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  • 1
    Online Resource
    Online Resource
    Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models. (2017)
    London :Taylor & Francis Group,
    Details
    Keywords: Flood forecasting. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (275 pages)
    Edition: 1st ed.
    ISBN: 9781351652568
    Series Statement: IHE Delft PhD Thesis Series
    URL: https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=4824709
    DDC: 363.34930112
    Language: English
    Note: Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Acknoledgments -- Summary -- Samenvatting -- Sommario -- Table of Contents -- 1: Introduction -- 1.1 Background -- 1.1.1 Flood Forecasting and Early Warning Systems -- 1.1.2 Hydrological and Hydrodynamic Modelling -- 1.1.3 Uncertainty in Hydrological and Hydrodynamic Modelling -- 1.1.4 Data Assimilation -- 1.1.5 Citizen Science -- 1.2 Motivation -- 1.3 Terminology -- 1.4 Research Objectives -- 1.5 Outline of the Thesis -- 2: Case Studies and Models -- 2.1 Introduction -- 2.2 Case 1 - Brue Catchment (uk) -- 2.2.1 Catchment Description -- 2.2.2 Model Description -- 2.3 Case 2 - Bacchiglione Catchment (italy) -- 2.3.1 Catchment Description -- 2.3.2 Model Description -- 2.4 Case 3 - Trinity and Sabine Rivers (usa) -- 2.4.1 Rivers Description -- 2.4.2 Model Description -- 2.5 Case 4 - Synthetic River Reach -- 3: Data Assimilation Methods -- 3.1 Introduction -- 3.2 Direct Insertion -- 3.3 Nudging Scheme -- 3.4 Kalman Filter -- 3.5 Ensemble Kalman Filter -- 3.6 Asynchronous Ensemble Kalman Filter -- 4: Assimilation of Synchronous Data in Hydrological Models -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Assimilation of Intermittent Observations -- 4.2.2 Observation and Model Error -- 4.2.3 Generation of Synthetic Observations -- 4.3 Experimental Setup -- 4.3.1 Experiment 4.1: Streamflow Data from Static Physical (stph) Sensors -- 4.3.2 Experiment 4.2: Streamflow Data from Static Social (stsc) Sensors -- 4.3.3 Experiment 4.3: Intermittent Streamflow Data from Static Social (stsc) Sensors -- 4.3.4 Experiment 4.4: Heterogeneous Network of Static Physical (stph) and Static Social (stsc) Sensors -- 4.4 Results and Discussion -- 4.4.1 Experiment 4.1 -- 4.4.2 Experiment 4.2 -- 4.4.3 Experiment 4.3 -- 4.4.4 Experiment 4.4 -- 4.5 Conclusions. , 5: Assimilation of Asynchronous Data in Hydrological Models -- 5.1 Introduction -- 5.2 Methodology -- 5.2.1 Assimilation of Asynchronous Observations -- 5.2.2 Observation and Model Error -- 5.2.3 Generation of Synthetic Observations -- 5.3 Experimental Setup -- 5.3.1 Experiment 5.1: Observations from a Single Static Social (stsc) Sensor -- 5.3.2 Experiments 5.2: Observations from Distributed Static Physical (stph) and Static Social (stsc) Sensors -- 5.4 Results and Discussion -- 5.4.1 Experiment 5.1 -- 5.4.2 Experiment 5.2 -- 5.5 Conclusions -- 6: Assimilation of Synchronous Data in Hydraulic Models -- 6.1 Introduction -- 6.2 Methodology -- 6.2.1 Data Assimilation Methods -- 6.2.2 Observation and Model Error -- 6.2.3 Streamflow Observations -- 6.3 Experimental Setup -- 6.3.1 Experiment 6.1: Effect of Different Da Methods -- 6.3.2 Experiment 6.2: Effect of Sensors Location on Kf Performances -- 6.4 Results and Discussions -- 6.4.1 Experiment 6.1 -- 6.4.2 Experiment 6.2 -- 6.5 Conclusions -- 7: Assimilation of Synchronous Data in a Cascade of Models -- 7.1 Introduction -- 7.2 Methodology -- 7.2.1 Data Assimilation Method -- 7.2.2 Observation and Model Error -- 7.2.3 Generation of Synthetic Observations -- 7.3 Experimental Setup -- 7.3.1 Experiment 7.1: Assimilation of Data from Static Physical (stph) Sensors -- 7.3.2 Experiment 7.2: Assimilation of Data from Static Social (stsc) Sensors -- 7.3.3 Experiment 7.3: Assimilation of Data from Dynamic Social (dysc) Sensors -- 7.3.4 Experiment 7.4: Realistic Scenarios of Engagements -- 7.4 Results and Discussion -- 7.4.1 Experiment 7.1 -- 7.4.2 Experiment 7.2 -- 7.4.3 Experiment 7.3 -- 7.4.4 Experiment 7.4 -- 7.5 Conclusions -- 8: Conclusions and Recommendations -- 8.1 Overview -- 8.2 Research Outcomes -- 8.3 Limitations and Recommendations -- References -- List of Acronyms -- List of Table -- List of Figures. , About the Author.
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  • 2
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    The challenge of unprecedented floods and droughts in risk management (2022)
    In:  EPIC3Nature, 608(7921), pp. 80-86, ISSN: 0028-0836
    Details
    Publication Date: 2022-09-02
    Description: Risk management has reduced vulnerability to floods and droughts globally, yet their impacts are still increasing. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
    Format: application/pdf
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  • 3
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    Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts (2023)
    Copernicus Publications
    In:  EPIC3Earth System Science Data, Copernicus Publications, 15(5), pp. 2009-2023, ISSN: 1866-3508
    Details
    Publication Date: 2024-04-22
    Description: As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human-water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, 10.5880/GFZ.4.4.2023.001).
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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