Estimating the Relative Impact of Measurement, Parameter, and Flow Law Errors on Discharge from the Surface Water and Ocean Topography Mission

Abstract

Abstract The Surface Water and Ocean Topography (SWOT) mission will allow the estimation of discharge in rivers wider than 100 m, filling important gaps in the network of in situ measurements. This novel source of discharge observations has the potential to enable significant progress toward closing Earth’s water budget and creating new understanding of the water cycle. Quantifying the uncertainty in the SWOT estimates of discharge, mapping the error sources, and understanding their relative importance is essential to the fulfillment of this potential. Here, we break the SWOT discharge production process into its essential parts: 1) retrieval of river width and water surface heights and slopes, 2) estimation of unobservable parameters, and 3) computation of discharge with the selected flow law, and through a Monte Carlo simulation study, we assess the sensitivity of the overall discharge error to each of these parts. We analyze the discharge error characteristics in terms of bias, error standard deviation, and the correlation between true and retrieved discharges and map the contribution of the essential discharge production elements to each of the error metrics. Our study revealed that biases in parameters are the most important source of discharge biases, yet we found that a larger than expected fraction of the discharge biases can be attributed to observation errors. Surprisingly, we found that parameter biases are also the most important contributor to discharge error standard deviation and to the deterioration of the correlation between truth and retrieved discharges, which were previously thought of as being controlled by observation errors.