Ecological Modeling and Simulation Using Error and Uncertainty Analysis Methods (Project CS-1097).

Parameter uncertainty in hydrologic modeling is commonly evaluated, but assessing the impact of spatial input data uncertainty in spatially descriptive ‘distributed’ models is not common. This study compares the significance of uncertainty in spatial input data and model parameters on the output uncertainty of a distributed hydrology and sediment transport model, HYdrology Simulation using Time-ARea method (HYSTAR). The Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm was used to quantify parameter uncertainty of the model. Errors in elevation and land cover layers were simulated using the Sequential Gaussian/Indicator Simulation (SGS/SIS) techniques and then incorporated into the model to evaluate their impact on the outputs relative to those of the parameter uncertainty. This study demonstrated that parameter uncertainty had a greater impact on model output than did errors in the spatial input data. In addition, errors in elevation data had a greater impact on model output than did errors in land cover data. Thus, for the HYSTAR distributed hydrologic model, accuracy and reliability can be improved more effectively by refining parameters rather than further improving the accuracy of spatial input data and by emphasizing the topographic data over the land cover data.

Uncertainty is the intrinsic property of spatial data and one of important factors affecting the course of spatial data mining. There are diversiform forms for the essentiality and aspect of uncertainty in the spatial objects of geographic information system. Essentiality of uncertainty may consist of the components of randomicity, fuzzy, chaos, etc. And the latter, i.e. aspect of uncertainty, may include error uncertainty, location uncertainty, attribute uncertainty, topology uncertainty, inconsonance uncertainty, immaturity uncertainty and so on. Spatial data mining, which is based on uncertainty, is the course of discovering knowledge in the spatial data including the attribute of uncertainty. Spatial data is the necessary object operated by spatial data mining and its uncertainty can exist in the whole process of data input, data processing, data output, etc. Uncertainty can affect directly or indirectly the quality of spatial data mining. Specially, uncertainty of spatial data can affect directly or indirectly the veracity and reliability of ultimate decision-makings and may lead to produce false results and even reverse conclusions. Spatial data mining based on uncertainty has taken synchronously into account the two associated factors of uncertainty of data and spatial data mining. Therefore, due to considering the uncertainty, it can enhance the reliability of discovering knowledge and improve the veracity and reliability of spatial as well as raise the efficiency and practicability for the decision-makings made by operation departments. In this paper, aiming at massive spatial data belonging to the Government Geographic Information System, some work has been done under the conditions of summarizing existing relevant theory and technology of spatial data mining and uncertainty and carry out necessary practice.

Uncertainties pervade spatial data mining. This paper proposes a method of spatial data mining handling randomness and fuzziness simultaneously. First, the uncertainties in spatial data mining are presented via characteristics, spatial data, knowledge discovery and knowledge representation. Second, the aspects of the uncertainties in spatial data mining are briefed. They often appear simultaneously, but most of the existing methods cannot deal with spatial data mining with more than one uncertainty. Third, cloud model is presented to mine spatial data with both randomness and fuzziness. It may also act as an uncertainty transition between a qualitative concept and its quantitative data, which is the basis of spatial data mining in the contexts of uncertainties. Finally, a case study on landslide-monitoring data mining is given. The results show that the proposed method can well deal with randomness and fuzziness during the process of spatial data mining.

Modelling and visualizing multiple spatial uncertainties

Uncertainty analysis have attracted increasing attention of both theory and application over the last decades. Owing to the complex of surrounding, uncertainty analysis of rainfall in urban area is very little. Existing literatures on uncertainty analysis paid less attention on gauge density and rainfall intensity. Therefore, this study focuses on urban area, which a good complement to uncertainty research. In this study, gauge density was investigated with carefully selecting of gauge to covering evenly. Rainfall intensity data were extracted from one rainfall event at begin, summit and ending phases of rainfall process. Three traditional methods (Ordinary Kriging, RBF and IDW) and three machine methods (RF, ANN and SVM) were investigated for the uncertainty analysis. The result shows that (1) gauge density has important influence on the interpolation accuracy, and the higher gauge density means the higher accuracy. (2) The uncertainty is progressively stable with the increasing of rainfall intensity. (3) Geostatistic methods has better result than the IDW and RBF owing to considering spatial variability. The selected machine learning methods have good performance than traditional methods. However, the complex training processing and without spatial variability may reduce its practicability in modern flood management. Therefore, the combining of traditional methods and machine learning will be the good paradigm for spatial interpolation and uncertainty analysis.

Thirty‐seven years post‐eruption, erosion of the debris avalanche at Mount St Helens continues to supply sediment to the Toutle–Cowlitz River system in quantities that have the potential to lower the Level of Protection (LoP) against flooding unacceptably, making this one of the most protracted gravel‐bed river disasters to date. The Portland District, US Army Corps of Engineers (USACE) recently revised its long‐term plan for sediment management (originally published in 1985), in order to maintain the LoP above the Congressionally‐authorized level, while reducing impacts on fish currently listed under the Endangered Species Act, and minimizing the overall cost of managing sediment derived from erosion at Mount St Helens. In revising the plan, the USACE drew on evidence gained from sediment monitoring, modelling and uncertainty analysis, coupled with assessment of future LoP trends under a baseline scenario (continuation of the 1985 sediment management strategy) and feasible alternatives. They applied geomorphological principles and used engineering science to develop a phased Sediment Management Plan that allows for uncertainty concerning future sediment yields by implementing sediment management actions only as, and when, necessary. The phased plan makes best use of the potential to enhance the sediment trap efficiency and storage capacity of the existing Sediment Retention Structure (SRS) by incrementally raising its spillway and using novel hydraulic structures to build islands in the North Fork Toutle River (NFTR) and steepen the gradient of the sediment plain upstream of the structure. Dredging is held in reserve, to be performed only when necessary to react to unexpectedly high sedimentation events or when the utility of other measures has been expended. The engineering‐geomorphic principles and many of the measures in the phased Sediment Management Plan are transferrable to other gravel‐bed river disasters. The overriding message is that monitoring and adaptive management are crucial components of long‐term sediment‐disaster management, especially in volcanic landscapes where future sediment yields are characterized by uncertainty and natural variability. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

Multi-objective spatial optimization problems require spatial data input that can contain uncertainties. Via the validation of constraints and the computation of objective values this uncertainty propagates to the Pareto fronts. Here, we develop a method to quantify the uncertainty in Pareto fronts by finding the extreme lower and upper bound of the range of optimal values in the objective space, i.e. the Pareto interval. The method is demonstrated on a land use allocation problem with initial land use (for objectives and constraints) and soil fertility (for one objective) as uncertain input data. Pareto intervals resulting from uncertain land use data were wide and irregularly shaped, whereas the ones from uncertain soil data were narrow and regularly shaped. Furthermore, in some objective-space regions, optimal land use patterns remained relatively stable under uncertainty, while elsewhere they were clouded. This information can be used to select solutions robust to spatial input data uncertainty.

Introduction: The greatest part of constructed dams belongs to embankment dams and there are many examples of their failures throughout history. About one-third of the world’s dam failures have been caused by flood overtopping, which indicates that flood overtopping is an important factor affecting reservoir projects’ safety. Moreover, because of a poor understanding of the randomness of floods, reservoir water levels during flood seasons are often lowered artificially in order to avoid overtopping and protect the lives and property of downstream residents. So, estimation of dam overtopping risk with regard to uncertainties is more important than achieving the dam’s safety. This study presents the procedure for risk evaluation of dam overtopping due to various uncertaintiess in inflows and reservoir initial condition. Materials and Methods: This study aims to present a practical approach and compare the different uncertainty analysis methods in the evaluation of dam overtopping risk due to flood. For this purpose, Monte Carlo simulation and Latin hypercube sampling methods were used to calculate the overtopping risk, evaluate the uncertainty, and calculate the highest water level during different flood events. To assess these methods from a practical point of view, the Maroon dam was chosen for the case study. Figure. 1 indicates the work procedure, including three parts: 1) Identification and evaluation of effective factors on flood routing and dam overtopping, 2) Data collection and analysis for reservoir routing and uncertainty analysis, 3) Uncertainty and risk analysis. Figure 1- Diagram of dam overtopping risk evaluation Results and Discussion: Figure 2 shows the results of the computed overtopping risks for the Maroon Dam without considering the wind effect, for the initial water level of 504 m as an example. As it is shown in Figure. 2, the trends of the risk curves computed by the different uncertainty analysis methods are similar. As it can be seen, the risk curves computed by the LHS are slightly higher than those curves computed by the MCS method. Also as it is observed, the differences between risk values of the two methods increase in longer return periods. Variations of overtopping risk with increasing the initial water level and return period related to overtopping risk in the 2-year return period for the initial water level of 470 m are shown in Table1. The results show that elongation of return period plays a more important role in increasing the risk, than the increase of initial water level. T Method 2→2 2→50 2→100 2→1000 2→5000 2→10000 470→470 MCS 1 5 9 23 42.36 58 470→478 2 7 15.6 37 58.34 79 470→485 5.6 13.6 28.6 55.6 85.67 112.6 470→493 10.3 32.6 54 95.6 127.34 152 470→504 40.3 83 117.3 165 200.34 224.3 470→470 LHS 1 5.34 11 25.3 43 60.3 470→478 2.3 8.6 18 39.3 60.67 84 470→485 5.3 17.3 32.6 58.3 89 114.6 470→493 13.3 37.6 57.6 97 133.34 160.3 470→504 41.6 87.3 119.6 173 205 233.3 Figure 2- Overtopping risk in the initial water level of 504 m, without considering the wind effect Conclusions: This study applies MCS and LHS methods to analyze the uncertainty and evaluate the dam overtopping risk consideringthe uncertainties in input variables, such as quintile of flood peak discharge, initial levels of water and spill coefficients. The results show that the uncertainty of water level calculated by MCS is higher than that calculated by LHS. In addition, the overtopping risk calculated by LHS is higher than that calculated by MCS. Furthermore, the increase of inflow rate influences the variations of the overtopping risk more than the increase of the return period. In addition, evaluation of the results indicates that the overtopping risk is an important issue in the Maroon dam. So, a comprehensiverisk analysis procedure in conjunction with uncertainty gives very important information for decision makers to make better judgments in dam operation based on uncertainty in inputs.

Spatial data refers to extracting and mining the hidden, implicit, valid, novel and interesting spatial or non-spatial patterns or rules from large-amount, incomplete, noisy, fuzzy, random, and practical spatial databases. In which an important issue but remains underdeveloped is to reveal and handle the uncertainties in spatial data mining. In This work, uncertainty of spatial data is briefly analyzed firstly, including the types and origins of uncertainty, their models of measurement and propagation. Then, some uncertainty factors in operation of spatial data are discussed and some uncertainty handling methods are adopted, including maximum variance data discretization and fuzzy belief function. Finally, we think the process of spatial data can be regarded as a complex system, a linear serial processing system in engineering control systems. An uncertainty propagation model of spatial data - fuzzy logic uncertainty propagation model with credibility factor is developed. Moreover, several key problems about uncertainty handling and propagation in spatial data are put forward.

Uncertainty analysis is a key element of sound techno-economic analysis (TEA) of CO2 Capture and Storage (CCS) technologies and systems, and in the communication of TEA results. Many CCS technologies are relatively novel, with only few large-scale projects constructed and in operation to date. Therefore, uncertainties in technology performance and costs are often substantial, making it imperative that they be characterized and reported. Although uncertainty analysis itself is not novel, with some methods already frequently used by the CCS TEA community, a document that provides a comprehensive overview of methods and approaches, as well as guidance on their selection and use, is still lacking. Given its importance, we seek to fill this gap by providing a critical review of uncertainty analysis methods along with guidance on the selection and use of these methods for CCS TEAs, highlighting good practice and examples from the CCS literature. The paper starts by identifying the different audiences for CCS TEAs, the different modelling approaches available for CCS technology performance and cost analysis, and the different roles that uncertainty analysis may play. It then continues to discuss established, as well as emerging, uncertainty analysis methods and addresses how and when each method is best used, as well as common pitfalls. We argue that the most commonly used method of one-parameter-at-a-time ‘local’ sensitivity analysis may often be a suboptimal choice, and that other approaches may be more suitable or lead to more insight, especially since uncertainty analysis software is becoming more widespread and easier to use. Finally, the paper discusses the benefits of advanced uses of uncertainty analysis in, for instance, the design of CCS experiments or in the design and planning of CCS infrastructure. Sound uncertainty analysis has an important role to play in TEAs of CCS technologies and systems, and there are many opportunities to bring the use of uncertainty analysis to a higher level than currently practiced. This review of and guidance on available methods is intended to help accelerate continued methods development and their application to more robust and meaningful CCS performance and costing studies.

Satellites, while offering excellent spatial coverage, determine precipitation indirectly, using algorithms that transform satellite-sensed radiance (either emitted or scattered) from clouds or raindrops into precipitation. A large uncertainty is associated with satellite precipitation estimates, stemming from unknown variation in space and time of the physical and statistical relationships between precipitation and satellite-sensed radiance. To mitigate this, satellite algorithms must be calibrated and verified using surface precipitation sampled from different climate regimes and seasons. Recently developed statistical techniques have been used effectively to reduce spatial sampling error associated with sparsely distributed raingages and thereby improve our understanding of satellite algorithm quality. This paper provides an example of satellite precipitation validation, including a description of the types of satellite data used to estimate precipitation, as well as the results from a major project (the Global Precipitation Climatology Project [GPCP]), to estimate global precipitation through a combination of satellite and raingage products. In addition, a recently developed procedure to investigate spatial averaging, scaling, and uncertainty analysis will be used to examine the GPCP product. Specifically, uncertainty analysis applied to comparisons between satellite monthly rainfall estimates and rainfall estimates constructed from Pacific atoll-sited raingauge sites will be discussed.

The National Weather Service (NWS) maintains the National Operational Hydrologic Remote Sensing Center, based in Minneapolis, to generate operational hydrologic products from in situ and remotely sensed snow cover data sets. The Center maintains an Airborne Snow Survey Program, a Satellite Hydrology Program, and a Snow Estimation and Updating Program. In all three programs, the Center relies heavily on the use of multiple Geographic Information Systems (GIS) to process, analyze, and distribute spatial snow cover data sets. Real-time, airborne snow water equivalent data and satellite areal extent of snow cover data are used operationally by the National Weather Service, the U.S. Army Corps of Engineers and other Federal, state, and private agencies when issuing spring flood outlooks, water supply outlooks, river and flood forecasts, and reservoir inflow forecasts. The remotely sensed and interpolated, gridded, snow water equivalent data products are generated by hydrologists in the Minneapolis office and distributed electronically, in near real-time, to NWS and non-NWS end-users in both alphanumeric and graphic format. The reliable, real-time, snow water equivalent information is critical to water managers and disaster emergency services officials who are required to make decisions with regard to snow melt flooding, reservoir regulation, and water supply allocation.KeywordsSnow CoverGeographic Information SystemSnow Water EquivalentNational Weather ServiceGeographic Resource Analysis Support SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Recent decades have seen tremendous increases in the quantity of empirical ecological data collected by individual investigators, as well as through research networks such as FLUXNET (Baldocchi et al., 2001). At the same time, advances in computer technology have facilitated the development and implementation of large and complex land surface and ecological process models. Separately, each of these information streams provides useful, but imperfect information about ecosystems. To develop the best scientific understanding of ecological processes, and most accurately predict how ecosystems may cope with global change, integration of empirical and modeling approaches is necessary. However, true integration - in which models inform empirical research, which in turn informs models (Fig. 1) - is not yet common in ecological research (Luo et al., 2011). The goal of this workshop, sponsored by the Department of Energy, Office of Science, Biological and Environmental Research (BER) program, was to bring together members of the empirical and modeling communities to exchange ideas and discuss scientific practices for increasing empirical - model integration, and to explore infrastructure and/or virtual network needs for institutionalizing empirical - model integration (Yiqi Luo, University of Oklahoma, Norman, OK, USA). The workshop included presentations and small group discussions that coveredmore » topics ranging from model-assisted experimental design to data driven modeling (e.g. benchmarking and data assimilation) to infrastructure needs for empirical - model integration. Ultimately, three central questions emerged. How can models be used to inform experiments and observations? How can experimental and observational results be used to inform models? What are effective strategies to promote empirical - model integration?« less

An efficient D-vine copula-based coupling uncertainty analysis for variable-stiffness composites

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