Development of a global ~90m water body map using multi-temporal Landsat images

This paper describes the development of a Global 3 arc-second Water Body Map (G3WBM), using an automated algorithm to process multi-temporal Landsat images from the Global Land Survey (GLS) database. We used 33,890 scenes from 4 GLS epochs in order to delineate a seamless water body map, without cloud and ice/snow gaps. Permanent water bodies were distinguished from temporal water-covered areas by calculating the frequency of water body existence from overlapping, multi-temporal, Landsat scenes. By analyzing the frequency of water body existence at 3 arc-second resolution, the G3WBM separates river channels and floodplains more clearly than previous studies. This suggests that the use of multi-temporal images is as important as analysis at a higher resolution for global water body mapping. The global totals of delineated permanent water body area and temporal water-covered area are 3.25 and 0.49 million km2 respectively, which highlights the importance of river-floodplain separation using multi-temporal images. The accuracy of the water body classification was validated in Hokkaido (Japan) and in the contiguous United States using an existing water body databases. There was almost no commission error, and about 70% of lakes > 1 km2 shows relative water area error < 25%. Though smaller water bodies (< 1 km2) were underestimated mainly due to omission of shoreline pixels, the overall accuracy of the G3WBM should be adequate for larger scale research in hydrology, biogeochemistry, and climate systems and importantly includes a quantification of the temporal nature of global water bodies

A Physically Based Empirical Localization Method for Assimilating Synthetic SWOT Observations of a Continental-Scale River: A Case Study in the Congo Basin

Water resource management has faced challenges in recent decades due to limited in situ observations and the limitations of hydrodynamic modeling. Data assimilation techniques have been proposed to improve hydrodynamic model outputs of local rivers (river length &#8804; 1500 km) using synthetic observations of the future Surface Water and Ocean Topography (SWOT) satellite mission to overcome limited in situ observations and the limitations of hydrodynamic modeling. However, large-scale data assimilation schemes require computationally efficient filtering techniques, such as the Local Ensemble Transformation Kalman Filter (LETKF). Expansion of the assimilation domain to maximize observations is limited by error covariance caused by limited ensemble size in complex river networks, such as the Congo River. Therefore, we tested the LETKF algorithm in a continental-scale river (river length &gt; 1500 km) using a physically based empirical localization method to maximize the observations available while filtering error covariance areas. Physically based empirical local patches were derived separately for each river pixel, considering spatial auto-correlations. An observing system simulation experiment (OSSE) was performed using empirical localization parameters to evaluate the potential of our method for estimating discharge. We found our method could improve discharge estimates considerably without affected from error covariance while fully using the available observations. We compared this experiment using empirical localization parameters with conventional fixed-shape local patches of different sizes. The empirical local patch OSSE showed the lowest normalized root mean square error of discharge for the entire Congo basin. Extending the conventional local patch without considering spatial auto-correlation results in very large errors in LETKF assimilation due to error covariance between small tributaries. The empirical local patch method has the potential to overcome the limitations of conventional local patches for continental-scale rivers using SWOT observations