An overview of current applications, challenges, and future trends in distributed process-based models in hydrology

S. Fatichi
E. R. Vivoni
F. L. Ogden
V. Y. Ivanov
B. Mirus
D. Gochis
C. W. Downer
M. Camporese
J. H. Davison
B. Ebel
N. Jones
J. Kim
G. Mascaro
R. Niswonger
P. Restrepo
R. Rigon
C. Shen
M. Sulis
David G. Tarboton, Utah State University


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.