Testing the premise of a proposed SWOT discharge algorithm that accounts for river-storage mass conservation

Abstract:

Lakes and reservoirs are essential storage units of the global drainage system. These stores, through detaining and releasing water mass at their interface, influence downstream river discharge. This can be especially pronounced for reservoirs, where water regulation is artificial. Improving discharge estimations at the store-river interface is thus critical to the river discharge products of the upcoming Surface Water and Ocean Topography (SWOT) mission, but has not been adequately addressed by the existing discharge algorithms. Here, we evaluate the feasibility of a new method that aims at improving the estimations of reservoir inflow and outflow, by modifying the MetroMan algorithm to account for the mass conservation between reservoirs and their adjacent rivers. This method bridges together reservoir storage variation (ΔV), which is relatively easy for SWOT to measure, to an improved parameterization of the inflow and outflow channel properties (e.g., roughness coefficient and baseflow cross-section area) that are not observable to SWOT but are necessary for discharge calculations. A fundamental premise of this method is that the discharge difference (ΔQ) between SWOT-observable inflow and outflow reaches is the first-order control on ΔV, so the algorithm’s channel parameterization can rely largely on SWOT’s measurements of the observable hydraulic variables. To test this premise, we collected all SWOT-visible reservoirs (>6.25 ha) in the contiguous US, where i) main-stem inflow/outflow reaches are also visible to SWOT (>100 m) and ii) long-term ΔQ and ΔV observations are available via gage measurements. Our preliminary results show the existence of such a premise in most studied reservoirs. However, uncertainties of the reservoir-river mass conservation appear to amplify in arid regions or during flood seasons, suggesting the importance of other controls such as i) reservoir surface evaporation and ii) lateral inflow from the tributaries that are too small to be observed by SWOT. These preliminary results support the overall feasibility of our algorithm, and motivate us to further constrain the uncertainty by accounting for additional controls (e.g., reservoir evaporation and lateral inflow) through a synergy of multi-source remote sensing and modeling datasets.