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Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation
Author(s) -
Pathiraja S.,
Moradkhani H.,
Marshall L.,
Sharma A.,
Geenens G.
Publication year - 2018
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2018wr022627
Subject(s) - data assimilation , computer science , parametric statistics , synthetic data , errors in variables models , hydrological modelling , propagation of uncertainty , uncertainty quantification , chaotic , gaussian , streamflow , data mining , algorithm , machine learning , statistics , mathematics , artificial intelligence , meteorology , climatology , drainage basin , cartography , quantum mechanics , geography , geology , physics
The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real‐world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data‐driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low‐dimensional chaotic dynamics and a real hydrologic experiment for one‐day‐ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.