TY - JOUR
T1 - Understanding the Information Content in the Hierarchy of Model Development Decisions
T2 - Learning From Data
AU - Gharari, Shervan
AU - Gupta, Hoshin V.
AU - Clark, Martyn P.
AU - Hrachowitz, Markus
AU - Fenicia, Fabrizio
AU - Matgen, Patrick
AU - Savenije, Hubert H.G.
N1 - Publisher Copyright:
© 2021. The Authors.
PY - 2021/6
Y1 - 2021/6
N2 - Process-based hydrological models seek to represent the dominant hydrological processes in a catchment. However, due to unavoidable incompleteness of knowledge, the construction of “fidelius” process-based models depends largely on expert judgment. We present a systematic approach that treats models as hierarchical assemblages of hypotheses (conservation principles, system architecture, process parameterization equations, and parameter specification), which enables investigating how the hierarchy of model development decisions impacts model fidelity. Each model development step provides information that progressively changes our uncertainty (increases, decreases, or alters) regarding the input-state-output behavior of the system. Following the principle of maximum entropy, we introduce the concept of “minimally restrictive process parameterization equations—MR-PPEs,” which enables us to enhance the flexibility with which system processes can be represented, and to thereby investigate the important role that the system architectural hypothesis (discretization of the system into subsystem elements) plays in determining model behavior. We illustrate and explore these concepts with synthetic and real-data studies, using models constructed from simple generic buckets as building blocks, thereby paving the way for more-detailed investigations using sophisticated process-based hydrological models. We also discuss how proposed MR-PPEs can bridge the gap between current process-based modeling and machine learning. Finally, we suggest the need for model calibration to evolve from a search over “parameter spaces” to a search over “function spaces.”.
AB - Process-based hydrological models seek to represent the dominant hydrological processes in a catchment. However, due to unavoidable incompleteness of knowledge, the construction of “fidelius” process-based models depends largely on expert judgment. We present a systematic approach that treats models as hierarchical assemblages of hypotheses (conservation principles, system architecture, process parameterization equations, and parameter specification), which enables investigating how the hierarchy of model development decisions impacts model fidelity. Each model development step provides information that progressively changes our uncertainty (increases, decreases, or alters) regarding the input-state-output behavior of the system. Following the principle of maximum entropy, we introduce the concept of “minimally restrictive process parameterization equations—MR-PPEs,” which enables us to enhance the flexibility with which system processes can be represented, and to thereby investigate the important role that the system architectural hypothesis (discretization of the system into subsystem elements) plays in determining model behavior. We illustrate and explore these concepts with synthetic and real-data studies, using models constructed from simple generic buckets as building blocks, thereby paving the way for more-detailed investigations using sophisticated process-based hydrological models. We also discuss how proposed MR-PPEs can bridge the gap between current process-based modeling and machine learning. Finally, we suggest the need for model calibration to evolve from a search over “parameter spaces” to a search over “function spaces.”.
KW - catchment
KW - general or miscellaneous
KW - model calibration
KW - modeling
KW - uncertainty assessment
UR - http://www.scopus.com/inward/record.url?scp=85108811945&partnerID=8YFLogxK
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U2 - 10.1029/2020WR027948
DO - 10.1029/2020WR027948
M3 - Article
AN - SCOPUS:85108811945
SN - 0043-1397
VL - 57
JO - Water Resources Research
JF - Water Resources Research
IS - 6
M1 - e2020WR027948
ER -