Enhancing the accessibility and interactions of regional hydrologic projections for water managers
Growing challenges of climate change require urgent shifts in scientific research to inform environmental decision-making. In the context of water management of the Colorado River Basin (CRB), the impacts of intensifying forest disturbances under climate change are not well understood but expected t...
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Veröffentlicht in: | Environmental modelling & software : with environment data news 2023-09, Vol.167, p.105763, Article 105763 |
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Sprache: | eng |
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Zusammenfassung: | Growing challenges of climate change require urgent shifts in scientific research to inform environmental decision-making. In the context of water management of the Colorado River Basin (CRB), the impacts of intensifying forest disturbances under climate change are not well understood but expected to have major consequences. To address this, we developed a web-based tool, CRB-Scenario-Explorer, that provides interactive visual assessments of modelled future CRB hydrology scenarios and detailed documentation of our approach to support mindful interpretations. Water manager feedback and user-experience principles were incorporated to achieve a user-centered design. Stakeholders confirmed the effectiveness of the web-based tool in assisting with the discovery that future CRB hydrology appears more sensitive to climate uncertainty than forest disturbances, which can be used to brief leaders and spark discourse around risk management. CRB-Scenario-Explorer thus exemplifies a novel and effective method to increase the accessibility, applicability, and transparency of environmental science research.
•Web-based tool developed to visualize scenarios of future Colorado River hydrology.•Water manager feedback incorporated into intuitive user-centered interface design.•Interactive visualizations show how forest reduction scenarios increase streamflow.•Guided analyses reveal hydrology more sensitive to climate than forest uncertainties.•Progressive disclosure allows deeper understanding of results for decision-makers. |
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ISSN: | 1364-8152 1873-6726 |
DOI: | 10.1016/j.envsoft.2023.105763 |