|Paper title||Automatic Multiscale-based Peak Detection Retracker using Physically-based model Fitting (AMPDR-PF): a new approach to improve river level estimation|
|Form of presentation||Poster|
Rivers play an important role in regulating and distributing inland water resources in the processes of the hydrological cycle of the earth, which is an important factor for the steady development of regional economy and climate change. The river width, water level and flow velocity are important parameters to characterize the changes in river discharge. With the rapid development of remote sensing technology and hydrological models, the width, velocity and slope of rivers can be effectively estimated. But the monitoring river water level in high-precision is not effective, especially for small and medium-sized river basins, due to the low spatial and temporal resolution and the lack of measurement precision. We develop a new retracker to process the inland water altimetry waveforms, called AMPDR-PF (Automatic Multiscale-based Peak Detection Retracker using Physically-based model Fitting). We compare the water level estimated by AMPDR-PF with water level from official altimeter products in the River Rhine. We finally use it to estimate river discharge in the Rhine river.
The mix of quantitative and qualitative methods (AMPDR-PF) are considered to retrack the inland water altimetry waveforms for improving the accuracy of the river level at different spatial scales. Point of departure are combining the advantages of the AMPDR method and SAMOSA+ methods. Moreover, the new method allow for sensitivity analysis in different altimeter data such as Sentinel-3A/3B and Sentinel-6, accuracy validation such as the standard deviations of overpasses, RMSEs (the root-mean-squared errors) compared with the tide gauge at different spatial scales. Time-series of Water Surface Elevation (WSE) from multiple virtual stations are built after correcting for the river mean slope. Additionally, time-series of river width and river slope are generated from Sentinel-1 and Sentinel-2 images and DEM data by using Google Earth Engine. Then the river discharge is estimated by the rating curve. Meanwhile, the river discharge is evaluated using standard methods and compared with other products.