TY - JOUR
T1 - An overview of current applications, challenges, and future trends in distributed process-based models in hydrology
AU - Fatichi, Simone
AU - Vivoni, Enrique R.
AU - Ogden, Fred L.
AU - Ivanov, Valeriy Y.
AU - Mirus, Benjamin
AU - Gochis, David
AU - Downer, Charles W.
AU - Camporese, Matteo
AU - Davison, Jason H.
AU - Ebel, Brian
AU - Jones, Norm
AU - Kim, Jongho
AU - Mascaro, Giuseppe
AU - Niswonger, Richard
AU - Restrepo, Pedro
AU - Rigon, Riccardo
AU - Shen, Chaopeng
AU - Sulis, Mauro
AU - Tarboton, David
N1 - Funding Information:
This article resulted from discussions held during the session “High Resolution Hydrologic Modeling: Challenges and Avenues for Development” at the American Geophysical Union Fall Meeting 2013 in San Francisco, USA. Luke McGuire, USGS, provided a useful review of an earlier draft of this manuscript. We thank Massimo Dotti for the references about astrophysics. SF thanks the support of the Stavros Niarchos Foundation and the ETH Zurich Foundation (Grant ETH-29 14-2 ). VI acknowledges the support of NSF Grant EAR 1151443 . MS acknowledges financial support from SFB/TR32 (Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modeling, and Data Assimilation) funded by the Deutsche Forschungsgemeinschaft (DFG). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Publisher Copyright:
© 2016 Elsevier B.V.
PY - 2016/6/1
Y1 - 2016/6/1
N2 - Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth's system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty.
AB - Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth's system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty.
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U2 - 10.1016/j.jhydrol.2016.03.026
DO - 10.1016/j.jhydrol.2016.03.026
M3 - Review article
AN - SCOPUS:84961741436
VL - 537
SP - 45
EP - 60
JO - Journal of Hydrology
JF - Journal of Hydrology
SN - 0022-1694
ER -