Day 4

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Paper title DAHITI - Next generation of water level time series of inland waters
Authors
  1. Christian Schwatke Technical University of Munich (DGFI-TUM) Speaker
  2. Daniel Scherer
  3. Denise Dettmering Technical University of Munich
Form of presentation Poster
Topics
  • A7. Hydrology and Water Cycle
    • A7.01 Inland Water Storage and Runoff: Modeling, In Situ Data and Remote Sensing
Abstract text For more than two decades, satellite altimetry has demonstrated the potential to derive water level time series of inland waters. Nowadays, accuracies of water level time series between few centimeters for large lakes and few decimeters for smaller lakes and rivers can be achieved. However, there is still potential for quality improvements when optimizing the processing strategy, for example in view of retracking algorithms, off-nadir effects, or outlier rejection.

In 2015, DGFI-TUM published the first DAHITI approach that is based on an extended outlier rejection and a Kalman filter approach. In this poster, we present an updated DAHITI approach, which considers the following aspects for deriving high-accurate water level time series for small inland waters: First, detailed analysis of the altimeter sub-waveforms is performed in order to detect that part of the radar echo that can be assigned to the water bodies of interest. Additionally, off-nadir reflections are analyzed and taken into account, in order to derive reliable error information of the water level time series. This step is also the first step of the outlier rejection, which is extended by applying other criteria. For example, this additionally contains a detection of ice coverage. In order to achieve long-term consistent and homogenous water level time series, the latest geophysical corrections and models are applied and a multi-mission crossover analysis is performed for all altimeter missions.

We present preliminary results for selected inland waters, which are validated by using in-situ data. The results of the new DAHITI approach show a significant improvement of the accuracy of water level time series and its errors estimation.