One of the most dramatic events in river environments is the natural diversion, or avulsion, of a channel across its floodplain. Though rarely witnessed, avulsions can cause massive floods, and over geologic time they create most of the fluvial stratigraphic record. Avulsions exhibit behavior ranging from reoccupying abandoned channels to constructing new channels and splay complexes. To quantify avulsion behavior, or style, we measure avulsion-related floodplain disturbance in modern environments. We show that for 63 avulsions from Andean, Himalayan, and New Guinean basins, avulsion style correlates with channel morphology and changes systematically downstream. Avulsions in braided rivers reoccupy abandoned channels, whereas avulsions in meandering rivers often produce flooding and sediment deposition during channel construction. These downstream changes in avulsion style can explain the abrupt transition from channel-dominated to floodplain-dominated facies commonly observed in foreland basin stratigraphy. These dynamics also explain why some avulsions are more hazardous than others.

The world's river deltas may collapse under the combined effects of rising sea levels, subsidence, and reduced sediment supply. Saving these deltaic environments requires quantifying processes driving collapse. In the Mississippi River Delta, rapid land loss offers an important opportunity to test existing theories for marsh collapse. We use Landsat images to examine how pond expansion by edge retreat contributes to land loss over 34 years in the Atchafalaya‐Vermillion, Terrebonne, and Barataria basins of the Mississippi Delta. Tracking the area changes in ponds on the marsh surface, we find a striking consistency between pond expansion direction and the dominant wind direction and show that wind‐generated waves are capable of causing edge erosion. Expansion rate increases rapidly for ponds wider than 300 m in Terrebonne and Barataria basins. From this, we suggest that ponds in Atchafalaya‐Vermillion basin are stable, whereas ponds in Terrebonne and Barataria are unstable.

Basemap and Planet Fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub-weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets main differences when used to monitor OFRs. When comparing the OFRs surface area derived from Basemap and Planet Fusion to an independent validation dataset, both datasets had high agreement (r² ≥ 0.87), and small uncertainties, with a mean absolute percent error (MAPE) between 7.05% and 10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had r² ≥ 0.55, and 70% of the OFRs had MAPE <5%. In general, both datasets can be employed to monitor OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year. The uncertainties in surface area classification decreased as the OFRs increased in size. In addition, the surface area time series can have high variability, depending on the OFR environmental conditions (e.g., presence of vegetation inside the OFR). Our findings suggest that both datasets can be used to monitor OFRs sub-weekly, seasonal, and inter-annual surface area changes; therefore, these datasets can help improve freshwater management by allowing better assessment and management of the OFRs.

In this work we explore three methods for quantifying ecosystem vegetation responses spatially and temporally using Google’s Earth Engine, implementing an Ecosystem Moisture Stress Index (EMSI) to monitor vegetation health in agricultural, pastoral, and natural landscapes across the entire era of spaceborne remote sensing. EMSI is the multitemporal standard (z) score of the Normalized Difference Vegetation Index (NDVI) given as I, for a pixel (x,y) at the observational period t. The EMSI is calculated as: z_xyt = (I_xyt − µ_xyT)/σ_xyT, where the index value of the observational date (I_xyt) is subtracted from the mean (µ_xyT) of the same date or range of days in a reference time series of length T (in years), divided by the standard deviation (σ_xyT), during the same day or range of dates in the reference time series. EMSI exhibits high significance (z > |2.0 ± 1.98σ|) across all geographic locations and time periods examined. Our results provide an expanded basis for detection and monitoring: (i) ecosystem phenology and health; (ii) wildfire potential or burn severity; (iii) herbivory; (iv) changes in ecosystem resilience; and (v) change and intensity of land use practices. We provide the code and analysis tools as a research object, part of the findable, accessible, interoperable, reusable (FAIR) data principles.