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Uncertainty in predictions of forest carbon dynamics: separating driver error from model error

ECOLOGICAL APPLICATIONS 2011年第5期21卷 1506-1522页

作者： Spadavecchia, L. Williams, M. Law, B. E. Univ Edinburgh Sch Geosci Edinburgh EH9 3JN Midlothian Scotland Univ Edinburgh NERC Ctr Terr Carbon Dynam Edinburgh EH9 3JN Midlothian Scotland Oregon State Univ Corvallis OR 97331 USA

We present an analysis of the relative magnitude and contribution of parameter and driver uncertainty to the confidence intervals on estimates of net carbon fluxes. Model parameters may be difficult or impractical to measure, while driver fields are rarely complete, with data gaps due to sensor failure and sparse observational networks. Parameters are generally derived through some optimization method, while driver fields may be interpolated from available data sources. For this study, we used data from a young ponderosa pine stand at Metolius, Central Oregon, and a simple daily model of coupled carbon and water fluxes (DALEC). An ensemble of acceptable parameterizations was generated using an ensemble Kalman filter and eddy covariance measurements of net C exchange. Geostatistical simulations generated an ensemble of meteorological driving variables for the site, consistent with the spatiotemporal autocorrelations inherent in the observational data from 13 local weather stations. Simulated meteorological data were propagated through the model to derive the uncertainty on the CO2 flux resultant from driver uncertainty typical of spatially extensive modeling studies. Furthermore, the model uncertainty was partitioned between temperature and precipitation. With at least one meteorological station within 25 km of the study site, driver uncertainty was relatively small (similar to 10% of the total net flux), while parameterization uncertainty was larger, similar to 50% of the total net flux. The largest source of driver uncertainty was due to temperature (8% of the total flux). The combined effect of parameter and driver uncertainty was 57% of the total net flux. However, when the nearest meteorological station was >100 km from the study site, uncertainty in net ecosystem exchange (NEE) predictions introduced by meteorological drivers increased by 88%. Precipitation estimates were a larger source of bias in NEE estimates than were temperature estimates, although the bia

关键词： carbon dynamics data assimilation ensemble Kalman filter geostatistics product-sum covariance model process-based modeling

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Using Physics-Encoded GeoAI to Improve the Physical Realism of Deep Learning′s Rainfall-Runoff Responses under Climate Change

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 133卷

作者： Li, Heng Hu, Yuqian Zhang, Chunxiao Shen, Dingtao Xu, Bingli Chen, Min Chu, Wenhao Li, Rongrong China Univ Geosci Beijing Sch Informat Engn Beijing Peoples R China Minist Nat Resources Beijing Fangshan Comprehens Explorat Observat & Res Stn Beijing Peoples R China Cent China Normal Univ Key Lab Geog Proc Anal & Simulat Hubei Prov Wuhan 430079 Peoples R China Cent China Normal Univ Coll Urban & Environm Sci Wuhan 430079 Peoples R China Acad Army Armored Forces Dept Informat & Commun Beijing Peoples R China Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ PRC Nanjing Jiangsu Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Resou Nanjing Jiangsu Peoples R China Chinese Univ Hong Kong Inst Space & Earth Informat Sci Shatin Hong Kong Peoples R China

Recent research has shown that deep learning (DL) faces physical realism challenges in predicting runoff responses under climate change, mainly due to DL's data dependence and lack of process understanding. In this study, a physics-encoded neural network model (dNN) was developed to adress this. dNN enables a fully processbased way to training and prediction by encoding process-based modeling knowledge into the DL architecture, including the water balance principle and causal linkages of catchment hydrological processes. To examine whether dNN can produce reliable runoff responses under warming scenarios, we first conducted regional training for dNN on daily runoff in 29 catchment in California. Two process-based models, EXP-HYDRO and HBV, were then developed as benchmarks. Both dNN and a pure data-driven LSTM were forced under warming scenarios, and the monthly hydrographs and total runoff ratios metrics were evaluated relative to the benchmarks. The results demonstrated: (1) For monthly hydrographs, dNN exhibited advantages in capturing cold-season runoff increase and warm-season recession than LSTM, effectively predicting the changes and trends in monthly runoff under warming scenarios;(2) For total runoff ratios, dNN predicted fewer catchments with increased runoff, indicating it can better maintain the total water budget under warming scenarios;(3) Through the synergy with physics, dNN was able to reasonably infer unobserved snowpack dynamics under warming scenarios. These results highlight the credibility and necessity of considering physics for DL in predicting runoff responses under climate change. Overall, this study provides a promising solution for considering physics in DL to further improve the process understanding in changing environments.

关键词： Climate Change Deep Learning process-based modeling Physics-Encoded Hybrid modeling Runoff responses

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Soil water depletion and replenishment during first- and early second-rotation Eucalyptus globulus plantations with deep soil profiles

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AGRICULTURAL AND FOREST METEOROLOGY 2011年第12期151卷 1568-1579页

作者： Mendham, D. S. White, D. A. Battaglia, M. McGrath, J. F. Short, T. M. Ogden, G. N. Kinal, J. CSIRO Ecosyst Sci Hobart Tas 7001 Australia CRC Forestry Hobart Tas 7001 Australia CSIRO Ecosyst Sci Wembley WA 6913 Australia CRC Forestry Wembley WA 6913 Australia CRC Future Farm Ind Crawley WA 6009 Australia WA Dept Environm & Conservat Dwellingup WA 6213 Australia

Eucalyptus globulus plantations are thought to use stored soil water when planted on ex-agricultural sites, and we hypothesized that this is likely to affect productivity of 2nd and later rotation plantations because the next rotations have access to less stored soil water. We used a combination of experiments and modeling to understand the impact of E. globulus plantations on soil water stores over the first rotation and early second rotation. The experiments were conducted at 3 contrasting sites in southwestern Australia, and modeling was used to extrapolate the results to other climatic zones. Soil water dynamics were assessed to 8 m depth under a range of management options, including spacing and nitrogen addition in the first rotation, and coppice or seedling re-establishment in the 2nd rotation. We found that soil water stores declined over the course of the first rotation at all sites, with some (incomplete) annual replenishment evident at the higher rainfall sites, but less replenishment at depth, especially in the lower rainfall sites. Only the wettest of the 3 sites fully replenished with soil water after harvest of the 1st rotation. Plots with higher stocking rates had higher soil water depletion early in the rotation, although by the end of the first rotation, most treatments had similar soil water deficits of around 800 mm at all of the sites. Of the sites that were responsive to N fertilizer, there was a strong differential in the degree of soil water deficit between N treatments, with N fertilized trees using more of the soil water store each year, but also producing more wood. A process-based plantation growth model, CABALA, was found to be adequate for predicting soil water dynamics under the range of management options that we explored, and we applied it to understanding the potential replenishment of soil under 2nd rotation plantations in a range of climatic zones within the E. globulus estate in south-western Australia. This modeling showed that

关键词： Soil water deficit E. globulus CABALA process-based modeling

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Bayesian calibration of mathematical models: Optimization of model structure and examination of the role of process error covariance

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ECOLOGICAL INFORMATICS 2013年 18卷 107-116页

作者： Ramin, Maryam Arhonditsis, George B. Univ Toronto Ecol Modelling Lab Dept Phys & Environm Sci Toronto ON M1C 1A4 Canada

The integration of Bayesian inference techniques with mathematical modeling offers a promising means to improve ecological forecasts and management actions over space and time, while accounting for the uncertainty underlying model predictions. In this study, we address two important questions related to the ramifications of the statistical assumptions typically made about the model structural error and the prospect of Bayesian calibration to guide the optimization of model complexity. Regarding the former issue, we examine statistical formulations that whether postulate conditional independence or explicitly accommodate the covariance among the error terms for various model endpoints. Our analysis evaluates the differences in the posterior parameter patterns and predictive performance of a limiting nutrient (phosphate)-phytoplankton-zooplankton-detritus (particulate phosphorus) model calibrated with three alternative statistical configurations. The lessons learned from this exercise are combined with those from a second comparative analysis that aims to optimize model structure. In particular, we selected three formulas of the zooplankton mortality term (linear, hyperbolic, sigmoidal) and examine their capacity to determine the posterior parameterization as well as the reproduction of the observed ecosystem patterns. Our analysis suggests that the statistical characterization of the model error as well as the mathematical representation of specific ecological processes can be influential to the inference drawn by a modeling exercise. Our findings could be useful when selecting the most suitable statistical framework for model calibration and/or making informative decisions about model structure optimization. In the absence of adequate prior knowledge, we also advocate the use of Bayesian model averaging for obtaining weighted averages of the forecasts from different model structures and/or statistical descriptions of the process error terms. (C) 2013 Elsevier B.V. A

关键词： process-based modeling Eutrophication Bayesian inference process error covariance Model averaging Model complexity optimization

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Plant Traits are Key Determinants in Buffering the Meteorological Sensitivity of Net Carbon Exchanges of Arctic Tundra

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JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES 2018年第9期123卷 2675-2694页

作者： Lopez-Blanco, Efren Lund, Magnus Christensen, Torben R. Tamstorf, Mikkel P. Smallman, Thomas L. Slevin, Darren Westergaard-Nielsen, Andreas Hansen, Birger U. Abermann, Jakob Williams, Mathew Aarhus Univ Dept Biosci Arctic Res Ctr Roskilde Denmark Univ Edinburgh Sch Geosci Edinburgh Midlothian Scotland Lund Univ Dept Phys Geog & Ecosyst Sci Lund Sweden Univ Copenhagen Dept Geosci & Nat Resource Management Ctr Permafrost CENPERM Copenhagen Denmark Greenland Survey Asiaq Nuuk Greenland

The climate sensitivity of carbon (C) cycling in Arctic terrestrial ecosystems is a major unknown in the Earth system. There is a lack of knowledge about the mechanisms that drive the interactions between photosynthesis, respiration, and changes in C stocks across full annual cycles in Arctic tundra. We use a calibrated and validated model (soil-plant-atmosphere;SPA) to estimate net ecosystem exchange (NEE), gross primary production (GPP), ecosystem respiration (R-eco), and internal C processing across eight full years. SPA's carbon flux estimates are validated with observational data obtained from the Greenland Ecosystem Monitoring program in West Greenland tundra. Overall, the model explained 73%, 73%, and 50% of the variance in NEE, GPP, and R-eco, respectively, and 85% of the plant greenness variation. Flux data highlighted the insensitivity of growing season NEE to interannual meteorological variability, due to compensatory responses of photosynthesis and ecosystem respiration. In this modelling study, we show that this NEE buffering is the case also for full annual cycles. We show through a sensitivity analysis that plant traits related to nitrogen are likely key determinants in the compensatory response, through simulated links to photosynthesis and plant respiration. Interestingly, we found a similar temperature sensitivity of the trait-flux couplings for GPP and R-eco, suggesting that plant traits drive the stabilization of NEE. Further, model analysis indicated that wintertime periods decreased the C sink by 60%, mostly driven by litter heterotrophic respiration. This result emphasizes the importance of wintertime periods and allows a more comprehensive understanding of full annual C dynamics.

关键词： Arctic tundra ecosystem exchange gross primary production ecosystem respiration plant traits process-based modeling

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Ericoid mycorrhizal fungi mediate the response of ombrotrophic peatlands to fertilization: a modeling study

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NEW PHYTOLOGIST 2023年第1期238卷 80-95页

作者： Shao, Siya Wu, Jianghua He, Hongxing Moore, Tim R. Bubier, Jill Larmola, Tuula Juutinen, Sari Roulet, Nigel T. McGill Univ Dept Geog Montreal PQ H3A 0G4 Canada Dartmouth Coll Dept Biol Sci Hanover NH 03755 USA Mem Univ Newfoundland Sch Sci & Environm Environm & Sustainabil St John NF A1C 5S7 Canada Mt Holyoke Coll Dept Environm Studies South Hadley MA 01075 USA Nat Resources Inst Finland Luke Helsinki 00790 Finland Finnish Meteorol Inst Helsinki 00560 Finland

Ericaceous shrubs adapt to the nutrient-poor conditions in ombrotrophic peatlands by forming symbiotic associations with ericoid mycorrhizal (ERM) fungi. Increased nutrient availability may diminish the role of ERM pathways in shrub nutrient uptake, consequently altering the biogeochemical cycling within bogs. To explore the significance of ERM fungi in ombrotrophic peatlands, we developed the model MWMmic (a peat cohort-based biogeochemical model) into MWMmic-NP by explicitly incorporating plant-soil nitrogen (N) and phosphorus (P) cycling and ERM fungi processes. The new model was applied to simulate the biogeochemical cycles in the Mer Bleue (MB) bog in Ontario, Canada, and their responses to fertilization. MWMmic_NP reproduced the carbon(C)-N-P cycles and vegetation dynamics observed in the MB bog, and their responses to fertilization. Our simulations showed that fertilization increased shrub biomass by reducing the C allocation to ERM fungi, subsequently suppressing the growth of underlying Sphagnum mosses, and decreasing the peatland C sequestration. Our species removal simulation further demonstrated that ERM fungi were key to maintaining the shrub-moss coexistence and C sink function of bogs. Our results suggest that ERM fungi play a significant role in the biogeochemical cycles in ombrotrophic peatlands and should be considered in future modeling efforts.

关键词： carbon cycling ericaceous shrub ericoid mycorrhizal (ERM) fungi nutrient cycling ombrotrophic peatland process-based modeling Sphagnum

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Learning population dynamics models from data and domain knowledge

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ECOLOGICAL MODELLING 2003年第2-3期170卷 129-140页

作者： Dzeroski, S Todorovski, L Jozef Stefan Inst Dept Intelligent Syst Ljubljana 1000 Slovenia

This paper is concerned with integrating knowledge-based modeling or modeling from first principles, with data-driven or automated modeling of dynamic systems. The approach presented here includes methods for equation discovery: unlike mainstream system identification methods, which work under the assumption that the form of the equations is known, equation discovery systems explore a space of possible equation structures. We propose a formalism for representing knowledge about processes in population dynamics domains, and a method to transform such knowledge into an operational form that could be used by equation discovery systems. We also describe the extensions of the equation discovery system LAGRAMGE necessary to incorporate this kind of knowledge in the process of equation discovery. (C) 2003 Elsevier B.V. All rights reserved.

关键词： equation discovery data-driven modeling knowledge-based modeling machine learning population dynamics process-based modeling structural modeling

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Global pattern and change of cropland soil organic carbon during 1901-2010: Roles of climate, atmospheric chemistry, land use and management

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Geography and Sustainability 2020年第1期1卷 59-69页

作者： Wei Ren Kamaljit Banger Bo Tao Jia Yang Yawen Huang Hanqin Tian Department of Plant&Soil Sciences College of AgricultureFood and EnvironmentUniversity of KentuckyLexingtonKY 40506USA Plant Agriculture-University of Guelph Ontario N1G 2W1Canada Department of Forestry Mississippi State UniversityMississippi StateMS 39762USA International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences Auburn UniversityAuburnAL 36849USA

Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)*** managed sustainably,soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the ***,the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity,complicated mechanisms,and multiple influencing ***,we use a process-based agroecosystem model(DLEM-Ag)in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change,elevated CO2,nitrogen deposition,land cover change,and land management practices such as nitrogen fertilizer use and *** results show that the total SOC and SOC density in the 2000s increased by 125%and 48.8%,respectively,compared to the early 20th *** SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer *** analysis suggests that climate change reduced approximately 3.2%(or 2,166 Tg C)of the total SOC over the past 110 *** results indicate that croplands have a large potential to sequester C through implementing better land use management practices,which may partially offset SOC loss caused by climate change.

关键词： Global cropland Soil organic carbon Climate change Land management process-based modeling

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High Expectations and Open Questions: Using High-Frequency Proxy Data to Calibrate a Catchment-Scale Phosphorus Model

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ACS ES&T WATER 2024年第5期4卷 2135-2143页

作者： Lannergard, Emma E. Sandstrom, Sara Nilsson, Elin Widen Lewan, Elisabet Futter, Martyn N. Swedish Univ Agr Sci Dept Aquat Sci & Assessment S-75007 Uppsala Sweden Swedish Univ Agr Sci Dept Soil & Environm Box 7014 S-75007 Uppsala Sweden

High-frequency (HF) in situ sensor data capture temporal water quality variability. Using HF data to calibrate catchment-scale water quality models remains uncommon, despite previous studies demonstrating the value of HF data in reducing uncertainty in model output. This study aims to provide new insights regarding the use of HF proxy data [turbidity as a proxy for total phosphorus (TP) and total suspended solids (TSS)] compared to low-frequency (LF) data for model calibration. Four calibration data sets were used to calibrate a catchment-scale water quality model: monthly grab samples of TSS and TP (LF) and HF in situ sensor turbidity proxies for TSS and TP, alone and in combinations. These calibration data sets resulted in significant differences in model output, with HF data sets projecting more variable results and affecting modeled TP/TSS concentrations, loads, and performance statistics. Multiobjective calibration using HF proxy data highlighted important knowledge gaps concerning the theoretical and practical implications of using HF turbidity for model calibration, which is why it should be used with caution in future studies. Further research areas include investigating the nature of proxy relationships, such as linearity/nonlinearity, exploring the concentration range coverage, and an improved understanding of turbidity and TSS- and TP-generating processes.

关键词： high-frequency monitoring proxy relationships INCA-PEco process-based modeling turbidity total phosphorus total suspended solids

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PERSPECTIVES AND ASPECTS IN POEML Supporting Adaptation in Learning processes

PERSPECTIVES AND ASPECTS IN POEML <i>Supporting Adaptation i...

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4th International Conference on Software and Data Technologies

作者： Caeiro, Manuel Anido, Luis Llamas, Martin Univ Vigo Dept Telemat Engn Vigo 36310 Spain

ISBN: (纸本)9789896740092

Adaptation is a key feature of e-learning systems. The educational process performed by the support of e-learning systems should be carried out taking into account the cognitive and other particular characteristics of the students, of the teachers and of the environments. However, this adaptation is not a simple process. It implies the conjunction of technical and pedagogical issues. This paper introduces a solution to support adaptation in the context of an Educational modeling Languages (EML). EMLs have been conceived to allow us the modeling of learning units, enabling the expression of different pedagogical approaches. Eventually, these models are intended to control and support the development of the corresponding learning units.

关键词： Model-driven engineering process-based modeling e-Learning Educational modeling languages

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