Title

Monthly Streamflow Forecasting Using Gaussian Process Regression

Keywords

Gaussian Process Regression; Hydrologic similarity; Machine learning theory; Probabilistic streamflow forecasting; Water/energy interactions

Abstract

Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and management. In this work, Gaussian Process Regression (GPR), an effective kernel-based machine learning algorithm, is applied to probabilistic streamflow forecasting. GPR is built on Gaussian process, which is a stochastic process that generalizes multivariate Gaussian distribution to infinite-dimensional space such that distributions over function values can be defined. The GPR algorithm provides a tractable and flexible hierarchical Bayesian framework for inferring the posterior distribution of streamflows. The prediction skill of the algorithm is tested for one-month-ahead prediction using the MOPEX database, which includes long-term hydrometeorological time series collected from 438 basins across the U.S. from 1948 to 2003. Comparisons with linear regression and artificial neural network models indicate that GPR outperforms both regression methods in most cases. The GPR prediction of MOPEX basins is further examined using the Budyko framework, which helps to reveal the close relationships among water-energy partitions, hydrologic similarity, and predictability. Flow regime modification and the resulting loss of predictability have been a major concern in recent years because of climate change and anthropogenic activities. The persistence of streamflow predictability is thus examined by extending the original MOPEX data records to 2012. Results indicate relatively strong persistence of streamflow predictability in the extended period, although the low-predictability basins tend to show more variations. Because many low-predictability basins are located in regions experiencing fast growth of human activities, the significance of sustainable development and water resources management can be even greater for those regions. © 2014 Elsevier B.V.

Publication Date

4-16-2014

Publication Title

Journal of Hydrology

Volume

511

Number of Pages

72-81

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.jhydrol.2014.01.023

Socpus ID

84893444157 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84893444157

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