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

0022-1694/$ see front matter 2009 Elsevier B.V. A doi:10.1016/j.jhydrol.2009.03.039 * Corresponding author. Fax: +814 863 7304. E-mail address: [email protected] (C.J. Duffy). 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 largescale 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 streamflow 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 streamflow 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. 2009 Elsevier B.V. All rights reserved.