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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.
Book Synopsis Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models by : Maurizio Mazzoleni
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.
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.
Book Synopsis Improving Hydrologic Prediction Via Data Assimilation, Data Fusion and High-resolution Modeling by : Arezoo Rafieei Nasab
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.
Accurate forecasting of floods is a long-standing challenge in hydrology and water management. Data assimilation (DA) is a popular technique used to improve forecast accuracy by updating the model states in real time using the uncertainty-quantified actual and model-simulated observations. A particular challenge in DA concerns the ability to improve the prediction of hydrologic extremes, such as floods, which have particularly large impacts on society. Almost all DA methods used today are based on least squares minimization. As such, they are subject to conditional bias (CB) in the presence of observational uncertainties which often leads to under- and over-prediction of the predict and over the upper and lower tails, respectively. To address the adverse impact of CB in DA, conditional bias penalized Kalman filter (CBPKF) and conditional bias penalized ensemble Kalman filter (CBEnKF) have recently been proposed which minimize a weighted sum of the error variance and expectation of the CB squared. Whereas CBPKF and CBEnKF significantly improve the accuracy of the estimates over the tails, they deteriorate performance near the median due to the added penalty. To address the above, this work introduces CB-aware DA, which adaptively weights the CB penalty term in real time, and assesses the flow-dependent information content in observation and model prediction using the degrees of freedom for signal (DFS), which serves as a skill score for information fusion. CB-aware DA is then comparatively evaluated with ensemble Kalman filter in which the marginal information content of observations and its flow dependence are assessed given the hydrologic model used. The findings indicate that CB-aware DA with information content analysis offers an objective framework for improving DA performance for prediction of extremes and dynamically balancing the predictive skill of hydrologic models, quality and frequency of hydrologic observations, and scheduling of DA cycles for improving operational flood forecasting cost-effectively.
Book Synopsis Improving Flood Forecasting Using Conditional Bias-aware Assimilation of Streamflow Observations and Dynamic Assessment of Flow-dependent Information Content by : Haojing Shen
Download or read book Improving Flood Forecasting Using Conditional Bias-aware Assimilation of Streamflow Observations and Dynamic Assessment of Flow-dependent Information Content written by Haojing Shen and published by . This book was released on 2021 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate forecasting of floods is a long-standing challenge in hydrology and water management. Data assimilation (DA) is a popular technique used to improve forecast accuracy by updating the model states in real time using the uncertainty-quantified actual and model-simulated observations. A particular challenge in DA concerns the ability to improve the prediction of hydrologic extremes, such as floods, which have particularly large impacts on society. Almost all DA methods used today are based on least squares minimization. As such, they are subject to conditional bias (CB) in the presence of observational uncertainties which often leads to under- and over-prediction of the predict and over the upper and lower tails, respectively. To address the adverse impact of CB in DA, conditional bias penalized Kalman filter (CBPKF) and conditional bias penalized ensemble Kalman filter (CBEnKF) have recently been proposed which minimize a weighted sum of the error variance and expectation of the CB squared. Whereas CBPKF and CBEnKF significantly improve the accuracy of the estimates over the tails, they deteriorate performance near the median due to the added penalty. To address the above, this work introduces CB-aware DA, which adaptively weights the CB penalty term in real time, and assesses the flow-dependent information content in observation and model prediction using the degrees of freedom for signal (DFS), which serves as a skill score for information fusion. CB-aware DA is then comparatively evaluated with ensemble Kalman filter in which the marginal information content of observations and its flow dependence are assessed given the hydrologic model used. The findings indicate that CB-aware DA with information content analysis offers an objective framework for improving DA performance for prediction of extremes and dynamically balancing the predictive skill of hydrologic models, quality and frequency of hydrologic observations, and scheduling of DA cycles for improving operational flood forecasting cost-effectively.
Flooding is the costliest natural disaster in the United States and tragically often leads to loss of life. Flood prediction, response and mitigation are therefore critical areas of research and have been for many decades. Hydrologic and hydraulic models are a key component of flood prediction methods and highly detailed models have been implemented in many areas of high risk which often correspond to areas with high population. However, the high cost and complexity of highly detailed models means that many areas of the US are not covered by flood prediction early warning systems. Recent increases in computational power and increased resolution and coverage of remotely sensed data have allowed for the development of a continental scale streamflow prediction system known as the National Water Model which is currently forecasting streamflow values for over 2.7 million stream reaches across the US. Flood inundation predictions can be derived from the National Water Model using digital elevation data to extract reach-scale rating curves and therefore river stage height. Using the height above nearest drainage method, flood inundation maps can be created from the stage height at relatively low computational cost at continental scale. The National Water Model is currently operating as a deterministic model for short-term predictions and does not currently include an estimate of the uncertainty in these predictions. The final streamflow values are at the end of a chain of models which originate from precipitation forecasts and go through rainfall-runoff and finally routing modules. The total uncertainty in the streamflow predictions is therefore a function of the uncertainty in each step. Uncertainty analysis commonly relies on an assessment of uncertainty in model parameters and boundary conditions, the use of perturbed inputs or through comparison of several different models of the same systems. Estimated uncertainty from the first model in a chain can then be propagated to the next model and so on until a final estimate is achieved. Unfortunately, the National Water Model is operated on a super computer and the details of the model are not available for perturbation analysis. One step in the National Water Model hourly cycle is the assimilation of USGS gage data which allows for corrections to the model state before the forecast simulation is made. This excludes USGS gage data from being used as a verification dataset. Even so, it is still an informative exercise to compare NWM predictions at these sites. There are numerous local and regional gaging stations which are not assimilated into the National Water Model and can be used as an independent check on the model output. Recent flooding in the Llano River basin in central Texas provides an opportunity to compare National Water Model predictions to both USGS and non-USGS gage readings. This thesis presents an assessment of the error in National Water Model predictions in the Llano River basin
Book Synopsis Error Assessment of National Water Model Analysis & Assimilation and Short-range Forecasts by : Andrew Austin-Petersen
Download or read book Error Assessment of National Water Model Analysis & Assimilation and Short-range Forecasts written by Andrew Austin-Petersen and published by . This book was released on 2018 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Flooding is the costliest natural disaster in the United States and tragically often leads to loss of life. Flood prediction, response and mitigation are therefore critical areas of research and have been for many decades. Hydrologic and hydraulic models are a key component of flood prediction methods and highly detailed models have been implemented in many areas of high risk which often correspond to areas with high population. However, the high cost and complexity of highly detailed models means that many areas of the US are not covered by flood prediction early warning systems. Recent increases in computational power and increased resolution and coverage of remotely sensed data have allowed for the development of a continental scale streamflow prediction system known as the National Water Model which is currently forecasting streamflow values for over 2.7 million stream reaches across the US. Flood inundation predictions can be derived from the National Water Model using digital elevation data to extract reach-scale rating curves and therefore river stage height. Using the height above nearest drainage method, flood inundation maps can be created from the stage height at relatively low computational cost at continental scale. The National Water Model is currently operating as a deterministic model for short-term predictions and does not currently include an estimate of the uncertainty in these predictions. The final streamflow values are at the end of a chain of models which originate from precipitation forecasts and go through rainfall-runoff and finally routing modules. The total uncertainty in the streamflow predictions is therefore a function of the uncertainty in each step. Uncertainty analysis commonly relies on an assessment of uncertainty in model parameters and boundary conditions, the use of perturbed inputs or through comparison of several different models of the same systems. Estimated uncertainty from the first model in a chain can then be propagated to the next model and so on until a final estimate is achieved. Unfortunately, the National Water Model is operated on a super computer and the details of the model are not available for perturbation analysis. One step in the National Water Model hourly cycle is the assimilation of USGS gage data which allows for corrections to the model state before the forecast simulation is made. This excludes USGS gage data from being used as a verification dataset. Even so, it is still an informative exercise to compare NWM predictions at these sites. There are numerous local and regional gaging stations which are not assimilated into the National Water Model and can be used as an independent check on the model output. Recent flooding in the Llano River basin in central Texas provides an opportunity to compare National Water Model predictions to both USGS and non-USGS gage readings. This thesis presents an assessment of the error in National Water Model predictions in the Llano River basin
The book presents the recent advances on testing and experimentation in civil engineering, especially in the branches of geotechnics, transportation, hydraulics, and natural resources. It includes advances in physical modelling, monitoring techniques, data acquisition and analysis, and provides an invaluable contribution for the installation of new civil engineering experimental facilities. The first part of the book covers the latest advances in testing and experimentation in key domains of geotechnics: soil mechanics and geotechnical engineering, rock mechanics and rock engineering, and engineering geology. Some of the topics covered include new developments in topographic survey acquisition for applied mapping and in situ geotechnical investigations; laboratory and in situ tests to estimate the relevant parameters needed to model the behaviour of rock masses and land structures; monitoring and inspection techniques designed for offshore wind foundations. The second part of the book highlights the relevance of testing and monitoring in transportation. Full-scale accelerated pavement testing, and instrumentation becomes even more important nowadays when, for sustainability purposes, non-traditional materials are used in road and airfield pavements. Innovation in testing and monitoring pavements and railway tracks is also developed in this part of the book. Intelligent traffic systems are the new traffic management paradigm, and an overview of new solutions is addressed here. Finally, in the third part of the book, trends in the field and laboratory measurements and corresponding data analysis are presented according to the different hydraulic domains addressed in this publication, namely maritime hydraulics, surface water and river hydraulics and urban water.
Book Synopsis Advances on Testing and Experimentation in Civil Engineering by : Carlos Chastre
Download or read book Advances on Testing and Experimentation in Civil Engineering written by Carlos Chastre and published by Springer Nature. This book was released on 2022-08-17 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents the recent advances on testing and experimentation in civil engineering, especially in the branches of geotechnics, transportation, hydraulics, and natural resources. It includes advances in physical modelling, monitoring techniques, data acquisition and analysis, and provides an invaluable contribution for the installation of new civil engineering experimental facilities. The first part of the book covers the latest advances in testing and experimentation in key domains of geotechnics: soil mechanics and geotechnical engineering, rock mechanics and rock engineering, and engineering geology. Some of the topics covered include new developments in topographic survey acquisition for applied mapping and in situ geotechnical investigations; laboratory and in situ tests to estimate the relevant parameters needed to model the behaviour of rock masses and land structures; monitoring and inspection techniques designed for offshore wind foundations. The second part of the book highlights the relevance of testing and monitoring in transportation. Full-scale accelerated pavement testing, and instrumentation becomes even more important nowadays when, for sustainability purposes, non-traditional materials are used in road and airfield pavements. Innovation in testing and monitoring pavements and railway tracks is also developed in this part of the book. Intelligent traffic systems are the new traffic management paradigm, and an overview of new solutions is addressed here. Finally, in the third part of the book, trends in the field and laboratory measurements and corresponding data analysis are presented according to the different hydraulic domains addressed in this publication, namely maritime hydraulics, surface water and river hydraulics and urban water.
Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Book Synopsis Flood Forecasting Using Machine Learning Methods by : Fi-John Chang
Download or read book Flood Forecasting Using Machine Learning Methods written by Fi-John Chang and published by MDPI. This book was released on 2019-02-28 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
The National Water Center (NWC) started using the National Water Model (NWM) in 2016. The NWM delivers state-of-the-science hydrologic forecasts in the nation. The NWM aims at operationally forecasting streamflow in more than 2,000,000 river reaches while currently river forecasts are issued for 4,000. The NWM is a specific configuration of the community WRF-Hydro Land Surface Model (LSM) which has recently been introduced to the hydrologic community. The WRF-Hydro model, itself, uses another newly-developed LSM called Noah-MP as the core hydrologic model. In WRF-Hydro, Noah-MP results (such as soil moisture and runoff) are passed to routing modules. Riverine water level and discharge, among other variables, are outputted by WRF-Hydro. The NWM, WRF-Hydro, and Noah-MP have recently been developed and more research for operational accuracy is required on these models. The overarching goal in this dissertation is improving the ability of these three models in simulating and forecasting hydrological variables such as streamflow and soil moisture. Therefore, data assimilation (DA) is implemented on these models throughout this dissertation. The results show that short-range forecasts are significantly sensitive to the initial condition and its associated uncertainty. It is shown that quantification of this uncertainty can improve the forecasts by approximately 80%. The findings of this dissertation highlight the importance of DA to extract the information content from the observations and then incorporate this information into the land surface models. The findings could be beneficial for flood forecasting in research and operation.
Book Synopsis Ensemble Data Assimilation for Flood Forecasting in Operational Settings by :
Download or read book Ensemble Data Assimilation for Flood Forecasting in Operational Settings written by and published by . This book was released on 2018 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: The National Water Center (NWC) started using the National Water Model (NWM) in 2016. The NWM delivers state-of-the-science hydrologic forecasts in the nation. The NWM aims at operationally forecasting streamflow in more than 2,000,000 river reaches while currently river forecasts are issued for 4,000. The NWM is a specific configuration of the community WRF-Hydro Land Surface Model (LSM) which has recently been introduced to the hydrologic community. The WRF-Hydro model, itself, uses another newly-developed LSM called Noah-MP as the core hydrologic model. In WRF-Hydro, Noah-MP results (such as soil moisture and runoff) are passed to routing modules. Riverine water level and discharge, among other variables, are outputted by WRF-Hydro. The NWM, WRF-Hydro, and Noah-MP have recently been developed and more research for operational accuracy is required on these models. The overarching goal in this dissertation is improving the ability of these three models in simulating and forecasting hydrological variables such as streamflow and soil moisture. Therefore, data assimilation (DA) is implemented on these models throughout this dissertation. The results show that short-range forecasts are significantly sensitive to the initial condition and its associated uncertainty. It is shown that quantification of this uncertainty can improve the forecasts by approximately 80%. The findings of this dissertation highlight the importance of DA to extract the information content from the observations and then incorporate this information into the land surface models. The findings could be beneficial for flood forecasting in research and operation.
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.
Book Synopsis Modelling Uncertainty in Flood Forecasting Systems by : Shreeda Maskey
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.
This volume provides an introduction for flood risk management practitioners, up-to-date methods for analysis of uncertainty and its use in risk-based decision making. It addresses decision making for both short-term (real-time forecasting) and long-term (flood risk planning under change) situations. It aims primarily at technical practitioners involved in flood risk analysis and flood warning, including hydrologists, engineers, flood modelers, risk analysts and those involved in the design and operation of flood warning systems. Many experienced practitioners are now expected to modify their way of working to fit into the new philosophy of flood risk management. This volume helps them to undertake that task with appropriate attention to the surrounding uncertainties. The book will also interest and benefit researchers and graduate students hoping to improve their knowledge of modern uncertainty analysis.
Book Synopsis Applied Uncertainty Analysis For Flood Risk Management by : Keith J Beven
Download or read book Applied Uncertainty Analysis For Flood Risk Management written by Keith J Beven and published by World Scientific. This book was released on 2014-01-13 with total page 685 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides an introduction for flood risk management practitioners, up-to-date methods for analysis of uncertainty and its use in risk-based decision making. It addresses decision making for both short-term (real-time forecasting) and long-term (flood risk planning under change) situations. It aims primarily at technical practitioners involved in flood risk analysis and flood warning, including hydrologists, engineers, flood modelers, risk analysts and those involved in the design and operation of flood warning systems. Many experienced practitioners are now expected to modify their way of working to fit into the new philosophy of flood risk management. This volume helps them to undertake that task with appropriate attention to the surrounding uncertainties. The book will also interest and benefit researchers and graduate students hoping to improve their knowledge of modern uncertainty analysis.
A comprehensive reference on data assimilation and inverse problems, and their applications across a broad range of geophysical disciplines, ideal for researchers and graduate students. It highlights the importance of data assimilation for understanding dynamical processes of the Earth and its space environment, and summarises recent advances.
Book Synopsis Applications of Data Assimilation and Inverse Problems in the Earth Sciences by : Alik Ismail-Zadeh
Download or read book Applications of Data Assimilation and Inverse Problems in the Earth Sciences written by Alik Ismail-Zadeh and published by Cambridge University Press. This book was released on 2023-06-30 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive reference on data assimilation and inverse problems, and their applications across a broad range of geophysical disciplines, ideal for researchers and graduate students. It highlights the importance of data assimilation for understanding dynamical processes of the Earth and its space environment, and summarises recent advances.
