Recent Advances in Integrated Hydrologic Models: Integration of New Domains
Recent Advances in Integrated Hydrologic Models: Integration of New Domains
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DOI：
10.1016/j.jhydrol.2023.129515
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发表时间：
2023-04
影响因子：
6.4
通讯作者：
A. Brookfield;H. Ajami;R. Carroll;N. Tague;P.L.Sullivan;L. Condon
中科院分区：
文献类型：
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作者：
A. Brookfield;H. Ajami;R. Carroll;N. Tague;P.L.Sullivan;L. Condon
在过去的几十年里，水文模型已经从地表和地下的独立模型发展到可以在一个框架内捕获陆地水文循环的集成模型。近年来，这些耦合框架已经包含了生物地球化学过程、生态水文学、沉积和侵蚀、寒冷地区水文学、人类活动和大气过程。这种扩展是计算、数据和建模能力增强以及对驱动这些集成系统的流程理解加深的结果。在这里，我们回顾了将新流程和系统集成到现有陆地水文模型中的最新进展，并强调仍然存在的重大挑战和机遇。我们发现，目前可用和正在开发的模型如此之多，选择最合适的模型很困难，因此我们建议新建模者或新手建模者根据自己的需求找到最合适的代码。此外，参数化和校准这些模型所需的数据通常会限制它们的适用性和有用性。然而，除了非传统数据（例如遥感、定性数据）的数据同化之外，环境传感器和测量技术的进步也为解决这一问题提供了新的方法。随着我们扩展水文模型以集成更多流程和系统，我们的计算需求也随之增加。包括云和量子计算在内的计算平台的最新进展和新兴进展，除了使用机器学习来捕获某些过程之外，还将继续支持使用越来越大、越来越复杂的基于过程的模型。最后，我们强调，开发所有模型用户（而不仅仅是用于研究和开发的用户）都可以访问的最新科学模型至关重要。我们鼓励继续开发多样化的建模平台，考虑用户需求、数据可用性和计算资源。
Over the past several decades, hydrologic models have advanced from independent models of the surface and subsurface to integrated models that can capture the terrestrial hydrologic cycle within one framework. In recent years, these coupled frameworks have seen the inclusion of biogeochemical processes, ecohydrology, sedimentation and erosion, cold region hydrology, anthropogenic activities, and atmospheric processes. This expansion is the result of increased computational, data, and modeling capabilities and capacities, as well as improved understanding of the processes that drive these integrated systems. Here, we review these recent advances to integrate new processes and systems into existing terrestrial hydrologic models and highlight the significant challenges and opportunities that remain. We identify that with so many models currently available and in development, selecting the most appropriate model is difficult, and we suggest a path for new or novice modelers to find the most appropriate code based on their needs. In addition, data required to parameterize and calibrate these models can often constrain their applicability and usefulness. However, advances in environmental sensors and measurement technology, in addition to data assimilation of non-traditional data (e.g. remote sensing, qualitative data) are providing new ways of addressing this issue. As we expand hydrologic models to integrate more processes and systems, our computational demands also increase. Recent and emerging advances in computational platforms, including cloud and quantum computing, in addition to the use of machine learning to capture some processes, will continue to support the use of increasingly larger and more complex, process-based models. Finally, we highlight that it is critical to develop state-of-the-science models that are accessible to all model users, not just those applied for research and development. We encourage continued development of diverse modeling platforms, considering the user needs, data availability, and computational resources.