Download or read book Uncertainty Metrics for Coupled Watershed Models written by Geoff Parker and published by . This book was released on 2009 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Uncertainty and Sensitivity Analysis for Watershed Models with Calibrated Parameters written by Seunguk Lee and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis provides a critique and evaluation of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology, and provides an appraisal of sensitivity analysis methods for watershed models with calibrated parameters. The first part of this thesis explores the strengths and weaknesses of the GLUE methodology with commonly adopted subjective likelihood measures using a simple linear watershed model. Recent research documents that the widely accepted GLUE procedure for describing forecasting precision and the impact of parameter uncertainty in rainfall-runoff watershed models fails to achieve the intended purpose when used with an informal likelihood measure (Christensen, 2004; Montanari, 2005; Mantovan and Todini, 2006; Stedinger et al., 2008). In particular, GLUE generally fails to produce intervals that capture the precision of estimated parameters, and the distribution of differences between predictions and future observations. This thesis illustrates these problems with GLUE using a simple linear rainfall-runoff model so that model calibration is a linear regression problem for which exact expressions for prediction precision and parameter uncertainty are well known and understood. The results show that the choice of a likelihood function is critical. A likelihood function needs to provide a reasonable distribution for the model errors for the statistical inference and resulting uncertainty and prediction intervals to be valid. The second part of this thesis discusses simple uncertainty and sensitivity analysis for watershed models when parameter estimates result form a joint calibration to observed data. Traditional measures of sensitivity in watershed modeling are based upon a framework wherein parameters are specified externally to a model, so one can independently investigate the impact of uncertainty in each parameter on model output. However, when parameter estimates result from a joint calibration to observed data, the resulting parameter estimators are interdependent and different sensitivity analysis procedures should be employed. For example, over some range, evaporation rates may be adjusted to correct for changes in a runoff coefficient, and vice versa. As a result, descriptions of the precision of such parameters may be very large individually, even though their joint response is well defined by the calibration data. These issues are illustrated with the simple abc watershed model. When fitting the abc watershed model to data, in some cases our analysis explicitly accounts for rainfall measurement errors so as to adequately represent the likelihood function for the data given the major source of errors causing lack of fit. The calibration results show that the daily precipitation from one gauge employed provides an imperfect description of basin precipitation, and precipitation errors results in correlation among flow errors and degraded the goodness of fit.

Download or read book Parameter Estimation and Uncertainty Quantification in Water Resources Modeling written by Philippe Renard and published by Frontiers Media SA. This book was released on 2020-04-22 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical models of flow and transport processes are heavily employed in the fields of surface, soil, and groundwater hydrology. They are used to interpret field observations, analyze complex and coupled processes, or to support decision making related to large societal issues such as the water-energy nexus or sustainable water management and food production. Parameter estimation and uncertainty quantification are two key features of modern science-based predictions. When applied to water resources, these tasks must cope with many degrees of freedom and large datasets. Both are challenging and require novel theoretical and computational approaches to handle complex models with large number of unknown parameters.

Download or read book Uncertainty Quantification of Hydrologic Predictions and Risk Analysis written by Yurui Fan and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Stochastic Modeling and Uncertainty Assessment for Watershed Water Quality Management written by Yi Zheng and published by . This book was released on 2007 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex watershed water quality models have been increasingly used to support Total Maximum Daily Load (TMDL) development. However, systematic approaches for addressing the significant simulation uncertainty are lacking. For TMDLs supported by complex watershed models, defining the margin of safety (MOS) component through a rigorous uncertainty analysis remains a significant challenge. This study aimed to develop (1) a systematic approach of uncertainty analysis for complex watershed water quality models in the watershed management context; and (2) a framework for defining the MOS with an explicit consideration of uncertainty and degree of protection. A global sensitivity analysis technique was first applied to select critical model parameters. A framework for sources of uncertainty and their interactions was built. Based on this framework, Generalized Likelihood Uncertainty Estimation (GLUE) was initially evaluated as a potential approach for conducting stochastic simulation and uncertainty analysis for complex watershed models. The limitations of GLUE became evident, which led to the development of a new Bayesian approach, Management Objectives Constrained Analysis of Uncertainty (MOCAU). The concept Compliance of Confidence (CC) was then introduced to bridge the gap between modeling uncertainty and MOS. An optimization model was also developed for cost-minimized TMDLs. This study used WARMF as an example of a complex watershed model and constructed a synthetic watershed for developing and testing methodologies. The methodologies were also implemented to study the diazinon TMDL in the Newport Bay watershed (southern California). This research contributes to the theory of stochastic watershed water quality modeling, as well as to the practices of managing watershed water quality.

Download or read book Uncertainty and Sensitivity Analyses for Watershed Models written by Jennifer Benaman and published by . This book was released on 2003 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hydrological Uncertainty Quantification and Propagation in Multimodel Approaches written by Edom Melesse Moges and published by . This book was released on 2018 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model uncertainties and inaccuracies can limit the application of hydrological modeling as decision making tool. Analysis and insight derived from uncertain models can significantly undermine the implication of their results, recommendations and conclusions. This thesis intends to deal with the various sources of uncertainty in hydrological modeling, particularly multi-modeling approaches, by using different statistical, computational and physically-based diagnostic measures. The uncertainty and the proposed approaches are evaluated using various hydrologic problems including -- extreme event frequency analysis, rainfall-runoff modeling, and coupled surface and subsurface models. First, the significance of model averaging, particularly Bayesian Model Averaging (BMA), is demonstrated by exploring extensive data, fundamental theory, and systematic diagnostic measures. Second, the study integrated hydrological signature measures and a multi model integration approach - Hierarchical Mixture of Experts (HME), in order to reduce structural uncertainty. Third, the study developed uncertainty quantification and propagation framework for coupled hydrological models that can readily be transferred to other coupled models. Using the framework, the study explored uncertainty propagation and their interplay in coupled hydrological models. The findings from this study -- in terms of developing a systematic uncertainty quantification framework and model diagnostic approaches -- are expected to improve the applications of hydrological and environmental models in understanding the underlying physical processes and making improved predictions.

Download or read book Comparison of Uncertainty Analysis for Community Based Watershed Models written by Ray Dukes Smith and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improved Data Uncertainty Handling in Hydrologic Modeling and Forecasting Applications written by Hongli Liu and published by . This book was released on 2019 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: In hydrologic modeling and forecasting applications, many steps are needed. The steps that are relevant to this thesis include watershed discretization, model calibration, and data assimilation. Watershed discretization separates a watershed into homogeneous computational units for depiction in a distributed hydrologic model. Objective identification of an appropriate discretization scheme remains challenging in part because of the lack of quantitative measures for assessing discretization quality, particularly prior to simulation. To solve this problem, this thesis contributes to develop an a priori discretization error metrics that can quantify the information loss induced by watershed discretization without running a hydrologic model. Informed by the error metrics, a two-step discretization decision-making approach is proposed with the advantages of reducing extreme errors and meeting user-specified discretization error targets. In hydrologic model calibration, several uncertainty-based calibration frameworks have been developed to explicitly consider different hydrologic modeling errors, such as parameter errors, forcing and response data errors, and model structure errors. This thesis focuses on climate and flow data errors. The common way of handling climate and flow data uncertainty in the existing calibration studies is perturbing observations with assumed statistical error models (e.g., addictive or multiplicative Gaussian error model) and incorporating them into parameter estimation by integration or repetition with multiple climate and (or) flow realizations. Given the existence of advanced climate and flow data uncertainty estimation methods, this thesis proposes replacing assumed statistical error models with physically-based (and more realistic and convenient) climate and flow ensembles. Accordingly, this thesis contributes developing a climate-flow ensemble based hydrologic model calibration framework. The framework is developed through two stages. The first stage only considers climate data uncertainty, leading to the climate ensemble based hydrologic calibration framework. The framework is parsimonious and can utilize any sources of historical climate ensembles. This thesis demonstrates the method of using the Gridded Ensemble Precipitation and Temperature Estimates dataset (Newman et al., 2015), referred to as N15 here, to derive precipitation and temperature ensembles. Assessment of this framework is conducted using 30 synthetic experiments and 20 real case studies. Results show that the framework generates more robust parameter estimates, reduces the inaccuracy of flow predictions caused by poor quality climate data, and improves the reliability of flow predictions. The second stage adds flow ensemble to the previously developed framework to explicitly consider flow data uncertainty and thus completes the climate-flow ensemble based calibration framework. The complete framework can work with likelihood-free calibration methods. This thesis demonstrates the method of using the hydraulics-based Bayesian rating curve uncertainty estimation method (BaRatin) (Le Coz et al., 2014) to generate flow ensemble. The continuous ranked probability score (CRPS) is taken as an objective function of the framework to compare the scalar model prediction with the measured flow ensemble. The framework performance is assessed based on 10 case studies. Results show that explicit consideration of flow data uncertainty maintains the accuracy and slightly improves the reliability of flow predictions, but compared with climate data uncertainty, flow data uncertainty plays a minor role of improving flow predictions. Regarding streamflow forecasting applications, this thesis contributes by improving the treatment of measured climate data uncertainty in the ensemble Kalman filter (EnKF) data assimilation. Similar as in model calibration, past studies usually use assumed statistical error models to perturb climate data in the EnKF. In data assimilation, the hyper-parameters of the statistical error models are often estimated by a trial-and-error tuning process, requiring significant analyst and computational time. To improve the efficiency of climate data uncertainty estimation in the EnKF, this thesis proposes the direct use of existing climate ensemble products to derive climate ensembles. The N15 dataset is used here to generate 100-member precipitation and temperature ensembles. The N15 generated climate ensembles are compared with the carefully tuned hyper-parameter generated climate ensembles in ensemble flow forecasting over 20 catchments. Results show that the N15 generated climate ensemble yields improved or similar flow forecasts than hyper-parameter generated climate ensembles. Therefore, it is possible to eliminate the time-consuming climate relevant hyper-parameter tuning from the EnKF by using existing ensemble climate products without losing flow forecast performance. After finishing the above research, a robust hydrologic modeling approach is built by using the thesis developed model calibration and data assimilation methods. The last contribution of this thesis is validating such a robust hydrologic model in ensemble flow forecasting via comparison with the use of traditional multiple hydrologic models. The robust single-model forecasting system considers parameter and climate data uncertainty and uses the N15 dataset to perturb historical climate in the EnKF. In contrast, the traditional multi-model forecasting system does not consider parameter and climate data uncertainty and uses assumed statistical error models to perturb historical climate in the EnKF. The comparison study is conducted on 20 catchments and reveal that the robust single hydrologic model generates improved ensemble high flow forecasts. Therefore, robust single model is definitely an advantage for ensemble high flow forecasts. The robust single hydrologic model relieves modelers from developing multiple (and often distributed) hydrologic models for each watershed in their operational ensemble prediction system.

Download or read book End to end Flood Risk Assessment written by Hilary Katherine McMillan and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Issues in Global Environment Globalization and Global Change Research 2013 Edition written by and published by ScholarlyEditions. This book was released on 2013-05-01 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues in Global Environment—Globalization and Global Change Research: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Dendrochronologia. The editors have built Issues in Global Environment—Globalization and Global Change Research: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Dendrochronologia in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Global Environment—Globalization and Global Change Research: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Download or read book Uncertainty Analysis Coupled with a Basin wide Flood Forecast Model written by 林福如 and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Global Sensitivity and Uncertainty Analysis of Spatially Distributed Watershed Models written by Zuzanna B. Zajac and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The relationship between model uncertainty and alternative spatial data resolutions was studied to provide an illustration of how the procedure may be applied 16 for more informed decisions regarding planning of data collection campaigns. The results corroborate a proposed hypothetical nonlinear, negative relationship between model uncertainty and source data density. The inflection point in the curve, representing the optimal data requirements for the application, is identified for the data density between 1/4 and 1/8 of original data density. It is postulated that the inflection point is related to the characteristics of the spatial dataset (variogram) and the aggregation technique (model grid size). The framework proposed in this dissertation could be applied to any spatially distributed model and input, as it is independent from model assumptions.

Download or read book Uncertainty Propagation in Complex Coupled Flood Risk Models Using Numerical Weather Prediction and Weather Radars written by Yunqing Xuan and published by . This book was released on 2007 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Natural Hazard Uncertainty Assessment written by Karin Riley and published by John Wiley & Sons. This book was released on 2016-11-15 with total page 728 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainties are pervasive in natural hazards, and it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties to inform modeling efforts. In this monograph we provide a broad, cross-disciplinary overview of issues relating to uncertainties faced in natural hazard and risk assessment. We introduce some basic tenets of uncertainty analysis, discuss issues related to communication and decision support, and offer numerous examples of analyses and modeling approaches that vary by context and scope. Contributors include scientists from across the full breath of the natural hazard scientific community, from those in real-time analysis of natural hazards to those in the research community from academia and government. Key themes and highlights include: Substantial breadth and depth of analysis in terms of the types of natural hazards addressed, the disciplinary perspectives represented, and the number of studies included Targeted, application-centered analyses with a focus on development and use of modeling techniques to address various sources of uncertainty Emphasis on the impacts of climate change on natural hazard processes and outcomes Recommendations for cross-disciplinary and science transfer across natural hazard sciences This volume will be an excellent resource for those interested in the current work on uncertainty classification/quantification and will document common and emergent research themes to allow all to learn from each other and build a more connected but still diverse and ever growing community of scientists. Read an interview with the editors to find out more: https://eos.org/editors-vox/reducing-uncertainty-in-hazard-prediction

Download or read book Review of the New York City Watershed Protection Program written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-12-04 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: New York City's municipal water supply system provides about 1 billion gallons of drinking water a day to over 8.5 million people in New York City and about 1 million people living in nearby Westchester, Putnam, Ulster, and Orange counties. The combined water supply system includes 19 reservoirs and three controlled lakes with a total storage capacity of approximately 580 billion gallons. The city's Watershed Protection Program is intended to maintain and enhance the high quality of these surface water sources. Review of the New York City Watershed Protection Program assesses the efficacy and future of New York City's watershed management activities. The report identifies program areas that may require future change or action, including continued efforts to address turbidity and responding to changes in reservoir water quality as a result of climate change.

Download or read book Propagation of Uncertainty in a Watershed Model written by Igor Iskra and published by . This book was released on 2007 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: