Betterle, A; Schirmer, M; Botter, G: Flow dynamics at the continental scale: Streamflow correlation and hydrological similarity, Hydrological Processes, 33(4), 627-646 (2018), doi:10.1002/hyp.13350 [Link]

Streamflow variability in space and time critically affects anthropic water uses and ecosystem services. Unfortunately, spatiotemporal patterns of flow regimes are often unknown, as discharge measurements are usually recorded at a limited number of hydrometric stations unevenly distributed along river networks. Advances in understanding the physical processes that control the spatial patterns of river flows are therefore necessary to predict water availability at ungauged locations or to extrapolate pointwise streamflow observations. This work explores the use of the spatial correlation of river flows as a metric to quantify the similarity between hydrological responses of two catchments. Following a stochastic framework, 340,000 cross‐correlations between pairs of daily streamflows time series are predicted at a seasonal timescale across the contiguous United States using 413 catchments of the MOPEX dataset. Model predictions of streamflow correlation obtained in absence of run‐off information are successfully used to identify catchment outlets sharing similar discharge dynamics and flow regimes across a broad range of geomorphoclimatic conditions, without relying on calibration. The selection of reference streamgauges based on predicted streamflow correlation generally outperforms the selection based on spatial proximity, especially as the density of available gauged sections decreases. Interestingly, correlated outlets share a broad spectrum of hydrological signatures (mean discharge, flow variability, and recession properties), suggesting that catchments forced by analogous frequency and intensity of effective rainfall events might exhibit common geomorphoecological traits leading to similar hydrological responses. The proposed framework provides a physical basis to assist the regionalization of flow dynamics and to interpret the spatial variability of flow regimes along stream networks.


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last modified 2019-03-11