Spatial technologies for coastal management

Recent literature on building energy performance simulation leans toward implementing uncertainty analysis (UA), instead of deterministic solutions, to handle ever-existing and pivotal uncertainties in building design decision-making process. Variations in weather temperature, degradation of building envelope material properties over time, and random behavior of occupants, among all, are the key sources of uncertainty in building energy consumption predictions. The UA couples to the sensitivity analysis (SA) to identify the most influential inputs on the uncertainties of the building energy consumption. This paper describes a newly-developed UA and SA predictive tool for building energy performance simulations. Energy performance simulations are based on a resistance-capacitance thermal model for the building. For a hypothetical residential building in College Station, Texas, USA, the present work describes and compares predicted probability distribution and sensitivity indexes produced by the UA-SA tool using a transient (dynamic) response analysis (TRA) and static response analysis (SRA). For brevity, the analysis considers uncertainty only for the exterior walls’ parameters including thickness, thermal conductivity, heat transfer coefficient, density, and heat capacity; i.e., a five-dimensional problem is solved. Compared to the TRA, predictions from the SRA underestimate the annual energy consumption up to 30%; however, SRA is significantly faster. Nonetheless, sensitivity indexes from the SRA and TRA closely match.

An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the Soil and Water Assessment Tool

Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China

In 2019, the International Atomic Energy Agency launched a coordinated research project titled “Advancing the State of the Practice in Uncertainty and Sensitivity Methodologies for Severe Accident Analysis in Water Cooled Reactors of CANDU Types.” This project involved participation from 22 organizations representing 18 member states. The purpose of the project was to advance the understanding and characterization of the sources of uncertainty and assess their impacts on key water-cooled reactor figures of merit predicted by severe accident (SA) codes. Three organizations from three member states participated in the CANada Deuterium Uranium (CANDU) exercise: Canadian Nuclear Laboratories, Canada; Korea Atomic Energy Research Institute, Republic of Korea; and University Politehnica of Bucharest, Romania. Each contributor used its own SA code and uncertainty analysis tools and selected its own uncertain input variables with associated probability distributions. All three participants performed a station blackout scenario for a generic CANDU 6 station, using similar normal operating conditions and analysis assumptions. This paper presents the results of the uncertainty and sensitivity calculations from each contributor and discusses the key findings, lessons learned, and future work.

Uncertainty Analysis by Bayesian Inference

The Sea Level Affecting Marshes Model (SLAMM) was applied at six major estuaries along Florida’s Gulf Coast (Pensacola Bay, St. Andrews/Choctawhatchee Bays, Apalachicola Bay, Southern Big Bend, Tampa Bay and Charlotte Harbor) to provide quantitative and spatial information on how coastal ecosystems may change with sea level rise (SLR) and to identify how this information can be used to inform adaption planning. High resolution LiDAR-derived elevation data was utilized under three SLR scenarios: 0.7 m, 1 m and 2 m through the year 2100 and uncertainty analyses were conducted on selected input parameters at three sites. Results indicate that the extent, spatial orientation and relative composition of coastal ecosystems at the study areas may substantially change with SLR. Under the 1 m SLR scenario, total predicted impacts for all study areas indicate that coastal forest (-69,308 ha; -18%), undeveloped dry land (-28,444 ha; -2%) and tidal flat (-25,556 ha; -47%) will likely face the greatest loss in cover by the year 2100. The largest potential gains in cover were predicted for saltmarsh (+32,922 ha; +88%), transitional saltmarsh (+23,645 ha; na) and mangrove forest (+12,583 ha; +40%). The Charlotte Harbor and Tampa Bay study areas were predicted to experience the greatest net loss in coastal wetlands The uncertainty analyses revealed low to moderate changes in results when some numerical SLAMM input parameters were varied highlighting the value of collecting long-term sedimentation, accretion and erosion data to improve SLAMM precision. The changes predicted by SLAMM will affect exposure of adjacent human communities to coastal hazards and ecosystem functions potentially resulting in impacts to property values, infrastructure investment and insurance rates. The results and process presented here can be used as a guide for communities vulnerable to SLR to identify and prioritize adaptation strategies that slow and/or accommodate the changes underway.

As an important marine sedimentary geomorphic unit, the spatial distribution characteristics of tidal sand ridges hold significant guiding significance for oil and gas resource exploration and coastal zone management. This study systematically sorts out the morphological characteristics, sedimentary structures, and formation-evolution mechanisms of typical tidal sand ridges at home and abroad. Through comparative analysis, it is found that relevant research mainly focuses on two aspects: the statistical characterization parameters and sedimentary facies division of continental shelf sand ridges, as well as hydrodynamic-sedimentary coupling simulation and 3D geological modeling. Based on the integration of multi-source data, a multi-dimensional characterization system is proposed, which includes morphological parameters (aspect ratio, slope aspect angle), sedimentary sequences (grain size rhythm, ichnofossil assemblage), and hydrodynamic responses (tidal current rose diagram, vortex intensity). The applicability of this system is verified by previous comparative studies on tidal sand ridges in the offshore area of the Yangtze River Estuary and the North Sea.

Challenges and progress of uncertainty analysis for the pebble-bed high-temperature gas-cooled reactor

Abstract. Runoff generation processes and pathways vary widely between catchments. Credible simulations of solute and pollutant transport in surface waters are dependent on models which facilitate appropriate, catchment-specific representations of perceptual models of the runoff generation process. Here, we present a flexible, semi-distributed landscape-scale rainfall-runoff modelling toolkit suitable for simulating a broad range of user-specified perceptual models of runoff generation and stream flow occurring in different climatic regions and landscape types. PERSiST (the Precipitation, Evapotranspiration and Runoff Simulator for Solute Transport) is designed for simulating present-day hydrology; projecting possible future effects of climate or land use change on runoff and catchment water storage; and generating hydrologic inputs for the Integrated Catchments (INCA) family of models. PERSiST has limited data requirements and is calibrated using observed time series of precipitation, air temperature and runoff at one or more points in a river network. Here, we apply PERSiST to the river Thames in the UK and describe a Monte Carlo tool for model calibration, sensitivity and uncertainty analysis.

Integrative Data Analysis (IDA) encompasses a collection of methods for data synthesis that pools participant-level data across multiple studies. Compared with single-study analyses, IDA provides larger sample sizes, better representation of participant characteristics, and often increased statistical power. Many of the methods currently available for IDA have focused on examining developmental changes using longitudinal observational studies employing different measures across time and study. However, IDA can also be useful in synthesizing across multiple randomized clinical trials to improve our understanding of the comprehensive effectiveness of interventions, as well as mediators and moderators of those effects. The pooling of data from randomized clinical trials presents a number of methodological challenges, and we discuss ways to examine potential threats to internal and external validity. Using as an illustration a synthesis of 19 randomized clinical trials on the prevention of adolescent depression, we articulate IDA methods that can be used to minimize threats to internal validity, including (1) heterogeneity in the outcome measures across trials, (2) heterogeneity in the follow-up assessments across trials, (3) heterogeneity in the sample characteristics across trials, (4) heterogeneity in the comparison conditions across trials, and (5) heterogeneity in the impact trajectories. We also demonstrate a technique for minimizing threats to external validity in synthesis analysis that may result from non-availability of some trial datasets. The proposed methods rely heavily on latent variable modeling extensions of the latent growth curve model, as well as missing data procedures. The goal is to provide strategies for researchers considering IDA.

Technology advancements over the years have improved our understanding of the subsurface. This paper focuses on how integration of available geological and engineering datasets was used to de-risk the geological and petrophysical uncertainty of Reservoir B-1.1 in Oredo field in Niger Delta. The oil reservoirs in this field experienced late fair aquifer support. However, the producing gas reservoirs exhibit volumetric drive behavior so far. Analysis of logs, production, pressure and PVT data were carried out. Fluid and material balance (analytical) models were built and fluid model was tested for validity. There was 80% difference between the material balance volume and the static model volume. The material balance volume is supported by historical Bottom Hole Pressure surveys and recent wireline formation test acquired from a sidetracked well in the field. This wireline formation test shows substantive pressure depletion across the reservoir of interest, which can be attributed to production activities from the existing conduit. Based on the geological analysis, there is no evidence of compartmentalization to account for the pressure and production behavior of the well producing from this reservoir. Sensitivity and uncertainty analysis on selected parameters were carried out on the base case geological/static model to validate the Hydrocarbon in Place volume for history matching and performance prediction. The dynamic simulation run under uncertainty validated the material balance volume. This led to the revision of the petrophysical evaluation and update of the static model.

Uncertainty analysis of calibrated parameter values of an urban storm water quality model using metropolis Monte Carlo algorithm

Reliable evaluations of the radiological consequences of hypothetical severe accidents in nuclear power plants (NPPs) play a key role in supporting the emergency and preparedness plans during such abnormal events. Also triggered by the growing requests of the national regulators, one of the milestones of the Nuclear Safety Research program (NUSAFE) of the Karlsruhe Institute of Technology (KIT) is the improvement of the performance of the codes employed for such evaluations in the current and innovative NPPs, which are in operation and going to be built in EU and worldwide. Having this in mind, a calculation platform of state-of-art codes has been assessed at KIT to evaluate the accident progression, the radiological source term, and the behavior of the fission product dispersion to the environment by also taking into account the corresponding uncertainties. In particular, the CASMO5 lattice physics code, developed by Studsvik Scandpower, is used for a proper evaluation of the fuel inventory loaded in the reactor core. The European reference Accident Source Term Evaluation Code (ASTEC), developed by IRSN (France), is employed to evaluate the accident progression from the initiation to the fission produced release to the environment. The use of the KArlsruhe Tool for Uncertainty and Sensitivity Analysis (KATUSA), developed by KIT, aims at performing the uncertainty quantification of the source term. Finally, the Java Real-time On-line DecisiOn Support (JRODOS) is employed for analyzing the radiological consequences of the accident. The KIT platform has been employing since some years in the frame of the KIT participation to different European projects, i.e., MUSA, SASPAM-SA on SMRs, SANE. In the paper, an overall view of the KIT strategy on severe accident analyses will be provided, and examples of the application of the above-mentioned platform to generic NPPs will be provided, i.e., PWR Konvoi.

Abstract. The choice of different model structures and the subsequent parameter identification are relevant and usually have a strong subjective component. The impacts of various decisions associated with the internal configuration of a given watershed model need greater exploration to ensure confidence in hydrologic and water quality modeling results, yet they are often overlooked. In this study, a non-subjective approach to selecting alternative methods is developed. The goal is to assess the impact of different configurations of the Soil and Water Assessment Tool (SWAT) model on consequential hydrologic and water quality behavior. A total of 12 SWAT configurations, each containing a unique combination of alternative algorithms in estimating surface runoff, potential evapotranspiration, and water routing, were calibrated against flow and nutrient data using the Dynamically Dimensioned Search (DDS) optimization algorithm within the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT). The assessment ensures that non-subjective decisions are formed by fully exploring different model configurations and consequences in the optimization process. The accuracy of calibrated outputs was sensitive to the choice of alternative methods, especially for ammonia-N predictions: NSE values varied from 0.23 to 0.67 for streamflow, from 0.59 to 0.82 for sediment, and from -0.15 to 0.66 for ammonia-N. Similarly, uncertainty in the calibrated outputs varied among alternative methods: inclusion rate varied from 31% to 58% for streamflow, from 46% to 71% sediment, and from 25% to 87% for ammonia-N. The results highlight the significance of non-subjectivity in selecting alternative methods in the calibration of complex watershed models. The potential impact of selecting alternative methods should be fully explored in advance before further applications of complex watershed simulation models.

The fleet of Electric Vehicles is part of the Smart Grid ecosystem. The EV recharge process and its mathematical modeling is an important component that impacts on the stability and the security of energy supply. Global Sensitivity Analysis support tools are used to measure the relative importance of the inputs contributing to produce the model’s outputs. Recently, the energy flows being exchanged between Smart Grid and an EV fleet are part of the Transactive Energy Control scenario in which an economic incentive becomes an impacting parameter to investigate further because it can produce a substantial behavioral change. In this article the authors discuss about the experimental workflows used to support the impact assessment studies and about the role of Sensitivity and Uncertainty Analysis tools helping to optimize the EV fleet recharge processes.