The Poisson cluster rainfall generation model has been underutilized in urban hydrological simulations due to challenges in reproducing fine-scale extreme precipitation. This study addresses this issue by comprehensively validating four variants of Poisson cluster rainfall models for three types of urban hydrological simulations: surface flooding, combined sewer overflow, and blue-green infrastructure performance. We generated 200 years of synthetic rainfall time series using the Base model (basic Poisson cluster model) and three variants: the Weight model (emphasizing variance and skewness in calibration), the Sine model (replacing rectangular rain cells with bell shapes), and the Weight + Sine model. This process was applied to rainfall data from 39 stations across Switzerland. Our key findings are: (1) While the Base model systematically underestimates extreme values, the Weight, Sine, and Weight + Sine models effectively address this bias. The Sine model directly enhances the intensity of fine-scale extreme rainfall, while the Weight model increases intensity at coarser durations; (2) Selecting the appropriate rainfall model depends on the timescales to which the urban systems are sensitive. For simulations of urban flooding, where fine-scale rainfall variability is crucial, models with bell-shaped rain cells outperform others. In contrast, systems like combined sewer overflows and blue-green infrastructure, which respond to coarser timescale variations, are better simulated by models using the Weight modification; (3) In all three simulation cases, the Base model failed to reproduce the quantile and interannual variability of hydrological metrics. This highlights the necessity of model modifications for reliable use in system design.

Indem blau-grüne Infrastrukturen die Mischwasserentlastungen reduzieren, werden auch die Einträge von Mikroverunreinigungen verkleinert. Besonders Versickerungsflächen senken die eingeleiteten Volumina und Frachten in Oberflächengewässer. Klimawandelprognosen lassen eine Zunahme von Entlastungen erwarten. Blau-grüne Infrastrukturen können daher einen wichtigen Beitrag leisten, um unerwünschte Effekte zu vermeiden oder zu reduzieren.

Sustainable roofing configurations, including green and white roofs, can reduce rooftop surface temperatures compared to conventional surfaces and can therefore enhance photovoltaic (PV) system performance due to the temperature dependence of PV cells. Previous research, primarily experimental, recognized the synergy of combining PV with green or cool roofs. However, the influence of geographic and climatic factors on the performance of these combined systems, particularly in future climates affected by climate change, remains unclear. This work integrates three roof configurations (gravel, green, and white) into rooftop solar energy modeling across thirteen cities with different climate types, under current and future climate scenarios. Results indicate limited efficiency gains (< 2%) across all cities and climates, challenging previous findings. Yield is expected to increase in some cities receiving more solar irradiation in the future but decrease in others due to rising temperatures. Green and cool roofs can partially offset the effects of climate change on yield. PV-white roofs consistently outperform PV-green roofs, with the performance gap expected to widen in future climates. PV-green roofs excel in tropical climates with high irradiation and precipitation levels. Overall, the outcomes of this study inform the design and planning of sustainable buildings in response to climate change challenges.

Combined sewer overflows (CSOs), the discharge of untreated sewage mixed with stormwater into surface waters, are expected to increase under climate change as a result of more extreme rainfall. Blue-green infrastructure (BGI), such as bioretention cells and porous pavements, can help to reduce the amount of stormwater entering combined sewer systems, thus reducing CSO discharge. However, our understanding of the potential for BGI to mitigate CSOs in a future climate is still lacking, as performance is typically evaluated for individual BGI elements with fixed implementation areas under historical climate conditions or limited future scenarios. In response, this study investigates the performance of 30 combinations of BGI elements and implementation rates to prevent increases in CSOs under a range of future climate scenarios in an urban catchment near Zurich, Switzerland. Median total annual rainfall, projected to increase by as much as 46%, could double the median annual CSO volume and increase median annual CSO frequency by up to 52%. Four BGI combinations that include bioretention cells show the most promise to prevent increases in CSO volume and frequency in a future climate; and given the diverse responses of BGI elements to distinct rainfall patterns, their combinations can enhance CSO discharge reduction across varying climate patterns. BGI is also likely to become more cost-effective under future climatic conditions as projected increases in total rainfall led to larger CSO volume reductions obtained through BGI. However, there is a trade-off between robustness to climate change and cost-effectiveness, since CSO volume reduction capacity scales with BGI implementation rate but cost-effectiveness declines. Our study illustrates the effectiveness of various BGI combinations to prevent increases in CSOs in a future climate, calling for a range of BGI elements and implementation areas to be considered for urban drainage adaptation.

By infiltrating and retaining stormwater, Blue-Green Infrastructure (BGI) can help to reduce Combined Sewer Overflows (CSOs), one of the main causes of urban water pollution. Several studies have evaluated the ability of individual BGI types to reduce CSOs; however, the effect of combining these elements, likely to occur in reality, has not yet been thoroughly evaluated. Moreover, the CSO volume reduction potential of relevant components of the urban drainage system, such as detention ponds, has not been quantified using hydrological models. This study presents a systematic way to assess the potential of BGI combinations to mitigate CSO discharge in a catchment near Zurich (Switzerland). Sixty BGI combinations, including four BGI elements (bioretention cells, permeable pavement, green roofs, and detention ponds) and four different implementation rates (25%, 50%, 75%, and 100% of the available sewer catchment area) are evaluated for four runoff routing schemes. Results reveal that BGI combinations can provide substantial CSO volume reductions; however, combinations including detention ponds can potentially increase CSO frequency, due to runoff prolongation. When runoff from upstream areas is routed to the BGI, the CSO discharge reductions from combinations of BGI elements differ from the cumulative CSO discharge reductions achieved by individual BGI types, indicating that the sum of effects from individual BGI types cannot accurately predict CSO discharge in combined BGI scenarios. Moreover, larger BGI implementation areas are not consistently more cost-effective than small implementation areas, since the additional CSO volume reduction does not outweigh the additional costs. The best-performing BGI combination depends on the desired objective, being CSO volume reduction, CSO frequency reduction or cost-effectiveness. This study emphasizes the importance of BGI combinations and detention ponds in CSO mitigation plans, highlighting their critical factors—BGI types, implementation area, and runoff routing— and offering a novel and systematic approach to develop tailored BGI strategies for urban catchments facing CSO challenges.

Blue-green infrastructure (BGI), combining semi-natural and engineered elements, offers multifaceted benefits like stormwater management, water purification, heat mitigation, and habitat provision. However, current BGI designs prioritize engineering goals, overlooking its ecological potential. Here we advocate for integrating engineering and ecological objectives into BGI design to enhance performance and biodiversity. Through an interdisciplinary literature review, we emphasize the importance of species diversity, abundance, and ecological processes, to improve engineering performance and resilience, and lower management costs. We emphasize the importance of interdisciplinary collaboration to navigate trade-offs between engineering and ecological objectives, ultimately enabling us to engineer both for and with biodiversity.

Robust, quantitative understanding of the diverse ecological needs of species is needed to inform effective biodiversity conservation, now and in the future, but is lacking for most species. The advent of "big data" in ecology presents unprecedented opportunities to fill this gap and to disentangle the diverse drivers of biodiversity. Variable and model selection in sparse (small sample sizes for most species), high-dimensional (large pool of candidate predictors) problems is, however, non-trivial. Here, we employ cross-validated Bayesian projection predictive variable selection and shrinkage priors to identify, from a list of 70 ecological and biophysical candidate predictor variables, the minimal subset that best predicts the habitat preferences and distributions of 103 species of amphibians, birds, butterflies, dragonflies, and grasshoppers using the city of Zurich, Switzerland, as a case study. We contrast the predictive performance and ecological inference of models fit with the full set of predictors using shrinkage priors (exhaustive models) to models fit with a limited number of predictors obtained by compiling predictors from the full list of predictors using weakly informative priors (selective models). We show that exhaustive models excel in predictive performance, albeit at the cost of greater model complexity compared to selective models. Results from the selective models reveal the importance of access to aquatic habitat for a wide range of taxa, relative to other drivers such as urbanisation, vegetation and environmental hazards. These results are complemented by more nuanced insights from the exhaustive models into the importance of specific types of aquatic habitat (ponds, lakes, streams) and vegetation (herb, shrub, canopy cover) for the distribution of urban biodiversity, as well as the different spatial scales at which drivers are of predictive relevance. Our findings demonstrate the potential of shrinkage-based Bayesian variable selection to leverage big ecological data for species distribution modelling, and contribute to the development of concrete guidelines for urban planning and infrastructure design that account for biodiversity conservation.

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.

Green stormwater infrastructure (GSI) is growing in popularity to reduce combined sewer overflows (CSOs) and hydrologic simulation models are a tool to assess their reduction potential. Given the numerous and interacting water flows that contribute to CSOs, such as evapotranspiration (ET) and groundwater (GW), these models should ideally account for them. However, due to the complexity, simplified models are often used, and it is currently unknown how these assumptions affect estimates of CSOs, GSI effectiveness, and ultimately planning guidance. This study evaluates the effect on estimates of CSOs and GSI effectiveness when different flows and hydrologic processes are neglected. We modified an existing EPA SWMM model of a combined sewer system in Switzerland to include ET, GW, and upstream inflows. Historical rainfall data over 30 years are used to assess volume and duration of CSOs with and without three types of GSI (bioretention basins, permeable pavements and green roofs). Results demonstrate that neglect of certain flows in modelling can alter CSO volumes from -15 % to 40 %. GSI effectiveness also varies considerably, resulting in differences in simulated percent of CSO volume reduced from 8 % to 35 %, depending on the GSI type and modeled flow or process. Representation of GW within models is particularly crucial when infiltrating GSI are present, as CSOs could increase in certain subcatchments due to higher GW levels from increased infiltration. When basing GSI planning decisions on modeled estimates of CSOs, all relevant hydrologic processes should be included to the extent possible, and uncertainty and assumptions should always be considered.

Climate change is currently reshaping precipitation patterns, intensifying extremes, and altering runoff dynamics. Particularly susceptible to these impacts are combined sewer systems (CSS), which convey both stormwater and wastewater and can lead to combined sewer overflow (CSO) discharges during heavy rainfall. Green infrastructure (GI) can help mitigate these discharges and enhance system resilience under historical conditions; however, the quantification of its effect on resilience in a future climate remains unknown in the literature. This study employs a modified Global Resilience Analysis (GRA) framework for continuous simulation to quantify the impact of climate change on CSS resilience, particularly CSOs. The study assesses the efficacy of GI interventions (green roofs, permeable pavements, and bioretention cells) under diverse future rainfall scenarios based on EURO-CORDEX regional climate models (2085–2099) and three Representative Concentration Pathways (2.6, 4.5, 8.5 W/m²). The findings underscore a general decline in resilience indices across the future rainfall scenarios considered. Notably, the total yearly CSO discharge volume increases by a range of 145 % to 256 % in response to different rainfall scenarios. While GI proves effective in increasing resilience, it falls short of offsetting the impacts of climate change. Among the GI options assessed, green roofs routed to pervious areas exhibit the highest adaptive capacity, ranging from 9 % to 22 % at a system level, followed by permeable pavements with an adaptation capacity between 7 and 13 %. By linking the effects of future rainfall scenarios on CSO performance, this study contributes to understanding GI's potential as a strategic tool for enhancing urban resilience.

Green roof (GR) ecosystems function as a result of a series of complex interactions between plants and substrates, which are poorly understood. This ambiguity hinders the scientific application of GRs to tackle urban eco-environmental problems. The goal of this study was to clarify the response of plants to various abiotic and biotic characteristics of GR substrates, including physical properties, nutrients, and microbes. We systematically analyzed the statistical association between 43 indicators of plant and substrate characteristics using four GR substrates and one common plant type (Sedum lineare Thunb) in a subtropical monsoon climate. Height, coverage, root-shoot ratio, total carbon, total nitrogen, total phosphorus, total potassium, root vitality, and malondialdehyde levels were considered sensitive indicators of the plant's response to the substrate. Substrate nutrients and microbial biomass were most closely associated with the plant response, whereas the material properties of the substrate could indirectly affect plant growth by influencing substrate microbes and plant nutrients. Our results suggest that when optimizing GR substrates for plant responses, it is necessary to respect the plants and account for the abiotic and biotic relationships. It may be possible to manage GR performance in a more scientific and effective manner through the control of substrate microorganisms.

Flat roofs can employ a range of technologies to improve sustainability, such as photovoltaic (PV) panels, green roofs, cool roofs, or a combination of these options. Yet, weighing the benefits, costs, and performance of different roofing technologies is complex, especially when different stakeholders are involved. Decision analysis techniques, such as multi-criteria decision analysis (MCDA), can be used to systematically evaluate a diverse range of rooftop options to assess trade-offs in a quantitative way and avoid decision biases. This study offers a holistic comparison of different roof types, considering stakeholder preferences and uncertainty using MCDA. Ten flat roof options are compared, including black, gravel, cool, extensive green and semi-intensive green roofs, each with or without a rooftop PV installation, for nine objectives and three hypothetical stakeholder profiles. Performance is evaluated using building energy simulation, hydrologic modeling, and literature research. Uncertainty analyses are used to evaluate the effects of model assumptions on the MCDA results. For assumed preferences of an urban planner and environmentalist, semi-intensive green roofs with integrated PV installation are the best performing option; however, for a hypothetical building owner more concerned with costs, a gravel roof with PV ranks best. Uncertainty plays a role in the results, in particular, the uncertainty of the predicted outcome of options for the building owner, which can change the top-ranking options considerably. The uncertainty analyses are useful to identify consensus options over all three stakeholder types. Despite considerable uncertainty, extensive and semi-intensive green roofs with PV are recommended as relatively robust best-performing options.

Eine blau-grüne Infrastruktur aus Fliessgewässern, Pflanzenkläranlagen oder Gründächern kann den Siedlungsraum und vor allem Städte sowohl biodiverser als auch resilienter gegenüber dem Klimawandel machen. Voraussetzung ist die Zusammenarbeit von Ingenieur- und Naturwissenschaften.

Die Umsetzung Blau-Grüner Infrastrukturen ist in allen grösseren Schweizer Städten ein äusserst aktuelles Thema. Treibende Faktoren waren meist die Förderung der Biodiversität und die Not-wendigkeit von hitzemindernden Massnahmen im urbanen Raum, und weniger die Siedlungswas-serwirtschaft. Ausserhalb der Schweiz hingegen wurde die Umsetzung in den vergangenen Jahr-zehnten stark auch aus Sicht der Regenwasserbewirtschaftung und Sicherung der Wasserqualität in den oberflächlichen Gewässern vorangetrieben. Dieser Artikel fasst wichtige Erfahrungen und Erkenntnisse aus der weltweiten Umsetzung zusammen.

Managing and reducing combined sewer overflow (CSO) discharges is crucial for enhancing the resilience of combined sewer systems (CSS). However, the absence of a standardised resilience analysis approach poses challenges in developing effective discharge reduction strategies. To address this, our study presents a top-down method that expands the existing Global Resilience Analysis to quantify resilience performance in CSS. This approach establishes a link between threats (e.g., rainfall) and impacts (e.g., CSOs) through continuous and long-term simulation, accommodating various rainfall patterns, including extreme events. We assess CSO discharge impacts from a resilience perspective by introducing eight new metrics. We conducted a case study in Fehraltorf, Switzerland, analysing the performance of three green infrastructure (GI) types (bioretention cells, green roofs, and permeable pavements) over 38 years. The results demonstrated that GI enhanced all resilience indices, with variations observed in individual CSO performance metrics and their system locations. Notably, in Fehraltorf, green roofs emerged as the most effective GI type for improving resilience, while the downstream outfall displayed the highest resilience enhancement. Overall, our proposed method enables a shift from event-based to continuous simulation analysis, providing a standardised approach for resilience assessment. This approach informs the development of strategies for CSO discharge reduction and the enhancement of CSS resilience.

Green roofs can mitigate climate change through various mechanisms, including carbon sequestration, thermal process regulation, and building energy savings. While those mechanisms have been well studied, it remains unclear how to quantify carbon dioxide reduction and design green roofs properly for optimal performance. This review summarizes the processes, factors, and quantitative methods for carbon dioxide reduction from peer-reviewed literature. Firstly, the processes that account for how green roofs reduce carbon dioxide directly (by absorbing ambient CO₂ and storing it in biomass and substrate) and indirectly (thermal process regulation reduces energy consumption) are focused. The factors affecting CO₂ reduction (i.e., vegetation, substrate, building environment, and maintenance) are then discussed to investigate how these factors impact the performance of green roofs. Lastly, quantitative methods, including field experiments and simulation models to evaluate direct and indirect CO₂ reductions from green roofs, are discussed considering different spatial scales. The review found that a better understanding of green roofs' carbon reduction mechanisms is needed. Recommendations also should be developed on customized green roof system designs for various climate conditions. Lastly, there is a lack of green roof decarbonization evaluation at multiple spatial and temporal scales to effectively implement green roofs in urban areas. Overall, this comprehensive review facilitates an understanding of how green roofs reduce CO₂, the driving factors, and quantitative methods, which will advance research on designing and building green roofs as effective climate mitigation solutions in urban areas.

Reflected in the growing body of literature, urban heat mitigation is increasingly relevant as cities experience extreme heat, exacerbated by climate change and rapid urbanisation. Most studies focus on urban–rural temperature differences, known as the Urban Heat Island, which does not provide insight into urban heat dynamics. Here, we synthesise current knowledge on spatio-temporal variations of heat sources and sinks, showing that a targeted and absolute understanding of urban heat dynamics rather than an urban–rural comparison should be encouraged. We discuss mechanisms of heat sinks for microclimate control, provide a clear classification of Blue Green Systems and evaluate current knowledge of their effectiveness in urban heat mitigation. We consider planning and optimisation aspects of Blue Green Infrastructure (greenery and water bodies/features), interactions with hard surfaces and practices that ensure space and water availability. Blue Green Systems can positively affect urban microclimates, especially when strategically planned to achieve synergies. Effectiveness is governed by their dominant cooling mechanisms that show diurnal and seasonal variability and depend upon background climatic conditions and characteristics of surrounding urban areas. Situationally appropriate combination of various types of Blue Green Systems and their connectivity increases heat mitigation potential while providing multiple ecosystem services but requires further research.

Bevor sie grösstenteils in den Untergrund verlegt wurden, prägten Bäche das Bild der Schweiz, auch in der Stadt Zürich. Im Rahmen des naturnahen Wasserbaus werden in Zürich diese Bäche seit 35 Jahren wieder an die Oberfläche gebracht. In dieser Studie untersuchten wir die Vielfalt der Gefässpflanzen an wieder geöffneten Bächen in der Stadt Zürich.

Avant d’être en majeure partie enfouis, les ruisseaux étaient des marqueurs omniprésents du paysage en Suisse, y compris dans l’espace urbain. Depuis 35 ans, les petits cours d’eau de la ville de Zurich sont progressivement remis à ciel ouvert dans le cadre d’un plan de réaménagement tendant vers l’état naturel. Une étude s’est intéressée à la diversité des plantes vasculaires présentes dans le courant et au bord des ruisseaux ainsi revitalisés.

Due to their cooling ability, sustainable roofing configurations, such as green and cool roofs, have the potential to increase solar panel yield, which is temperature dependent. However, the influence of sustainable roofing configurations on panel yield is not yet considered in rooftop photovoltaic (PV) planning models; thus, the significance of these integrated systems cannot be evaluated. In order to quantify the potential benefits of sustainable rooftops on solar energy, the first goal of this study is to develop a method that systematically accounts for roof surface characteristics in the simulation of PV panel energy yield. To do so, a rooftop energy balance model is linked with a physically-based solar energy model (the System Advisor Model, SAM) to quantify the energy yield of PV installations on sustainable roofing configurations. Roof surface temperatures are first estimated using non-linear energy balance equations, then integrated into a revised version of SAM to simulate energy yield. This new method improves the accuracy of PV yield simulations, compared to prior assumptions of roof surface temperature equal to ambient temperature. This updated model is used for the second goal of the study, to understand how four roofing configurations (black membrane, rock ballasted, white membrane, and vegetated) influence PV panel yield, which is currently not well understood in cooler climates. For a flat rooftop PV installation near Zurich, Switzerland (temperate climate), results show that, compared to a conventional roof, green roofs can increase annual PV energy yield, on average, by 1.8%, whereas cool roofs can increase it by 3.4%. For the case-study installation, an inverse correlation between the 95th-quantile roof surface temperature and the PV energy yield was identified; an increase of 1 °C leads to a 71 kWh reduction in energy yield per year. Overall, cool roofs outperform green roofs in terms of increases in PV energy yield; however, potential improvements of both systems are non-negligible, even in relatively cooler climate regions like Switzerland. By providing a systematic method to evaluate the influence of the roofing configuration on PV energy yield, solar energy planners are able to differentiate between the benefits of traditional and sustainable rooftop configurations - the first step towards the coupling of distributed energy and sustainable building systems. In the future, this integrated method could be used as part of a holistic evaluation of the environmental, economic, and social objectives of green and cool roofs, as well as, other infrastructure systems.

Green roofs have the potential to offer numerous ecosystem services; however, they are rarely designed to achieve them. Instead, design is restricted by perceived structural and maintenance constraints, which consequently diminish the achievable benefits. For green roofs to improve sustainability and resilience of cities, their design should match their promised multi-functional application using performance-based design. The first step towards a comprehensive performance model is to synthesize design recommendations across disciplines to identify synergies and trade-offs in design objectives for multiple benefits. This study discusses design strategies that could alter the energy and water balance in the green roof in order to attenuate urban stormwater, increase building energy performance, mitigate urban heat, and improve the output of solar panels placed on top of green roofs. These benefits are mathematically linked to quantifiable processes (discharge rate, water content, evapotranspiration, sensible heat, net radiation, insulation, and thermal mass), forming the foundation for a performance-based design model. Design recommendations are then summarized for each process, followed by a discussion of synergies, trade-offs, and research needs that arise when green roofs are designed to achieve multiple functions. Selecting vegetation with high leaf area and albedo improves multiple benefits without affecting structural constraints, whereas choosing plants with low stomatal resistance leads to trade-offs between higher evapotranspiration and higher irrigation requirements. Trade-offs in substrate depth and properties including organic matter and moisture are also apparent. Interdisciplinary collaborations are needed to simulate and optimize design parameters based on stakeholder preferences related to co-benefits and constraints.

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.

Intensity-duration-frequency (IDF) curves, commonly used in stormwater infrastructure design to represent characteristics of extreme rainfall, are gradually being updated to reflect expected changes in rainfall under climate change. The modeling choices used for updating lead to large uncertainties; however, it is unclear how much these uncertainties affect the design and cost of stormwater systems. This study investigates how the choice of spatial resolution of the regional climate model (RCM) ensemble and the spatial adjustment technique affect climate-corrected IDF curves and resulting stormwater infrastructure designs in 34 US cities for the period 2020 to 2099. In most cities, IDF values are significantly different between three spatial adjustment techniques and two RCM spatial resolutions. These differences have the potential to alter the size of stormwater systems designed using these choices and affect the results of climate impact modeling more broadly. The largest change in the engineering decision results when the design storm is selected from the upper bounds of the uncertainty distribution of the IDF curve, which changes the stormwater pipe design size by five increments in some cases, nearly doubling the cost. State and local agencies can help reduce some of this variability by setting guidelines, such as avoiding the use of the upper bound of the future uncertainty range as a design storm and instead accounting for uncertainty by tracking infrastructure performance over time and preparing for adaptation using a resilience plan.