Flow dynamics at the continental scale: Streamflow correlation and hydrological similarity

Abstract

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.

Is the Hyporheic Zone Relevant beyond the Scientific Community?

“…• to understand geomorphic-climatic controls that underlie spatial patterns of streamflow dynamics to quantify hydrologically critical drivers of HEF across different scales [255,256]. A proper description of the spatial variability of hydrological processes would help clarify how the ecosystem services provided by HEFs can be extended and upscaled to entire river networks.…”

Streamflow prediction in “geopolitically ungauged” basins using satellite observations and regionalization at subcontinental scale

“…Similar flow dynamics (mean discharge, relative flow variability and catchment response rates) have been found between catchments having high spatial correlation of daily streamflow (p > 0.9), rather than catchments having spatial proximity (Archfield and Vogel, 2010;Betterle et al, 2017;Betterle et al, 2019). Instead of using streamflow for ungauged catchments (receptor), water level observations can be used to find the most highly correlated gauged catchments (donor).…”

Joint Spatial and Temporal Modeling for Hydrological Prediction

“…The correlation between stream flow is used as a means to understand spatial patterns of stream flow dynamics [3], [8]- [10], [45]. In addition to distance, we also calculate correlations based on the daily steam flow of the stations for the last five years.…”

“…For example, the upstream of a river can directly affect the runoff of the downstream, and the two nearby rivers are likely to have similar rainfall processes, which results in similar changes in runoff. The spatial relationships across rivers provide important signals and have great potential to advance the hydrological prediction performance [10], [37]. However, most of the current data-driven hydrological forecasting methods focus on modeling the temporal patterns, without considering the rich spatial characteristics.…”