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Sensitivity analysis of hydrological models: review and way forward
Author(s) -
Manjula Devak,
C. T. Dhanya
Publication year - 2017
Publication title -
journal of water and climate change
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 22
eISSN - 2408-9354
pISSN - 2040-2244
DOI - 10.2166/wcc.2017.149
Subject(s) - sensitivity (control systems) , computer science , imperfect , model selection , selection (genetic algorithm) , categorization , management science , machine learning , data mining , risk analysis (engineering) , artificial intelligence , engineering , philosophy , linguistics , electronic engineering , medicine
Different hydrological models provide diverse perspectives of the system being modeled, and inevitably, are imperfect representations of reality. Irrespective of the choice of models, the major source of error in any hydrological modeling is the uncertainty in the determination of model parameters, owing to the mismatch between model complexity and available data. Sensitivity analysis (SA) methods help to identify the parameters that have a strong impact on the model outputs and hence influence the model response. In addition, SA assists in analyzing the interaction between parameters, its preferable range and its spatial variability, which in turn influence the model outcomes. Various methods are available to perform SA and the perturbation technique varies widely. This study attempts to categorize the SA methods depending on the assumptions and methodologies involved in various methods. The pros and cons associated with each SA method are discussed. The sensitivity pertaining to the impact of space and time resolutions on model results is highlighted. The applicability of different SA approaches for various purposes is understood. This study further elaborates the objectives behind selection and application of SA approaches in hydrological modeling, hence providing valuable insights on the limitations, knowledge gaps, and future research directions.

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