A document by Daniel White. Click on the document to view its contents.

A document by Brady Jones. Click on the document to view its contents.

A document by Nicholas Christensen. Click on the document to view its contents.

Due to long term drought, engineered structures (e.g., dams and levees), and other stressors, river systems are at high risk of degradation. Riparian vegetation and river geomorphology are continuously changing. The change in river hydrology, geomorphology and riparian vegetation have cascading impacts on other ecological aspects of the river corridor system. In this study, spatiotemporal variations of the riparian vegetation and the river geomorphology have been characterized using machine learning techniques (in particular, random forest) over an evaluation period of three decades. The study area is the Middle Rio Grande, located in New Mexico, USA. For the study of vegetation, the normalized difference vegetation index (NDVI) was used. The land cover was classified, using Landsat images (1984 to 2020) collected from Landsat 5, 7 and 8, to determine the change in vegetation cover and river geomorphology. The trends of NDVI shows the increase in vegetation cover even during long term drought due to presence of groundwater dependent vegetation like cottonwoods. Similarly, the formation of new stable channel islands and narrowing of the channel are some major observations and changes in channel from this study. The availability of long-term datasets and machine learning algorithms in Google Earth Engine shows the potential in spatiotemporal analysis of riparian vegetation and river geomorphology. These long-term observations will help river managers to monitor the status of the riparian vegetation and the impacts on the river geomorphology.

Recent advances in Earth observation data and computing ability create exciting opportunities for national and global studies of human impacts to water resources. But, with a lack of complete databases of artificial levees, there remains a need to better understand how artificial levees impact floodplain extent at regional and larger scales. Here, we estimate river-floodplain disconnection in the contiguous United States using an incomplete artificial levee database, machine learning algorithms, and hydrogeomorphic floodplain delineation models. We tested different topographic, land use, and spatial variables with different machine learning techniques in a case study of seven geographically diverse HUC8 basins before applying the technique at the national scale. We found that a parsimonious random forest model without topographic variables was 97% accurate. When applied to areas within a national 100-year hydrogeomorphic floodplain, the model indicated the potential for more than 180,000 km of undocumented artificial levees, meaning that the National Levee Database (NLD) is about 20% complete. More than 62% of potential levees are concentrated in the Upper and Lower Mississippi and Missouri basins. The stream order distribution of potential and NLD levees are similar; however, potential levees are primarily located along stream orders 3 and 6 while the NLD locations are along stream orders 2, 3 and 4. Using this, we explored the national impacts of artificial levees on floodplain extent by comparing two hydrogeomorphic floodplains based on (1) an unmodified USGS 1 arc second DEM and (2) a modified DEM with known and potential levees erased from the topography. We found that the overall impact of artificial levee removal was to shift the location of flooding. Over 30% of the CONUS 100-year floodplain was cultivated or developed land use.

A document by Adnan Rajib. Click on the document to view its contents.