H41E-1378
On the Development of an Integrated Hydrologic, Hydraulic, and Inverse Modeling Approach for Estimating Discharges and Water Depths for Ungauged Rivers from Space
Thursday, 17 December 2015
Poster Hall (Moscone South)
Ganming LIU, The Ohio State University, School of Earth Sciences, Columbus, OH, United States, Frank W Schwartz, Ohio State University Main Campus, Columbus, OH, United States, Kuo-Hsin Tseng, National Central University, Center for Space and Remote Sensing Research, Taoyuan, Taiwan and C.K. Shum, Ohio State University, Columbus, OH, United States
Abstract:
The characterization of hydrologic processes in large river basins has been benefitting from a variety of remotely sensed data. These are useful in augmenting the conventional ground-surface and gage data that have long been available, or in providing what is often the only available information for ungauged river basins. The goal of this study is to demonstrate an innovative modeling approach that uses satellite data to enhance understanding of rivers, particularly ungauged rivers.
The paper describes a prototype system - SWAT-XG, coupling SWAT and XSECT models in a Genetic Algorithm framework, for estimating discharge and depth for ungauged rivers from space. SWAT-XG was rigorously tested in the Red River of the North basin by validating discharge and depth products from 2006 to 2010 using in-situ observations across the basin. Results show that SWAT-XG, calibrated against remotely sensed data alone (i.e., water levels from ENVISAT altimetry and water extents from LANDSAT), was able to provide estimates of daily and monthly river discharge with mean R2 values of 0.822 and 0.924, respectively, against data from three gaging stations on the main stem. SWAT-XG also simulated the discharges of smaller tributaries well (yielding a mean R2 of 0.809 over seven gaging stations), suggesting that the SWAT-XG is a powerful estimator of river discharge at a basin scale. Results also show that the SWAT-XG simulated river’s vertical dynamics quite well, providing water-depth estimates with an average R2 of 0.831.
We conclude that the SWAT-XG advances the ability to estimate discharge and water depth from space for ungauged rivers. SWAT-XG would help to solve global big data problem for river studies and offer potential for understanding and quantifying the global water cycles. This study also implies that in-situ discharge data may not be necessary for a successful hydrologic model calibration.
