Abstract
This paper studies how streamflow predictability varies with basin characteristics. We introduce an index of basin complexity that is based on a model of least statistical complexity that is needed to reliably predict daily streamflow of the basin. We then relate it with climate, vegetation and soil characteristics of the basin. Daily streamflow is modeled using k nearest neighbor model of lagged streamflow that predicts next time step streamflow based on the occurrences of similar streamflow events from the past. In order to calculate basin complexity, we identify difficult streamflow events of the basin and then use VapnikChervonenkis generalization theory, which trades off model performance with VapnikChervonenkis dimension (i.e., a measure of model complexity), to find a k nearest neighbor model of appropriate complexity for predicting a difficult streamflow event of the basin. The average of selected model complexities corresponding to difficult events is then defined as the basin's complexity. Basin complexity of 412 Model Parameter Estimation Experiment basins from continental United States are then related with its six basin characteristics. All the characteristics have been derived from the Model Parameter Estimation Experiment database to represent climate, vegetation and soil characteristics of the basins in a concise manner. Results find that more complex basins that are drier have less seasonal rainfall, vegetation with more storage capacity (i.e., smaller 5week Normalized Difference Vegetation Index gradient), and faster responsive soils. The results reaffirm prior observations that minimum complexity that is required to model a basin depends on its climate and landscape characteristics (e.g., complex models do not perform well in dry basins).
Original language  English 

Pages (fromto)  74037416 
Number of pages  14 
Journal  Water Resources Research 
Volume  54 
Issue number  10 
DOIs  
Publication status  Published  Sep 2018 
Keywords
 comparative hydrology
 complexity
 model selection
 modeling
 prediction uncertainty
 statistical learning theory
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Data and algorithms underlying "Hydrological interpretation of a statistical measure of basin complexity"
Pande, S. (Creator) & Moayeri, M. (Creator), TU Delft  4TU.ResearchData, 5 Sep 2018
DOI: 10.4121/uuid:08608567697046a0b14c3365732ece6b
Dataset/Software: Dataset

Data and algorithms underlying "Hydrological interpretation of a statistical measure of basin complexity"
Pande, S. (Creator) & Moayeri, M. (Creator), TU Delft  4TU.ResearchData, 5 Sep 2018
DOI: 10.4121/UUID:08608567697046A0B14C3365732ECE6B
Dataset/Software: Dataset