ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Copernicus  (10)
  • Princeton : Princeton University Press  (1)
Collection
Publisher
Language
Years
  • 1
    Monograph available for loan
    Monograph available for loan
    Ecological Forecasting (2017)
    Princeton : Princeton University Press
    Details Availability
    Call number: PIK N 531-17-91098
    Type of Medium: Monograph available for loan
    Pages: x. 270 Seiten , Diagramme
    ISBN: 9781400885459 , 9780691160573 (print)
    Language: English
    Note: Contents: 1. Introduction -- 1.1 Why Forecast? -- 1.2 The Informatics Challenge in Forecasting -- 1.3 The Model-Data Loop -- 1.4 Why Bayes? -- 1.5 Models as Scaffolds -- 1.6 Case Studies and Decision Support -- 1.7 Key Concepts -- 1.8 Hands-on Activities -- 2. From Models to Forecasts -- 2.1 The Traditional Modeler's Toolbox -- 2.2 Example: The Logistic Growth Model -- 2.3 Adding Sources of Uncertainty -- 2.4 Thinking Probabilistically -- 2.5 Predictability -- 2.6 Key Concepts -- 2.7 Hands-on Activities -- 3. Data, Large and Small -- 3.1 The Data Cycle and Best Practices -- 3.2 Data Standards and Metadata -- 3.3 Handling Big Data -- 3.4 Key Concepts -- 3.5 Hands-on Activities -- 4. Scientific Workflows and the Informatics of Model-Data Fusion -- 4.1 Transparency, Accountability, and Repeatability -- 4.2 Workflows and Automation -- 4.3 Best Practices for Scientific Computing -- 4.4 Key Concepts -- 4.5 Hands-on Activities -- 5. Introduction to Bayes -- 5.1 Confronting Models with Data -- 5.2 Probability 101 -- 5.3 The Likelihood -- 5.4 Bayes' Theorem -- 5.5 Prior Information -- 5.6 Numerical Methods for Bayes -- 5.7 Evaluating MCMC Output -- 5.8 Key Concepts -- 5.9 Hands-on Activities -- 6. Characterizing Uncertainty -- 6.1 Non-Gaussian Error -- 6.2 Heteroskedasticity -- 6.3 Observation Error -- 6.4 Missing Data and Inverse Modeling -- 6.5 Hierarchical Models and Process Error -- 6.6 Autocorrelation -- 6.7 Key Concepts -- 6.8 Hands-on Activities -- 7. Case Study: Biodiversity, Populations, and Endangered Species -- 7.1 Endangered Species -- 7.2 Biodiversity -- 7.3 Key Concepts -- 7.4 Hands-on Activities -- 8. Latent Variables and State-Space Models -- 8.1 Latent Variables -- 8.2 State Space -- 8.3 Hidden Markov Time-Series Model -- 8.4 Beyond Time -- 8.5 Key Concepts -- 8.6 Hands-on Activities -- 9. Fusing Data Sources -- 9.1 Meta-analysis -- 9.2 Combining Data: Practice, Pitfalls, and Opportunities -- 9.3 Combining Data and Models across Space and Time -- 9.4 Key Concepts -- 9.5 Hands-on Activities -- 10. Case Study: Natural Resources -- 10.1 Fisheries -- 10.2 Case Study: Baltic Salmon -- 10.3 Key Concepts -- 11. Propagating, Analyzing, and Reducing Uncertainty -- 11.1 Sensitivity Analysis -- 11.2 Uncertainty Propagation -- 11.3 Uncertainty Analysis -- 11.4 Tools for Model-Data Feedbacks -- 11.5 Key Concepts -- 11.6 Hands-on Activities -- Appendix A Properties of Means and Variances -- Appendix B Common Variance Approximations -- 12. Case Study: Carbon Cycle -- 12.1 Carbon Cycle Uncertainties -- 12.2 State of the Science -- 12.3 Case Study: Model-Data Feedbacks -- 12.4 Key Concepts -- 12.5 Hands-on Activities -- 13. Data Assimilation 1: Analytical Methods -- 13.1 The Forecast Cycle -- 13.2 Kalman Filter -- 13.3 Extended Kalman Filter -- 13.4 Key Concepts -- 13.5 Hands-on Activities -- 14. Data Assimilation 2: Monte Carlo Methods -- 14.1 Ensemble Filters -- 14.2 Particle Filter -- 14.3 Model Averaging and Reversible Jump MCMC -- 14.4 Generalizing the Forecast Cycle -- 14.5 Key Concepts -- 14.6 Hands-on Activities -- 15. Epidemiology -- 15.1 Theory -- 15.2 Ecological Forecasting -- 15.3 Examples of Epidemiological Forecasting -- 15.4 Case Study: Influenza -- 15.5 Key Concepts -- 16. Assessing Model Performance -- 16.1 Visualization -- 16.2 Basic Model Diagnostics -- 16.3 Model Benchmarks -- 16.4 Data Mining the Residuals -- 16.5 Comparing Model Performance to Simple Statistics -- 16.6 Key Concepts -- 16.7 Hands-on Activities -- 17. Projection and Decision Support -- 17.1 Projections, Predictions, and Forecasting -- 17.2 Decision Support -- 17.3 Key Concepts -- 17.4 Hands-on Activities -- 18. Final Thoughts -- 18.1 Lessons Learned -- 18.2 Future Directions -- References -- Index
    Location: A 18 - must be ordered
    Branch Library: PIK Library
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    BOOK
    Link to Library Catalog
    get availability status
  • 2
    facet.materialart.
    Unknown
    Benchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, Panama (2020)
    Copernicus
    Details
    Publication Date: 2020-06-15
    Description: Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include (a) the number of competing PFTs present in any simulation and (b) parameters that govern disturbance and height-based light competition. While single-PFT simulations are roughly consistent with observations of productivity at Barro Colorado Island, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e., the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early- and late-successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional-dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not and to better understand the relationships between these two types of model parameters to quantify sources of uncertainty in VDMs.
    Print ISSN: 1726-4170
    Electronic ISSN: 1726-4189
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 3
    facet.materialart.
    Unknown
    Climatic history of the northeastern United States during the past 3000 years (2017)
    Copernicus
    Details
    Publication Date: 2017-10-13
    Description: Many ecosystem processes that influence Earth system feedbacks – vegetation growth, water and nutrient cycling, disturbance regimes – are strongly influenced by multidecadal- to millennial-scale climate variations that cannot be directly observed. Paleoclimate records provide information about these variations, forming the basis of our understanding and modeling of them. Fossil pollen records are abundant in the NE US, but cannot simultaneously provide information about paleoclimate and past vegetation in a modeling context because this leads to circular logic. If pollen data are used to constrain past vegetation changes, then the remaining paleoclimate archives in the northeastern US (NE US) are quite limited. Nonetheless, a growing number of diverse reconstructions have been developed but have not yet been examined together. Here we conduct a systematic review, assessment, and comparison of paleotemperature and paleohydrological proxies from the NE US for the last 3000 years. Regional temperature reconstructions (primarily summer) show a long-term cooling trend (1000 BCE–1700 CE) consistent with hemispheric-scale reconstructions, while hydroclimate data show gradually wetter conditions through the present day. Multiple proxies suggest that a prolonged, widespread drought occurred between 550 and 750 CE. Dry conditions are also evident during the Medieval Climate Anomaly, which was warmer and drier than the Little Ice Age and drier than today. There is some evidence for an acceleration of the longer-term wetting trend in the NE US during the past century; coupled with an abrupt shift from decreasing to increasing temperatures in the past century, these changes could have wide-ranging implications for species distributions, ecosystem dynamics, and extreme weather events. More work is needed to gather paleoclimate data in the NE US to make inter-proxy comparisons and to improve estimates of uncertainty in reconstructions.
    Print ISSN: 1814-9324
    Electronic ISSN: 1814-9332
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 4
    facet.materialart.
    Unknown
    Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation (2018)
    Copernicus
    Details
    Publication Date: 2018-10-04
    Description: Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally demanding models and large datasets. We employ an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature and specification of both model and data uncertainties, and it introduced novel approaches to autocorrelation corrections on multiple data streams and emulating the sufficient statistics surface. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard brute-force MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model's parameters with comparable performance to the brute-force approach but reduced computation time by more than 2 orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (brute-force) Bayesian data assimilation techniques. Both models are constrained after assimilation of the observational data with the emulator method, reducing the uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data. Our study furthers efforts toward reducing model uncertainties, showing that the emulator method makes it possible to efficiently calibrate complex models.
    Print ISSN: 1726-4170
    Electronic ISSN: 1726-4189
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 5
    facet.materialart.
    Unknown
    The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 1: Model description (2019)
    Copernicus
    Details
    Publication Date: 2019-10-14
    Description: Earth system models (ESMs) have been developed to represent the role of terrestrial ecosystems on the energy, water, and carbon cycles. However, many ESMs still lack representation of within-ecosystem heterogeneity and diversity. In this paper, we present the Ecosystem Demography model version 2.2 (ED-2.2). In ED-2.2, the biophysical and physiological processes account for the horizontal and vertical heterogeneity of the ecosystem: the energy, water, and carbon cycles are solved separately for a series of vegetation cohorts (groups of individual plants of similar size and plant functional type) distributed across a series of spatially implicit patches (representing collections of micro-environments that have a similar disturbance history). We define the equations that describe the energy, water, and carbon cycles in terms of total energy, water, and carbon, which simplifies the differential equations and guarantees excellent conservation of these quantities in long-term simulation (
    Print ISSN: 1991-959X
    Electronic ISSN: 1991-9603
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 6
    facet.materialart.
    Unknown
    Climatic history of the northeastern United States during the past 3000 years (2016)
    Copernicus
    Details
    Publication Date: 2016-11-02
    Description: Many ecosystem processes that influence Earth system feedbacks, including vegetation growth, water and nutrient cycling, and disturbance regimes, are strongly influenced by multi-decadal to millennial-scale variations in climate that cannot be captured by instrumental climate observations. Paleoclimate information is therefore essential for understanding contemporary ecosystems and their potential trajectories under a variety of future climate conditions. With the exception of fossil pollen records, there are a limited number of northeastern US (NE US) paleoclimate archives that can provide constraints on its temperature and hydroclimate history. Moreover, the records that do exist have not been considered together. Tree-ring data indicate that the 20th century was one of the wettest of the past 500 years in the eastern US (Pederson et al., 2014), and lake-level records suggest it was one of the wettest in the Holocene (Newby et al., 2014); how such results compare with other available data remains unclear, however. Here we conduct a systematic review, assessment, and comparison of paleotemperature and paleohydrological proxies from the NE US for the last 3000 years. Regional temperature reconstructions are consistent with the long-term cooling trend (1000 BCE–1700 CE) evident in hemispheric-scale reconstructions, but hydroclimate reconstructions reveal new information, including an abrupt transition from wet to dry conditions around 550–750 CE. NE US paleo data suggest that conditions during the Medieval Climate Anomaly were warmer and drier than during the Little Ice Age, and drier than today. There is some evidence for an acceleration over the past century of a longer-term wetting trend in the NE US, and coupled with the abrupt shift from a cooling trend to a warming trend from increased greenhouse gases, may have wide-ranging implications for species distributions, ecosystem dynamics, and extreme weather events. More work is needed to gather paleoclimate data in the NE US, make inter-proxy comparisons, and improve estimates of uncertainty in the reconstructions.
    Print ISSN: 1814-9340
    Electronic ISSN: 1814-9359
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 7
    facet.materialart.
    Unknown
    The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 2: Model evaluation for tropical South America (2019)
    Copernicus
    Details
    Publication Date: 2019-10-14
    Description: The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.
    Print ISSN: 1991-959X
    Electronic ISSN: 1991-9603
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 8
    facet.materialart.
    Unknown
    Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation (2018)
    Copernicus
    Details
    Publication Date: 2018-02-26
    Description: Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov Chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally-demanding models and large data sets. We describe an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature, and introduced novel approaches to the specification of both model and data uncertainties, including bias and autocorrelation corrections on multiple data streams. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard "bruteforce" MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model’s parameters with comparable performance to the bruteforce approach, but reduced computation time by more than two orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (bruteforce) Bayesian data assimilation techniques. Both models are constrained after assimilation of the observational data with the emulator method, reducing the uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data. Our study furthers efforts toward reducing model uncertainties showing that the emulator method makes it possible to efficiently calibrate complex models. This efficient data assimilation method allows us to conduct more calibration experiments in relatively much shorter times, enabling constraining of numerous models using the expanding amount and types of data.
    Print ISSN: 1810-6277
    Electronic ISSN: 1810-6285
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 9
    facet.materialart.
    Unknown
    The biophysics, ecology, and biogeochemistry of functionally diverse, vertically- and horizontally-heterogeneous ecosystems: the Ecosystem Demography Model, version 2.2 – Part 2: Model evaluation (2019)
    Copernicus
    Details
    Publication Date: 2019-03-28
    Description: The Ecosystem Demography Model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical and biogeochemical cycles of dynamic ecosystems while considering the role of vertical structure of plant communities and the heterogeneity of such structures across the landscape. In a companion paper, we described in detail how the model solves the energy, water, and carbon cycles, and verified the excellent conservation of such properties in long-term simulation. Here, we present a thorough assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles, with focus on the Amazon forest. We used multiple measurements from eddy covariance towers, forest inventory plots and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple time scales ranging from sub-daily to century-long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes and the storage of water, energy and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 also simulates emerging properties of the ecosystem found in observations, such as the relationship between biomass and mortality rates and wood density, although the relationships predicted by the model were biased. We also identified some of the model limitations, such as the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration, and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in ESMs, to realistically represent the role of forest structure and composition on energy, water, and carbon cycles, as well as the priority areas for further model development.
    Print ISSN: 1991-9611
    Electronic ISSN: 1991-962X
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 10
    facet.materialart.
    Unknown
    The biophysics, ecology, and biogeochemistry of functionally diverse, vertically- and horizontally-heterogeneous ecosystems: the Ecosystem Demography Model, version 2.2 — Part 1: Model description (2019)
    Copernicus
    Details
    Publication Date: 2019-03-27
    Description: Earth System Models (ESMs) have been developed to represent the role of terrestrial ecosystems on the energy, water, and carbon cycles. However, many ESMs still lack representation of within-ecosystem heterogeneity and diversity. In this manuscript, we present the Ecosystem Demography Model version 2.2 (ED-2.2). In ED-2.2, the biophysical and physiological cycles account for the horizontal and vertical heterogeneity of the ecosystem: the energy, water, and carbon cycles are solved separately for each group of individual trees of similar size and functional group (cohorts) living in a micro-environment with similar disturbance history (patches). We define the equations that describe the energy, water, and carbon cycles in terms of total energy, water, and carbon, which simplifies the ordinary differential equations and guarantees excellent conservation of these quantities in long-term simulation ( 〈 0.1 % error over 50 years). We also show examples of ED-2.2 simulation results at single sites and across tropical South America. These results demonstrate the model's ability to characterize the variability of ecosystem structure, composition and functioning both at stand- and continental-scales. In addition, a detailed model evaluation was carried out and presented in a companion paper. Finally, we highlight some of the ongoing developments in ED-2.2 that aim at reducing the uncertainties identified in this study and the inclusion of processes hitherto not represented in the model.
    Print ISSN: 1991-9611
    Electronic ISSN: 1991-962X
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...