Improving Flood Forecasting Using Conditional Bias Aware Assimilation Of Streamflow Observations And Dynamic Assessment Of Flow Dependent Information Content
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Improving Flood Forecasting Using Conditional Bias Aware Assimilation Of Streamflow Observations And Dynamic Assessment Of Flow Dependent Information Content
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Author : Haojing Shen
language : en
Publisher:
Release Date : 2021
Improving Flood Forecasting Using Conditional Bias Aware Assimilation Of Streamflow Observations And Dynamic Assessment Of Flow Dependent Information Content written by Haojing Shen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Flood forecasting categories.
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.
Improving Flood Prediction Assimilating Uncertain Crowdsourced Data Into Hydrologic And Hydraulic Models
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Author : Maurizio Mazzoleni
language : en
Publisher: CRC Press
Release Date : 2017-03-16
Improving Flood Prediction Assimilating Uncertain Crowdsourced Data Into Hydrologic And Hydraulic Models written by Maurizio Mazzoleni and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-16 with Science categories.
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.
Flood Forecasting Using Machine Learning Methods
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Author : Fi-John Chang
language : en
Publisher: MDPI
Release Date : 2019-02-28
Flood Forecasting Using Machine Learning Methods written by Fi-John Chang and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-28 with Technology & Engineering categories.
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.
Hydrologic Data Assimilation For Operational Streamflow Forecasting
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Author : Feyera Aga Hirpa
language : en
Publisher:
Release Date : 2013
Hydrologic Data Assimilation For Operational Streamflow Forecasting written by Feyera Aga Hirpa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Ensemble Data Assimilation For Flood Forecasting In Operational Settings
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Author :
language : en
Publisher:
Release Date : 2018
Ensemble Data Assimilation For Flood Forecasting In Operational Settings written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computer simulation categories.
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.
Modelling Uncertainty In Flood Forecasting Systems
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Author : Shreeda Maskey
language : en
Publisher: CRC Press
Release Date : 2004-11-23
Modelling Uncertainty In Flood Forecasting Systems written by Shreeda Maskey and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-11-23 with Science categories.
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.
The Estimation Of Rainfall For Flood Forecasting Using Radar And Rain Gage Data
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Author : William J. Charley
language : en
Publisher:
Release Date : 1988
The Estimation Of Rainfall For Flood Forecasting Using Radar And Rain Gage Data written by William J. Charley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Flood forecasting categories.
Streamflow And Precipitation Data Assimilation Into The National Water Model
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Author : Leah Gage Huling
language : en
Publisher:
Release Date : 2020
Streamflow And Precipitation Data Assimilation Into The National Water Model written by Leah Gage Huling and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Flooding is a dangerous natural disaster that poses risk to property and personal safety world-wide. The state of Texas is especially vulnerable to floods; a region known as “Flash flood Alley” runs through central Texas, and the coastal region is prone to severe tropical storms and hurricanes. Flood warning systems exist in large cities throughout Texas, but there is no coordinated state-wide flood warning network. In this study, we investigate the feasibility of creating a locally intelligent, state-wide flood forecasting system by using local observational streamflow data to better inform a national forecasting model. A method is developed to integrate streamflow sensors and precipitation products into short-range, 18-hour National Water Model (NWM) forecasts through data assimilation (DA). Four-dimensional variational data assimilation is coupled with a mass-conservative Muskingum-Cunge flow routing scheme to propagate streamflow corrections upstream and downstream from the sensor locations. The model is applied to a rural study area in the Texas Hill Country, and the streamflow DA model creates improved forecasts products at the sensor location and over 15 miles upstream and downstream of the sensor. Proof of concept results from a simplified surface runoff model using precipitation corrections indicate improved streamflow profiles at the upstream location. With further validation and development, there is real potential in assimilating local data into the NWM to create a statewide flood forecasting network
Improvements In Flood Forecasting In Mountain Basins Through A Physically Based Distributed Model
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Author : Hernan Moreno Ramirez
language : en
Publisher:
Release Date : 2012
Improvements In Flood Forecasting In Mountain Basins Through A Physically Based Distributed Model written by Hernan Moreno Ramirez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Flood forecasting categories.
This doctoral thesis investigates the predictability characteristics of floods and flash floods by coupling high resolution precipitation products to a distributed hydrologic model. The research hypotheses are tested at multiple watersheds in the Colorado Front Range (CFR) undergoing warm-season precipitation. Rainfall error structures are expected to propagate into hydrologic simulations with added uncertainties by model parameters and initial conditions. Specifically, the following science questions are addressed: (1) What is the utility of Quantitative Precipitation Estimates (QPE) for high resolution hydrologic forecasts in mountain watersheds of the CFR?, (2) How does the rainfall-reflectivity relation determine the magnitude of errors when radar observations are used for flood forecasts?, and (3) What are the spatiotemporal limits of flood forecasting in mountain basins when radar nowcasts are used into a distributed hydrological model?. The methodology consists of QPE evaluations at the site (i.e., rain gauge location), basin-average and regional scales, and Quantitative Precipitation Forecasts (QPF) assessment through regional grid-to-grid verification techniques and ensemble basin-averaged time series. The corresponding hydrologic responses that include outlet discharges, distributed runoff maps, and streamflow time series at internal channel locations, are used in light of observed and/or reference data to diagnose the suitability of fusing precipitation forecasts into a distributed model operating at multiple catchments. Results reveal that radar and multisensor QPEs lead to an improved hydrologic performance compared to simulations driven with rain gauge data only. In addition, hydrologic performances attained by satellite products preserve the fundamental properties of basin responses, including a simple scaling relation between the relative spatial variability of runoff and its magnitude. Overall, the spatial variations contained in gridded QPEs add value for warm-season flood forecasting in mountain basins, with sparse data even if those products contain some biases. These results are encouraging and open new avenues for forecasting in regions with limited access and sparse observations. Regional comparisons of different reflectivity -rainfall (Z-R) relations during three summer seasons, illustrated significant rainfall variability across the region. Consistently, hydrologic errors introduced by the distinct Z-R relations, are significant and proportional (in the log-log space) to errors in precipitation estimations and stream flow magnitude. The use of operational Z-R relations without prior calibration may lead to wrong estimation of precipitation, runoff magnitude and increased flood forecasting errors. This suggests that site-specific Z-R relations, prior to forecasting procedures, are desirable in complex terrain regions. Nowcasting experiments show the limits of flood forecasting and its dependence functions of lead time and basin scale. Across the majority of the basins, flood forecasting skill decays with lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Both precipitation and flood forecasting skills are noticeably reduced for lead times greater than 30 minutes. Scale dependence of hydrologic forecasting errors demonstrates reduced predictability at intermediate-size basins, the typical scale of convective storm systems. Overall, the fusion of high resolution radar nowcasts and the convenient parallel capabilities of the distributed hydrologic model provide an efficient framework for generating accurate real-time flood forecasts suitable for operational environments.
Improving Operational Flood Forecasting Using Data Assimilation
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Author : Oldřich Rakovec
language : en
Publisher:
Release Date : 2014
Improving Operational Flood Forecasting Using Data Assimilation written by Oldřich Rakovec and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.