Integrating remotely sensed surface water dynamics into hydrologic signature modelling
Abstract. Extreme flow conditions in rivers have far-reaching environmental and economic consequences. The retention of surface water in lakes, wetlands, and floodplains can potentially modify the timing, duration, and magnitude of flow. However, efforts to explore the impact of surface water storage on discharge regimes have been limited in geographic extent. In this analysis, we calculated six hydrologic signatures, reflecting flashiness and high and low flow conditions, at 72 gaged watersheds across the conterminous United States. In addition to traditionally considered variables representing climate, land cover, topography, and soil, we incorporated a novel remote sensing (Sentinel-1 & 2) approach to study the contribution of surface water storage dynamics when modelling spatial variability in hydrologic signatures using random forest models. While climate variables explained much of the variability in the hydrologic signatures, models for five of the six signatures showed some degree of improvement in model performance when landscape characteristics were added with adjusted R2 improving 1.75 to 11.69 % and Akaike information criterions improving 0.24 % to 6.69 %. Automated variable selection can be indicative of the relative importance of certain variables over others. Using a forward selection process, five of the six signature models selected remotely sensed inundation variables with all five variables showing a significant (p<0.01) contribution to the respective model. More semi-permanent and permanent inundation within the floodplain (i.e., lakes along rivers), for example, was associated with lower wet season and annual flashiness. Further, greater seasonal floodplain inundation extent was associated with increases in peak flows, so that floodplain water storage was relevant to both flashiness and high flow signatures. Additionally, spatial variability in the amount of semi-permanent and permanent non-floodplain water significantly contributed to explaining spatial variability in the baseflow index. These findings suggest that surface water storage dynamics may help explain variability in streamflow signatures. Watershed management will benefit from an improved understanding of how surface water storage influences stream behaviour.
