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Cities such as Detroit, MI in the post-industrial Rust Belt region of the United States, have been experiencing a decline in both population and economy since the 1970's. These “shrinking cities” are characterized by aging infrastructure and increasing vacant areas, potentially resulting in more green space. While in growing cities research has demonstrated an “urban heat island” effect resulting from increased temperatures with increased urbanization, little is known about how this may be different if a city shrinks due to urban decline. We hypothesize that the changes associated with shrinking cities will have a measurable impact on their local climatology that is different than in areas experiencing increased urbanization. Here we present our analysis of historical temperature and precipitation records (1900–2020) from weather stations positioned in multiple shrinking cities from within the Rust Belt region of the United States and in growing cities within and outside of this region. Our results suggest that while temperatures are increasing overall, these increases are lower in shrinking cities than those cities that are continuing to experience urban growth. Our analysis also suggests there are differences in precipitation trends between shrinking and growing cities. We also highlight recent climate data in Detroit, MI in the context of these longer-term changes in climatology to support urban planning and management decisions that may influence or be influenced by these trends. 

Stormwater management is of great importance in large shrinking cities with aging and outdated infrastructure. Maintenance of vegetated areas, particularly referred to as green infrastructure, is often aimed at mitigating flooding and the urban heat island effect by stormwater storage and evaporative cooling, respectively. This approach has been applied in large cities as a cost-effective and eco-friendly solution. However, the ecohydrological processes and how the ecohydrology influences the function of green infrastructure and its potential to provide those ecosystem services are not well understood. In this study, continuous field measurements including air temperature, stomatal conductance, and phenocam images were taken in a 308 m2 bioswale retrofitted into a 4063 m2 parking lot on the Wayne State University campus in Detroit, Michigan over a two-year period. Our results suggest that plant characteristics such as water use efficiency impact the ecohydrological processes within bioswales and that retrofitted bioswales will need to be adapted over time to meet environmental demands to allow for full and sustained success. Therefore, projected shifts in precipitation regime change are expected to affect the performance of green infrastructure, and each bioswale needs to be developed and engineered to be able to adapt to changing rainfall patterns. 

Abstract Understanding the severity and extent of near surface critical zone (CZ) disturbances and their ecosystem response is a pressing concern in the face of increasing human and natural disturbances. Predicting disturbance severity and recovery in a changing climate requires comprehensive understanding of ecosystem feedbacks among vegetation and the surrounding environment, including climate, hydrology, geomorphology, and biogeochemistry. Field surveys and satellite remote sensing have limited ability to effectively capture the spatial and temporal variability of disturbance and CZ properties. Technological advances in remote sensing using new sensors and new platforms have improved observations of changes in vegetation canopy structure and productivity; however, integrating measures of forest disturbance from various sensing platforms is complex. By connecting the potential for remote sensing technologies to observe different CZ disturbance vectors, we show that lower severity disturbance and slower vegetation recovery are more difficult to quantify. Case studies in montane forests from the western United States highlight new opportunities, including evaluating post‐disturbance forest recovery at multiple scales, shedding light on understory vegetation regrowth, detecting specific physiological responses, and refining ecohydrological modeling. Learning from regional CZ disturbance case studies, we propose future directions to synthesize fragmented findings with (a) new data analysis using new or existing sensors, (b) data fusion across multiple sensors and platforms, (c) increasing the value of ground‐based observations, (d) disturbance modeling, and (e) synthesis to improve understanding of disturbance.