More than half of all streams globally are non-perennial, and thus dynamic due to their expanding and retracting nature. Field mapping in conjunction with observational data from gauges and/or in-situ loggers is a typical approach for studying non-perennial stream dynamics, but these approaches underrepresent their spatiotemporal variability. High-resolution distributed hydrological modeling promises to bridge this gap, thanks to advances in model physics, remote sensing, and computational power. As the first step towards this goal, we investigate the capability of distributed hydrologic modeling to capture stream dynamics in Upper Blue River Basin, OK. Coupled Routing and Excess STorage (CREST), a distributed hydrological model, is used to simulate spatiotemporally varied streamflow at 10-meter spatial resolution and daily time steps. USGS stream gauge data and in-situ state logger data are used to calibrate and validate the simulation at the watershed outlet and small headwater tributaries, respectively. Dynamic Surface Water Estimate (DSWE), a LANDSAT product is also compared with simulated water presence in high-order streams. Results show that the CREST model can capture low-moderate streamflow values at the watershed outlet with a log-NSE value over 0.7 in the validation period, while underestimating high flow values due to the daily time step. Also, the calibrated model can accurately estimate wet/dry status as monitored by in-situ state loggers in nine headwater catchments. The dynamic stream networks are mapped over 2510 stream segments using the CREST simulation. Non-perennial streams are the most dynamic in small headwater tributaries (contributing area <2 km2) and high-order streams are sustained by perennial flow. As hydrologic interpretation of the stream dynamics, the interannual and seasonal variability in rainfall and evapotranspiration is well reflected by the water occurrence in streams. Across various catchments, a consistent threshold behavior is found between drainage density and unit discharge, indicating the control of runoff generation on the flowing stream networks. The mapping also identifies differences in stream dynamics caused by heterogeneities in land cover and soil properties. Given the prevalence of dynamical streams worldwide, our analysis illustrates the potential for mapping them using distributed hydrologic models.
All Science Journal Classification (ASJC) codes
- Water Science and Technology