Explanation and probabilistic prediction of hydrological signatures with statistical boosting algorithms

Journal
Remote sensing  
Volume
13
Issue
3
Start Page
1
End Page
23
Article Number
333
Citation
Remote Sensing 13 (1): 1-23 (2021)
Publisher DOI
10.3390/rs13030333
Scopus ID
2-s2.0-85099780284
Publisher
MDPI
Hydrological signatures, i.e., statistical features of streamflow time series, are used to char-acterize the hydrology of a region. A relevant problem is the prediction of hydrological signatures in ungauged regions using the attributes obtained from remote sensing measurements at ungauged and gauged regions together with estimated hydrological signatures from gauged regions. The relevant framework is formulated as a regression problem, where the attributes are the predictor variables and the hydrological signatures are the dependent variables. Here we aim to provide probabilistic predictions of hydrological signatures using statistical boosting in a regression setting. We predict 12 hydrological signatures using 28 attributes in 667 basins in the contiguous US. We provide formal assessment of probabilistic predictions using quantile scores. We also exploit the statistical boosting properties with respect to the interpretability of derived models. It is shown that probabilistic predictions at quantile levels 2.5% and 97.5% using linear models as base learners exhibit better performance compared to more flexible boosting models that use both linear models and stumps (i.e., one-level decision trees). On the contrary, boosting models that use both linear models and stumps perform better than boosting with linear models when used for point predictions. More-over, it is shown that climatic indices and topographic characteristics are the most important attributes for predicting hydrological signatures.
Subjects
Catchment hydrology
Flow indices
Flow metrics
Hydrological processes
Hydrological uncertainty
Large sample hydrology
Prediction in ungauged basins
Quantile regression
Statistical boosting
Streamflow signatures
DDC Class
600: Technology