Uncertain Future Conditions Research Articles

High-resolution estimates of water availability for the Iberian Peninsula under climate scenarios

Water availability is of paramount importance for sustainable development and environmental planning, specifically in regions such as the Iberian Peninsula, renowned for diverse landscapes and varying climatic conditions. Due to climate change, understanding the potential impacts on water resources becomes essential for effective water management strategies. This research effort aims to assess future potential water availability for the Iberian Peninsula in different climate scenarios, employing cutting-edge water resource modelling techniques integrated within a geographic information system (GIS) framework. In this study, potential water availability is defined as the annual demand for water that can be satisfied at a specific point in the fluvial network with certain reliability. An ensemble of state-of-the-art climate models is utilised to project runoff for the Iberian Peninsula during the mid- and late-twenty-first century periods. These climate projections were subsequently processed using the GIS-based water resource management model, WAAPA, to derive potential water availability under a range of realistic hypotheses. The results indicate that anticipated shifts in precipitation patterns will lead to alterations in hydrological regimes across the region, significantly impacting future water availability. By using GIS-based methodologies, we can facilitate the identification of vulnerable areas susceptible to changes in water availability, offering spatially explicit information along the main rivers of the Iberian Peninsula for decision-makers and stakeholders. High-resolution spatial outputs from this research and detailed water availability estimates serve as valuable input for integrated water resource management and climate change adaptation planning. By combining advanced GIS-based hydrological modelling with climate scenarios, this research presents a robust framework for assessing water resources amidst a changing climate, applicable to other regions struggling with analogous challenges. Ultimately, our study provides vital insights for policymakers and stakeholders, empowering them to make informed decisions and devise adaptive measures to ensure sustainable use of water resources despite uncertain future climatic conditions.

Evaluating the potential for continuous update of enhanced forest inventory attributes using optical satellite data

Abstract Timely and detailed inventories of forest resources are of critical importance to guiding sustainable forest management decisions. As forests occur across large spatial extents, remotely sensed data are often used to augment conventional forest inventory measurements. When combined with field plot measurements, airborne laser scanning (ALS) data can be used to derive detailed enhanced forest inventories (EFIs), which provide spatially explicit and wall-to-wall characterizations of forest attributes. However, these EFIs represent a static point in time, and the dynamic nature of forests, coupled with increasing disturbance and uncertain future conditions, generates a need for the continuous updating of forest inventories. This study used a time series of optical satellite data to update an EFI generated for a large (~690 000 ha) forest management unit in Ontario, Canada, at a two-week interval. The two-phase approach involved first building a relationship between single-year EFI attributes (2018) and spectral variables representing within-year slope, amplitude, and trend of a time series (2000–21) of 14 spectral bands and indices. For each of the 20 strata representing different species groups and site productivity classes, a k-nearest neighbor (kNN) model was developed to impute seven common EFI attributes: aboveground biomass, basal area, stem density, Lorey’s height, quadratic mean diameter, and stem volume. Across all strata, models were generally accurate, with relative root mean square error ranging from 11.47% (canopy cover) to 31.82% (stem volume). In the second phase of the approach, models were applied across the entire study area at two-week intervals in order to assess the capacity of the methodology for characterizing change in EFI attributes over a three-year period. Outputs from this second phase demonstrated the potential of the approach for characterizing changes in EFI values in areas experiencing no change or non-stand replacing disturbances. The methods developed herein can be used for EFI update for any temporal interval, thereby enabling more informed decisions by forest managers to prescribe treatments or understand the current state of forest resources.

Evaluating alternatives to improve drinking water supply within a complex and uncertain human-environment system: case of the Port-au-Prince Metropolitan Region

ABSTRACT Improving decision making in urban water supply systems under a wide range of changing human-environmental scenarios represents a challenging issue, especially when there is a high level of uncertainty and the available information is limited or scarce. To address this concern, the present study applied a decision aiding process to evaluate long-term planning solutions for the Port-au Prince Metropolitan Region’s water supply in Haiti, which is recognized for having a low coverage of water services and great lack of monitoring data. The methodological framework assessed pre-specified alternatives to face uncertain future conditions, with the main emphasis of being aware of how these uncertainties can be framed in the water management practices. To discover the most robust management solutions, a tradeoff analysis and multi-objective optimization allowed to explore the performance of alternatives across many future states of the world and multiple criteria. The results highlighted that human factors associated with the increase of per-capita water use and population growth posed a greater risk on water supply coverage than climatic factors. In addition, the decision support can offer a comprehensive representation of potential supply/demand futures, and provides decision-makers with the opportunity to discuss a posteriori the most appropriate water supply solution under an extensive variety of information with severe uncertainty.

Adaptive Silviculture and Climate Change—A Forced Marriage of the 21st Century?

Climatic changes significantly impact forest ecosystems, inevitably affecting forestry and forest-related industry. Considering that most forests are actively managed, there is a need to define the future risks and set a strategy for forestry and silviculture in a changing world. This review provides insight into the new challenges and opportunities forest management and silviculture face in the coming decades. There is sound recognition of risk factors expected from climate change, yet great uncertainty exists in the predictions of the response of forests to new conditions. Additionally, the stakeholders’ interests in the goods and services offered by forests are changing, and this also needs to be taken into account in future forest management. Undoubtedly, the goal of future forestry and silviculture in the 21st century will be primarily to ensure the continuity and sustainability of the forest. Sustainable use of goods and ecosystem services from forests will be directly related to the continuity and sustainability of the forest in the future. Adaptive forest management aims to promote the adaptive capacity of forests to new conditions resulting from climate change. If adaptation efforts are effective, adaptive forest management should be a kind of risk management. There is no one-fits-all strategy for adaptation to uncertain future conditions. Silviculture in the 21st century is expected to be more conducive to adapting forests to changes. Operational silvicultural activities should focus on ensuring the resilience and adaptation of forests to future environmental conditions. Modern silviculture offers activities that fall within the scope of contemporary close-to-nature silviculture practices. However, some of the currently applied practices will require review and modification to be applicable under new conditions. This review also identifies the need to fill knowledge gaps in order to develop more effective and flexible adaptation strategies to foster sustainable forest development and, thus, sustainable forestry.

Scenario Development for Evaluating Carbon Capture and Utilization Concepts Using Steel Mill Exhaust Gases with Linear Optimization Models

Utilizing exhaust gases from the steel mill generation to produce chemicals presents a promising avenue for carbon capture and utilization (CCU) concepts. Employing a model-based mathematical approach, specifically mixed-integer linear programming (MILP), enables the identification of optimal production concepts. To evaluate the long-term feasibility under uncertain future conditions, the construction of hypothetical scenarios to depict possible future states is necessary. This study introduces novel and tailored scenarios for a specific CCU concept aimed at producing methanol, ammonia, urea and/or acetic acid from steel mill exhaust gases by the year 2040 to enhance decision-making processes for identifying the optimal concept. These scenarios provide comprehensive insights into potential future conditions, spanning technical, economic and ecological domains. Unlike prior studies that focus on individual key factors, this approach involves analyzing the interactions of 24 identified key factors within the investigated CCU concept. The method yields five distinct scenarios: (1) Business as Usual (BAU), (2) CO2 Reduction and Renewable Energy Target (RE-Boom), (3) Technical Improvement and Market Booming (Market-Boom), (4) Energy and Market Crisis (Crisis) and (5) Hydrogen Booming (H2-Boom). These five scenarios can be directly integrated into MILP models, enhancing the significance of the optimization results for identifying the optimal CCU concept.

Towards adaptive resilience for the future of integrated water systems planning

Abstract The integrated water systems (IWSs) concept involves managing water quantity and quality through dynamic interactions. This paper reviews the terrestrial water cycle, focusing on resilience and adaptive planning (AP) approaches within IWSs. We examine how integrating these approaches can improve IWS management and planning, addressing their inherent complexities. Using a performance-based resilience definition, we consider the system’s ability to absorb, recover from and adapt to adverse events. The AP focuses on flexible management pathways for uncertain future conditions. Although both resilience and AP aim to enhance water system performance and address uncertainties, they differ in their assessment and implementation approaches. We propose an Adaptive Resilience Planning (ARP) framework that merges both approaches. The ARP uses resilience metrics for performance assessment and incorporates AP’s methods for conceptualising uncertainties and optimising management portfolios. Implementing the ARP framework raises four research questions: (1) holistic characterisation of uncertainties and options in IWSs, (2) using resilience metrics for IWS adaptation, (3) balancing trade-offs among management goals through optimal portfolio selection and (4) monitoring portfolio performance and uncertainties for informed adaptation. The ARP framework offers a structured method for dynamic and adaptive resilience planning, enhancing IWS management’s responsiveness to evolving challenges.

Exploring potential futures: Evaluating the influence of deep uncertainties in urban planning through scenario planning: A case study in Rome, Italy

Cities play a critical role in developing adaptable strategies to address the challenges posed by climate change. However, the inherent complexity of urban environments and their uncertain future conditions necessitate exploring innovative approaches and tools to assist the current planning practices. This paper presents a workflow rooted in model-based scenario planning for long-term adaptation planning given uncertain futures. To demonstrate the workflow’s effectiveness, a pertinent case study was conducted in a flood-prone area of Rome. The study employed a land-use change model to examine potential urban growth patterns, considering the uncertain implementation of new poles of attraction. This interdisciplinary study constitutes an initial stride toward implementing uncertainty within urban planning frameworks. Future prospects encompass the integration of multiple models for cross-scale analysis, embracing further critical environmental and social aspects. This research contributes to advancing urban resilience strategies. It enhances the understanding of adapting to an uncertain future in the face of climate change, as urban areas must embrace comprehensive planning to ensure flexible adaptation when faced with climate-driven uncertainties in long-term planning. In conclusion, the study underscores that embracing uncertainty is a challenge and a pivotal opportunity to shape resilient and adaptable urban futures.

Towards Adaptive Water Management—Optimizing River Water Diversion at the Basin Scale under Future Environmental Conditions

The degree of success of river water diversion planning decisions is affected by uncertain environmental conditions. The adaptive water management framework incorporates this uncertainty at all stages of management. While the most effective form of adaptive management requires experimental comparison of practices, the use of optimization modeling is convenient for conducting exploratory simulations to evaluate the spatiotemporal implications of current water diversion management decisions under future environmental changes. We demonstrate such an explorative modeling approach by assessing river water availability for diversion in a river basin in Northern Spain under two future environmental scenarios that combine climate and land use change. An evolutionary optimization method is applied to identify and reduce trade-offs with Supporting Ecosystem Services linked to environmental flow requirements for relevant local freshwater species. The results show that seasonal shifts and spatial heterogeneity of diversion volumes are the main challenges for the future diversion management of the Pas River. Basin-scale diversion management should take into account the seasonal planning horizon and the setting of tailored diversion targets at the local-level to promote the implementation of adaptive management. The presented assessment can help with strategic placement of diversion points and timing of withdrawals, but it also provides deeper insight into how optimisation can support decision-making in managing water diversion under uncertain future environmental conditions.

Managed diversion lead time effects on flood response in a tidal freshwater river

AbstractRiver diversions are a commonly used water management tool throughout the world. Low‐gradient coastal rivers exhibit complex floodplain interactions and are subject to multiple uncertain drivers of future flood risk. The interacting factors make quantification of downstream diversion effects on ambient flood hazards in coastal riverine ecosystems challenging. This article analyzes the effects of a managed ecological diversion (MED) on downstream flood hazards in the flood‐prone tidal freshwater Vermilion River system situated within the Atchafalaya Basin region of Louisiana, USA. A flexible and efficient hydraulic and hydrologic (H&H) modeling setup and counterfactual approach is utilized to develop and analyze 60 simulation scenarios aimed at quantifying the effects of an existing baseflow augmentation program on downstream flood hazards under uncertain future conditions. The analysis demonstrated the significance of preemptive diversion closure on the downstream flood levels in downstream rivers. The role of wetland topography and residual floodplain storage on MED‐induced flood response was also highlighted. A variety of uncertain parameter/forcing scenarios were also evaluated to establish robustness of the findings and to extend the results to more general settings. The study provides insights on downstream flood response induced by MEDs, which are expected to become more prevalent in the future.

Continuous monitoring and sub-annual change detection in high-latitude forests using Harmonized Landsat Sentinel-2 data

Spatially and temporally precise monitoring of forest resources is becoming increasingly vital in rapidly changing environmental and economic conditions. However, recent developments in moderate resolution satellite imagery have led to spectrally consistent and temporally dense datasets capable of capturing both seasonal variation and long-term trajectories. Previous studies have demonstrated the ability of continuous monitoring algorithms to detect transitional changes such as timber harvesting at sub-annual scales. However, less is known about the ability to use these algorithms in areas such as high-latitude forests which have large data gaps over winter due to prolonged snow cover. This study investigated the use of a time series of 7-day Harmonized Landsat Sentinel-2 (HLS) composites along with the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) algorithm for change detection and monitoring in two managed forest areas in Canada. On average, both sites had no data periods (over winter) lasting ∼200 days. Despite this, BEAST successfully detected change, with overall accuracies of 88.7% and 97.3% for both sites when compared to an independent validation dataset derived from visual interpretation of PlanetScope images. A simulation of change detection in near-real time revealed that approximately 75% of changes were detected within 7 and 11 valid images for each site. Furthermore, monitoring the trend slope of the spectral index showed that areas with a new declining slope were typically about twice as likely to occur in photointerpreted polygons of non-stand replacing disturbance, indicating that such a technique could be used as an early warning signal of fine-scale changes such as those due to insect defoliation or drought. Overall, this study demonstrated the efficacy of a continuous change detection and monitoring framework, which could be used to provide insights at previously unavailable temporal and spatial scales, thereby allowing forest managers the ability to quickly react to changes in uncertain future conditions.

Water and Environmental Systems Management Under Uncertainty: From Scenario Construction to Robust Solutions and Adaptation

This paper presents and discusses concepts, models, and methods for defining strategies, plans, and actions to achieve the sustainable development of water and environmental systems in a context of uncertainty. The complexity of such systems, including human and natural landscapes and their interactions, is a tremendous challenge with regard to decision-making processes. The future that is now being designed involves a myriad of uncertainties, climate and non-climate related, that request comprehensive decision frameworks involving multiple processes (institutional, political, social, economic, biophysical, etc.) to prevent disagreements and barriers from impeding the achievement of sustainable decisions. When it comes to assessing responses to future scenarios (or different states of the world), the idea of ​​robustness can include introducing the concept of adaptation. New terms such as “multiple plausible futures” and “deep uncertainty” have been emerging. How past frameworks should give rise to new frameworks so that decisions to be taken on water and environmental systems management and infrastructure planning are adapted to uncertain future conditions are the main issues tackled. The limitations on predicting the future and controlling and managing water and environmental systems mean that policy makers and society in general, especially knowledge-producing centres, need to shift from rhetoric to intervention, to tackle the many changing tendencies of today. Deciding now, at the present time, which has already been the future, the future of the next generations is an intricate and demanding task.

Diversity and enrichment of breeding material for resilience in European forests

Delivering material selected for breeding purposes into the wild in the context of sustainable forest management might reduce the levels of genetic diversity of future forests in comparison to that of natural populations. Another consequence might be a reduction of their resilience under uncertain future climatic and socio-economic conditions if these new populations lack adaptability. Despite the long tradition of breeding activities in Europe, there is still a need to assess the impact of genetically enriched material on forests’ resilience. In this study, we address (1) the genetic diversity of selected material compared to its wild ancestors, and (2) how to enrich breeding material to support forests’ resilience under changing socio-environmental conditions. We analysed 16 study cases of selected material delivered from breeding activities in four European forest tree species (Pinus halepensis Mill., Pinus nigra J.F. Arnold, Pinus pinaster Ait. and Populus nigra L.) with different levels of breeding. To answer these two questions, we first assessed and compared the genetic diversity of selected material versus natural populations using both putatively neutral and adaptive (based on diverging selection) Single Nucleotide Polymorphisms (SNPs). We then suggest how to enrich these populations for resilience under future climatic conditions by defining a core collection for each species including material from populations that will likely disappear under future conditions. Thanks to the large SNP datasets available for our focal species, we were able to detect some trends in our data. Expected and observed heterozygosity values for selected populations were almost always identical. The selected material showed small but significant genetic differentiation from their original population and their inbreeding coefficient was generally lower. However, the level of genetic improvement (i.e. low vs high) was not correlated with the observed genetic differences between selected material and natural populations.The genetic characterization of natural populations distributed across the species range, and the future projection of their range stability, made it possible to identify core-collections that would significantly enrich breeding populations under uncertain future environmental conditions.

COVID-19 impacts on mobility and travel demand

Since the beginning of the COVID-19 pandemic, many travel restriction policies were implemented to reduce further spread of the virus. These measures significantly affected travel demand to levels which could not have been anticipated by most planners in transportation agencies. As the pandemic has proven to have significant short-term impacts, it is anticipated that some of these impacts may translate to longer-term impacts on overall travel behavior and the movement of people and goods. Beyond the pandemic, the observed travel patterns during this period also provides a great opportunity for planners to assess policies such as work from home and remote learning as strategies to manage travel demand. This study provides a scenario analysis framework to re-evaluate travel demand forecasts under uncertain future conditions using the Maryland Statewide Transportation Model (MSTM). Model parameters associated with working from home, household income, changes in discretionary travel, distance learning, increased e-commerce, vehicle occupancy and mode choice were identified. Parameter values were assigned under the various scenarios using employer surveys on workforce teleworking and observed data on e-commerce growth and shopping behavior. The main findings of this study capture the sensitivities of systemwide vehicle miles travel, and vehicle hours travel under different scenarios and implications on future investment decisions.The study found that future investments under the scenarios remain beneficial to systemwide performance and therefore justified. Although this study focuses on the state of Maryland, the scenario framework and parameter definitions can be used in other states or agencies within a travel demand model environment.

Featured Collection Introduction: Severe Sustained Drought Revisited: Managing the Colorado River System in Times of Water Shortage 25 Years Later — Part I

Featured Collection Introduction: Severe Sustained Drought Revisited: Managing the Colorado River System in Times of Water Shortage 25 Years Later — Part I

Climate- and fire-smart landscape scenarios call for redesigning protection regimes to achieve multiple management goals

Integrated management of biodiversity and ecosystem services (ES) in heterogeneous landscapes requires considering the potential trade-offs between conflicting objectives. The UNESCO's Biosphere Reserve zoning scheme is a suitable context to address these trade-offs by considering multiple management zones that aim to minimise conflicts between management objectives. Moreover, in Mediterranean ecosystems, management and planning also needs to consider drivers of landscape dynamics such as wildfires and traditional farming and forestry practices that have historically shaped landscapes and the biodiversity they host. In this study, we applied a conservation planning approach to prioritise the allocation of management zones under future landscape and climate scenarios. We tested different landscape management scenarios reflecting the outcomes of climate-smart and fire-smart policies. We projected the expected landscape dynamics and associated changes on the distribution of 207 vertebrate species, 4 ES and fire hazard under each scenario. We used Marxan with Zones to allocate three management zones, replicating the Biosphere Reserves zoning scheme (“Core area”, “Buffer zone” and “Transition area”) to address the various management objectives within the Biosphere Reserve. Our results show that to promote ES supply and biodiversity conservation, while also minimising fire hazard, the reserve will need to: i) Redefine its zoning, especially regarding Core Areas, which need a considerable expansion to help mitigate changes in biodiversity and accommodate ES supply under expected changes in climate and species distribution. ii) Revisit current management policies that will result in encroached landscapes prone to high intensity, uncontrollable wildfires with the potential to heavily damage ecosystems and compromise the supply of ES. Our results support that both climate- and fire-smart policies in the Meseta Ibérica can help develop multifunctional landscapes that help mitigate and adapt to climate change and ensure the best possible maintenance of biodiversity and ES supply under uncertain future climate conditions.

Quantifying the Impacts of Modelling Assumptions on Accuracy and Computational Efficiency for Integrated Water-Energy System Simulations Under Uncertain Climate

Jointly managing water and energy systems, rather than treating each system independently, is recognised as an approach that can lead to a more cost-effective and reliable supply, which is particularly critical in water-rich and developing countries. This has motivated the development of various integrated water-energy simulators, each one catering for specific modelling needs through the use of specific sets of modelling assumptions, e.g., representing water and energy with balance equations, or dedicated river flow and power network equations. In this context, it becomes critical to assess the effectiveness of different modelling assumptions to improve the design of water-energy simulators. In particular, it is important to develop a methodology that can identify, based on a systematic assessment process, the portfolios of modelling assumptions that better capture the uncertain future conditions in the water and energy sectors, e.g., climate-driven stresses and shocks such as water scarcity, temperature rise, etc. To address this challenge, this paper proposes a Mixed Integer Linear Programming (MILP) integrated water-energy system simulation methodology designed to adapt and quantify different modelling assumptions under various weather-related conditions (e.g., water scarcity and high temperatures). The models were developed to capture the characteristics of non-pressurised water systems (e.g., channels and rivers) and electricity systems. The methodology is used to investigate typical modelling assumptions (e.g., temporal resolutions and water and power system models) and novel approaches to model the impacts of high temperatures on generation capacity to capture the effects of extreme weather on power generation. The methodology is demonstrated on the Ghanaian integrated water-energy system. The results highlight the benefits in terms of computational costs and modelling accuracy, of the customisable simulation, and provide guidance to select adequate modelling assumptions.

A distributionally ambiguous two-stage stochastic approach for investment in renewable generation

The optimal expansion of a power system with reduced carbon footprint entails dealing with uncertainty about the distribution of the random variables involved in the decision process. Optimisation under ambiguity sets provides a mechanism to suitably deal with such a setting. For two-stage stochastic linear programs, we propose a new model that is between the optimistic and pessimistic paradigms in distributionally robust stochastic optimisation. When using Wasserstein balls as ambiguity sets, the resulting optimisation problem has nonsmooth convex constraints depending on the number of scenarios and a bilinear objective function. We propose a decomposition method along scenarios that converges to a solution, provided a global optimisation solver for bilinear programs with polyhedral feasible sets is available. The solution procedure is applied to a case study on expansion of energy generation that takes into account sustainability goals for 2050 in Europe, under uncertain future market conditions.

Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning

Accurate degradation trajectory and future life are the key information of a new generation of intelligent battery and electrochemical energy storage systems. It is very challenging to obtain accurate predictions against uncertain application conditions by using only a few known historical data. In this article, we extend the widely studied remaining useful life (RUL) prediction to the prediction of charge and discharge capacity trajectories under both fixed and random future operating conditions. This is achieved by developing a general deep learning framework cored by recurrent neural network (RNN) which integrates future current plan and few early capacity-voltage data as inputs. As a case study, we have experimented with 77 commercial batteries cycled under fixed and random operating conditions. We demonstrate that the median root mean square error (RMSE) of prediction can be within 2.4% for NMC/graphite batteries and 2.3% for LFP/graphite batteries by using 3.8% of the whole life data only. Compared with the existing methods, the proposed framework predicts more accurately and has a very balanced performance for both fixed and random future conditions. This work highlights the promise of actively forecasting the future of batteries based on RNN.

INTEGRATING CAPABILITY DEVELOPMENT PROCESSES ‒ NECESSARY ACTION TO INCREASE EFFECTIVENESS OF MILITARY ACTIONS

In the increasingly uncertain future conditions, because the lost time cannot be recovered, the only one within reach remains the future. Thus, defence planning is about planning for an uncertain future, about reducing the level of uncertainty, or about increasing the level of tolerance for uncertainty. Therefore, we consider that it is time to move on to prospective planning, to the management components reprioritization, with an emphasis on foresight, especially as we observe the security environment new specifics, in order to choose, develop and maintain those capabilities to meet current and future challenges. In this article we will analyze all these aspects, in an attempt to highlight the fact that the efficient integration of those elements, which lead to obtaining and maintaining optimal capabilities, must represent a synergistic action in order to increase the effectiveness of military actions.

Scenario planning for transport practitioners

Scenario planning helps in contemplating how the future may develop and can be especially important when needing to make sense of uncertainty – something now pertinent to the transport sector.Accordingly, scenario planning is moving from the periphery of strategic transport planning towards becoming a more normalised and integral contribution. By examining rather than ignoring a range of uncertainties about the future, scenarios can be developed that enable an exploration of different futures, in turn improving transport planning. Scenarios can be narrative based, represented quantitatively, or combine ‘storytelling and number crunching’. Both the process of creating them and of representing the scenarios, deepen an appreciation of uncertainty about the future. In turn this allows planners and policymakers to better understand potential outcomes and challenges and determine how to address these. Scenarios can also be used to identify and assess candidate measures for influencing the transport system, testing these against a range of uncertain future conditions. This helps to identify measures that together can help form a strategy that is more robust.Drawing upon the combined experience of its authors, this paper provides insights into the development of scenarios and their use to improve decision making in transport planning. It offers advice on how to help ensure the scenario development process is credible, how to produce a coherent set of scenarios and how to ensure they are used to engage key stakeholders and to enable policymakers to confidently develop their strategic thinking and plans.