TY - JOUR
T1 - Recent advances in integrated hydrologic models
T2 - Integration of new domains
AU - Brookfield, A. E.
AU - Ajami, H.
AU - Carroll, R. W.H.
AU - Tague, C.
AU - Sullivan, P. L.
AU - Condon, L. E.
N1 - Funding Information: This material is based upon work supported by the National Science and Engineering Research Council Discovery Grant (PI Brookfield), National Science Foundation under Grant No. (PL Sullivan: NSF 1904527 and 2121694 and PI Tague 2012821), the Department of Energy under Grant No. (PL Sullivan: DE-SC0020146) and (coPI Carroll: DE-AC02-05CH11231), and the USDA National Institute of Food and Agriculture Hatch funds (PI Ajami CA-R-ENS-5147-H) and National Science Foundation Grant No. (EAR-2012821) Publisher Copyright: © 2023 Elsevier B.V.
PY - 2023/5
Y1 - 2023/5
N2 - 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.
AB - 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.
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U2 - 10.1016/j.jhydrol.2023.129515
DO - 10.1016/j.jhydrol.2023.129515
M3 - Review article
SN - 0022-1694
VL - 620
JO - Journal of Hydrology
JF - Journal of Hydrology
M1 - 129515
ER -