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  • 1
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 11 June 2013, Vol.110(24), pp.9824-9
    Description: The Cyanobacteria Prochlorococcus and Synechococcus account for a substantial fraction of marine primary production. Here, we present quantitative niche models for these lineages that assess present and future global abundances and distributions. These niche models are the result of neural network, nonparametric, and parametric analyses, and they rely on 〉35,000 discrete observations from all major ocean regions. The models assess cell abundance based on temperature and photosynthetically active radiation, but the individual responses to these environmental variables differ for each lineage. The models estimate global biogeographic patterns and seasonal variability of cell abundance, with maxima in the warm oligotrophic gyres of the Indian and the western Pacific Oceans and minima at higher latitudes. The annual mean global abundances of Prochlorococcus and Synechococcus are 2.9 ± 0.1 × 10(27) and 7.0 ± 0.3 × 10(26) cells, respectively. Using projections of sea surface temperature as a result of increased concentration of greenhouse gases at the end of the 21st century, our niche models projected increases in cell numbers of 29% and 14% for Prochlorococcus and Synechococcus, respectively. The changes are geographically uneven but include an increase in area. Thus, our global niche models suggest that oceanic microbial communities will experience complex changes as a result of projected future climate conditions. Because of the high abundances and contributions to primary production of Prochlorococcus and Synechococcus, these changes may have large impacts on ocean ecosystems and biogeochemical cycles.
    Keywords: Climate Change ; Marine Biogeochemistry ; Microbial Biogeography ; Ecosystem ; Prochlorococcus -- Growth & Development ; Seawater -- Microbiology ; Synechococcus -- Growth & Development
    ISSN: 00278424
    E-ISSN: 1091-6490
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  • 2
    Language: English
    In: Journal of Hydrology, April 2018, Vol.559, pp.954-971
    Description: This essay illustrates some recent developments to the DiffeRential Evolution Adaptive Metropolis (DREAM) MATLAB toolbox of Vrugt (2016) to delineate and sample the behavioural solution space of set-theoretic likelihood functions used within the GLUE (Limits of Acceptability) framework (Beven and Binley, 1992, 2014; Beven and Freer, 2001; Beven, 2006). This work builds on the DREAM algorithm of Sadegh and Vrugt (2014) and enhances significantly the accuracy and CPU-efficiency of Bayesian inference with GLUE. In particular it is shown how lack of adequate sampling in the model space might lead to unjustified model rejection.
    Keywords: Glue ; Limits of Acceptability ; Markov Chain Monte Carlo ; Posterior Sampling ; Dream ; Dream(Loa) ; Sufficiency ; Hydrological Modelling ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 3
    Language: English
    In: Journal of Hydrology, October 2015, Vol.529, pp.1095-1115
    Description: In the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we focus on large-scale hydrologic modeling and analyze the effect of parameter and rainfall data uncertainty on simulated discharge dynamics with the global hydrologic model PCR-GLOBWB. We use three rainfall data products; the CFSR reanalysis, the ERA-Interim reanalysis, and a combined ERA-40 reanalysis and CRU dataset. Parameter uncertainty is derived from Latin Hypercube Sampling (LHS) using monthly discharge data from five of the largest river systems in the world. Our results demonstrate that the default parameterization of PCR-GLOBWB, derived from global datasets, can be improved by calibrating the model against monthly discharge observations. Yet, it is difficult to find a single parameterization of PCR-GLOBWB that works well for all of the five river basins considered herein and shows consistent performance during both the calibration and evaluation period. Still there may be possibilities for regionalization based on catchment similarities. Our simulations illustrate that parameter uncertainty constitutes only a minor part of predictive uncertainty. Thus, the apparent dichotomy between simulations of global-scale hydrologic behavior and actual data cannot be resolved by simply increasing the model complexity of PCR-GLOBWB and resolving sub-grid processes. Instead, it would be more productive to improve the characterization of global rainfall amounts at spatial resolutions of 0.5° and smaller.
    Keywords: Global Hydrological Modeling ; Forcing Uncertainty ; Model Calibration ; Parameter Uncertainty ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 4
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 16 January 2007, Vol.104(3), pp.708-11
    Description: In the last few decades, evolutionary algorithms have emerged as a revolutionary approach for solving search and optimization problems involving multiple conflicting objectives. Beyond their ability to search intractably large spaces for multiple solutions, these algorithms are able to maintain a diverse population of solutions and exploit similarities of solutions by recombination. However, existing theory and numerical experiments have demonstrated that it is impossible to develop a single algorithm for population evolution that is always efficient for a diverse set of optimization problems. Here we show that significant improvements in the efficiency of evolutionary search can be achieved by running multiple optimization algorithms simultaneously using new concepts of global information sharing and genetically adaptive offspring creation. We call this approach a multialgorithm, genetically adaptive multiobjective, or AMALGAM, method, to evoke the image of a procedure that merges the strengths of different optimization algorithms. Benchmark results using a set of well known multiobjective test problems show that AMALGAM approaches a factor of 10 improvement over current optimization algorithms for the more complex, higher dimensional problems. The AMALGAM method provides new opportunities for solving previously intractable optimization problems.
    Keywords: Algorithms ; Biological Evolution ; Computer Simulation
    ISSN: 0027-8424
    E-ISSN: 10916490
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  • 5
    Language: English
    In: Environmental Modelling and Software, January 2016, Vol.75, pp.273-316
    Description: Bayesian inference has found widespread application and use in science and engineering to reconcile Earth system models with data, including prediction in space (interpolation), prediction in time (forecasting), assimilation of observations and deterministic/stochastic model output, and inference of the model parameters. Bayes theorem states that the posterior probability, of a hypothesis, is proportional to the product of the prior probability, ( ) of this hypothesis and the likelihood, of the same hypothesis given the new observations, , or . In science and engineering, often constitutes some numerical model, ℱ(x) which summarizes, in algebraic and differential equations, state variables and fluxes, all knowledge of the system of interest, and the unknown parameter values, are subject to inference using the data . Unfortunately, for complex system models the posterior distribution is often high dimensional and analytically intractable, and sampling methods are required to approximate the target. In this paper I review the basic theory of Markov chain Monte Carlo (MCMC) simulation and introduce a MATLAB toolbox of the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm developed by Vrugt et al. (2008a, 2009a) and used for Bayesian inference in fields ranging from physics, chemistry and engineering, to ecology, hydrology, and geophysics. This MATLAB toolbox provides scientists and engineers with an arsenal of options and utilities to solve posterior sampling problems involving (among others) bimodality, high-dimensionality, summary statistics, bounded parameter spaces, dynamic simulation models, formal/informal likelihood functions (GLUE), diagnostic model evaluation, data assimilation, Bayesian model averaging, distributed computation, and informative/noninformative prior distributions. The DREAM toolbox supports parallel computing and includes tools for convergence analysis of the sampled chain trajectories and post-processing of the results. Seven different case studies illustrate the main capabilities and functionalities of the MATLAB toolbox.
    Keywords: Bayesian Inference ; Markov Chain Monte Carlo (Mcmc) Simulation ; Random Walk Metropolis (Rwm) ; Adaptive Metropolis (Am) ; Differential Evolution Markov Chain (De-MC) ; Prior Distribution ; Likelihood Function ; Posterior Distribution ; Approximate Bayesian Computation (ABC) ; Diagnostic Model Evaluation ; Residual Analysis ; Environmental Modeling ; Bayesian Model Averaging (BMA) ; Generalized Likelihood Uncertainty Estimation (Glue) ; Multi-Processor Computing ; Extended Metropolis Algorithm (Ema) ; Engineering ; Environmental Sciences ; Computer Science ; Ecology
    ISSN: 1364-8152
    E-ISSN: 1873-6726
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  • 6
    In: Eos, Transactions American Geophysical Union, 07 January 2014, Vol.95(1), pp.7-7
    Description: I am proud to introduce Soroosh Sorooshian, distinguished professor of civil and environmental engineering and Earth system science at the University of California, Irvine, as the 2013 Horton medalist. Soroosh is an exemplary scholar, who through collaborations with Hoshin Gupta and numerous other talented students and researchers, has made many landmark contributions to hydrology. I will highlight what I consider to be his principal contributions.
    Keywords: 2013 Robert E. Horton Medal
    ISSN: 0096-3941
    E-ISSN: 2324-9250
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  • 7
    Language: English
    In: Geoderma, 2011, Vol.163(3), pp.150-162
    Description: This paper demonstrates for the first time the use of Markov Chain Monte Carlo (MCMC) simulation for parameter inference in model-based soil geostatistics. We implemented the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm to jointly summarize the posterior distribution of variogram parameters and the coefficients of a linear spatial model, and derive estimates of predictive uncertainty. The DREAM method runs multiple different Markov chains in parallel and jumps in each chain are generated from a discrete proposal distribution containing a fixed multiple of the difference of the states of randomly chosen pairs of other chains. This approach automatically scales the orientation and scale of the proposal distribution, and is especially designed to maintain detailed balance and ergodicity, thereby generating an exact approximation of the posterior probability density function (pdf) of the parameters of the linear model and variogram. This approach is tested using three different data sets from Australia involving variogram estimation of soil thickness, kriging of soil pH, and spatial prediction of soil organic carbon content. The results showed some advantages of MCMC over the conventional method of moments and residual maximum likelihood (REML) estimation. The posterior pdf derived with MCMC conveys important information about parameter uncertainty, multi-dimensional parameter correlation, and thus how many significant parameters are warranted by the calibration data. Parameter uncertainty constitutes only a small part of total prediction uncertainty for the case studies considered here. The prediction accuracies using MCMC and REML are similar. The variogram estimated using conventional approaches (method of moments, and without simulation) lies within the 95% prediction uncertainty interval of the posterior distribution derived with DREAM. Altogether our results show that conventional kriging and regression-kriging still remain a viable option for production mapping. ► An adapted Markov Chain Monte Carlo (MCMC) method was used for parameter inference in model-based soil geostatistics. ► The DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm automatically tunes the scale and orientation of the proposal distribution facilitates an easy implementation for geostatistical parameter inference. ► This paper shows the variation of soil variograms due to parameter uncertainty, which can be quite large with increasing lag. ► The posterior probability density function (pdf) derived with MCMC conveys important information about parameter uncertainty, and multi-dimensional parameter correlation.
    Keywords: Generalized Linear Mixed Model ; Bayesian Inference ; Bayesian Kriging ; Regression-Kriging ; Best Linear Unbiased Predictor ; Pedometrics ; Agriculture
    ISSN: 0016-7061
    E-ISSN: 1872-6259
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  • 8
    Language: English
    In: Ecological Applications, 01 December 2015, Vol.25(8)
    Description: Photosynthetic capacity, determined by light harvesting and carboxylation reactions, is a key plant trait that determines the rate of photosynthesis; however, in Earth System Models (ESMs) at a reference temperature, it is either a fixed value for a given plant functional type or derived from a linear function of leaf nitrogen content. In this study, we conducted a comprehensive analysis that considered correlations of environmental factors with photosynthetic capacity as determined by maximum carboxylation (Vc,m) rate scaled to 25°C (i.e., Vc,25; μmol CO2·m–2·s–1) and maximum electron transport rate (Jmax) scaled to 25°C (i.e., J25; μmol electron·m–2·s–1) at the global scale. Our results showed that the percentage of variation in observed Vc,25 and J25 explained jointly by the environmental factors (i.e., day length, radiation, temperature, and humidity) were 2–2.5 times and 6–9 times of that explained by area-based leaf nitrogen content, respectively. Environmental factors influenced photosynthetic capacity mainly through photosynthetic nitrogen use efficiency, rather than through leaf nitrogen content. The combination of leaf nitrogen content and environmental factors was able to explain ~56% and ~66% of the variation in Vc,25 and J25 at the global scale, respectively. As a result, our analyses suggest that model projections of plant photosynthetic capacity and hence land–atmosphere exchange under changing climatic conditions could be substantially improved if environmental factors are incorporated into algorithms used to parameterize photosynthetic capacity in ESMs.
    Keywords: Basic Biological Sciences ; Environmental Sciences ; Climate Change ; Climate Variables ; Earth System Models ; Leaf Nitrogen Content ; Photosynthetic Capacity ; Plant Traits ; Environmental Sciences ; Biology ; Ecology
    ISSN: 1051-0761
    E-ISSN: 1939-5582
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  • 9
    Language: English
    In: Advances in Water Resources, Jan, 2013, Vol.51, p.457(22)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.advwatres.2012.04.002 Byline: Jasper A. Vrugt (a)(b), Cajo J.F. ter Braak (c), Cees G.H. Diks (d), Gerrit Schoups (e) Keywords: Particle filtering; Bayes; Sequential Monte Carlo; Markov chain Monte Carlo; DREAM; Parameter and state estimation Abstract: During the past decades much progress has been made in the development of computer based methods for parameter and predictive uncertainty estimation of hydrologic models. The goal of this paper is twofold. As part of this special anniversary issue we first shortly review the most important historical developments in hydrologic model calibration and uncertainty analysis that has led to current perspectives. Then, we introduce theory, concepts and simulation results of a novel data assimilation scheme for joint inference of model parameters and state variables. This Particle-DREAM method combines the strengths of sequential Monte Carlo sampling and Markov chain Monte Carlo simulation and is especially designed for treatment of forcing, parameter, model structural and calibration data error. Two different variants of Particle-DREAM are presented to satisfy assumptions regarding the temporal behavior of the model parameters. Simulation results using a 40-dimensional atmospheric "toy" model, the Lorenz attractor and a rainfall-runoff model show that Particle-DREAM, P-DREAM.sub.(VP) and P-DREAM.sub.(IP) require far fewer particles than current state-of-the-art filters to closely track the evolving target distribution of interest, and provide important insights into the information content of discharge data and non-stationarity of model parameters. Our development follows formal Bayes, yet Particle-DREAM and its variants readily accommodate hydrologic signatures, informal likelihood functions or other (in)sufficient statistics if those better represent the salient features of the calibration data and simulation model used. Author Affiliation: (a) Department of Civil and Environmental Engineering, University of California, Irvine, USA (b) Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands (c) Biometris, Wageningen University and Research Centre, 6700 AC, Wageningen, The Netherlands (d) Center for Nonlinear Dynamics in Economics and Finance, University of Amsterdam, The Netherlands (e) Department of Water Management, Delft University of Technology, Delft, The Netherlands
    Keywords: Computer Simulation -- Models ; Computer Simulation -- Technology Application ; Markov Processes -- Models ; Markov Processes -- Technology Application ; Monte Carlo Methods -- Models ; Monte Carlo Methods -- Technology Application ; Hydrology -- Models ; Hydrology -- Technology Application
    ISSN: 0309-1708
    Source: Cengage Learning, Inc.
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  • 10
    Language: English
    In: Vadose Zone Journal, 2013, Vol.12(1), p.0
    Description: Geophysical methods offer several key advantages over conventional subsurface measurement approaches, yet their use for hydrologic interpretation is often problematic. Here, we introduce theory and concepts of a novel Bayesian approach for high-resolution soil moisture estimation using traveltime observations from crosshole Ground Penetrating Radar (GPR) experiments. The recently developed Multi-try DiffeRential Evolution Adaptive Metropolis with sampling from past states, MT-DREAM(ZS) is being used to infer, as closely and consistently as possible, the posterior distribution of spatially distributed vadose zone soil moisture and/or porosity under saturated conditions. Two differing and opposing model parameterization schemes are being considered, one involving a classical uniform grid discretization and the other based on a discrete cosine transformation (DCT). We illustrate our approach using two different case studies involving geophysical data from a synthetic water tracer infiltration study and a real-world field study under saturated conditions. Our results demonstrate that the DCT parameterization yields the most accurate estimates of distributed soil moisture for a large range of spatial resolutions, and superior MCMC convergence rates. In addition, DCT is admirably suited to investigate and quantify the effects of model truncation errors on the MT-DREAM(ZS) inversion results. For the field example, lateral anisotropy needs to be enforced to derive reliable soil moisture variability. Our results also demonstrate that the posterior soil moisture uncertainty derived with the proposed Bayesian procedure is significantly larger than its counterpart estimated from classical smoothness-constrained deterministic inversions. Comment: in Vadose Zone Journal, 12 (2013)
    Keywords: Physics - Geophysics;
    ISSN: Vadose Zone Journal
    E-ISSN: 1539-1663
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