Search for: All records

The planning, design, and maintenance of stormwater infrastructure must be informed by changing rainfall patterns due to climate change. However, there is little consensus on how future climate information should be used, or how uncertainties introduced by use of different methods and datasets should be characterized or managed. These uncertainties exacerbate existing challenges to using climate information on local or municipal scales. Here we analyze major cities in the U.S., 48 of which developed climate adaptation and resilience plans. Given the prevalence of depth duration frequency (DDF) curves for planning infrastructure for rainfall, we then assessed the underlying climate information used in these 48 plans to show how DDF curves used for resilience planning and the resulting outcomes can be affected by stakeholders’ methodological choices and datasets. For rainfall extremes, many resilience plans varied by trend detection method, data preprocessing steps, and size of study area, and all used only one of the available downscaled climate projection datasets. We evaluate the implications of uncertainties across five available climate datasets and show the level of climate resilience to extreme rainfall depends on the dataset selected for each city. We produce risk matrices for a broader set of 77 U.S. cities to highlight how local resilience strategies and decisions are sensitive to the climate projection dataset used in local adaptation plans. To help overcome barriers to using climate information, we provide an open dataset of future daily rainfall values for 2-, 5-, 10-, 25-, 50-, and 100 years annual recurrence intervals for 77 cities and compare resilience outcomes across available climate datasets that each city can use for comparison and for robust resilience planning. Because of uncertainty in climate projections, our results highlight the importance of no-regret and flexible resilience strategies that can be adjusted with new climate information.

 

We autonomously directed a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or “drone” to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, were varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones.

 

Infrastructure are at the center of three trends: accelerating human activities, increasing uncertainty in social, technological, and climatological factors, and increasing complexity of the systems themselves and environments in which they operate. Resilience theory can help infrastructure managers navigate increasing complexity. Engineering framings of resilience will need to evolve beyond robustness to consider adaptation and transformation, and the ability to handle surprise. Agility and flexibility in both physical assets and governance will need to be emphasized, and sensemaking capabilities will need to be reoriented. Transforming infrastructure is necessary to ensuring that core systems keep pace with a changing world.

 

As climate change alters precipitation patterns, stakeholders will need to understand how performance of green stormwater infrastructure (GSI) could change in response. As an alternative to using on-site monitoring, which may not always feasible, we propose that changes in performance could be tracked using annual rainfall measures (e.g., maximum daily rainfall per year). We estimated performance of GSI in 17 U.S. cities using rainfall measures by establishing linear relationships with specific performance metrics (e.g., frequency of discharge). Prediction accuracy was evaluated in 2 cities for the period 2020 to 2060 by comparing performance predicted from rainfall trends from regional climate models (RCMs) with simulated performance in SWMM using the same RCMs as input. Findings suggest that tracking rainfall measures can provide insight into the hydrologic performance of green infrastructure by predicting the direction of change, as well as, the magnitude within 25% to 50% percent change.