Yeosang Yoon

Improving Temporal Coverage of the SWOT Mission Using Spatiotemporal Kriging

The upcoming Surface Water and Ocean Topography (SWOT) satellite mission will measure water surface elevation, its spatial and temporal derivatives, and inundated area. These observations can be used to estimate river discharge at a global scale. SWOT will measure a given area on mid-latitude rivers two or three times per 22-day repeat cycle. In this paper, we suggest an interpolation-based method of estimating water height for times without SWOT observations (i.e., in between SWOT overpasses). A local space-time ordinary kriging (LSTOK) method is developed. Two sets of synthetic SWOT observations are generated by corrupting two different types of true river height with the instrument error. The true river heights are extracted from: 1) simulation of the LISFLOOD-FP hydrodynamic model, and from 2) in situ gage measurements from five USGS gages. Both of these synthetic SWOT observations datasets are important for the following reasons. The model-based dataset provides a complete spatiotemporal picture of river height that is unavailable from in situ measurements, but neglects the effects of e.g. human management actions on river dynamics. On the other hand, the gage-based dataset samples only five locations on the river (1,050 km in length), but represents all effects of human management, tributaries, or other influences on river heights, which are not included in the model. The results are evaluated by a comparison with truth and simple linear interpolation estimates as a first-guess. The model-based experiment shows the LSTOK recovered the river heights with a mean spatial and temporal root mean square error (RMSE) of 11 cm and 12 cm, respectively; these accuracies show a 46% and 54% improvement compared to the RMSEs of the linear interpolation estimates. The gage-based experiment shows a temporal RMSE of 32 cm on average; the LSTOK estimates show a 23% improvement over the linear interpolation estimates. The degradation in performance of the LSTOK for the gage-based analysis as compared to the model-based analysis is apparently due to the effects of human management on river dynamics. Further work is needed to model the effects of human management, and to extend the analysis to consider river tributaries and the main stem of the river simultaneously.

Improved error estimates of a discharge algorithm for remotely sensed river measurements: Test cases on Sacramento and Garonne Rivers

We present an improvement to a previously presented algorithm that used a Bayesian Markov Chain Monte Carlo method for estimating river discharge from remotely sensed observations of river height, width, and slope. We also present an error budget for discharge calculations from the algorithm. The algorithm may be utilized by the upcoming Surface Water and Ocean Topography (SWOT) mission. We present a detailed evaluation of the method using synthetic SWOT-like observations (i.e., SWOT and AirSWOT, an airborne version of SWOT). The algorithm is evaluated using simulated AirSWOT observations over the Sacramento and Garonne Rivers that have differing hydraulic characteristics. The algorithm is also explored using SWOT observations over the Sacramento River. SWOT and AirSWOT height, width, and slope observations are simulated by corrupting the “true” hydraulic modeling results with instrument error. Algorithm discharge root mean square error (RMSE) was 9% for the Sacramento River and 15% for the Garonne River for the AirSWOT case using expected observation error. The discharge uncertainty calculated from Mannings equation was 16.2% and 17.1%, respectively. For the SWOT scenario, the RMSE and uncertainty of the discharge estimate for the Sacramento River were 15% and 16.2%, respectively. A method based on the Kalman filter to correct errors of discharge estimates was shown to improve algorithm performance. From the error budget, the primary source of uncertainty was the a priori uncertainty of bathymetry and roughness parameters. Sensitivity to measurement errors was found to be a function of river characteristics. For example, Steeper Garonne River is less sensitive to slope errors than the flatter Sacramento River.

Estimating Flood Discharges in Reservoir-Regulated River Basins by Integrating Synthetic SWOT Satellite Observations and Hydrologic Modeling

AbstractLakes and reservoirs are widely used for water supply and flood control, especially during large storm events. In hydrologic modeling applications, accounting for the regulated behavior of reservoirs distributed throughout a river system poses a significant challenge, especially during flood events, when detailed reservoir operation rules and strategies are implemented. Building on this problem, this study addresses this question: Can we model reservoir water storage changes and outlet discharges based on satellite measurements of river water surface elevation and inundated areas especially during the flood event? A method is presented and evaluated using synthetic observations as a proxy for measurements from the forthcoming surface water and ocean topography (SWOT) satellite mission. The May 2010 flood event in the Cumberland River Basin is used as a case study. Based on synthetic SWOT observation, time series of water storage changes are generated and evaluated for eight reservoirs. As expected...