## 5 Discussion  
### Assessment of Automated Target Detection  
With 5708 targets, about 1000 more water bodies were identified in this study (applying an occurrence threshold of 5%) compared to the 4647 found with GLWD Level 1 and 2 data (see Figure 3). However, the datasets differ not only by the additional water bodies identified in this study, but also by some missing targets which are contained in GLWD.  
As example, Figure 6 shows an area along the Red River at the border between Arkansas and Oklahoma. Several water bodies identified in this study are not part of GLWD Level 1 or 2, such as the lakes Jim Chapman, McGee Creek, Hinkle and some smaller targets east of the Jim Chapman Lake; however, these are contained in WGHM. However, some water bodies are not identified, even though they are available in GLWD; this could be because they are too small, as with Lake Gillham--i.e., consistently narrower than 630 m--and removed by the morphological operations (Section 3.1), or because they are connected to a river (by water occurrence pixels above 5%) and removed manually (Section 3.3) as they could not be distinguished from the river. Furthermore, the different sizes of some lakes are noteworthy. For example, Lake Hugo and Lake Wister are both much smaller in GLWD than in this study.  
Sometimes, the separation between a lake and river is not clear, since the method is not able to distinguish between a narrow reservoir and a wide river. In these cases, part of the river is identified as a lake area (e.g.,s Lake Broken Bow and Millwood Lake). In rare cases, such targets can cause an error in the overflight statistics when the satellite track crosses the connected remaining river but not the water body that is the actual target. On a basin-wide average, the water surface area differs by 1710 km\({}^{2}\) (about 5%) with respect to GLWD (see Figure 3).  
Figure 5: Missed WGHM storage changes as a function of minimal lake sizes for different altimetry configurations.  
Other challenges arise due to the definition of targets. For example, the lakes Barkley and Kentucky, as shown in Figure 7, are actually one water body connected by the unregulated Barkley Canal. While they are automatically identified as one target in this study, as indicated by the green polygon in Figure 7, they are treated separately in the GLWD dataset. Another challenge in terms of definition is the extent of a reservoir upstream of the respective dam. There is no clear border between the river and the reservoir, and thus the size of both lakes is larger in GLWD compared to this study. The comparison also shows that the water body outline identified in this study is more detailed and accurate than the GLWD data.  
Figure 6: Isolated occurrence of detected targets (green), GLWD Level 1 (red) and 2 (orange) and WGHM data (white hatched) surrounding the Red River at the border between Arkansas and Oklahoma. Additionally, the GSW water occurrence is shown as background.  
Challenging conditions are also found in the coastal wetlands, where some possible targets cannot be distinguished from the ocean or connected water bodies. In rare cases, targets are removed by the erosional step or the POI is not within the area of high occurrence of a water body (e.g., an island), resulting in incorrect labeling and isolation. Both errors occur predominantly in narrow dendritic reservoirs.  
By changing the pixel sizes of the morphological operation, the method can be optimized to find more or less narrow structures. However, automated processes will never be able to distinguish a bridge over a reservoir (which should be removed from the images) from an equally sized dam of a reservoir (which should be left in the image). The applied parameters have proved to be a good compromise to detect most of the relevant water bodies without identifying too many unwanted targets. However, since no ground truth is available, fine-tuning based on statistical validation for the entire MRB is not possible.