On the use of streamflow transformations for hydrological model calibration

The calibration of hydrological models through the use of automatic algorithms aims at identifying parameter sets that minimize the deviation of simulations from observations (often streamflows). Further, the choice of objective function (i.e. the criterion or combination of criteria for optimizatio...

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Bibliographic Details
Published in:Hydrology and earth system sciences 2024-11, Vol.28 (21), p.4837-4860
Main Authors: Thirel, Guillaume, Santos, Léonard, Delaigue, Olivier, Perrin, Charles
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Calibration
Catchment area
Earth Sciences
Efficiency
Functions (mathematics)
Hydrologic models
Hydrology
Hypothesis testing
Logarithms
Mathematical functions
Mathematical models
Objective function
Parameter identification
Precipitation
Sciences of the Universe
Stream discharge
Stream flow
Streamflow
Time series
Transformations (mathematics)
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Summary:The calibration of hydrological models through the use of automatic algorithms aims at identifying parameter sets that minimize the deviation of simulations from observations (often streamflows). Further, the choice of objective function (i.e. the criterion or combination of criteria for optimization) can significantly impact the parameter set values identified as optimal by the algorithm. This article discusses how mathematical transformations, which are sometimes applied to the target variable before calculating the objective function, impact model simulations. Such transformations, for example square root or logarithmic, aim at increasing the weight of errors made in specific ranges of a hydrograph. We show in a catchment set that the impact of these transformations on the obtained time series can sometimes be different from their expected behaviour. Extreme transformations, such as squared or inverse squared transformations, lead to models that are specialized for extreme streamflows but show poor performance outside the range of the targeted streamflows and are less robust. Other transformations, such as the power 0.2 and the Box–Cox and logarithmic transformations, can be categorized as more generalist and show good performance for the medium range of streamflows, along with acceptable performance for extreme streamflows.
ISSN:1607-7938
1027-5606
1607-7938
DOI:10.5194/hess-28-4837-2024