tian Engel, Bernard A. Shao, Gang Arnold, Jeffrey G. Srinivasan, Raghavan Kiniry, James R. Purdue Univ Dept Agr & Biol Engn W Lafayette IN 47907 USA Purdue Univ Dept Forest & Nat Resources W Lafayette IN 47907 USA USDA ARS Grassland Soil & Water Res Lab Temple TX 76502 USA Texas A&M Univ Spatial Sci Lab Coll Stn Temple TX 76502 USA

Short-rotation woody crops (SRWCs) such as Populus have great potential as biofuel feedstocks. Biomass yields and yield stability at potential sites are important considerations when SRWCs are widely planted. The process-based, daily time-step simulation model Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) offers promise as a useful tool to evaluate tree growth over large ranges of conditions. The objective of this study was to develop algorithms and growth parameters of hybrid poplar 'Tristis #1' (Populus balsamifera L. x Populus tristis Fisch) and eastern cottonwood (Populus deltoides Bartr.) in ALMANAC and to improve simulation of leaf area index (LAI) and plant biomass as well as biomass partitioning. ALMANAC with the improved algorithms for LAI and weight of falling leaves was applied to hybrid poplar plots in Wisconsin and cottonwood plots in Mississippi, and the modeled biomass yield and LAI were compared with measured data to modify and evaluate the location-specific ALMANAC models. Improved algorithms for LAI and biomass simulation and suggested values and potential parameter ranges for hybrid poplar and cottonwood were reasonable (Nash-Sutcliffe model efficiency (NSE) 0.81 +/- 0.99 and R (2) 0.76 +/- 0.99). ALMANAC with modified algorithms and parameters for Populus growth realistically simulated LAI, aboveground woody biomass, and root biomass of Populus. Thus, this model can be used for biofeedstock production modeling for Populus. The improved algorithms of LAI and biomass simulation for tree growth should also be useful for other process-based models, such as Soil and Water Assessment Tool (SWAT), Environmental Policy Integrated Climate (EPIC), and Agricultural Policy/Environmental eXtender (APEX).

关键词： Bioenergy Short-rotation woody crops Hybrid poplar Cottonwood process-based models Biofuel production modeling

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Are we unnecessarily constraining the agility of complex process-based models?

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WATER RESOURCES RESEARCH 2015年第1期51卷 716-728页

作者： Mendoza, Pablo A. Clark, Martyn P. Barlage, Michael Rajagopalan, Balaji Samaniego, Luis Abramowitz, Gab Gupta, Hoshin Univ Colorado Dept Civil Environm & Architectural Engn Boulder CO 80309 USA Univ Colorado Cooperat Inst Res Environm Sci Boulder CO 80309 USA Natl Ctr Atmospher Res Res Applicat Lab Boulder CO 80307 USA UFZ Helmholtz Ctr Environm Res Leipzig Germany Univ New S Wales Climate Change Res Ctr Sydney NSW Australia Univ New S Wales ARC Ctr Excellence Climate Syst Sci Sydney NSW Australia Univ Arizona Dept Hydrol & Water Resources Tucson AZ 85721 USA

In this commentary we suggest that hydrologists and land-surface modelers may be unnecessarily constraining the behavioral agility of very complex physics-based models. We argue that the relatively poor performance of such models can occur due to restrictions on their ability to refine their portrayal of physical processes, in part because of strong a priori constraints in: (i) the representation of spatial variability and hydrologic connectivity, (ii) the choice of model parameterizations, and (iii) the choice of model parameter values. We provide a specific example of problems associated with strong a priori constraints on parameters in a land surface model. Moving forward, we assert that improving hydrological models requires integrating the strengths of the physics-based modeling philosophy (which relies on prior knowledge of hydrologic processes) with the strengths of the conceptual modeling philosophy (which relies on data driven inference). Such integration will accelerate progress on methods to define and discriminate among competing modeling options, which should be ideally incorporated in agile modeling frameworks and tested through a diagnostic evaluation approach.

关键词： hydrology process-based models model agility sensitivity analysis

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Accounting for herbaceous communities in process-based models will advance our understanding of "grassy" ecosystems

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GLOBAL CHANGE BIOLOGY 2023年第23期29卷 6453-6477页

作者： Wilcox, Kevin R. Chen, Anping Avolio, Meghan L. Butler, Ethan E. Collins, Scott Fisher, Rosie Keenan, Trevor Kiang, Nancy Y. Knapp, Alan K. Koerner, Sally E. Kueppers, Lara Liang, Guopeng Lieungh, Eva Loik, Michael Luo, Yiqi Poulter, Ben Reich, Peter Renwick, Katherine Smith, Melinda D. Walker, Anthony Weng, Ensheng Komatsu, Kimberly J. Univ North Carolina Greensboro Greensboro NC 27412 USA Univ Wyoming Laramie WY USA Colorado State Univ Dept Biol Ft Collins CO 80523 USA Colorado State Univ Grad Degree Program Ecol Ft Collins CO 80523 USA Johns Hopkins Univ Earth & Planetary Sci Baltimore MD 21210 USA Univ Minnesota Dept Forest Resources St Paul MN USA Univ New Mexico Dept Biol Albuquerque NM 87131 USA CICERO Ctr Int Cimate Res Oslo Norway Lawrence Berkeley Natl Lab Climate & Ecosyst Sci Div Berkeley CA 94720 USA NASA Goddard Inst Space Studies New York NY 10025 USA Univ Oslo Nat Hist Museum Oslo Norway Univ Calif Santa Cruz Dept Environm Studies Santa Cruz CA 95064 USA Cornell Univ Sch Integrat Plant Sci Ithaca NY USA NASA Biospher Sci Lab GSFC Greenbelt MD USA Western Sydney Univ Hawkesbury Inst Environm Penrith NSW Australia US Forest Serv Northern Reg Missoula MT USA Oak Ridge Natl Lab Environm Sci Div Oak Ridge TN USA Oak Ridge Natl Lab Climate Change Sci Inst Oak Ridge TN USA Columbia Univ Ctr Climate Syst Res New York NY USA

Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process-based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models;second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet. To accurately forecast the future of "grassy" ecosystems, which provide many of Earth's key ecosystem services, we must improve our understanding and representation of herbaceous

关键词： biogeochemistry ecology ecophysiology plant competition plant growth process-based models vegetation demographic models

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Comparison of process-based models to quantify nutrient flows and greenhouse gas emissions associated with milk production

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AGRICULTURE ECOSYSTEMS & ENVIRONMENT 2017年 237卷 31-44页

作者： Veltman, Karin Jones, Curtis D. Gaillard, Richard Cela, Sebastian Chase, Larry Duval, Benjamin D. Izaurralde, R. Cesar Ketterings, Quirine M. Li, Changsheng Matlock, Marty Reddy, Ashwan Rotz, Alan Salas, William Vadas, Peter Jolliet, Olivier Univ Michigan Sch Publ Hlth Dept Environm Hlth Sci Ann Arbor MI 48109 USA Univ Maryland Dept Geog Sci College Pk MD 20742 USA USDA ARS Madison WI USA Cornell Univ Dept Anim Sci Ithaca NY 14853 USA Texas A&M Univ Texas Agrilife Res & Extens 720 East Blacklands Rd Temple TX 76502 USA Univ New Hampshire Inst Study Earth Oceans & Space EOS Durham NH 03824 USA Appl Geosolut AGS Durham NH USA Univ Arkansas Coll Engn Fayetteville AR 72701 USA USDA ARS Pasture Syst & Watershed Management Res Unit University Pk PA 16802 USA New Mexico Inst Min & Technol Dept Biol Socorro NM 87801 USA

Assessing and improving the sustainability of dairy production systems is essential to secure future food production. This requires a holistic approach to reveal trade-offs between emissions of the different greenhouse gases (GHG) and nutrient-based pollutants and to ensure that interactions between farm components are taken into account. process-based models are essential to support whole-farm mass balance accounting. However, since variation between process-based model results can be large, there is a need to compare and better understand the strengths and limitations of various models. Here, we use a whole-farm mass-balance approach to compare five process-based models in terms of predicted carbon (C), nitrogen (N) and phosphorus (P) flows and potential global warming impact (GWI) associated with milk production at the animal, field and farm-scale. We include two whole-farm models complemented by two field-scale models and one animal-based model. A whole-farm mass-balance framework was used to facilitate model comparison at different scales. GWIs were calculated from predicted emissions of methane (CH4) and nitrous oxide (N2O) and soil C change. Results show that predicted whole-farm GWIs were similar for the two whole farm models, ManureDNDC and IFSM, With a predicted GWI of 9.3 and 10.8 Gg CO(2)eq. year(-1) for ManureDNDC and IFSM, respectively. Enteric CH4 emissions were the single most important source of greenhouse gas emissions contributing 47%-70% of the total farm GWI. Model predictions were comparable, that is, within a factor of 1.5, for most flows related to the animal, barn and manure management system. In contrast, predicted field emissions of N2O and ammonia (NH3) to air, N and P losses to the hydrosphere and soil C change, were highly variable across models. This indicates that there is a need to further our understanding of soil and crop N, P and C flows and that measurement data on nutrient and C flows are particularly needed for the field. In ad

关键词： Nutrient flows Greenhouse gas (GHG) emissions process-based models Milk production Whole-farm mass-balance

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Modelling the spatiotemporal dynamics of cropland soil organic carbon by integrating process-based models differing in structures with machine learning

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JOURNAL OF SOILS AND SEDIMENTS 2023年第7期23卷 2816-2831页

作者： Zhang, Xiu Xie, Enze Chen, Jian Peng, Yuxuan Yan, Guojing Zhao, Yongcun Chinese Acad Sci Inst Soil Sci State Key Lab Soil & Sustainable Agr Nanjing 210008 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Chinese Acad Sci Nanjing 211135 Peoples R China

PurposeIntegrating process-based models with machine learning (ML) is an important way to derive spatially-explicit information on the dynamics of cropland soil organic carbon (SOC) stock. However, the performance of integrating process-based models with different structures remains unclear. The purpose of this study is to develop integrated models by combining multiple process-based models with ML for improving the spatiotemporal dynamics modelling of cropland *** total of 1219 cropland SOC stock data (0-20 cm, sampled in 1980, 2000, and 2015) were collected from southern Jiangsu Province of China. Three models were built by integrating either Rothamsted carbon model (RothC), microbial-mineral carbon stabilization model (MIMICS), or the both with space-for-time random forest (RF-SFTS).ResultsThe overall spatiotemporal patterns of SOC dynamics that modelled by RothC, MIMICS, the integration of RothC with RF-SFTS (RF-RothC), the integration of MIMICS with RF-SFTS (RF-MIMICS), and the integration of RothC and MIMICS with RF-SFTS (RF-RothC-MIMICS) were similar, but their prediction accuracies differed significantly. The RF-RothC-MIMICS model had the best performance among the six models, being a lowest root mean squared error (RMSE) of 0.79 kg C m(-2) and a highest coefficient of determination (R-2) of 0.54, compared to the prediction accuracy of other *** process-based models differing in structures with ML significantly improved the spatiotemporal modelling of SOC dynamics. Outputs of this study may provide some guidance on more realistic projection of SOC dynamics under future changes in climate, land use, and management practices.

关键词： Soil organic carbon process-based models Machine learning Space for time substitution Model integration

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Procedural justice and process-based models: Understanding how practitioners utilise Community Protection Notices to regulate anti-social behaviour

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CRIMINOLOGY & CRIMINAL JUSTICE 2024年第3期24卷 629-647页

作者： Heap, Vicky Black, Alex Rodgers, Zoe Sheffield Hallam Univ Sheffield England Sheffield Hallam Univ Criminol Sheffield England Sheffield Hallam Univ Dept Law & Criminol Sheffield England Sheffield Hallam Univ Sheffield S10 2BQ England

Community Protection Notices (CPNs) were created and introduced in England and Wales through the Anti-Social Behaviour, Crime and Policing Act (2014). They are used to prevent and/or require specific actions by an individual or organisation, where existing behaviour has 'a detrimental effect on the quality of life of those in the locality'. A wide range of criminal justice actors can issue Community Protection Notices, and they require a low/no standard of evidence to do so. Breach of a Community Protection Notice results in a Fixed Penalty Notice of 100 pound or a possible criminal conviction. Using procedural justice theory as an analytical framework, our research is the first to investigate how Community Protection Notices are constructed, evidenced and monitored by the authorising bodies. The findings highlight divergent local practices, which sometimes lack procedural safeguards and adherence to Home Office statutory guidance. We propose 10 empirically based recommendations for policy and legislative changes to Community Protection Notice issuing practices.

关键词： Anti-social behaviour compliance legitimacy procedural justice process-based models proportionality

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