: Decades of research on multifunctional Green Infrastructure (GI) has yet to translate into holistic implementation in the built environment. This oversight stems from assumptions that many ecosystem services occur passively and thus potential synergies are overlooked during planning and design. This study offers specific guidance for coordinating GI planning, design, and construction by examining the current state of academic literature on these aspects. It identifies 15 GI elements (e.g., green roofs) and 15 objectives (e.g., biodiversity) to collectively consider before implementation. The literature tends to isolate discussions of “engineered” GI elements with water-related objectives, while more “natural” GI are linked to biodiversity and human well-being. Coordinating across GI objectives and elements remains imperative, but evaluating too many options risks a paradox of choice. This study recommends short-term adherence to principles of adaptive design and, in the long-term, reemphasizes multifunctionality assessments, inter and transdisciplinary collaboration, and political will.

Vulnerable communities are disproportionately placed in low-lying, flood-prone neighborhoods, with deficient infrastructure and limited access to shelter. Here we present a methodology to study the risk of urban floods in tandem with access to shelter to reduce the risk of flooding to communities and prevent a natural hazard from turning into a human disaster. We integrate access to national emergency shelters into a national risk index for riverine floods in eight U.S. cities at the block group level using clustering techniques. The results show shelters are more accessible for inner-city residents regardless of the risk level, and communities with high risk of flood and low access to shelter are disproportionately home to the underserved populations of Asians and the elderly. The outcomes delineate the disparity in equity related to urban floods and support plans and policy needs by identifying and prioritizing areas to improve emergency responses and resource allocations.

Manning's roughness coefficient, n, is used to describe channel roughness, and is a widely sought‐after key parameter for estimating and predicting flood propagation. Due to its control of flow velocity and shear stress, n is critical for modeling timing of floods and pollutants, aquatic ecosystem health, infrastructural safety, and so on. While alternative formulations exist, open‐channel n is typically regarded as temporally constant, determined from lookup tables or calibration, and its spatiotemporal variability was never examined holistically at large scales. Here, we developed and analyzed a continental‐scale n dataset (along with alternative formulations) calculated from observed velocity, slope, and hydraulic radius in 200,000 surveys conducted over 5,000 U.S. sites. These large, diverse observations allowed training of a Random Forest (RF) model capable of predicting n (or alternative parameters) at high accuracy (Nash Sutcliffe model efficiency >0.7) in space and time. We show that predictable time variability explains a large fraction (∼35%) of n variance compared to spatial variability (50%). While exceptions abound, n is generally lower and more stable under higher streamflow conditions. Other factorial influences on n including land cover, sinuosity, and particle sizes largely agree with conventional intuition. Accounting for temporal variability in n could lead to substantially larger (45% at the median site) estimated flow velocities under high‐flow conditions or lower (44%) velocities under low‐flow conditions. Habitual exclusion of n temporal dynamics means flood peaks could arrive days before model‐predicted flood waves, and peak magnitude estimation might also be erroneous. We therefore offer a model of great practical utility.

Urbanization typically results in increased imperviousness which alters suspended sediment yield and impacts geomorphic and ecological processes within urban streams. Therefore, there is an increasing interest in the ability to predict suspended sediment yield. This study assesses the combined impact of urban development and increased precipitation on suspended sediment yield in the Cuyahoga River using statistical modeling. Historical satellite-based land-cover data was combined with precipitation and suspended sediment yield data to create a Multiple Linear Regression (MLR) model for the Cuyahoga watershed. An R² value of 0.71 was obtained for the comparison between the observed and predicted results based on limited land-use and land-cover data. The model also shows that every 1 mm increase in the mean annual precipitation has the potential to increase the mean annual suspended sediment yield by 860 tons/day. Further, a 1 km² increase in developed land area has the potential to increase mean annual suspended sediment yield by 0.9 tons/day. The framework proposed in this study provides decision makers with a measure for assessing the potential impacts of future development and climate alteration on water quality in the watershed and implications for stream stability, dam and flood management, and in-stream and near-stream infrastructure life.