Climate change is leading to more extreme precipitation events, which require new ways of managing stormwater, particularly in urban areas. Nature-based solutions (NBS) have become an increasingly popular way of providing additional stormwater retention and detention, as well as supporting urban biodiversity and access to nature. However, monitoring of the hydrological performance of NBS is often limited. To date most literature has focused on monitoring methodologies for specific sites and types of NBS, using remote sensing and modeling methods, or measuring benefits of NBS for urban heat mitigation. More comprehensive measurement strategies are needed to improve design, inform maintenance, and provide data that can encourage the adoption of NBS. To address this gap, this tutorial review provides specific recommendations for the instrumentation and in situ monitoring of common types and scales of NBS, as well as in-depth discussion of monitoring methods and hydrological performance for two specific NBS installations in the Chicago region. Based on these findings, we make recommendations for consistent hydrological assessment of NBS and development of common metrics that allow for comparison regionally and across different types of NBS.

Simultaneous pressures of climate change and increasing populations in urban areas have resulted in new stresses on stormwater infrastructure. Altered precipitation patterns require robust and versatile management strategies for stormwater, resulting in increased consideration of greenspace as infrastructure for communities with significant flood risk. There is particular interest in natural, minimally-engineered green infrastructure (GI). Such greenspaces can be heterogeneous and difficult to characterize but are often straightforwardly modelled as black-box systems within a landscape. Many natural sites cannot be approached so simply due to highly permeable interfaces with surrounding landscapes and it is often impossible to monitor the surrounds at anywhere near the same spatial or temporal resolution as within the boundaries of a study site, resulting in uncertainty about the actual benefit of natural greenspace for adjacent communities. We explored water storage in an urban green space, identifying spatio-temporal patterns of internal dynamics to holistically understand site behavior. A dense sensor network in a prairie wetland nature preserve within the Chicago metro area produced 4+ years of high-resolution surface and subsurface water level, soil moisture, precipitation, and air and water temperature data. Responses to weather events in the short term and to climate-driven seasonal effects in the longer term are then described via the combination of GIS methods and signal processing approaches. Power spectral and cross-correlation analyses contribute understanding of relevant timescales for further investigation. Applying hydrograph analysis methods to water level time series yields important statistics about the response of water table elevations throughout the prairie complex, including baseflow elevations and relaxation times. These statistics are used to develop spatial maps of event response as a function of site properties and to identify seasonal effects. Understanding the expected response of stormwater storage in a natural greenspace to a precipitation event has valuable utility for conservation groups and stormwater management utilities. The synthesis of these methods contribute to development of planning tools for siting, design, and management of GI.

Localized and severe storms can cause citywide flooding, leading drainage systems to surcharge and overflow to nearby water courses. Urban catchments feature high degrees of imperviousness and heterogeneity, often resulting in highly nonlinear hydrologic responses with shorter time of concentration, lag times, and sharper peak flows. Additionally, due to population and economic growth, urban drainage systems have attempted to evolve to more efficiently drain surface waters and reduce vulnerabilities. A critical outcome of this evolution is the need for finer spatio-temporal resolution rainfall measurements and hydrological modeling. As the major driving mechanism, the spatio-temporal variability in rainfall is acknowledged as a key source of uncertainty for urban hydrological modeling. The objective of this research is to revisit the impact of the temporal and spatial resolution of rainfall measurements on urban hydrological applications. We first provide a quantitative analysis of the spatiotemporal structure and variability of rainfall using both a 9-member hourly rain gauge network spaced ~10 km apart and a single WSR-88D dual-polarimetric weather radar with precipitation resolved every 5 minutes at ~500 m. Precipitation data from each observing system extracted at different time steps is aggregated within urban catchments and compared for three typical intense storms over a set of urban catchments located in Chicago Metropolitan area. Then the rain-runoff dynamics for 9 geographically-diverse (relative to the underneath sewer system) and differently-sized catchments are examined utilizing MetroFlow – a coupled hydrologic and hydraulic modeling system. Finally, city-wide flooding risks are simulated by routing the predicted surface runoff through the as-built sewer system. Additional mitigating storage capacity is also considered by numerical modeling the deep tunnel and reservoir in construction. The sensitivity of urban flood variables (i.e., mean and peak depth as well as duration) to rainfall spatiotemporal resolution is analyzed. Our results complement and advance the limited literature attempting to resolve the ideal resolution of rainfall data relevant for urban hydrology and stormwater management.

Localized and severe storms can cause citywide flooding, leading drainage systems to surcharge and overflow to nearby water courses. Urban catchments feature high degrees of imperviousness and heterogeneity, often resulting in highly nonlinear hydrologic responses with shorter time of concentration, lag times, and sharper peak flows. Additionally, due to population and economic growth, urban drainage systems have attempted to evolve to more efficiently drain surface waters and reduce vulnerabilities. A critical outcome of this evolution is the need for finer spatio-temporal resolution rainfall measurements and hydrological modeling. As the major driving mechanism, the spatio-temporal variability in rainfall is acknowledged as a key source of uncertainty for urban hydrological modeling. The objective of this research is to revisit the impact of the temporal and spatial resolution of rainfall measurements on urban hydrological applications. We first provide a quantitative analysis of the spatiotemporal structure and variability of rainfall using both a 9-member hourly rain gauge network spaced ~10 km apart and a single WSR-88D dual-polarimetric weather radar with precipitation resolved every 5 minutes at ~500 m. Precipitation data from each observing system extracted at different time steps is aggregated within urban catchments and compared for three typical intense storms over a set of urban catchments located in Chicago Metropolitan area. Then the rain-runoff dynamics for 9 geographically-diverse (relative to the underneath sewer system) and differently-sized catchments are examined utilizing MetroFlow – a coupled hydrologic and hydraulic modeling system. Finally, city-wide flooding risks are simulated by routing the predicted surface runoff through the as-built sewer system. Additional mitigating storage capacity is also considered by numerical modeling the deep tunnel and reservoir in construction. The sensitivity of urban flood variables (i.e., mean and peak depth as well as duration) to rainfall spatio-temporal resolution is analyzed. Our results complement and advance the limited literature attempting to resolve the ideal resolution of rainfall data relevant for urban hydrology and stormwater management.