Green stormwater infrastructure (GSI) provides critical ecosystem services including flood mitigation, pollutant removal, habitat creation, and temperature regulation, among others. These ecosystem services can degrade over time if green infrastructure is not properly inspected and maintained, such as the detection and removal of invasive weeds; however, doing so is a challenge as they spatially disperse and rely on costly and intensive in-person site visits. The system-wide coverage of satellite systems has the potential to directly address this need, yet the application of satellite remote sensing to identify and track the maintenance needs of urban water resources BMPs is underexplored. Therefore, novel approaches are needed that can utilize both drone and satellite data to monitor and assess these practices in urban environments.

We refine the newly developed seismicity catalog of the Apollo 17 Lunar Seismic Profiling Experiment using quality control algorithms, and speculate on the source mechanisms of thousands of catalogued events using cross-correlation and calculation of event and waveform properties. We first sort these events into two types – “repeating’ or “isolated’ – based on whether or not the event shares similar waveforms with at least one other event. Repeating events are characterized by minimal time between signal onset and maximum, a prevalence during early sunrise, a periodicity consistent with the diurnal cycle, and a preferential direction due east, coincident with the Lunar Module. Isolated events, on the other hand, are distributed throughout the lunar day, originate from a wide variety of directions, and are characterized by a more gradual increase in amplitude. Many repeating events are likely caused by thermal expansion of the Lunar Module or its trapped volatiles, while most isolated events may be caused by thermal fracturing of rock or sliding of regolith along crater slopes. Across both the repeating and isolated events, we pinpoint 43 moonquakes whose time-of-day and waveform characteristics are consistent with thermally-induced fracture of several boulders at the Apollo 17 site. Future lunar missions, particularly those sensitive to ground motion, should be aware of thermally-induced, repeating seismic signals caused by nearby boulders and structures.

Detecting and attributing changes in mean precipitation is particularly challenging due to the strong influence of natural internal variability and poor performance of climate models in its simulations. There is still lack of research on human influence on mean precipitation in China. Here we use four observational datasets and CMIP6 simulations, with percentage precipitation anomaly (PPA) as a metric, to analyze precipitation changes in China and successfully detect human activity fingerprints based on the optimal fingerprinting method. Results show an increasing trend in annual mean precipitation across most regions since the 1960s, which CMIP6 models are generally able to reproduce. Human influence on China’s mean precipitation changes is detectable and separable from natural forcings. Across different regions, anthropogenic signals are detected in three sub-climatic regions: Northwest China, Northeast China, and Tibetan Plateau. Three-signal analysis further reveals that the increase in China’s mean precipitation is primarily driven by greenhouse gas forcing.