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Abstract Collaborative governance has emerged as a promising approach for addressing complex water sustainability issues, with purported benefits from enhanced democracy to improved environmental outcomes. Collaborative processes are often assumed to be inherently more equitable than traditional governance approaches due to their goal of engaging diverse actors in the development of policy and management solutions. However, when collaborative water governance processes ignore issues of politics and power in their design, they risk creating or even exacerbating existing inequities. How, then, can collaborative water governance processes be designed to enhance, rather than undermine, equity? To answer this question, we first conduct an extensive review of the collaborative governance literature to identify common design features of collaborative processes, which each present potential benefits and challenges for actualizing equitable collaborative water governance. After critically discussing these design features, we explore how they are executed through two case studies of collaborative water governance in western North America: groundwater governance reform in California and transnational Colorado River Delta governance. In reflecting on these cases, we chart an agenda for future collaborative water governance research and practice that moves beyond engaging diverse actors to promoting equity among them. This article is categorized under:Human Water > Water GovernanceScience of Water > Water and Environmental ChangeEngineering Water > Planning Water
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A comprehensive review of deep learning applications in hydrology and water resources
Abstract The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research that incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.
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- Award ID(s):
- 1633098
- PAR ID:
- 10290895
- Date Published:
- Journal Name:
- Water Science and Technology
- Volume:
- 82
- Issue:
- 12
- ISSN:
- 0273-1223
- Page Range / eLocation ID:
- 2635 to 2670
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2166/wst.2020.369
