ssp - cabra -巴西集水区水流情景预测

IF 2.4 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
André Almagro, Paulo Tarso S. Oliveira, André S. Ballarin
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引用次数: 0

摘要

气候变化对全世界的水文通量产生重大影响,对巴西的影响显著,包括频繁发生的严重干旱和洪水。因此,准确的流量预测对于推进水资源工程、改善水资源管理和为气候适应战略提供信息至关重要。在这里,我们介绍了巴西流域的流量情景预测(SSP-CABra),它提供了735个巴西流域的长期每日流量模拟。这些模拟是使用5种不同复杂程度的水文模型生成的,由10个基于cmip6的历史(1980-2013)和未来(2015-2100;SSP2-4.5和SSP5-8.5)期的偏差校正气候模拟所强迫。SSP-CABra解决了巴西大规模水文建模的一个关键空白,为研究人员和决策者提供了有价值的见解。尽管其广泛的适用性,数据集包括不同地区的不同性能模型;用户应在当地评估模型技能,以确保适当使用,特别是用于决策或极端事件分析。尽管如此,通过利用多种模型和气候情景,SSP-CABra不仅支持减轻气候变化对水安全的影响,而且还促进了对模型性能和区域水文行为的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SSP-CABra—Streamflow Scenarios Projections for Brazilian Catchments

SSP-CABra—Streamflow Scenarios Projections for Brazilian Catchments

SSP-CABra—Streamflow Scenarios Projections for Brazilian Catchments

SSP-CABra—Streamflow Scenarios Projections for Brazilian Catchments

Climate change has significant impacts on hydrological fluxes worldwide, with pronounced effects in Brazil, including intense and recurrent droughts and floods. Accurate streamflow prediction is therefore essential for advancing water resources engineering, improving water resources management, and informing climate adaptation strategies. Here, we introduce the Streamflow Scenarios Projections for Brazilian Catchments (SSP-CABra), which provides long-term to daily streamflow simulations for 735 Brazilian catchments. These simulations are generated using five hydrological models of varying complexity, forced by 10 bias-corrected CMIP6-based climate simulations for historical (1980–2013) and future (2015–2100; SSP2-4.5 and SSP5-8.5) periods. SSP-CABra addresses a critical gap in large-scale hydrological modelling for Brazil, offering valuable insights for researchers and policymakers. Despite its broad applicability, the dataset includes models with varying performance across regions; users should assess model skill locally to ensure appropriate use, particularly for decision-making or extreme event analyses. Still, by leveraging multiple models and climate scenarios, SSP-CABra supports not only the mitigation of climate change impacts on water security, but also advances the understanding of model performance and regional hydrological behaviour.

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来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
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