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 Advances In Data based Approaches For Hydrologic Modeling And Forecasting written by Bellie Sivakumar and published by World Scientific. This book was released on 2010-08-10 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Applications of Machine Learning in Hydroclimatology written by Roshan Karan Srivastav and published by Springer. This book was released on 2024-10-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models written by Maurizio Mazzoleni and published by CRC Press. This book was released on 2017-03-16 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Modelling Uncertainty in Flood Forecasting Systems written by Shreeda Maskey and published by CRC Press. This book was released on 2004-11-23 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Hydrometeorology written by Kevin Sene and published by Springer Nature. This book was released on with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advances in Hydrologic Forecasts and Water Resources Management written by Fi-John Chang and published by MDPI. This book was released on 2021-01-20 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.

Download or read book Atmospheric Data Analysis written by Roger Daley and published by Cambridge University Press. This book was released on 1993-11-26 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended to fill a void in the atmospheric science literature, this self-contained text outlines the physical and mathematical basis of all aspects of atmospheric analysis as well as topics important in several other fields outside of it, including atmospheric dynamics and statistics.

Download or read book Improved Uncertainty Assessment of Hydrologic Models Using Data Assimilation and Stochastic Filtering written by Hamid Moradkhani and published by . This book was released on 2004 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Using Prediction Uncertainty Analysis to Design Hydrologic Monitoring Networks written by Michael N Fienen and published by CreateSpace. This book was released on 2014-08-01 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: The importance of monitoring networks for resource-management decisions is becoming more recognized, in both theory and application. Quantitative computer models provide a science-based framework to evaluate the efficacy and efficiency of existing and possible future monitoring networks. In the study described herein, two suites of tools were used to evaluate the worth of new data for specific predictions, which in turn can support efficient use of resources needed to construct a monitoring network. The approach evaluates the uncertainty of a model prediction and, by using linear propagation of uncertainty, estimates how much uncertainty could be reduced if the model were calibrated with addition information (increased a priori knowledge of parameter values or new observations). The theoretical underpinnings of the two suites of tools addressing this technique are compared, and their application to a hypothetical model based on a local model inset into the Great Lakes Water Availability Pilot model are described. Results show that meaningful guidance for monitoring network design can be obtained by using the methods explored. The validity of this guidance depends substantially on the parameterization as well; hence, parameterization must be considered not only when designing the parameter-estimation paradigm but also-importantly-when designing the prediction-uncertainty paradigm.

Download or read book Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models written by Maurizio Mazzoleni and published by CRC Press. This book was released on 2017-03-16 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Hydrological Data Driven Modelling written by Renji Remesan and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

Download or read book Improving Hydrologic Prediction Via Data Assimilation Data Fusion and High resolution Modeling written by Arezoo Rafieei Nasab and published by . This book was released on 2017 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: With population growth, urbanization and climate change, accurate and skillful monitoring and prediction of water resources and water-related hazards are becoming increasingly important to maintaining and improving the quality of life for human beings and well-being of the ecosystem in which people live. Because most hydrologic systems are driven by atmospheric processes that are chaotic, hydrologic processes operate at many different scales, and the above systems are almost always under-observed, there are numerous sources of error in hydrologic prediction. This study aims to advance the understanding of these uncertainty sources and reduce the uncertainties to the greatest possible extent. Toward that end, we comparatively evaluate two data assimilation (DA) techniques ensemble Kalman filter (EnKF) and maximum likelihood ensemble filter (MLEF) to reduce the uncertainty in initial conditions of soil moisture. Results show MLEF is a strongly favorable technique for assimilating streamflow data for updating soil moisture. In most places, precipitation is by far the most important forcing in hydrologic prediction. Because radars do not measure precipitation directly, radar QPEs are subject to various sources of error. In this study, the three Next Generation Radar (NEXRAD)-based QPE products, the Digital Hybrid Scan Reflectivity (DHR), Multisensor Precipitation Estimator (MPE) and Next Generation Multisensor QPE (Q2), and the radar QPE from the Collaborative Adaptive Sensing of the Atmosphere (CASA) radar are comparatively evaluated for high-resolution hydrologic modeling in the Dallas-Fort Worth Metroplex (DFW) area. Also, since they generally carry complementary information, one may expect to improve accuracy by fusing multiple QPEs. This study develops and comparatively evaluates four different techniques for producing high-resolution QPE by fusing multiple radar-based QPEs. Two experiments were carried out for evaluation; in one, the MPE and Q2 products were fused and, in the other, the MPE and CASA products were fused. Result show that the Simple Estimation (SE) is an effective, robust and computationally inexpensive data fusion algorithm for QPE. The other main goal of this study is to provide accurate spatial information of streamflow and soil moisture via distributed hydrologic modeling. Toward that end, we evaluated the NWS's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) over the Trinity River Basin for several headwater basins. We also develop a prototype high resolution flash flood prediction system for Cities of Fort Worth, Arlington and Grand Prairie, a highly urbanized area. Ideally, the higher the resolution of distributed modeling and the precipitation input is, the more desirable the model output is as it provides better spatiotemporal specificity. There are, however, practical limits to the resolution of modeling. To test and ascertain the limits of high-resolution polarimetric QPE and distributed hydrologic modeling for advanced flash flood forecasting in large urban area, we performed sensitivity analysis to spatiotemporal resolution. The results indicate little consistent pattern in dependence on spatial resolution while there is a clear pattern for sensitivity to temporal resolution. More research is needed, however, to draw firmer conclusions and to assess dependence on catchment scale.

Download or read book Report of a Workshop on Predictability and Limits To Prediction in Hydrologic Systems written by National Research Council and published by National Academies Press. This book was released on 2002-05-01 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Committee on Hydrologic Science (COHS) of the National Research Council (NRC) is engaged in studying the priorities and future strategies for hydrologic science. In order to involve a broad community representation, COHS is organizing workshops on priority topics in hydrologic science. These efforts will culminate in reports from the NRC on the individual workshops as well as a synthesis report on strategic directions in hydrologic science. The first workshop-Predictability and Limits-to-Prediction in Hydrologic Systems-was held at the National Center for Atmospheric Research in Boulder, Colorado, September 21-22, 2000. Fourteen technical presentations covered basic research and understanding, model formulations and behavior, observing strategies, and transition to operational predictions.

Download or read book Radar Hydrology written by Yang Hong and published by CRC Press. This book was released on 2018-09-03 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Radar Hydrology: Principles, Models, and Applications provides graduate students, operational forecasters, and researchers with a theoretical framework and practical knowledge of radar precipitation estimation. The only text on the market solely devoted to radar hydrology, this comprehensive reference: Begins with a brief introduction to radar Focuses on the processing of radar data to arrive at accurate estimates of rainfall Addresses advanced radar sensing principles and applications Covers radar technologies for observing each component of the hydrologic cycle Examines state-of-the-art hydrologic models and their inputs, parameters, state variables, calibration procedures, and outputs Discusses contemporary approaches in data assimilation Concludes with methods, case studies, and prediction system design Includes downloadable MATLAB® content Flooding is the #1 weather-related natural disaster worldwide. Radar Hydrology: Principles, Models, and Applications aids in understanding the physical systems and detection tools, as well as designing prediction systems.

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 Handbook of Hydrometeorological Ensemble Forecasting written by Qingyun Duan and published by Springer. This book was released on 2016-05-06 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hydrometeorological prediction involves the forecasting of the state and variation of hydrometeorological elements -- including precipitation, temperature, humidity, soil moisture, river discharge, groundwater, etc.-- at different space and time scales. Such forecasts form an important scientific basis for informing public of natural hazards such as cyclones, heat waves, frosts, droughts and floods. Traditionally, and at most currently operational centers, hydrometeorological forecasts are deterministic, “single-valued” outlooks: i.e., the weather and hydrological models provide a single best guess of the magnitude and timing of the impending events. These forecasts suffer the obvious drawback of lacking uncertainty information that would help decision-makers assess the risks of forecast use. Recently, hydrometeorological ensemble forecast approaches have begun to be developed and used by operational collection of hydrometeorological services. In contrast to deterministic forecasts, ensemble forecasts are a multiple forecasts of the same events. The ensemble forecasts are generated by perturbing uncertain factors such as model forcings, initial conditions, and/or model physics. Ensemble techniques are attractive because they not only offer an estimate of the most probable future state of the hydrometeorological system, but also quantify the predictive uncertainty of a catastrophic hydrometeorological event occurring. The Hydrological Ensemble Prediction Experiment (HEPEX), initiated in 2004, has signaled a new era of collaboration toward the development of hydrometeorological ensemble forecasts. By bringing meteorologists, hydrologists and hydrometeorological forecast users together, HEPEX aims to improve operational hydrometeorological forecast approaches to a standard that can be used with confidence by emergencies and water resources managers. HEPEX advocates a hydrometeorological ensemble prediction system (HEPS) framework that consists of several basic building blocks. These components include:(a) an approach (typically statistical) for addressing uncertainty in meteorological inputs and generating statistically consistent space/time meteorological inputs for hydrological applications; (b) a land data assimilation approach for leveraging observation to reduce uncertainties in the initial and boundary conditions of the hydrological system; (c) approaches that address uncertainty in model parameters (also called ‘calibration’); (d) a hydrologic model or other approach for converting meteorological inputs into hydrological outputs; and finally (e) approaches for characterizing hydrological model output uncertainty. Also integral to HEPS is a verification system that can be used to evaluate the performance of all of its components. HEPS frameworks are being increasingly adopted by operational hydrometeorological agencies around the world to support risk management related to flash flooding, river and coastal flooding, drought, and water management. Real benefits of ensemble forecasts have been demonstrated in water emergence management decision making, optimization of reservoir operation, and other applications.