Дисертації з теми "Hydrologic Ensemble Prediction Systems"
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Brochero, Darwin. "Hydroinformatics and diversity in hydrological ensemble prediction systems." Thesis, Université Laval, 2013. http://www.theses.ulaval.ca/2013/29908/29908.pdf.
Повний текст джерелаIn this thesis, we tackle the problem of streamflow probabilistic forecasting from two different perspectives based on multiple hydrological models collaboration (diversity). The first one favours a hybrid approach for the evaluation of multiple global hydrological models and tools of machine learning for predictors selection, while the second one constructs Artificial Neural Network (ANN) ensembles, forcing diversity within. This thesis is based on the concept of diversity for developing different methodologies around two complementary problems. The first one focused on simplifying, via members selection, a complex Hydrological Ensemble Prediction System (HEPS) that has 800 daily forecast scenarios originating from the combination of 50 meteorological precipitation members and 16 global hydrological models. We explore in depth four techniques: Linear Correlation Elimination, Mutual Information, Backward Greedy Selection, and Nondominated Sorting Genetic Algorithm II (NSGA-II). We propose the optimal hydrological model participation concept that identifies the number of meteorological representative members to propagate into each hydrological model in the simplified HEPS scheme. The second problem consists in the stratified selection of data patterns that are used for training an ANN ensemble or stack. For instance, taken from the database of the second and third MOdel Parameter Estimation eXperiment (MOPEX) workshops, we promoted an ANN prediction stack in which each predictor is trained on input spaces defined by the Input Variable Selection application on different stratified sub-samples. In summary, we demonstrated that implicit diversity in the configuration of a HEPS is efficient in the search for a HEPS of high performance.
Velazquez, Zapata Juan Alberto. "Evaluation of hydrological ensemble prediction systems for operational forecasting." Thesis, Université Laval, 2010. http://www.theses.ulaval.ca/2010/27792/27792.pdf.
Повний текст джерелаVelázquez, Zapata Juan Alberto. "Evaluation of hydrological ensemble prediction systems for operational forecasting." Doctoral thesis, Université Laval, 2010. http://hdl.handle.net/20.500.11794/22245.
Повний текст джерелаXu, Jing. "Hydrological post-processing of streamflow forecasts issued from single-model and multimodel ensemble prediction systems." Doctoral thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/69503.
Повний текст джерелаHydrological simulations and forecasts are subject to various sources of uncertainties. Forecast uncertainties are unfortunately inevitable when conducting the deterministic analysis of a dynamical system. The cascade of uncertainty originates from different components of the forecasting chain, such as the chaotic nature of the atmosphere, various initial conditions and boundaries, necessarily imperfect hydrologic modeling, and the inconsistent stationnarity assumption in a changing environment. Ensemble forecasting is a powerful tool to represent error growth in the dynamical system and to capture the uncertainties associated with different sources. Thiboult et al. (2016) constructed a 50,000-member great ensemble that accounts for meteorological forcing uncertainty, initial conditions uncertainty, and structural uncertainty. This large ensemble can also be separated into sub-components to untangle the three main sources of uncertainties mentioned above. In asimilar experiment, another multimodel hydrological ensemble forecasting system implemented for different catchments was produced by Emixi Valdez. However,in the latter case, model outputs were simply pooled together, considering the members equiprobable. Although multimodel hydrological ensemble forecasting systems can be considered very comprehensive, they can still underestimate the total uncertainty. For instance, the meteorological forecasts in there search of Thiboult et al. (2016) were pre-tested on some watersheds. It was found out that the forecasting performance of data assimilation fades away quickly as the lead time progresses. In addition, operational forecasts users may not able to perfectly utilize all the forecasting tools (i.e., meteorological ensemble forcing, data assimilation, and multimodel) jointly. Therefore, there is still room for improvement to enhance the forecasting skill of such systems through proper statistical post-processing.The global objective of this research is to explore the proper use and predictive skill of various statistical post-processing algorithms by testing them on single-model and multimodel ensemble stream flow forecasts. First, we tested the post-processing skills of Affine kernel dressing (AKD) and Non-dominated sorting genetic algorithm II (NSGA-II) over single-model H-EPSs. Those two methods are theoretically/technically distinct yet are both non-parametric. They do not require the raw ensemble members to follow a specific parametric distribution.AKD-transformed ensembles and the Pareto fronts generated with NSGA-II demonstrated the superiority of post-processed ensembles compared to raw ensembles. Both methods where efficient at eliminating biases and maintaining a proper dispersion for all forecasting horizons. For multimodel ensembles, two post-processors, namely Bayesian model averaging (BMA) and the integrated copula-BMA, are compared for deriving a pertinent joint predictive distribution of daily streamflow forecasts issued by five different single-model hydrological ensemble prediction systems (H-EPSs). BMA assign weights to different models. Forecasts from all models are then combined to generate more skillful and reliable probabilistic forecasts. BMA weights quantify the level of confidence one can have regarding each candidate hydrological model and lead to a predictive probabilistic density function (PDF) containing information about uncertainty. BMA improves the overall quality of forecasts mainly by maintaining the ensemble dispersion with the lead time. It also improves the reliability and skill of multimodel systems that only include two sources of uncertainties compared to the 50,000-member great ensemble from Thiboult et al (2016). Furthermore, Thiboult et al. (2016) showed that the meteorological forecasts they used were biased and unreliable on some catchments. BMA improves the accuracy and reliability of the hydrological forecasts in that case as well.However, BMA suffers from limitations pertaining to its conditional probability density functions (PDFs), which must follow a known parametric distribution form (e.g., normal, gamma). On the contrary, Copula-BMA predictive model fully relaxes this constraint and also eliminates the power transformation step. In this study, eleven univariate marginal distributions and six copula functions are explored in a Copula-BMA framework for comprehensively reflecting the dependence structure between pairs of forecasted and observed streamflow. Results demonstrate the superiority of the Copula-BMAcompared to BMA in eliminating biases and maintaining an appropriate ensemble dispersion for all lead-times.
Wood, Andrew W. "Using climate model ensemble forecasts for seasonal hydrologic prediction /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/10205.
Повний текст джерелаDuncan, Andrew Paul. "The analysis and application of artificial neural networks for early warning systems in hydrology and the environment." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/17569.
Повний текст джерелаCunningham, Jeffrey G. "Applying ensemble prediction systems to Department of Defense operations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Mar%5FCunningham.pdf.
Повний текст джерелаThesis Advisor(s): Carlyle H. Wash, Patrick A. Harr. "March 2006." Includes bibliographical references (p. 129). Also available online.
Pearman, Douglas W. "Evaluating tropical cyclone forecast track uncertainty using a grand ensemble of ensemble prediction systems." Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5465.
Повний текст джерелаThe skill of a combined grand ensemble (GE), which is constructed from three operational global ensemble prediction systems (EPS), is evaluated with respect to the probability forecast of a tropical cyclone (TC) being within a specified area. Anisotropic probability ellipses are defined from the GE to contain 68% of the ensemble members. Forecast reliability is based on whether the forecast verifying position is within the ellipse. A sharpness parameter is based on the size of the GE-based probability ellipse relative to other operational forecast probability ellipses. For the 2010 Atlantic TC season, results indicate that the GE ellipses exhibit a high degree of reliability whereas the operational probability circle tends to be over-dispersive. Additionally, the GE ellipse tends to be sharper than the operational product for forecast intervals beyond 48 hours. The size and shape of the GE ellipses varied with TC track types, which suggests that information about the physics of the flow-dependent system is retained whereas isotropic probability ellipses may not reflect variability associated with track type. It is concluded that the GE probability ellipse demonstrates utility for combined EPS to enhance probabilistic forecasts for use as TC-related decision aids, as there is a potential for reducing the sizes of warning areas.
Sağlam, Şenay Yaşar. "The role of confidence and diversity in dynamic ensemble class prediction systems." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1940.
Повний текст джерелаShrestha, Rajesh Raj. "River flood prediction systems : towards complementary hydrodynamic, hydrological and data driven models with uncertainty analysis /." Karlsruhe : Institut für Wasser und Gewässerentwicklung Universität Karlsruhe (TH), 2005. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014799092&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Повний текст джерелаMASCARO, GIUSEPPE. "Assessing uncertainty propagation of precipitation input in hydrometeorological ensemble forecasting systems." Doctoral thesis, Università degli Studi di Cagliari, 2008. http://hdl.handle.net/11584/265979.
Повний текст джерелаPincini, Giacomo. "Forecast of high-impact weather over Italy: performance of global and limited-area ensemble prediction systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16207/.
Повний текст джерелаKeller, Julia Henriette [Verfasser]. "Diagnosing the Downstream Impact of Extratropical Transition Using Multimodel Operational Ensemble Prediction Systems / Julia Henriette Keller." Karlsruhe : KIT Scientific Publishing, 2013. http://www.ksp.kit.edu.
Повний текст джерелаVich, Ramis Maria del Mar. "Design of ensemble prediction systems based on potential vorticity perturbations and multiphysics. Test for western Mediterranean heavy precipitation events." Doctoral thesis, Universitat de les Illes Balears, 2012. http://hdl.handle.net/10803/84075.
Повний текст джерелаThe main goal of this thesis is to improve the current prediction skill of potentially hazardous heavy precipitation weather events in the western Mediterranean region. We develop and test three different ensemble prediction systems (EPSs) that account for uncertainties present in both the numerical models and the initial conditions. To generate the EPSs we take advantage of the connection between potential vorticity (PV) structures and cyclones, and use different physical parameterization schemes. We obtain an improvement in forecast skill when using an EPS compared to a determinist forecast. The EPSs generated perturbing the initial conditions perform better in the statistical verification scores. The results of this Thesis show the utility and suitability of forecasting methods based on perturbing the upper-level precursor PV structures present in cyclonic situations. The results and strategies here discussed aim to be a basis for future studies making use of these methods.
Keller, Julia Henriette [Verfasser], and S. [Akademischer Betreuer] Jones. "Diagnosing the Downstream Impact of Extratropical Transition Using Multimodel Operational Ensemble Prediction Systems / Julia Henriette Keller. Betreuer: S. Jones." Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/1020230037/34.
Повний текст джерелаShadabi, Fariba, and N/A. "Medical Outcome Prediction: A Hybrid Artificial Neural Networks Approach." University of Canberra. Information Sciences & Engineering, 2007. http://erl.canberra.edu.au./public/adt-AUC20070816.130444.
Повний текст джерелаLi, Jianeng. "Research on a Heart Disease Prediction Model Based on the Stacking Principle." Thesis, Högskolan Dalarna, Informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-34591.
Повний текст джерелаGogonel, Adriana Geanina. "Statistical Post-Processing Methods And Their Implementation On The Ensemble Prediction Systems For Forecasting Temperature In The Use Of The French Electric Consumption." Phd thesis, Université René Descartes - Paris V, 2012. http://tel.archives-ouvertes.fr/tel-00798576.
Повний текст джерелаRoulin, Emmannuel. "Medium-range probabilistic river streamflow predictions." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209270.
Повний текст джерелаThe research began by analyzing the meteorological predictions at the medium-range (up to 10-15 days) and their use in hydrological forecasting. Precipitation from the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF) were used. A semi-distributed hydrological model was used to transform these precipitation forecasts into ensemble streamflow predictions. The performance of these forecasts was analyzed in probabilistic terms. A simple decision model also allowed to compare the relative economic value of hydrological ensemble predictions and some deterministic alternatives.
Numerical weather prediction models are imperfect. The ensemble forecasts are therefore affected by errors implying the presence of biases and the unreliability of probabilities derived from the ensembles. By comparing the results of these predictions to the corresponding observed data, a statistical model for the correction of forecasts, known as post-processing, has been adapted and shown to improve the performance of probabilistic forecasts of precipitation. This approach is based on retrospective forecasts made by the ECMWF for the past twenty years, providing a sufficient statistical sample.
Besides the errors related to meteorological forcing, hydrological forecasts also display errors related to initial conditions and to modeling errors (errors in the structure of the hydrological model and in the parameter values). The last stage of the research was therefore to investigate, using simple models, the impact of these different sources of error on the quality of hydrological predictions and to explore the possibility of using hydrological reforecasts for post-processing, themselves based on retrospective precipitation forecasts.
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La prévision des débits des rivières se fait traditionnellement sur la base de mesures en temps réel des précipitations sur les bassins-versant et des débits à l'exutoire et en amont. Ces données sont traitées dans des modèles mathématiques de complexité variée et permettent d'obtenir des prévisions précises pour des temps courts. Pour prolonger l'horizon de prévision à quelques jours – afin d'être en mesure d'émettre des alertes précoces – il est nécessaire de prendre en compte les prévisions météorologiques. Cependant celles-ci présentent par nature une dynamique sensible aux erreurs sur les conditions initiales et, par conséquent, pour une gestion appropriée des risques, il faut considérer les prévisions en termes probabilistes. Actuellement, les prévisions d'ensemble sont effectuées à l'aide d'un modèle numérique de prévision du temps avec des conditions initiales perturbées et permettent d'évaluer l'incertitude.
La recherche a commencé par l'analyse des prévisions météorologiques à moyen-terme (10-15 jours) et leur utilisation pour des prévisions hydrologiques. Les précipitations issues du système de prévisions d'ensemble du Centre Européen pour les Prévisions Météorologiques à Moyen-Terme ont été utilisées. Un modèle hydrologique semi-distribué a permis de traduire ces prévisions de précipitations en prévisions d'ensemble de débits. Les performances de ces prévisions ont été analysées en termes probabilistes. Un modèle de décision simple a également permis de comparer la valeur économique relative des prévisions hydrologiques d'ensemble et d'alternatives déterministes.
Les modèles numériques de prévision du temps sont imparfaits. Les prévisions d'ensemble sont donc entachées d'erreurs impliquant la présence de biais et un manque de fiabilité des probabilités déduites des ensembles. En comparant les résultats de ces prévisions aux données observées correspondantes, un modèle statistique pour la correction des prévisions, connue sous le nom de post-processing, a été adapté et a permis d'améliorer les performances des prévisions probabilistes des précipitations. Cette approche se base sur des prévisions rétrospectives effectuées par le Centre Européen sur les vingt dernières années, fournissant un échantillon statistique suffisant.
A côté des erreurs liées au forçage météorologique, les prévisions hydrologiques sont également entachées d'erreurs liées aux conditions initiales et aux erreurs de modélisation (structure du modèle hydrologique et valeur des paramètres). La dernière étape de la recherche a donc consisté à étudier, à l'aide de modèles simples, l'impact de ces différentes sources d'erreur sur la qualité des prévisions hydrologiques et à explorer la possibilité d'utiliser des prévisions hydrologiques rétrospectives pour le post-processing, elles-même basées sur les prévisions rétrospectives des précipitations.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
Steinberg, Rebecca M. "Predicting Post-Mining Hydrologic Effects of Underground Coal Mines in Ohio throughMultivariate Statistical Analyses and GIS Tool Building." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1555429886192267.
Повний текст джерелаYoung, Roland Michael Brendon. "Predictability of a laboratory analogue for planetary atmospheres." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:b4f483a6-437c-4914-b94e-cb04d996b337.
Повний текст джерелаSarath, M. "At-site and Multisite Probabilistic Forecasting of Streamflow." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/5126.
Повний текст джерелаLin, Shu-Chen, and 林淑真. "Fractals and Chaos on the Analysis and Prediction of Nonlinear Hydrologic Systems." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/65671694228916805369.
Повний текст джерела國立成功大學
水利及海洋工程學系
87
The fractal geometry and the chaotic dynamics are two important components in nonlinear sciences. They are powerful tools to analyze the complex hydrologic systems currently. In this study, we focus the attention on hydrosciences. Eight topics are concerned and described as follows: (1) estimation of point-fractal dimension and its variability for hydrologic process, (2) analysis and clustering for areal fractal characteristics, (3) identification of time-scale law and fractal approach of frequency analysis for hydrologic variable, (4) existence identification of attractor for daily streamflow data, (5) complexity comparison of a system with different transformation, (6) mechanism description of subset regression model by phase-space concept, (7) nonlinear modeling of streamflow process and its one-lead time forecasting and (8) nonlinear model for multi-lead time forecasting in areal rainfall of watershed. Firstly, the fractal geometry is used to analyze the tempo-spatial variation for hydrologic variables and three parts are included. On the estimation of point-fractal dimension and its variability for hydrologic process, the capacity dimension, information dimension and correlation dimension are calculated. The results show that the rainfall and runoff processes are both multi-fractals. The former is far more complex than the latter. When the observation time-scale increases, the lacunarity decreases. The decreasing patterns are different in degree and have a strong non-uniform tendency on the temporal distribution. On the analysis and clustering for areal fractal characteristics, the spatial variation and explanation with the temporal dimension of rainfall are the same of our understanding from experience. As for the relation between the temporal dimension of watershed runoff process and the spatial dimension of river basin network, they are correlative in degree. Not only the lacunarity measure can be served as the verification tool, once the systems have the same dimensions, but also is a powerful tool to detect or test whether the external impact or not. On the identification of time-scale law and fractal approach of frequency analysis for hydrologic variable, the rainfall is evident that scale invariance exists in time and clustering will decrease in accord with the increase of threshold. Under the variation of threshold, it can be verified that the maximum values of the homogenous scale-invariant interval are just the same. In addition, taking the probability-scale law on different levels of threshold, the relation can be established between the saturation scale (return period) and the threshold (design hydrologic variable). The methodology is different from the traditional one, i.e. frequency analysis method. When the time-scale we concerned is increasing up to one-year, the fractal scaling law of storm can be established readily. Secondly, the chaotic dynamics is used to analyze the temporal variation of hydrologic variables and three parts are also included. On the existence identification of attractor for the daily streamflow data, the correlation dimension and maximum Lyapunov exponent are calculated. The results show that the chaotic attractor exists and suggests that it may be described by 3 or 4 variables. This information can be used to reconstruct the motion trajectory of a system. On the complexity comparison of a system with different transformation, four types of data are selected and the complexity is white noise, daily rainfall, daily streamflow and Lorenz attractor in descending order. Only the white noise is apparently in disorder, the others have some kinds of pattern or structure. This measure shows the power on the verification. Once the data are transformed by the integration and moving average, the complexity will be decreased. It implies that the model can be established with a simpler way and have a longer lead-time predictability. On the mechanism description of subset regression model by phase-space concept, the subset-order model has the dual properties, the framework of mathematical theory and the mechanism of the physical explanation, and this result is proved by the case studies. Moreover, the multi-dimensional nonlinear subset-order model is extended to the tempo-spatial related model. Finally, the nonlinear model is used for the time series simulation and forecasting and two aspects are concerned: (1)On the nonlinear modeling of streamflow process and its one-lead time forecasting, many models are compared. The results shows that the combined phase-space theory with the artificial neural network framework is excellent and it is powerful on the learning of nonlinear behavior for the artificial neural network. (2)On the nonlinear model for multi-lead time forecasting in areal rainfall of watershed, it is verified that the nonlinear model based on the chaotic dynamics is superior to the linear one based on the time-lag correlation. When the lead-time for forecasting is increased, the former is apparently better than the latter on the overall performance and it is still robust. If a time series is proved a chaotic process, it belongs a nonlinear problem. It must come from the nonlinear point of view and not treated as a linear one. Many modern concepts and tools are used to analyze the complex hydrologic systems in this study, but much of them are also introduced the results from the traditional one. The purpose is to provide a reference and a comparison with different methodologies. Viewing the differences, we overcome the shortcomings, instead of the strong points of the other. The description of complex system will be improved in advance.