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  • Wiley  (3)
  • Oxford University Press  (1)
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  • 1
    Publication Date: 2015-06-24
    Description: Benchmarking the quality of river discharge data and understanding its information content for hydrological analyses is an important task for hydrologic science. There is a wide variety of techniques to assess discharge uncertainty. However, few studies have developed generalised approaches to quantify discharge uncertainty. This study presents a generalised framework for estimating discharge uncertainty at many gauging stations with different errors in the stage-discharge relationship. The methodology utilises a non-parametric LOWESS regression within a novel framework that accounts for uncertainty in the stage-discharge measurements, scatter in the stage-discharge data and multi-section rating curves. The framework was applied to 500 gauging stations in England and Wales and we evaluated the magnitude of discharge uncertainty at low, mean and high flow points on the rating curve. The framework was shown to be robust, versatile and able to capture place-specific uncertainties for a number of different examples. Our study revealed a wide range of discharge uncertainties (10–397% discharge uncertainty interval widths), but the majority of the gauging stations (over 80%) had mean and high flow uncertainty intervals of less than 40%. We identified some regional differences in the stage-discharge relationships, however the results show that local conditions dominated in determining the magnitude of discharge uncertainty at a gauging station. This highlights the importance of estimating discharge uncertainty for each gauging station prior to using those data in hydrological analyses. This article is protected by copyright. All rights reserved.
    Print ISSN: 0043-1397
    Electronic ISSN: 1944-7973
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 2
    Publication Date: 2014-04-11
    Description: Large uncertainties in streamflow projections derived from downscaled climate projections of precipitation and temperature can render such simulations of limited value for decision making in the context of water resources management. New approaches are being sought to provide decision makers with robust information in the face of such large uncertainties. We present an alternative approach that starts with the stakeholder's definition of vulnerable ranges for relevant hydrologic indicators. Then, the modeled system is analyzed to assess under what conditions these thresholds are exceeded. The space of possible climates and land use combinations for a watershed is explored to isolate sub-spaces that lead to vulnerability, while considering model parameter uncertainty in the analysis. We implement this concept using classification and regression trees (CART) that separate the input space of climate and land use change into those combinations that lead to vulnerability and those that do not. We test our method in a Pennsylvania watershed for nine ecological and water resources related streamflow indicators for which an increase in temperature between 3°C to 6 °C and change in precipitation between -17% and 19% is projected. Our approach provides several new insights, for example we show that even small decreases in precipitation (~5%) combined with temperature increases greater than 2.5ºC can push the mean annual runoff into a slightly vulnerable regime. Using this impact and stakeholder driven strategy, we explore the decision-relevant space more fully and provide information to the decision maker even if climate change projections are ambiguous.
    Print ISSN: 0043-1397
    Electronic ISSN: 1944-7973
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 3
    Publication Date: 2018-03-14
    Description: Motivation Alternative splicing is a biological process of fundamental importance in most eukaryotes. It plays a pivotal role in cell differentiation and gene regulation and has been associated with a number of different diseases. The widespread availability of RNA-Sequencing capacities allows an ever closer investigation of differentially expressed isoforms. However, most tools for differential alternative splicing (DAS) analysis do not take split reads, i.e. the most direct evidence for a splice event, into account. Here, we present DIEGO, a compositional data analysis method able to detect DAS between two sets of RNA-Seq samples based on split reads. Results The python tool DIEGO works without isoform annotations and is fast enough to analyze large experiments while being robust and accurate. We provide python and perl parsers for common formats. Availability and implementation The software is available at: www.bioinf.uni-leipzig.de/Software/DIEGO . Contact steve@bioinf.uni-leipzig.de Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 4
    Publication Date: 1973-01-01
    Print ISSN: 0023-4753
    Topics: Geosciences , Physics
    Published by Wiley
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