Post-processing bias correction of streamflow forecasts can be useful in the hydrologic modeling workflow to fine-tune forecasts for operations, water management, and decision-making. Hydrologic model runoff simulations include errors, uncertainties, and biases, leading to less accuracy and precision for applications in real-world scenarios. We used random forest regression to correct biases and errors in streamflow predictions from the U.S. National Water Model (NWM) long-range streamflow forecasts, considering U.S. Geological Survey (USGS) gauge station measurements as a proxy for true streamflow. We used other features in model training, including watershed characteristics, time fraction of year, and lagged streamflow values, to help the model perform better in gauged and ungauged areas. We assessed the effectiveness of the bias correction technique by comparing the difference between forecast and actual streamflow before and after the bias correction model was employed. We also explored advances in hydroinformatics and cloud computing by creating and testing this bias correction capability within the Google Cloud Console environment to avoid slow and unnecessary data downloads to local devices, thereby streamlining the data processing and storage within the cloud. This demonstrates the possibility of integrating our method into the NWM real-time forecasting workflow. Results indicate reasonable bias correction is possible using the random forest regression machine learning technique. Differences between USGS discharge and NWM forecasts are less than the original difference observed after being run through the random forest model. The main issue concerning the forecasts from the NWM is that the error increases further from the reference time or start of the forecast period. The model we created shows significant improvement in streamflow the further the times get from the reference time. The error is reduced and more uniform throughout all the time steps of the 30-day long-range forecasts.

Le système climatique génère un attracteur étrange, décrit par une distribution de probabilité, nommée la mesure SRB (Sinai-Ruelle-Bowen). Cette mesure décrit l'état et sa dynamique du système. Le but de cette thèse est d'une part de quantifier les modifications de cette mesure quand le climat change. Pour cela, la distance de Wasserstein venant de la théorie du transport optimal, permet de mesurer finement les différences entre distributions de probabilités. Appliquée à un modèle jouet de Lorenz non autonome, elle a permis de détecter et quantifier l'altération due à un forçage similaire à celui du forçage anthropique. La même méthodologie a été appliquée à des simulations de scénarios RCP du modèle de l'IPSL. Des résultats cohérents avec les différents scénarios ont été retrouvés. D'autre part, la théorie du transport optimal fournit un contexte théorique pour la correction de biais dans un contexte stationnaire : une méthode de correction de biais est équivalente à une loi jointe de probabilité. Une loi jointe particulière est sélectionnée grâce à la distance de Wasserstein (méthode Optimal Transport Correction, OTC). Cette approche étend les méthodes de corrections en dimension quelconque, corrigeant en particulier les dépendances spatiales et inter-variables. Une extension dans le cas non-stationnaire a également été proposée (méthode dynamical OTC, dOTC). Ces deux méthodes ont été testées dans un contexte idéalisé, basé sur un modèle de Lorenz, et sur des données climatiques (une simulation climatique régionale corrigée avec des ré-analyses SAFRAN). / The climate system generates a strange attractor, described by a probability distribution, called the SRB measure (Sinai-Ruelle-Bowen). This measure describes the state and dynamic of the system. The goal of this thesis is first, to quantify the modification of this measure when climate changes. For this, the Wasserstein distance, stemming from the optimal transport theory, allows us determine accurately the differences between probability distributions. Used on a non-autonomous Lorenz toy model, this metric allows us to detect and quantify the alteration due to a forcing similar to anthropogenic forcing. This methodology has been applied to simulation of RCP scenarios from the IPSL model. The results are coherent with different scenarios. Second, the optimal transport gives a theoretical context for stationary bias correction: a bias correction method is equivalent to a joint probability law. A specific joint law is selected with the Wasserstein distance (Optimal Transport Correction method, OTC). This approach allows us extending bias correction methods in any dimension, correcting spatial and inter-variables dependences. An extension in the non-stationary context has been also developed (dynamical OTC method, dOTC). Those two methods have been tested in an idealized case, based on a Lorenz model, and on climate dataset (a regional climate simulation corrected with respect to the SAFRAN reanalysis).

Background: Response bias can distort treatment effect estimates and inferences in clinical trials. Although prevention, quantification, and adjustments have been developed, current methods are not applicable when subject-level reliability is used as the measure of response bias. Thus, the objective of the current study is to develop, test, and recommend a series of bias correction strategies for use in these cases. Methods: Monte Carlo simulation and logistic regression modeling were used to develop the strategies, examining the collective impact of sample size (N), effect size (ES), reliability distribution, and response style on estimating the treatment effect size in a series of hypothetical clinical trials. The strategies included a linear (LW), quadratic (QW), or cubic weight (CW) applied to the subject-level reliability; a reliability threshold (%); or a combination of the two (W-%). Bias and percent relative root mean square error (RRMSE (%)) were calculated for each treatment effect estimate and RRMSE (%) was compared to inform the bias correction recommendations. Results: The following recommendations are made for each N and ES combination: N=200/ES=small: no adjustment, N=200/ES=medium: 40%-LW, N=200/ES=large: 40%-QW, N=2000/ES=small: 40%-LW, N=2000/ES=medium: 55%-CW, N=2000/ES=large: 75%-CW, N=20000/ES=small: 70%-CW, N=20000/ES=medium: 85%-CW, N=20000/ES=large: 95%-CW. Conclusion: Employing these bias correction strategies in clinical trials where subject-level reliability can be calculated will decrease error and increase accuracy of estimates and validity of inferences.

A naive approach to filtering for feedback control of dynamic systems that is robust and stable is proposed. Simulations are run on the filters presented to investigate the robustness properties of each filter. Each simulation with the comparison of the filters is carried out using the usual mean squared error. The filters to be included are the classic Kalman filter, Krein space Kalman, two adjustments to the Krein filter with input modeling and a second uncertainty parameter, a newly developed filter called the Naive filter, bias corrected Naive, exponentially weighted moving average (EWMA) Naive, and bias corrected EWMA Naive filter.

No description available.

Water resource planning and management practices in the southeastern United States may be vulnerable to climate change. This vulnerability has not been quantified, and decision makers, although generally concerned, are unable to appreciate the extent of the possible impact of climate change nor formulate and adopt mitigating management strategies. Thus, this dissertation aims to fulfill this need by generating decision worthy data and information using an integrated climate change assessment framework. To begin this work, we develop a new joint variable spatial downscaling technique for statistically downscaling gridded climatic variables to generate high-resolution, gridded datasets for regional watershed modeling and assessment. The approach differs from previous statistical downscaling methods in that multiple climatic variables are downscaled simultaneously and consistently to produce realistic climate projections. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. In the second component of the integrated assessment framework, we develop a data-driven, downward hydrological watershed model for transforming the climate variables obtained from the downscaling procedures to hydrological variables. The watershed model includes several water balance elements with nonlinear storage-release functions. The release functions and parameters are data driven and estimated using a recursive identification methodology suitable for multiple, inter-linked modeling components. The model evolves from larger spatial/temporal scales down to smaller spatial/temporal scales with increasing model structure complexity. For ungauged or poorly-gauged watersheds, we developed and applied regionalization hydrologic models based on stepwise regressions to relate the parameters of the hydrological models to observed watershed responses at specific scales. Finally, we present the climate change assessment results for six river basins in the southeastern United States. The historical (baseline) assessment is based on climatic data for the period 1901 through 2009. The future assessment consists of running the assessment models under all IPCC A1B and A2 climate scenarios for the period from 2000 through 2099. The climate assessment includes temperature, precipitation, and potential evapotranspiration; the hydrology assessment includes primary hydrologic variables (i.e., soil moisture, evapotranspiration, and runoff) for each watershed.

In recent years, there has been a surge of research interest in the analysis of time series and spatial data. While on one hand more and more sophisticated models are being developed, on the other hand the resulting theory and estimation process has become more and more involved. This dissertation addresses the development of statistical inference procedures for data exhibiting dependencies of varied form and structure. In the first work, we consider estimation of the mean squared prediction error (MSPE) of the best linear predictor of (possibly) nonlinear functions of finitely many future observations in a stationary time series. We develop a resampling methodology for estimating the MSPE when the unknown parameters in the best linear predictor are estimated. Further, we propose a bias corrected MSPE estimator based on the bootstrap and establish its second order accuracy. Finite sample properties of the method are investigated through a simulation study. The next work considers nonparametric inference on spatial data. In this work the asymptotic distribution of the Discrete Fourier Transformation (DFT) of spatial data under pure and mixed increasing domain spatial asymptotic structures are studied under both deterministic and stochastic spatial sampling designs. The deterministic design is specified by a scaled version of the integer lattice in IRd while the data-sites under the stochastic spatial design are generated by a sequence of independent random vectors, with a possibly nonuniform density. A detailed account of the asymptotic joint distribution of the DFTs of the spatial data is given which, among other things, highlights the effects of the geometry of the sampling region and the spatial sampling density on the limit distribution. Further, it is shown that in both deterministic and stochastic design cases, for "asymptotically distant" frequencies, the DFTs are asymptotically independent, but this property may be destroyed if the frequencies are "asymptotically close". Some important implications of the main results are also given.

L’étude de la variabilité du climat est désormais indispensable pour anticiper les conséquences des changements climatiques futurs. Nous disposons pour cela de quantité de données issues de modèles de circulation générale (GCMs). Néanmoins, ces modèles ne permettent qu’une résolution partielle des interactions entre le climat et les activités humaines entre autres parce que ces modèles ont des résolutions spatiales souvent trop faibles. Il existe aujourd’hui toute une variété de modèles répondant à cette problématique et dont l’objectif est de générer des variables climatiques à l’échelle locale àpartir de variables à grande échelle : ce sont les modèles de régionalisation ou encore appelés modèles de réduction d’échelle spatiale ou de downscaling en anglais.Cette thèse a pour objectif d’approfondir les connaissances à propos des modèles de downscaling statistiques (SDMs) parmi lesquels on retrouve plusieurs approches. Le travail s’articule autour de quatre objectifs : (i) comparer des modèles de réduction d’échelle statistiques (et dynamiques), (ii) étudier l’influence des biais des GCMs sur les SDMs au moyen d’une procédure de correction de biais, (iii) développer un modèle de réduction d’échelle qui prenne en compte la non-stationnarité spatiale et temporelle du climat dans un contexte de modélisation dite spatiale et enfin, (iv) établir une définitiondes saisons à partir d’une modélisation des régimes de circulation atmosphérique ou régimes de temps.L’intercomparaison de modèles de downscaling a permis de mettre au point une méthode de sélection de modèles en fonction des besoins de l’utilisateur. L’étude des biais des GCMs révèle une influence indéniable de ces derniers sur les sorties de SDMs et les apports de la correction des biais. Les différentes étapes du développement d’un modèle spatial de réduction d’échelle donnent des résultats très encourageants. La définition des saisons par des régimes de temps se révèle être un outil efficace d’analyse et de modélisation saisonnière.Tous ces travaux de “Climatologie Statistique” ouvrent des perspectives pertinentes, non seulement en termes méthodologiques ou de compréhension de climat à l’échelle locale, mais aussi d’utilisations par les acteurs de la société. / The study of climate variability is vital in order to understand and anticipate the consequences of future climate changes. Large data sets generated by general circulation models (GCMs) are currently available and enable us to conduct studies in that direction. However, these models resolve only partially the interactions between climate and human activities, namely du to their coarse resolution. Nowadays there is a large variety of models coping with this issue and aiming at generating climate variables at local scale from large-scale variables : the downscaling models.The aim of this thesis is to increase the knowledge about statistical downscaling models (SDMs) wherein there is many approaches. The work conducted here pursues four main goals : (i) to discriminate statistical (and dynamical) downscaling models, (ii) to study the influences of GCMs biases on the SDMs through a bias correction scheme, (iii) to develop a statistical downscaling model accounting for climate spatial and temporal non-stationarity in a spatial modelling context and finally, (iv) to define seasons thanks to a weather typing modelling.The intercomparison of downscaling models led to set up a model selection methodology according to the end-users needs. The study of the biases of the GCMs reveals the impacts of those biases on the SDMs simulations and the positive contributions of the bias correction procedure. The different steps of the spatial SDM development bring some interesting and encouraging results. The seasons defined by the weather regimes are relevant for seasonal analyses and modelling.All those works conducted in a “Statistical Climatologie” framework lead to many relevant perspectives, not only in terms of methodology or knowlegde about local-scale climate, but also in terms of use by the society.

<p>  </p> <p>The management of water resources in East Africa is inherently challenged by rainfall variability and the uneven spatial distribution of freshwater resources. In addition to these issues, meteorological and water data collection has been inconsistent over the past decades, and unclearly defined purposes or end goals for collected data have left many datasets ineffectively curated. In light of the data intensiveness of current modelling and planning methods, data scarcity and inaccessibility have become substantial impediments to informed decision-making. Among the outputs of this research are 1) a revised technique for evaluating bias correction performance on reanalysis data for use in regions where precipitation data is temporally discontinuous which can potentially be applied to other types of climate data as well, 2) a new methodology for quantifying qualitative information contained in legislation and official documents and websites for the assessment of relationships between documented meteorological and water data policies and resulting outcomes in terms of data availability and accessibility, and 3) a fresh look at data needs and the value data holds with respect to water resources decision-making and management in the region.</p>

[pt] O Brasil sempre foi um país que teve sua matriz elétrica pautada majoritariamente em fontes renováveis, mais especificamente na hídrica. Com passar dos anos, esta tem se diversificado e demonstrado uma maior participação da fonte eólica. Para melhor explorála, pesquisas visando modelar seu comportamento são essenciais. Entretanto, não é sempre que se tem dados de velocidade do vento e de geração eólica disponíveis em quantidade e nas localidades de interesse. Esses dados são primordiais para identificar potenciais locais de instalação de parques eólicos, melhorar o desempenho dos existentes e estimular pesquisas de previsão e simulação da geração eólica que são entradas para auxiliar na melhor performance do planejamento e da operação do setor elétrico brasileiro. Na carência de dados de velocidade do vento, uma alternativa é o uso de dados vindos de base de reanálises. Elas disponibilizam longos históricos de dados de variáveis climáticas e atmosféricas para diversos pontos do globo terrestre e de forma gratuita. Desta forma, a primeira contribuição deste trabalho teve como foco a verificação da representatividade dos dados de velocidade do vento, disponibilizados pelo MERRA-2, no território brasileiro. Seguindo as recomendações da literatura, utilizou-se técnicas de interpolação, extrapolação e correção de viés para melhorar a adequação as velocidades fornecidas pela base de reanalise as que acontecem na altura dos rotores das turbinas dos parques eólicos. Em uma segunda contribuição combinou-se os dados do MERRA-2 com os de potência medidas em parques eólicos brasileiros para modelar de modo estocástico e não paramétrico a relação existente entre a velocidade e potência nas turbinas eólicas. Para isto utilizou-se as técnicas de clusterização, estimação das curvas de densidade e simulação. Por fim, em uma terceira contribuição, desenvolveu-se um aplicativo, no ambiente shiny, para disponibilizar as metodologias desenvolvidas nas duas primeiras contribuições. / [en] Brazil s energy landscape has historically relied heavily on renewable sources, notably hydropower, with wind energy emerging as a significant contributor in recent years. Understanding and harnessing the potential of wind energy necessitates robust modeling of its behavior. However, obtaining comprehensive wind speed and generation data, particularly in specific locations of interest, remains a challenge. In the absence of wind speed data, an alternative is to use data from a reanalysis database. They provide long histories of data on climatic and atmospheric variables for different parts of the world, free of charge. Therefore, the first contribution of this work focused on verifying the representativeness of wind speed data made available by MERRA-2 in Brazilian territory. Following literature recommendations, interpolation, extrapolation, and bias correction techniques were used to improve the adequacy of the speeds provided by the reanalysis based on those that occur at the height of the wind farm turbine rotors. In a second contribution, MERRA-2 data was combined with power measured in Brazilian wind farms to model in a stochastic and non-parametric way the relationship between speed and power in wind turbines. For this purpose, clustering, density curve estimation, and simulation techniques were used. Finally, the research culminates in the development of an application within the Shiny environment, offering a user-friendly platform to access and apply the methodologies devised in the preceding analyses. By making these methodologies readily accessible, the application facilitates broader engagement and utilization within the research community and industry practitioners alike.