Numerous large river basins of the world have few and irregular observations of the components of the terrestrial hydrological cycle with the exception of stream gauges at a few locations and at the outlet along with sparsely distributed rain gauges. Using observations from satellite sensors and output from global land surface models, it is possible to study these under-observed river basins. With populations greater than a billion people, some of these rivers (e.g., the Ganga-Brahmaputra, the Yangtze, the Nile and the Mekong) are the economic engines of the countries they transect, yet thorough assessment of their flow dynamics and variability in regard to water resource management is still lacking. In this paper, we use soil moisture (0-2m) and surface runoff from the NASA Global Land Data Assimilation System (GLDAS), evapotranspiration, and Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and rainfall from the Tropical Rainfall Measuring Mission (TRMM) and total water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) to examine variability of individual water balance components. To this end, understanding the inter-annual and intra-seasonal variability and the spatial variability of the water balance components in the major river basins of the world will help to plan for improved management of water resources for the future.

AirSWOT, an experimental airborne Ka‐band interferometric synthetic aperture radar, was developed for hydrologic research and validation of the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission (to be launched in 2021). AirSWOT and SWOT aim to improve understanding of surface water processes by mapping water surface elevation (WSE) and water surface slope (WSS) in rivers, lakes, and wetlands. However, the utility of AirSWOT for these purposes remains largely unexamined. We present the first investigation of AirSWOT WSE and WSS surveys over complex, low‐relief, wetland‐river hydrologic environments, including (1) a field‐validated assessment of AirSWOT WSE and WSS precisions for lakes and rivers in the Yukon Flats Basin, an Arctic‐Boreal wetland complex in eastern interior Alaska; (2) improved scientific understanding of surface water flow gradients and the influence of subsurface permafrost; and (3) recommendations for improving AirSWOT precisions in future scientific and SWOT validation campaigns. AirSWOT quantifies WSE with an RMSE of 8 and 15 cm in 1 and 0.0625 km² river reaches, respectively, and 21 cm in lakes. This indicates good utility for studying hydrologic flux, WSS, geomorphic processes, and coupled surface/subsurface hydrology in permafrost environments. This also suggests that AirSWOT supplies sufficient precision for validating SWOT WSE and WSS over rivers, but not lakes. However, improvements in sensor calibration and flight experiment design may improve precisions in future deployments as may modifications to data processing. We conclude that AirSWOT is a useful tool for bridging the gap between field observations and forthcoming global SWOT satellite products.

In flat homogenous terrain such as in Cambodia and Vietnam, the monsoon season brings significant and consistent flooding between May and November. To monitor flooding in the Lower Mekong region, the near real-time NASA Flood Extent Product (NASA-FEP) was developed using seasonal normalized difference vegetation index (NDVI) differences from the 250 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) sensor compared to daily observations. The use of a percentage change interval classification relating to various stages of flooding reduces might be confusing to viewers or potential users, and therefore reducing the product usage. To increase the product usability through simplification, the classification intervals were compared with other commonly used change detection schemes to identify the change classification scheme that best delineates flooded areas. The percentage change method used in the NASA-FEP proved to be helpful in delineating flood boundaries compared to other change detection methods. The results of the accuracy assessments indicate that the −75% NDVI change interval can be reclassified to a descriptive ‘flood’ classification. A binary system was used to simplify the interpretation of the NASA-FEP by removing extraneous information from lower interval change classes.

Climate change is linked to changing precipitation, yet uncertainty remains as to whether and how hydroclimatic changes will influence flood frequency, magnitude, and timing. Part of this uncertainty is because few regional‐scale studies have analyzed changes in floods produced by different mechanisms. We identify six flood generating mechanisms (FGMs) based on prominent meteorological processes and moisture supply to the soil column in the Western U.S.; these include large‐scale frontal storms (atmospheric and non‐atmospheric rivers), monsoons, convective storms, snowmelt, and rain‐on‐snow. We assess trends in the frequency, magnitude, and timing of floods produced by different FGMs across 119 basins during 1960–2018. Overall, we find decreased frequency and magnitude of rain‐on‐snow‐driven floods, increased frequency and magnitude of convective‐storm‐driven floods, and a temporal shift to earlier in the year for snowmelt‐driven floods. The flood characteristics are generally stable for floods produced by other FGMs.

Remote sensing technologies have seen extraordinary improvements in both spatial resolution and accuracy recently. In particular, airborne laser scanning systems can now provide data for surface modeling with unprecedented resolution and accuracy, which can effectively support the detection of sub-meter surface features, vital for landslide mapping. Also, the easy repeatability of data acquisition offers the opportunity to monitor temporal surface changes, which are essential to identifying developing or active slides. Specific methods are needed to detect and map surface changes due to landslide activities. In this paper, we present a methodology that is based on fusing probabilistic change detection and landslide surface feature extraction utilizing multi-temporal Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) to map surface changes demonstrating landslide activity. The proposed method was tested in an area with numerous slides ranging from 200 m² to 27,000 m² in area under low vegetation and tree cover, Zanesville, Ohio, USA. The surface changes observed are probabilistically evaluated to determine the likelihood of the changes being landslide activity related. Next, based on surface features, a Support Vector Machine (SVM) quantifies and maps the topographic signatures of landslides in the entire area. Finally, these two processes are fused to detect landslide prone changes. The results demonstrate that 53 out of 80 inventory mapped landslides were identified using this method. Additionally, some areas that were not mapped in the inventory map displayed changes that are likely to be developing landslides.

Areas of lakes that support emergent aquatic vegetation emit disproportionately more methane than open water but are under‐represented in upscaled estimates of lake greenhouse gas emissions. These shallow areas are typically less than ∼1.5 m deep and can be detected with synthetic aperture radar (SAR). To assess the importance of lake emergent vegetation (LEV) zones to landscape‐scale methane emissions, we combine airborne SAR mapping with field measurements of vegetated and open‐water methane flux. First, we use Uninhabited Aerial Vehicle SAR data from the NASA Arctic‐Boreal Vulnerability Experiment to map LEV in 4,572 lakes across four Arctic‐boreal study areas and find it comprises ∼16% of lake area, exceeding previous estimates, and exhibiting strong regional differences (averaging 59 [50–68]%, 22 [20–25]%, 1.0 [0.8–1.2]%, and 7.0 [5.0–12]% of lake areas in the Peace‐Athabasca Delta, Yukon Flats, and northern and southern Canadian Shield, respectively). Next, we account for these vegetated areas through a simple upscaling exercise using paired methane fluxes from regions of open water and LEV. After excluding vegetated areas that could be accounted for as wetlands, we find that inclusion of LEV increases overall lake emissions by 21 [18–25]% relative to estimates that do not differentiate lake zones. While LEV zones are proportionately greater in small lakes, this relationship is weak and varies regionally, underscoring the need for methane‐relevant remote sensing measurements of lake zones and a consistent criterion for distinguishing wetlands. Finally, Arctic‐boreal lake methane upscaling estimates can be improved with more measurements from all lake zones.