Improved Data Uncertainty Handling in Hydrologic Modeling and Forecasting Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Improved Data Uncertainty Handling in Hydrologic Modeling and Forecasting Applications write by Hongli Liu. This book was released on 2019. Improved Data Uncertainty Handling in Hydrologic Modeling and Forecasting Applications available in PDF, EPUB and Kindle. 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.
Advances In Data-based Approaches For Hydrologic Modeling And Forecasting
Advances In Data-based Approaches For Hydrologic Modeling And Forecasting - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advances In Data-based Approaches For Hydrologic Modeling And Forecasting write by Bellie Sivakumar. This book was released on 2010-08-10. Advances In Data-based Approaches For Hydrologic Modeling And Forecasting available in PDF, EPUB and Kindle. This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each — stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Applications of Machine Learning in Hydroclimatology
Applications of Machine Learning in Hydroclimatology - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Applications of Machine Learning in Hydroclimatology write by Roshan Karan Srivastav. This book was released on 2024-10-24. Applications of Machine Learning in Hydroclimatology available in PDF, EPUB and Kindle. Applications of Machine Learning in Hydroclimatology is a comprehensive exploration of the transformative potential of machine learning for addressing critical challenges in water resources management. The book explores how artificial intelligence can unravel the complexities of hydrological systems, providing researchers and practitioners with cutting-edge tools to model, predict, and manage these systems with greater precision and effectiveness. It thoroughly examines the modeling of hydrometeorological extremes, such as floods and droughts, which are becoming increasingly difficult to predict due to climate change. By leveraging AI-driven methods to forecast these extremes, the book offers innovative approaches that enhance predictive accuracy. It emphasizes the importance of analyzing non-stationarity and uncertainty in a rapidly evolving climate landscape, illustrating how statistical and frequency analyses can improve hydrological forecasts. Moreover, the book explores the impact of climate change on flood risks, drought occurrences, and reservoir operations, providing insights into how these phenomena affect water resource management. To provide practical solutions, the book includes case studies that showcase effective mitigation measures for water-related challenges. These examples highlight the use of machine learning techniques such as deep learning, reinforcement learning, and statistical downscaling in real-world scenarios. They demonstrate how artificial intelligence can optimize decision-making and resource management while improving our understanding of complex hydrological phenomena. By utilizing machine learning architectures tailored to hydrology, the book presents physics-guided models, data-driven techniques, and hybrid approaches that can be used to address water management issues. Ultimately, Applications of Machine Learning in Hydroclimatology empowers researchers, practitioners, and policymakers to harness machine learning for sustainable water management. It bridges the gap between advanced AI technologies and hydrological science, offering innovative solutions to tackle today's most pressing challenges in water resources.
Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models
Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models write by Maurizio Mazzoleni. This book was released on 2017-03-16. Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models available in PDF, EPUB and Kindle. In recent years, the continued technological advances have led to the spread of low-cost sensors and devices supporting crowdsourcing as a way to obtain observations of hydrological variables in a more distributed way than the classic static physical sensors. The main advantage of using these type of sensors is that they can be used not only by technicians but also by regular citizens. However, due to their relatively low reliability and varying accuracy in time and space, crowdsourced observations have not been widely integrated in hydrological and/or hydraulic models for flood forecasting applications. Instead, they have generally been used to validate model results against observations, in post-event analyses. This research aims to investigate the benefits of assimilating the crowdsourced observations, coming from a distributed network of heterogeneous physical and social (static and dynamic) sensors, within hydrological and hydraulic models, in order to improve flood forecasting. The results of this study demonstrate that crowdsourced observations can significantly improve flood prediction if properly integrated in hydrological and hydraulic models. This study provides technological support to citizen observatories of water, in which citizens not only can play an active role in information capturing, evaluation and communication, leading to improved model forecasts and better flood management.
Modelling Uncertainty in Flood Forecasting Systems
Modelling Uncertainty in Flood Forecasting Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Modelling Uncertainty in Flood Forecasting Systems write by Shreeda Maskey. This book was released on 2004-11-23. Modelling Uncertainty in Flood Forecasting Systems available in PDF, EPUB and Kindle. Like all natural hazards, flooding is a complex and inherently uncertain phenomenon. Despite advances in developing flood forecasting models and techniques, the uncertainty in forecasts remains unavoidable. This uncertainty needs to be acknowledged, and uncertainty estimation in flood forecasting provides a rational basis for risk-based criteria. This book presents the development and applications of various methods based on probablity and fuzzy set theories for modelling uncertainty in flood forecasting systems. In particular, it presents a methodology for uncertainty assessment using disaggregation of time series inputs in the framework of both the Monte Carlo method and the Fuzzy Extention Principle. It reports an improvement in the First Order Second Moment method, using second degree reconstruction, and derives qualitative scales for the interpretation of qualitative uncertainty. Application is to flood forecasting models for the Klodzko catchment in POland and the Loire River in France. Prospects for the hybrid techniques of uncertainty modelling and probability-possibility transformations are also explored and reported.
