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  • 1
    Publication Date: 2015-07-23
    Description: In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.
    Print ISSN: 1027-5606
    Electronic ISSN: 1607-7938
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 2
    Publication Date: 2014-09-10
    Description: In operational hydrology, estimation of predictive uncertainty of hydrological models used for flood modelling is essential for risk based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analyzing and predicting uncertainty. However, case studies comparing performance of these methods, most particularly predictive uncertainty methods, are limited. This paper focuses on two predictive uncertainty methods that differ in their methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC), aiming at identifying possible advantages and disadvantages of these methods (both estimating residual uncertainty) based on their comparative performance. We test these two methods on several catchments (from UK) that vary in its hydrological characteristics and models. Special attention is given to the errors for high flow/water level conditions. Furthermore, normality of model residuals is discussed in view of clustering approach employed within the framework of UNEEC method. It is found that basin lag time and forecast lead time have great impact on quantification of uncertainty (in the form of two quantiles) and achievement of normality in model residuals' distribution. In general, uncertainty analysis results from different case studies indicate that both methods give similar results. However, it is also shown that UNEEC method provides better performance than QR for small catchments with changing hydrological dynamics, i.e. rapid response catchments. We recommend that more case studies of catchments from regions of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features be tested.
    Print ISSN: 1812-2108
    Electronic ISSN: 1812-2116
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
    Publication Date: 2023-06-29
    Description: Data collection and management constitutes the foundation for all analysis and modeling tasks required for achieving water, food, energy, and environmental security. UN-Water identifies data and information as one of the five accelerators where efforts and investments should be concentrated to achieve the Sustainable Development Goal 6 (SDG 6). Availability of open and free data resources can greatly contribute to enhanced implementation capacity in water research. In this context, it is crucial that data are freely accessible, openly available, and reliable. Open datasets are available online, are accessible in machine-readable formats (i.e. not pdfs or reports), and are obtainable by the public. Free refers to the availability of datasets with licenses that allow sharing at no cost to individuals, researchers, institutions, or projects. However, such datasets are dispersed across various websites and repositories. This hampers visibility and findability, which in turn results in limited use for generation of new knowledge or transferability of insights. This highlights a critical need for improving visibility of existing hydrological datasets, dedicated projects, and repositories. Crowdsourced scientific data collection efforts enable collaborative work and provide potential solutions to these issues. In this talk, we present a crowdsourced hydrology initiative aimed at establishing a global catalog of open and free datasets. This inventory will provide a database of static links, data DOIs, description of datasets along with key references and supporting information. We report on our progress and challenges in designing such an initiative with the hope that others will benefit.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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