Extreme precipitation events are intensifying due to a warming climate, which, in some cases, is leading to increases in flooding. Detection of flood extent is essential for flood disaster management and prevention. However, it is challenging to delineate inundated areas through most publicly available optical and short-wavelength radar data, as neither can “see” through dense forest canopies. The 2018 Hurricane Florence produced heavy rainfall and subsequent record-setting riverine flooding in North Carolina, USA. NASA/JPL collected daily high-resolution full-polarized L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data between September 18th and 23rd. Here, we use UAVSAR data to construct a flood inundation detection framework through a combination of polarimetric decomposition methods and a Random Forest classifier. Validation of the established models with compiled ground references shows that the incorporation of linear polarizations with polarimetric decomposition and terrain variables significantly enhances the accuracy of inundation classification, and the Kappa statistic increases to 91.4% from 64.3% with linear polarizations alone. We show that floods receded faster near the upper reaches of the Neuse, Cape Fear, and Lumbee Rivers. Meanwhile, along the flat terrain close to the lower reaches of the Cape Fear River, the flood wave traveled downstream during the observation period, resulting in the flood extent expanding 16.1% during the observation period. In addition to revealing flood inundation changes spatially, flood maps such as those produced here have great potential for assessing flood damages, supporting disaster relief, and assisting hydrodynamic modeling to achieve flood-resilience goals.

River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26-year record of entirely remotely sensed volumetric channel water storage anomaly (VCWS) on 26 major world rivers. We find mainstem VCWS climatology amplitude (VCWSCA) represents an appreciable amount of basin-wide terrestrial water storage variability (median 2.2%, range 0.05-13.8% across world rivers), despite the fact that mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate VCWSCA (within {plus minus}50%) in only 19.2 % and 23.1 % of rivers considered (by model). These findings demonstrate VCWS is a useful measurement for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.

Rivers are an important source of freshwater that support societal needs and natural ecosystems, functioning as both collectors for watersheds and distributors along river corridors. Human-made infrastructure (dams, roads, canals) of various kinds have been built on and along rivers to access drinking water, generate energy, mitigate floods, and support industrial and agricultural production. However, due to the long and inconsistent history of constructing and recording these structures, we lack a globally consistent knowledge about where different types of infrastructure are. Here, we used a simple yet consistent method to visually locate and classify different infrastructures that could act as obstructions on rivers that are wider than 30 meters (total length ~2.1 million km globally). Our approach is based on Google Maps’ high resolution satellite images, which for many places have meter-scale resolution. We recently completed global-scale mapping and classifying different obstructions, and are conducting quality checks. In total, we identified ≥ 40,000 unique obstructions, including large dams and smaller weirs, control structures, partial barriers, as well as low-head dams that are often not included in other databases. This Global River Obstruction Dataset, or GROD, once fully validated, will be freely available to the public. We anticipate that it will be of wide interest to hydrological modeling, aquatic ecosystem, geomorphology, and water resource management communities.

Lakes are often defined by seasonal cycles. The seasonal timing, or phenology, of many lake processes, such as primary productivity, are changing in response to human activities. However, long-term records exist for few lakes, and extrapolating patterns observed in these lakes to entire landscapes is exceedingly difficult using the limited number of in situ observations that are available. Limited landscape level observations means we do not know how common shifts in lake phenology are at macroscales. Here, we use a new remote sensing dataset, LimnoSat-US, to analyze U.S. summer lake color phenology between 1984 and 2020 across more than 26,000 lakes. Our results show that summer lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of phytoplankton succession. The frequency with which lakes transition from one phenology group to another is tied to lake and landscape level characteristics. Lakes with high discharge and low variation in their seasonal extent are generally more stable while lakes in areas with high interannual variations in climate and catchment population density show less stability. Our research reveals previously unexamined spatiotemporal patterns in lake seasonality and demonstrates the utility of LimnoSat-US, which, with over 22 million remote sensing observations of lakes, creates novel opportunities to systematically examine changing lotic ecosystems at a national scale.

To help store water, facilitate navigation, generate energy, mitigate floods, and support industrial and agricultural production, people have built and continue to build obstructions to natural flow in rivers. However, due to the long and complex history of constructing and removing such obstructions, we lack a globally consistent record of their locations and types. Here, we used a consistent method to visually locate and classify obstructions on 2.1 million km of large rivers (width ≥ 30m) globally. We based our mapping on Google Earth Engine’s high resolution images from 2018–2020, which for many places have meter-scale resolution. The resulting dataset, the Global River Obstruction Database (GROD), consists of 29,877 unique obstructions, covering six different obstruction types: dam, lock, low head dam, channel dam, and two types of partial dams. By classifying a subset of the obstructions multiple times, we are able to show high classification consistency (87% mean balanced accuracy) for the three types of obstructions that fully intersect rivers: dams, low head dams, and locks. The classification of the three types of partial obstructions are somewhat less consistent (61% mean balanced accuracy). Overall, by comparing GROD to similar datasets, we estimate GROD likely captured 90% of the obstructions on large rivers. We anticipate that GROD will be of wide interest to the hydrological modeling, aquatic ecology, geomorphology, and water resource management communities.

Within Arctic deltas, surficial hydrologic connectivity of lakes to nearby river channels influences physical processes like sediment transport and ice phenology as well as biogeochemical processes such as photochemistry. As the Arctic hydrologic cycle is impacted by climate change, it is important to quantify temporal variability in connectivity. However, current connectivity detection methods are either spatially limited due to data availability constraints or have been applied at only a single time step. Additionally, the relationship between connectivity and lake ice is still poorly quantified. In this study, we present a multitemporal classification and validation of lake connectivity in the Colville River Delta, AK. We introduce a connectivity detection algorithm based on remote sensing of water color that is expandable to other high-sediment Arctic deltas. Comparison to validation datasets suggests that detection of high vs. low connectivity lakes is accurate in 69.5–85.5% of cases. Connectivity temporally varies in about 20% of studied lakes and correlates strongly with discharge and lake elevation, supporting the idea that future changes in discharge will be drivers of future changes in connectivity. Lakes that are always highly connected start and end ice break up an average of 26 and 16 days earlier, respectively, compared to lakes that are never connected. Because spring and summer ice conditions drive Arctic lake photochemistry processes, our research suggests that surface connectivity is an important parameter to consider when studying biogeochemistry of Arctic delta lakes.

River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26-year record of entirely remotely sensed volumetric channel water storage (CWS) change on 26 major world rivers. We find mainstem volumetric CWS climatology amplitude (CA) represents an appreciable amount of basin-wide terrestrial water storage variability (median 2.78%, range 0.04-12.54% across world rivers), despite mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate CA (within ±50%) in only 11.5 % (CaMa-Flood) and 30.7 % (HyMap) of rivers considered. These findings demonstrate volumetric CWS is a useful quantity for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.