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25 results for “timing uncertainty”
Ensemble projections (+ uncertainties) of contemporary (2012-2031) and future (2081-2100) mean annual plankton/phytoplankton/zooplankton species diversity (and species turn-over in time) for the global surface open ocean.
<p><em><strong>Gridded spatial fields (raster objects) containing the species distribution models (SDMs) projections of mean annual plankton total plankton, phytoplankton and zooplankton species diversity from Benedetti et al. (2021). </strong></em></p> <p>The present .grd file ('rasterStack' object in R) contain the fields of mean annual surface plankton/phytoplankton/zooplankton species diversity for the contemporary (2012-2031) and future (2081-2100) conditions of the global open ocean (i.e., data underlying those maps in Figure 1 and Figure 3 of Benedetti et al., 2021). Layers quantifying the uncertainty (i.e., the variablity across models projections estimated through the standard deviation) in ensemble projections were also added (i.e., data underlying the maps in Supplementary Figure 4). See the Methods section of Benedetti et al. (2021) for a full description of the methodology and the ensemble SDMs forecasting framework. The raster layers follow the 1°x1° cell grid of the World Ocean Atlas (https://www.ncei.noaa.gov/).</p> <p>In short, we empirically modelled the monthly and mean annual diversity patterns stemming from the distribution of 860 plankton species (336 phytoplankton, 524 zooplankton) spanning 13 phyla, 71 orders and 324 genera through an ensemble approach based on SDMs. The considered species cover a wide range of traits and functions, representing 10 major plankton functional groups (PFGs; three phytoplankton and seven zooplankton groups). We compiled the species occurrence records from various data sources (available here: https://zenodo.org/record/5101349#.YO7Dqm469lM) and aggregated them onto a monthly-resolved 1°x1° grid, excluding observations from regions where the seafloor is shallower than 200 m. We matched these binned open ocean records with observation-based climatologies of environmental predictors (temperature, dissolved oxygen concentration, solar irradiance, macronutrients concentration, chlorophyll a concentration) that reflect the climatic and biogeochemical conditions of the surface open ocean. Four types of SDMs (generalized linear models, generalized additive models, artificial neural networks, and random forests) were fitted to model the species’ current environmental habitat suitability patterns. For each SDMs, we used four alternative pools of predictors. Assuming niche conservatism, we projected each of the 16 resulting species-level habitat suitability models into the future using outputs from five ESMs belonging to the Coupled Model Intercomparison Project 5 (CMIP5) that were forced by the Representative Concentration Pathway 8.5 (RCP8.5) scenario of high greenhouse gas concentrations. To this end, we first computed the modelled monthly climatologies of the selected predictors for the 2012-2031 and 2081-2100 periods, and derive the future monthly anomalies from the differences between these two time periods. These anomalies were added to the observation-based monthly climatologies (i.e., those used to train the SDMs) to estimate the future environmental conditions of the ocean, and projected the SDMs in these future conditions. Finally, we estimated the mean annual present and future alpha diversity (species richness; SR) and beta diversity (species turnover through time) patterns for both trophic levels, for each cell, from the ensemble of SDMs. SR ensembles are estimated as the sum of all species’ habitat suitability patterns averaged across all 80 possible combinations (i.e., "ensemble members") of SDMs (n = 4), ESMs (n = 5) and predictor pools (n = 4). To assess the uncertainties of our diversity projections based on the ensemble members, we compute the interquartile range of the 80 ensemble members SR projections. We calculate species turnover as the change in mean annual species composition between present and future time based on Jaccard’s dissimilarity index and by decomposing this total turnover into the true species turnover (ST, also known as species replacement) and the nestedness (SR change) components. Numerous tests are conducted to ensure the robustness of the results with regard to the spatially and temporally highly uneven sampling effort as well as with regard to the relative role of different predictors.</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
Timing uncertainty in collective risk dilemmas encourages group reciprocation and polarization
<p><span><span><span><span><span><span><span><span><span><span><span>Social dilemmas are often shaped by actions involving uncertain returns only achievable in the future, such as climate action or voluntary vaccination. In this context, uncertainty may produce non-trivial effects. Here, we assess experimentally — through a collective risk dilemma — the effect of timing uncertainty, i.e. how uncertainty about when a target needs to be reached affects the participants' behaviours. We show that timing uncertainty prompts not only early generosity but also polarised outcomes, where participants' total contributions are distributed unevenly. Furthermore, analysing participants' behaviour under timing uncertainty reveals an increase in reciprocal strategies. A data-driven game-theoretical model captures the self-organizing dynamics underpinning these behavioural patterns. Timing uncertainty thus casts a shadow on the future that leads participants to respond early, while reciprocal strategies appear to be important for group success. Yet, the same uncertainty also leads to inequity and polarisation, necessitating the inclusion of new incentives handling these societal issues. </span></span></span></span></span></span></span></span></span></span></span></p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
The Effect of QPF on Real-time Deterministic Hydrologic Forecast Uncertainty
<p>The use of Quantitative Precipitation Forecast (QPF) in hydrologic forecasting is commonplace, but QPF is subject to considerable error. When QPF is included as a model forcing in the hydrological forecast process, significant error is passed to subsequent hydrologic predictions. Two questions arise: (1) are the resulting observed hydrologic forecast errors sufficiently large to suggest the use of zero QPF in the forecast process; if the use of QPF is indicated, (2) how many periods (hours) of QPF (1-, 6-, 12-,..., 72-h...) should be used? Also, do forecast conditions exist under which the use of QPF should be different? This study presents results from two real-time hydrologic forecast experiments, focused on the NOAA/NWS Ohio River Forecast Center (OHRFC). The experiments rely on forecasts from subbasins at 38 forecast point locations, ranging in drainage area, geographic location within the Ohio River Valley, and watershed response time. Results from an experiment, spanning all flow ranges, for the August 10, 2007 - August 31, 2009 period, show that non-zero QPF produces smaller hydrologic forecast error than zero QPF. A second experiment, January 23, 2009 through September 15, 2010, suggests that QPF should be limited to 6- to 12-h duration for flood forecasts. Beyond 12-h, hydrologic forecast error increases substantially across all forecast ranges, but errors are much larger for flood forecasts. Increased durations of QPF produce smaller forecast error than shorter QPF durations only for non-flood forecasts. Experimental results are shown to be consistent with NWS, April 2001 to October 2016, forecast verification statistics for the OHRFC.</p>
Timing uncertainty in collective risk dilemmas encourages group reciprocation and polarization
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Supplementary Data: Real-time optimal flood control decision making under uncertainty
<p>The files in this record contain data for real-time optimal flood control decision making and risk propagation under multiple uncertainties considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Pubugou Reservoir data;</li> <li>Source code and results of the Martingale Model of Forecast Evolution (MMFE);</li> <li>Source code and results of the SMAA-2 model;</li> <li>Source code and results of SMAA-TOPSIS model;</li> <li>Source code and results of the stochastic programming with recourse model.</li> </ul>
Data for "Uncertainties too large to predict tipping times of major Earth system components from historical data"
<p>All data needed to reproduce the figures from the Science Advances manuscript "Uncertainties too large to predict tipping times of major Earth system components from historical data" by Ben-Yami et al.</p> <p>Figs12SamplePathData.zip and Fig3SamplePathData.zip includes the generated synthetic timeseries of the conceptual models in Figs 1-3.</p> <p>The .txt files are the different AMOC observational time series, with the file names structured as dataset_fingerprint.txt. C18 and C18_2GMT are the subpolar gyre SSTs minus one times and twice the global mean SSTs, respectively. The dipole fingerprint is as defined in the text. The first column is the date, and the second column the fingeprint values. The values are monthly means and thus the value for the day of the month in the date is not significant.</p>
Time-Dependent Probabilistic Tsunami Inundation Assessment Using Mode Decomposition to Assess Uncertainty for an Earthquake Scenario
<p>This is the dataset of the manuscript submitted to JGR Ocean (Feb 2021).</p>
Constraining Bedrock Groundwater Residence Times in a Mountain System with Environmental Tracer Observations and Bayesian Uncertainty Quantification: Modeling and Data Package
<p>Here we present field observations of dissolved noble gases (He, Ne, Ar, Kr, and Xe), Chloroflourcarbons (CFCs), Sulfurhexaflouride (SF6), and tritium (3H) sampled from the PLM1, PLM6, and PLM7 wells in the East River Colorado (USA) sampled in May, 2021. This observation dataset, along with the presented python modeling scripts to interpret the data, can aide in quantifying groundwater residence times and recharge conditions. The README files describes the directories and scripts.</p>
Narratives on the present and the future in the time of Covid-19 pandemic: Uncertainty, subjective feeling and the role of positive anticipatory states.
<p><strong>Narratives on the present and the future in the time of Covid-19 pandemic: Uncertainty, subjective feeling and the role of positive anticipatory states.</strong></p>
Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector. Energy Policy (2023). Supplementary Information on Assumptions and Results
<p>This dataset supplements the article with the title "Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector", published in Energy Policy. </p> <p>The dataset contains the following:</p> <ul> <li>The Latin Hypercube Sample of the multipliers that are applied to the key input parameters of ETSAP-TIAM in order to generate 1000 different states of the world regarding economic and demographic growth, energy resources potentials, energy technology costs, climate sensitivity and radiative forcing, LULUCF CO<sub>2</sub> sink potential, CO<sub>2</sub> sequestration potential, and decoupling between energy consumption and economic development. The multipliers are sampled from the underlying probability distributions described in the article. </li> <li>The results (at the global scale) from four scenario families for each one of the 1000 wofld states. These scenario families are: <ul> <li>BASE_SSP2: Describes the development of the global energy system consistent with recent trends and policies.</li> <li>2C_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 2 °C as the maximum post-industrial temperature change from 2020 to 2100.</li> <li>2C_SSP2_DA30: Delayed climate action. The climate change mitigation policies of 2C_SSP2 start in 2030. </li> <li>1p5c_OS_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 1.5 °C as the maximum post-industrial temperature change from 2020 onwards to 2100</li> </ul> </li> </ul> <p>Key results included in the dataset are: Temperature change, Radiative Forcing, GHG concentrations, CO2 emissions, Marginal abatement cost, Electricity Supply Primary Energy Consumption, and Annual Total Global Energy System Cost.</p> <p>The dataset also includes sectoral results regarding energy consumption and use, such as shares of different electric uses, shares of hydrogen consumption in end-use sectors, hydrogen supply, Demand electrification by sector, Renewable energy consumption by sector, Alternative fuels consumption in transport, and Total final energy consumption by sector. </p>
Routing short-haul trucks under the uncertainties of travel time and service time
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Uncertainty in times of COVID-19: Raw survey data
<p>Data from a survey of consumer expectations</p> <p>From April 24, 2020, through June 22, 2020, Fabian Lange and Lars Vilhuber conducted the survey "Uncertainty in COVID-19 times". The survey is a single-question survey focusing on people's anticipation about social distancing rules and firm closures during the 2020 COVID-19 health crisis.</p>
Data Set and Replication Package of Paper on Handling Environmental Uncertainty in Design Time Access Control Analysis
<p>Data set and replication package for Paper "Handling Environmental Uncertainty in Design Time Access Control Analysis".</p> <p>The data set contains an overview of used case studies, with illustrations and descriptions.</p> <p>The replication package contains the implemented application as well as model instances of every case study used for the evaluation.</p>
Importance of Parametric Uncertainty in Predicting Probability Distributions for Burst Wait-Times in Fissile Systems
<p>In accordance with EPSRC funding requirements this folder contains all raw data relevant to the named paper: Importance of Parametric Uncertainty in Predicting Probability Distributions for Burst Wait-Times in Fissile Systems.</p>
Supporting data for "Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions"
<p>Supporting data for "Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions" submitted to Water Resources Research.</p> <p>Contains:</p> <ul> <li>SWMM template files.</li> <li>Objective function values for all model runs.</li> <li>Jupyter Notebooks used to create figures and tables for the article.</li> <li>Copy of rainfall runoff data available from <a href="https://doi.org/10.5281/zenodo.3931582">https://doi.org/10.5281/zenodo.3931582</a></li> </ul> <p>For python implementations of the Hydrograph Matching Algorithm (Ewen 2011) see <a href="https://doi.org/10.5281/zenodo.3923792">https://doi.org/10.5281/zenodo.3923792</a></p> <p>Ewen, John. “Hydrograph Matching Method for Measuring Model Performance.” <em>Journal of Hydrology</em> 408, no. 1–2 (September 2011): 178–87. <a href="https://doi.org/10.1016/j.jhydrol.2011.07.038">https://doi.org/10.1016/j.jhydrol.2011.07.038</a>.</p>
Data from: Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors
Calibration is the rate-determining step in every molecular clock analysis and, hence, considerable effort has been expended in the development of approaches to distinguish good from bad calibrations. These can be categorized into a priori evaluation of the intrinsic fossil evidence, and a posteriori evaluation of congruence through cross-validation. We contrasted these competing approaches and explored the impact of different interpretations of the fossil evidence upon Bayesian divergence time estimation. The results demonstrate that a posteriori approaches can lead to the selection of erroneous calibrations. Bayesian posterior estimates are also shown to be extremely sensitive to the probabilistic interpretation of temporal constraints. Furthermore, the effective time priors implemented within an analysis differ for individual calibrations when employed alone and in differing combination with others. This compromises the implicit assumption of all calibration consistency methods, that the impact of an individual calibration is the same when used alone or in unison with others. Thus, the most effective means of establishing the quality of fossil-based calibrations is through a priori evaluation of the intrinsic palaeontological, stratigraphic, geochronological and phylogenetic data. However, effort expended in establishing calibrations will not be rewarded unless they are implemented faithfully in divergence time analyses.
Data from: Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media
In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen–Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.
Estimating uncertainty in divergence times among three-spined stickleback clades using the multispecies coalescent
<p>Incomplete lineage sorting (ILS) can lead to biased divergence time estimates. To explore if and how ILS has influenced the results of a recent study of worldwide phylogeny of three-spined sticklebacks (Gasterosteus aculeatus), we estimated divergence times among major clades by applying both a concatenation approach and the multispecies coalescent (MSC) model to single-nucleotide polymorphisms. To further test the influence of different calibration strategies, we applied different calibrations to the root and to younger nodes in addition to the ones used in the original study. Both the updated calibrations and the application of the MSC model influenced divergence time estimates, sometimes significantly. The new divergence time estimates were more ancient than in the previous study for older nodes, whereas the estimates of younger nodes were not strongly affected by the re-analyses. However, given the applied MSC method employs a simple substitution model and cannot account for changes in population size, we suggest that different analytical approaches and calibration strategies should be used in order to explore uncertainty in divergence time estimates. This study provides a valuable reference timeline for the ages of worldwide three-spined stickleback populations and emphasizes the need to embrace, rather than obscure, uncertainties around divergence time estimates.</p>
Data from: Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media
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