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Climate change effects are not uniform and have disproportionate impacts among different groups of people within communities. It is therefore important to understand the underlying issues of intersectionality for climate change adaptation and human well-being. This paper aims to measure human capabilities and freedom of choice by analyzing perceived climate change impacts and current climate change adaptation ability among ethnic and non-ethnic communities in Bangladesh. This study applies a range of participatory rural appraisal tools and key informant interviews to assess impacts of climate change when considering gender and ethnicity. Women in the coastal regions have less access to resources and services because of social capital and cultural practices and this directly or indirectly influences their adaptation to climate change. Women have limited or no participation in decision-making processes at family or community levels and this impacts their vulnerability and well-being. In consequence, women’s capabilities must be focused on moderating their vulnerability and risk, and developing effective adaptation to the adverse impacts of climate change and natural hazards.

The diffusion of the smartphone and the urban sprawl is pushing both private and public actors to revisit the concept of demand-responsive transport (DRT). This paper provides a historical overview of DRT experiences, understanding their pros and cons. In addition, it presents the case study of Mokumflex, a 12-month DRT pilot program that replaced the regular bus service in low-density areas of Amsterdam, the Netherlands. Based on a close collaboration with the private enterprise that operated the service and also with the local bus operator, we performed an empirical before and after comparison. These insights help to understand the impacts of DRT systems and support (future) design of DRT and public transport. A set of indicators was chosen for the intermodal comparison: travel distances, ridership, costs, Greenhouse Gases (GHG), emissions and population's perception. Ridership dropped from 78.1 passengers/day to 15.9 passengers/day, however, for being “demand-tailored”, passenger-km reduced even more, going from 1252.8 km/day to 136.6 km/day, hence reducing the costs and GHG emissions per passenger. In regards to population's perception, the system enjoyed a good evaluation.

AbstractAssessment of the impact of climate change on water resources over land requires knowledge on the origin of the precipitation and changes therein toward the future. We determine the origin of precipitation over the Mississippi River basin (MRB) using high-resolution (~25 km) climate model simulations for present and future climate (RCP4.5). Moisture resulting in precipitation over the MRB is tracked back in time using Eulerian offline moisture tracking, in order to find out from where this water originally evaporated (i.e., the moisture sources). We find that the most important continental moisture sources are the MRB itself and the area southwest of the basin. The two most relevant oceanic sources are the Gulf of Mexico/Caribbean and the Pacific. The distribution of sources varies per season, with more recycling of moisture within the basin during summer and more transport of moisture from the ocean toward the basin in winter. In future winters, we find an increase in moisture source from the oceans (related to higher sea surface temperatures), resulting in more precipitation over the MRB. In future summers, we find an approximately 5% decrease in moisture source from the basin itself, while the decrease in precipitation is smaller (i.e., lower recycling ratios). The results here are based on one climate model, and we do not study low-frequency climate variability. We conclude that Mississippi’s moisture sources will become less local in a future climate, with more water originating from the oceans.

A new Proportional-Integral (PI) tuning method based on Linear Matrix Inequalities (LMIs) is presented. In particular, an LMI-based optimal control problem is solved to obtain a sparse feedback that provides the PI tuning. The ASCE Test Canal 1 is used as a case study. Using a linearised model of the canal, different tunings for the design of the PI controller are developed and tested using the software Sobek. Furthermore, the proposed method is also compared with other tunings proposed for the same canal available in the literature. Our results show that the proposed method reduces by half the maximum errors with respect to other assessed alternatives and minimizes undesired mutual interactions between canal pools. Also, our method improves the optimality degree of the PI tuning by 30%. Therefore, it is concluded that the LMI based PI controllers lead to satisfactory performance in regulating water levels and canal flows/structure outflows, outperforming other tested alternatives, thus becoming a useful tool for irrigation canal control.

Nature-based solutions (NBSs) are measures reflecting the ‘cooperation with nature’ approach: mitigating fluvial flood risk while being cost-effective, resource-efficient, and providing numerous environmental, social, and economic benefits. Since 2015, the United Nations (UN) 2030 Agenda has provided UN member states with goals, targets, and indicators to facilitate an integrated approach focusing on economic, environmental, and social improvements simultaneously. The aim of this study is to evaluate the contribution of fluvial NBSs to the UN 2030 Agenda, using all its components: Sustainable Development Goals (SDGs), targets, and indicators. We propose a four-step framework with inputs from the UN 2030 Agenda, scientific literature, and case studies. The framework provides a set of fluvial flooding indicators that are linked to SDG indicators of the UN 2030 Agenda. Finally, the fluvial flooding indicators are tested by applying them to a case study, the Eddleston Water Project, aiming to examine its contribution to the UN 2030 Agenda. This reveals that the Eddleston Water Project contributes to 9 SDGs and 33 SDG targets from environmental, economic, societal, policy, and technical perspectives. Our framework aims to enhance the systematic considerations of the SDG indicators, adjust their notion to the system of interest, and thereby enhance the link between the sustainability performance of NBSs and the UN 2030 Agenda.

Despite the growing calls to integrate realistic human behavior in sustainability science models, the representative rational agent prevails. This is especially problematic for climate change adaptation that relies on actions at various scales: from governments to individuals. Empirical evidence on individual adaptation to climate-induced hazards reveals diverse behavioral and social factors affecting economic considerations. Yet, implications of replacing the rational optimizer by realistic human behavior in nature–society systems models are poorly understood. Using an innovative evolutionary economic agent-based model we explore different framings regarding household adaptation behavior to floods, leveraging on behavioral data from a household survey in Miami, USA. We find that a representative rational agent significantly overestimates household adaptation diffusion and underestimates damages compared to boundedly rational behavior revealed from our survey. This “adaptation deficit” exhibited by a population of empirically informed agents is explained primarily by diverse “soft” adaptation constraints—awareness, social influences—rather than heterogeneity in financial constraints. Besides initial inequality disproportionally impacting low/medium adaptive capacity households post-flood, our findings suggest that even under a nearly complete adaptation diffusion, adaptation benefits are uneven, with late or less-efficient actions locking households to a path of higher damages, further exacerbating inequalities. Our exploratory modeling reveals that behavioral assumptions shape the uncertainty of physical factors, like exposure and objective effectiveness of flood-proofing measures, traditionally considered crucial in risk assessments. This unique combination of methods facilitates the assessment of cumulative and distributional effects of boundedly rational behavior essential for designing tailored climate adaptation policies, and for equitable sustainability transitions in general.