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  • 1
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    Climate change impacts on future snow, ice and rain runoff in a Swiss mountain catchment using multi-dataset calibration
    Elsevier BV ; 2017
    In:  Journal of Hydrology: Regional Studies Vol. 13 ( 2017-10), p. 222-239
    Details
    In: Journal of Hydrology: Regional Studies, Elsevier BV, Vol. 13 ( 2017-10), p. 222-239
    Type of Medium: Online Resource
    ISSN: 2214-5818
    URL: Article
    DOI: 10.1016/j.ejrh.2017.08.005
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2017
    detail.hit.zdb_id: 2814784-4
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  • 2
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    DOs and DON'Ts for using climate change information for water resource planning and management: guidelines for study design
    Elsevier BV ; 2018
    In:  Climate Services Vol. 12 ( 2018-12), p. 1-13
    Details
    In: Climate Services, Elsevier BV, Vol. 12 ( 2018-12), p. 1-13
    Type of Medium: Online Resource
    ISSN: 2405-8807
    URL: Article
    DOI: 10.1016/j.cliser.2018.07.002
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 2858351-6
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  • 3
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    Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges
    Informa UK Limited ; 2020
    In:  Hydrological Sciences Journal Vol. 65, No. 5 ( 2020-04-03), p. 712-725
    Details
    In: Hydrological Sciences Journal, Informa UK Limited, Vol. 65, No. 5 ( 2020-04-03), p. 712-725
    Type of Medium: Online Resource
    ISSN: 0262-6667 , 2150-3435
    URL: Article
    DOI: 10.1080/02626667.2019.1683182
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2180448-5
    SSG: 14
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  • 4
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    Risks and opportunities for a Swiss hydroelectricity company in a changing climate
    Copernicus GmbH ; 2020
    In:  Hydrology and Earth System Sciences Vol. 24, No. 7 ( 2020-07-29), p. 3815-3833
    Details
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 24, No. 7 ( 2020-07-29), p. 3815-3833
    Abstract: Abstract. Anticipating and adapting to climate change impacts on water resources requires a detailed understanding of future hydroclimatic changes and of stakeholders' vulnerability to these changes. However, impact studies are often conducted at a spatial scale that is too coarse to capture the specificity of individual catchments, and, importantly, the changes they focus on are not necessarily the changes most critical to stakeholders. While recent studies have combined hydrological and electricity market modeling, they tend to aggregate all climate impacts by focusing solely on reservoir profitability. Here, we collaborated with Groupe E, a hydroelectricity company operating several reservoirs in the Swiss pre-Alps, and we co-produced hydroclimatic projections tailored to support the upcoming negotiations of their water concession renewal. We started by identifying the vulnerabilities of their activities to climate change; together, we then selected streamflow and electricity demand indices to characterize the associated risks and opportunities. We provided Groupe E with figures showing the projected impacts, which were refined over several meetings. The selected indices enabled us to assess a variety of impacts induced by changes in (i) the seasonal water volume distribution, (ii) low flows, (iii) high flows, and (iv) electricity demand. This enabled us to identify key opportunities (e.g., the future increase in reservoir inflow in winter, when electricity prices have historically been high) and risks (e.g., the expected increase in consecutive days of low flows in summer and fall which is likely to make it more difficult to meet residual flow requirements). We highlight that the hydrological opportunities and risks associated with reservoir management in a changing climate depend on a range of factors beyond those covered by traditional impact studies. This stakeholder-centered approach, which relies on identifying stakeholder's needs and using them to inform the production and visualization of impact projections, is transferable to other climate impact studies, in the field of water resources and beyond.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    URL: Article
    URL: Article
    DOI: 10.5194/hess-24-3815-2020
    DOI: 10.5194/hess-24-3815-2020-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2100610-6
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  • 5
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    CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil
    Copernicus GmbH ; 2020
    In:  Earth System Science Data Vol. 12, No. 3 ( 2020-09-08), p. 2075-2096
    Details
    In: Earth System Science Data, Copernicus GmbH, Vol. 12, No. 3 ( 2020-09-08), p. 2075-2096
    Abstract: Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3679 gauges, as well as meteorological forcing (precipitation, evapotranspiration, and temperature) for 897 selected catchments. It also includes 65 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil, and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations, and their main spatial features. To facilitate comparisons with catchments from other countries, the data follow the same standards as the previous CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets for the United States, Chile, and Great Britain. CAMELS-BR (Brazil) complements the other CAMELS datasets by providing data for hundreds of catchments in the tropics and the Amazon rainforest. Importantly, precipitation and evapotranspiration uncertainties are assessed using several gridded products, and quantitative estimates of water consumption are provided to characterize human impacts on water resources. By extracting and combining data from these different data products and making CAMELS-BR publicly available, we aim to create new opportunities for hydrological research in Brazil and facilitate the inclusion of Brazilian basins in continental to global large-sample studies. We envision that this dataset will enable the community to gain new insights into the drivers of hydrological behavior, better characterize extreme hydroclimatic events, and explore the impacts of climate change and human activities on water resources in Brazil. The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709337 (Chagas et al., 2020).
    Type of Medium: Online Resource
    ISSN: 1866-3516
    URL: Article
    DOI: 10.5194/essd-12-2075-2020
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2475469-9
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  • 6
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    CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain
    Copernicus GmbH ; 2020
    In:  Earth System Science Data Vol. 12, No. 4 ( 2020-10-12), p. 2459-2483
    Details
    In: Earth System Science Data, Copernicus GmbH, Vol. 12, No. 4 ( 2020-10-12), p. 2459-2483
    Abstract: Abstract. We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological time series and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape, and human management characteristics across Great Britain. Daily time series covering 1970–2015 (a period including several hydrological extreme events) are provided for a range of hydro-meteorological variables including rainfall, potential evapotranspiration, temperature, radiation, humidity, and river flow. A comprehensive set of catchment attributes is quantified including topography, climate, hydrology, land cover, soils, and hydrogeology. Importantly, we also derive human management attributes (including attributes summarising abstractions, returns, and reservoir capacity in each catchment), as well as attributes describing the quality of the flow data including the first set of discharge uncertainty estimates (provided at multiple flow quantiles) for Great Britain. CAMELS-GB (Coxon et al., 2020; available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9) is intended for the community as a publicly available, easily accessible dataset to use in a wide range of environmental and modelling analyses.
    Type of Medium: Online Resource
    ISSN: 1866-3516
    URL: Article
    URL: Article
    DOI: 10.5194/essd-12-2459-2020
    DOI: 10.5194/essd-12-2459-2020-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2475469-9
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  • 7
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    Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0
    Copernicus GmbH ; 2021
    In:  Geoscientific Model Development Vol. 14, No. 8 ( 2021-08-05), p. 4865-4890
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 14, No. 8 ( 2021-08-05), p. 4865-4890
    Abstract: Abstract. Riverine flood hazard is the consequence of meteorological drivers, primarily precipitation, hydrological processes and the interaction of floodwaters with the floodplain landscape. Modeling this can be particularly challenging because of the multiple steps and differing spatial scales involved in the varying processes. As the climate modeling community increases their focus on the risks associated with climate change, it is important to translate the meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. This is due to the complexity and uncertainties of model cascades and the computational cost of flood inundation modeling. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (∼90 m) river flooding (fluvial) hazards. Thus, this framework is designed to be an accessible, computationally efficient tool using freely available data to enable greater uptake of this type of modeling. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data sets and thus can be applied anywhere in the world, but we use the Brahmaputra River in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework. This framework is designed to be driven by meteorology from observational data sets or climate model output. In this study, only observations are used to drive the models, so climate changes are not assessed. However, by comparing current and future simulated climates, this framework can also be used to assess impacts of climate change.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    DOI: 10.5194/gmd-14-4865-2021
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2021
    detail.hit.zdb_id: 2456725-5
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  • 8
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    Mapping (dis)agreement in hydrologic projections
    Copernicus GmbH ; 2018
    In:  Hydrology and Earth System Sciences Vol. 22, No. 3 ( 2018-03-12), p. 1775-1791
    Details
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 22, No. 3 ( 2018-03-12), p. 1775-1791
    Abstract: Abstract. Hydrologic projections are of vital socio-economic importance. However, they are also prone to uncertainty. In order to establish a meaningful range of storylines to support water managers in decision making, we need to reveal the relevant sources of uncertainty. Here, we systematically and extensively investigate uncertainty in hydrologic projections for 605 basins throughout the contiguous US. We show that in the majority of the basins, the sign of change in average annual runoff and discharge timing for the period 2070–2100 compared to 1985–2008 differs among combinations of climate models, hydrologic models, and parameters. Mapping the results revealed that different sources of uncertainty dominate in different regions. Hydrologic model induced uncertainty in the sign of change in mean runoff was related to snow processes and aridity, whereas uncertainty in both mean runoff and discharge timing induced by the climate models was related to disagreement among the models regarding the change in precipitation. Overall, disagreement on the sign of change was more widespread for the mean runoff than for the discharge timing. The results demonstrate the need to define a wide range of quantitative hydrologic storylines, including parameter, hydrologic model, and climate model forcing uncertainty, to support water resource planning.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    URL: Article
    URL: Article
    DOI: 10.5194/hess-22-1775-2018
    DOI: 10.5194/hess-22-1775-2018-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2100610-6
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  • 9
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    Hydrological Modeling to Evaluate Climate Model Simulations and Their Bias Correction
    American Meteorological Society ; 2018
    In:  Journal of Hydrometeorology Vol. 19, No. 8 ( 2018-08-01), p. 1321-1337
    Details
    In: Journal of Hydrometeorology, American Meteorological Society, Vol. 19, No. 8 ( 2018-08-01), p. 1321-1337
    Abstract: Variables simulated by climate models are usually evaluated independently. Yet, climate change impacts often stem from the combined effect of these variables, making the evaluation of intervariable relationships essential. These relationships can be evaluated in a statistical framework (e.g., using correlation coefficients), but this does not test whether complex processes driven by nonlinear relationships are correctly represented. To overcome this limitation, we propose to evaluate climate model simulations in a more process-oriented framework using hydrological modeling. Our modeling chain consists of 12 regional climate models (RCMs) from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) forced by five general circulation models (GCMs), eight Swiss catchments, 10 optimized parameter sets for the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV), and one bias correction method [quantile mapping (QM)]. We used seven discharge metrics to explore the representation of different hydrological processes under current climate. Specific combinations of biases in GCM–RCM simulations can lead to significant biases in simulated discharge (e.g., excessive precipitation in the winter months combined with a cold temperature bias). Other biases, such as exaggerated snow accumulation, do not necessarily impact temperature over the historical period to the point where discharge is affected. Our results confirm the importance of bias correction; when all catchments, GCM–RCMs, and discharge metrics were considered, QM improved discharge simulations in the vast majority of all cases. Additionally, we present a ranking of climate models according to their hydrological performance. Ranking GCM–RCMs is most meaningful prior to bias correction since QM reduces differences between GCM–RCM-driven hydrological simulations. Overall, this work introduces a multivariate assessment method of GCM–RCMs, which enables a more process-oriented evaluation of their simulations.
    Type of Medium: Online Resource
    ISSN: 1525-755X , 1525-7541
    URL: Article
    DOI: 10.1175/JHM-D-17-0189.1
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2018
    detail.hit.zdb_id: 2042176-X
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  • 10
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    Caravan - A global community dataset for large-sample hydrology
    Springer Science and Business Media LLC ; 2023
    In:  Scientific Data Vol. 10, No. 1 ( 2023-01-31)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2023-01-31)
    Abstract: High-quality datasets are essential to support hydrological science and modeling. Several CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist for specific countries or regions, however these datasets lack standardization, which makes global studies difficult. This paper introduces a dataset called Caravan (a series of CAMELS) that standardizes and aggregates seven existing large-sample hydrology datasets. Caravan includes meteorological forcing data, streamflow data, and static catchment attributes (e.g., geophysical, sociological, climatological) for 6830 catchments. Most importantly, Caravan is both a dataset and open-source software that allows members of the hydrology community to extend the dataset to new locations by extracting forcing data and catchment attributes in the cloud. Our vision is for Caravan to democratize the creation and use of globally-standardized large-sample hydrology datasets. Caravan is a truly global open-source community resource.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-023-01975-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2775191-0
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