4.7 Article

Detecting hydroclimatic change using spatio-temporal analysis of time series in Colorado River Basin

Journal

JOURNAL OF HYDROLOGY
Volume 374, Issue 1-2, Pages 1-15

Publisher

ELSEVIER
DOI: 10.1016/j.jhydrol.2009.03.039

Keywords

Singular spectrum analysis; Principal component analysis; Pattern classification; Change detection; Colorado River Basin

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It is generally accepted that the seasonal cycle of precipitation and temperature in cordillera of the western US exhibits a north-south pattern for annual, interannual and decadal time scales related to large-scale climate patterns. In this paper we explore these relationships, with special attention to the role of local and regional physiographic, hydrogeologic and anthropogenic conditions on low-frequency climate and terrestrial response modes. The goal is to try to understand the spatio-temporal structure in historical precipitation, temperature and stream flow records (P-T-Q) in terms of climate, physiography, hydrogeology, and human impacts. Spatial coherence in time series is examined by classification of factor loadings from principal component analysis. Classification pattern of P-T-Q stations indicate that local physiography, the hydrogeology, and anthropogenic factors transform atmospheric forcing and terrestrial response into unique clusters. To study the temporal structure, dominant low-frequency oscillatory modes are identified for a region from historical P-T-Q records using singular spectrum analysis. Noise-free time trajectories are reconstructed from the extracted low-frequency modes (seasonal-decadal) for each contributing watershed area corresponding to stream flow observation stations, and the phase-plane plots are obtained. Together, the spatial classification and phase plane provides a means of detecting how large-scale hydroclimatic patterns relate to major landforms and anthropogenic impacts across the CRB. The main result of this paper is that resolving the relative impact of basin-wide patterns of climate, physiography and anthropogenic factors (irrigation, dams, etc.) on runoff response can be a useful tool for detection and attribution for each source of variability. (C) 2009 Elsevier B.V. All rights reserved.

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