Farmers in Maharashtra, India, face water scarcity, leading to poor crop yields and farmer indebtedness. To improve water use efficiency and mitigate climate change impacts on livelihoods, governments promote micro-irrigation technologies. Still, the rate of adoption of these technologies remains low. This study aims to develop a complete overview of the socio-economic, psychological and contextual factors that influence adoption in a drought prone region of Maharashtra by combining two well-known models for understanding behavior, the RANAS model and the SSBC model. Our analysis used a mixed method approach. First, a logistic regression was made, using survey data from 419 farming households covering socio-economic and individual-level psychological factors from the RANAS-model. In addition, 22 qualitative semi-structured interviews were held to explore contextual, social, and personal-level factors, using insights from the SSBC model. The results show that farmers who are concerned with the availability of their water source and believe that getting water is becoming more difficult may not adopt micro irrigation systems. Prevalent norms influence farmers actions and choices. Strong financial abilities and technical skills are important drivers of the adoption of micro irrigation systems, in addition to the confidence in their abilities to buy and maintain them. Farmers who adopt micro irrigation systems are often more well-off than farmers adopting furrow irrigation systems and their ability to invest outweighs the importance of saving water through the adoption of efficient micro irrigation systems. Finally, we find that tremendous efforts, high uncertainty of the process of getting a micro-irrigation subsidy, combined with low trust in the government in the area, and feelings of unfairness negatively influence farmers and discourage them to adopt micro-irrigation. Taken together, our mixed-methods approach led to a more nuanced, technology-specific understanding of irrigation technology adoption beyond existing studies, offering valuable insights for designing more effective behavior change strategies and possible ways to encourage the adoption of water efficient irrigation technologies.

Urban areas face an increasing urgency to adapt to climate change, yet adaptation efforts remain insufficient. Addressing this adaptation gap requires an understanding of the psychological mechanisms and contextual influences shaping climate adaptation behaviour. Whereas behavioural scientists have explored citizens’ climate adaptation behaviours, the decision-making of policymakers is often overlooked despite its importance in closing the adaptation gap. To address this, we conducted a behavioural systems analysis to uncover behavioural dynamics that shape policymakers' decision-making, based on 32 semi-struct ured interviews and a workshop with Dutch urban climate adaptation policymakers. Combing thematic and content analysis with behavioural system mapping, our results highlight the importance of an integrated, dynamic system approach to understand psychological and contextual influences on policymakers' decision-making. We identified nine central themes reflecting key behavioural dynamics: reliance on precedents; fragmented roles and responsibilities; habitual thinking based on longstanding processes; policy stringency, clarity and process; conflicting political priorities; importance of individuals; externally-motivated action; illusion of local actor engagement; moving from awareness creation to mainstreaming. Furthermore, analysis of behavioural influences using the Theoretical Domains Framework identified policymakers’ beliefs about consequences, environmental context and resources, and goals as most influential determinants of behaviour. Behavioural system mapping revealed three impactful levers for interventions, namely supporting precedent identification, stimulating information gathering, and boosting mainstreaming processes, to improve decision-making in urban climate adaptation. By integrating behavioural insights and systems analysis, this work proposes a novel approach to analyse contextual, dynamic influences and determinants of behaviour that shape adaptation policymaking. Therefore, it aligns with recent calls from behavioural scientists highlighting the need for systemic approaches in behavioural science.

As cities expand and land becomes built over, more rainwater will run off rather than infiltrate or evapo(trans)pirate, increasing the likelihood of urban pluvial flooding. Stormwater management and planning is essential to ensure that urban areas are well adapted to climate change, involving cooperation between diverse actors with their own objectives. Current tools to support decision-making have a narrow technical focus and do not incorporate the multi-actor context. In this paper, we present a serious game called Urban dRain, developed with the aim to integrate technical assessment of blue, green and grey solutions and actor negotiation. In the game, participants are challenged to develop a stormwater management strategy for a Dutch neighbourhood in multiple rounds, first within their own separate groups, and then collectively. We present results from validation and play-testing the final game prototype with 70 students and researchers. Results show that the game supports socio-technical learning by encouraging players to come up with a range of stormwater management plans and negotiate for their individual goals while achieving a collective goal. The game demonstrates potential to bring actors with varying perspectives together and co-develop solutions to pluvial flooding, overcoming limitations of existing technology-focused tools.

Although causal evidence synthesis is critical for the policy sciences—whether it be analysis for policy or analysis of policy—its repeatable, systematic, and transparent execution remains challenging due to the growing volume, variety, and velocity of policy-relevant evidence generation as well as the complex web of relationships within which policies are usually situated. To address these shortcomings, we develop a novel, semi-automated approach to synthesizing causal evidence from policy-relevant documents. Specifically, we propose the use of natural language processing (NLP) for the extraction of causal evidence and subsequent homogenization of the text; causal mapping for the collation, visualization, and summarization of complex interdependencies within the policy system; and graph analytics for further investigation of the structure and dynamics of the causal map. We illustrate this approach by applying it to a collection of 28 articles on the emissions trading scheme (ETS), a policy instrument of increasing importance for climate change mitigation. In all, we find 300 variables and 284 cause-effect pairs in our input dataset (consisting of 4524 sentences), which are reduced to 70 unique variables and 119 cause-effect pairs after homogenization. We create a causal map depicting these relationships and analyze it to demonstrate the perspectives and policy-relevant insights that can be obtained. We compare these with select manually conducted, previous meta-reviews of the policy instrument, and find them to be not only broadly consistent but also complementary. We conclude that, despite remaining limitations, this approach can help synthesize causal evidence for policy analysis, policy making, and policy research.

We explore the dynamics of centralised and decentralised wastewater infrastructure across various scenarios and introduce novel insights into their performance regarding structural vulnerability, hydraulic capacity, and costs. This study determines circumstances under which infrastructure hybridisation outperforms traditional centralised infrastructure paradigms. We combined system analysis to map out the modelling problem with the model-based exploration of the transition space using the novel TURN-Sewers model. System diagramming was used to identify the parameters or combinations of parameters that significantly influence the performance indicators being assessed. This allowed the creation of relevant simulation scenarios to identify circumstances where a decentralised sewer system could outperform a centralised one. TURN-Sewers was applied to model the infrastructure maintenance and generation of new infrastructure over 20 years for a municipality on the Swiss Plateau, considering a population growth rate of 0.03 a⁻¹. Results show that decentralisation in expansion areas with higher densification can outperform the hydraulic performance and structural vulnerability of expanding centralised sanitary wastewater infrastructure. Decentralised systems can also offer economic advantages when capital expenditure costs for small-scale wastewater treatment plants are significantly reduced compared to current costs, particularly at higher discount rates, e.g. reaping effects of economies of scale. The findings of this study emphasise the potential of transition pathways towards decentralisation in urban water infrastructures and the value of models that allow the exploration of this transition space.

Decision-making is at the core of urban drainage asset management (UDAM), but its importance is often underestimated, leading to a lack of improvement of decision quality in practice. Therefore, our objective is to present fundamental concepts and theories of decision-making from literature and compare them with real-world experiences of observing, supporting, and participating in UDAM decisions in the Netherlands. The observations are contrasted against selected observations from other nations to illustrate the potential impact of key factors on decision-making processes and outcomes. From this, we observe that despite the available UDAM literature and experiences suggesting otherwise, decision-making in UDAM practice tends to focus on information acquisition, cognitive processing, and judgmental processes. This can lead to known decision biases such as protection of mindset and following fragmented, path-dependent processes influenced by formal and informal structures or institutions. To improve decision-making in UDAM, it is necessary to look beyond optimization of existing assets within the pre-existing technical paradigm and instead work toward aligning it with governing structures and processes for effective decision-making at a system level. While the existing evidence – although limited and mostly anecdotal – is compelling, it does not allow for generalization or validation of theoretical propositions against practical findings and vice versa. We therefore see a need for strengthened efforts into a more systematic study of current UDAM practices that incorporates existing theories and empirical insights on decision-making from several disciplines. This will foster accumulation of knowledge and mutual learning to enhance the research and practice of UDAM decision-making.

We present a new modular model called TURN-Sewers for exploring different adaptations of centralised wastewater infrastructure towards more decentralised wastewater systems under different urban development scenarios. The modular model is flexible and computationally efficient in exploring transitions at the city scale, allowing for the comparison of different policies and management strategies for sanitary wastewater infrastructure. TURN-Sewers includes independent modules that simulate the generation, dimensioning, deterioration, management, and calculation of performance indicators for different wastewater systems. This model can use readily available spatial information to support infrastructure planners and other stakeholders in exploring different transition pathways from centralised to decentralised wastewater infrastructure. An illustrative example demonstrates how TURN-Sewers can generate multiple future alternatives, define different infrastructure management strategies regarding system expansion, rehabilitation and transition, and assess the economic, hydraulic and structural impacts.

There is an urgent need for urban environments to be flood resilient due to increasing urbanization and climate change. This can be addressed by adopting sustainable drainage solutions (SuDS) in households. However, lack of knowledge and awareness among urban residents is a barrier. In this paper, we present an educational serious game called SuDSbury to overcome this barrier and a pre-/post-game survey-based evaluation to study whether the game can educate citizens (and to what degree). An exploratory study with 14 players across three game sessions suggests that playing SuDSbury induced changes in knowledge, comprehension, and personal norms regarding SuDS. However, comprehension of concepts related to urban drainage can be improved by increasing game realism. The game should be further tested with a larger sample and a diverse demographic of urban residents. The participants further found that SuDSbury is fun and engaging to play, making it suitable for broader public interventions.

Future climatic, demographic, technological, urban and socio-economic challenges call for more flexible and sustainable wastewater infrastructure systems. Exploratory modelling can help to investigate the consequences of these developments on the infrastructure. In order to explore large numbers of adaptation strategies, we need to re-balance the degree of realism of sewer network and ability to reflect key performance characteristics against the model's parsimony and computational efficiency. We present a spatially explicit algorithm for creating sanitary sewer networks that realistically represent key characteristics of a real system. Basic topographic, demographic and urban characteristics are abstracted into a squared grid of ‘Blocks’ which are the foundation for the sewer network's topology delineation. We compare three different pipe dimensioning approaches and found a good balance between detail and computational efficiency. With a basic hydraulic performance assessment, we demonstrate that we attain a computationally efficient and high-fidelity wastewater sewer network with adequate hydraulic performance. A spatial resolution of 250 m Block size in combination with a sequential Pipe-by-Pipe (PBP) design algorithm provides a sound trade-off between computational time and fidelity of relevant structural and hydraulic properties for exploratory modelling. We can generate a simplified sewer network (both topology and hydraulic design) in 18 s using PBP, versus 36 min using a highly detailed model or 1 s using a highly abstract model. Moreover, this simplification can cut up to 1/10th to 1/50th the computational time for the hydraulic simulations depending on the routing method implemented. We anticipate our model to be a starting point for sophisticated exploratory modelling into possible infrastructure adaptation measures of topological and loading changes of sewer systems for long-term planning.

Freshwater resources in coastal areas are under intense pressure from excessive groundwater extraction, which amplifies saltwater intrusion (SWI) into coastal freshwater aquifers, such as in the Mekong Delta. Studies that combine socioenvironmental data and households' psychological factors next to salinity measurement data to design groundwater conservation strategies are rare. In this study, these aspects are combined to explore their influence on the public willingness to conserve groundwater using a Bayesian belief network model. We analyzed 313 household survey data spread over three districts in the coastal province of Tra Vinh, located in the Vietnamese Mekong Delta. The level of salinity is significantly correlated with the willingness to conserve groundwater. The top three socioenvironmental characteristics that influence willingness are the level of salinity, type of employment - i.e., being a farmer - and frequency of being exposed to groundwater or SWI promotional activities. Social norm, i.e., perceived social pressure, is the most influential psychological factor that determines willingness. This study reveals an urgency for the local government to intervene and create social pressure regarding the issue.

Urban water management (UWM) is a complex problem characterized by multiple alternatives, conflicting objectives, and multiple uncertainties about key drivers like climate change, population growth, and increasing urbanization. Serious games are becoming a popular means to support decision-makers who are responsible for the planning and management of urban water systems. This is evident in the increasing number of articles about serious games in recent years. However, the effectiveness of these games in improving decision-making and the quality of their design and evaluation approaches remains unclear. To understand this better, in this paper, we identified 41 serious games covering the urban water cycle. Of these games, 15 were shortlisted for a detailed review. By using common rational decision-making and game design phases from literature, we evaluated and mapped how the shortlisted games contribute to these phases. Our research shows that current serious game applications have multiple limitations: lack of focus on executing the initial phases of decision-making, limited use of storytelling and adaptive game elements, use of low-quality evaluation design and explicit indicators to measure game outcomes, and lastly, lack of attention to cognitive processes of players playing the game. Addressing these limitations is critical for advancing purposeful game design supporting UWM.

While the burden of disease from well-studied drinking water contaminants is declining, risks from emerging chemical and microbial contaminants arise because of social, technological, demographic and climatological developments. At present, emerging chemical and microbial drinking water contaminants are not assessed in a systematic way, but reactively and incidence based. Furthermore, they are assessed separately despite similar pollution sources. As a result, risks might be addressed ineffectively. Integrated risk assessment approaches are thus needed that elucidate the uncertainties in the risk evaluation of emerging drinking water contaminants, while considering risk assessors’ values. This study therefore aimed to (1) construct an assessment hierarchy for the integrated evaluation of the potential risks from emerging chemical and microbial contaminants in drinking water and (2) develop a decision support tool, based on the agreed assessment hierarchy, to quantify (uncertain) risk scores. A multi-actor approach was used to construct the assessment hierarchy, involving chemical and microbial risk assessors, drinking water experts and members of responsible authorities. The concept of value- focused thinking was applied to guide the problem-structuring and model-building process. The development of the decision support tool was done using Decisi-o-rama, an open-source Python library. With the developed decision support tool (uncertain) risk scores can be calculated for emerging chemical and microbial drinking water contaminants, which can be used for the evidence-based prioritisation of actions on emerging chemical and microbial drinking water risks. The decision support tool improves existing prioritisation approaches as it combines uncertain indicator levels with a multi-stakeholder approach and integrated the risk assessment of chemical and microbial contaminants. By applying the concept of value-focused thinking, this study addressed difficulties in evidence-based decision-making related to emerging drinking water contaminants. Suggestions to improve the model were made to guide future research in assisting policy makers to effectively protect public health from emerging drinking water risks.

Environmental decisions are complex as they are multi-dimensional, highly interdisciplinary and not only involve multiple stakeholders with conflicting objectives, but also many possible alternatives with uncertain consequences. The difficulty lies in making trade-offs between tough value trade-offs on the one hand while appreciating uncertain impacts of alternatives on the other. To support decisions tackling such problems, a combination of multi-criteria decision analysis (MCDA) and environmental models is promising yet limited by the available MCDA software. Here, we present Decisi-o-rama, an open-source Python MCDA library for single and sets (portfolios) of alternatives in the context of multi-attribute value/utility theory (MAUT/MAVT). Its development was driven by four aspirations that are crucial for usability in the context of environmental decision-making: (1) interoperability, (2) uncertainty-awareness, (3) computational efficiency, and (4) integration with portfolio decisions. The results indicate that these aspirations are met, thus facilitating the adoption of MCDA methods by environmental researchers and practitioners.

Recent developments in high- and middle-income countries have exhibited a shift from conventional urban water systems to alternative solutions that are more diverse in source separation, decentralization, and modularization. These solutions include nongrid, small-grid, and hybrid systems to address such pressing global challenges as climate change, eutrophication, and rapid urbanization. They close loops, recover valuable resources, and adapt quickly to changing boundary conditions such as population size. Moving to such alternative solutions requires both technical and social innovations to coevolve over time into integrated socio-technical urban water systems. Current implementations of alternative systems in high- and middle-income countries are promising, but they also underline the need for research questions to be addressed from technical, social, and transformative perspectives. Future research should pursue a transdisciplinary research approach to generating evidence through socio-technical "lighthouse" projects that apply alternative urban water systems at scale. Such research should leverage experiences from these projects in diverse socio-economic contexts, identify their potentials and limitations from an integrated perspective, and share their successes and failures across the urban water sector.

Learning multi-cultural team competencies is important for engineering students to prepare for an increasingly global workspace. We evaluated the game BAFÁ BAFÁ with groups of Master students from varying engineering programmes using a mixed methods approach. The game experience of 118 participants was measured. These participants experienced the game overall positively, although difficulties to understand other players in the game triggered mild stress and confusion. 91respondents also completed questionnaires before and after the game about certain attitudes, skills, and values related to working with people from other cultures: willingness (attitude) and ability (skill) to understand those people; and appreciation (value) of working with them. We used paired t-tests and qualitative analysis to determine the game’s effectiveness: after playing the game the players’ willingness increased significantly (t(90) = 3.6, p=.001), but their ability to do so decreased
significantly (t(90) = 3.3, p=.001) and their appreciation remained constant (t(90) = 1.3, p=.195). The qualitative responses supported our quantitative results: after playing the BAFÁ BAFÁ game, players were more willing to understand people from other cultures. Moreover, players had become more aware of their own shortcomings in understanding people from other cultures. Finally, the learning effects were likely not caused by a test effect, since the appreciation (value) of working with people from other cultures had not increased after playing the game. We concluded that the BAFÁ BAFÁ game is a powerful instrument to embark upon teaching multi-cultural team skills, and therefore, to train more culturally aware engineers.

Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Because physical urban water infrastructure has life expectancies of up to 100 years or more, contemporary urban drainage systems are strongly influenced by historical decisions and implementations. The current decisions taken in sewer asset management will, therefore, have a long-lasting impact on the functionality and quality of future services provided by these networks. These decisions can be supported by different approaches ranging from various inspection techniques, deterioration models to assess the probability of failure or the technical service life, to sophisticated decision support systems crossing boundaries to other urban infrastructure. This paper presents the state of the art in sewer asset management in its manifold facets spanning a wide field of research and highlights existing research gaps while giving an outlook on future developments and research areas.

Sewer pipes are commonly inspected in situ with CCTV equipment. The CCTV footage is then reviewed by human operators in order to classify defects in the pipes and make a recommendation on possible interventions. This process is both labor-intensive and error-prone. Other researchers have suggested machine learning techniques to (partially) automate the human review of this footage, but the automated classifiers are often validated in artificial testing setups, leading to biased results that do not translate directly to operational impact. In this work, we discuss suitable evaluation metrics for this specific classification task — most notably ‘specificity at sensitivity’ and ‘precision at recall’ — and the importance of using a validation setup that includes a realistic ratio of images with defects to images without defects, and a sufficiently large dataset. We also introduce ‘leave-two-inspections-out’ cross validation, designed to eliminate a data leakage bias that would otherwise cause an overestimation of classifier performance. We designed a convolutional neural network (CNN) and applied this validation methodology to automatically detect the twelve most common defect types in a dataset of over 2 million CCTV images. With this dataset and our validation methodology, our CNN outperforms the state-of-the-art. Classification performance was highest for intruding and defective connections and lowest for porous pipes. While the CNN is not capable of fully automated classification at sufficient performance levels, we determined that if we augment the human operator with the CNN, this may reduce the required human labor by up to 60.5%.

A range of decision analysis methods are proposed to aid managers in solving complex problems. Due to a lack of systematic evaluation, it is difficult to say whether and why a certain intervention works in a given context. Claimed benefits often cannot be substantiated, hindering the uptake of decision analysis and impeding learning for more targeted design. Building on theories of persuasion, Rouwette et al (2003, 2009, 2011) developed and tested a framework to evaluate the performance of facilitated group model building (and by extension problem structuring). Although conditions for persuasion were given, the variable combinations could not predict the observed changes. I adapted the framework aiming to improve operationalization for problem structuring and MCDA interventions. I added variables to capture behavioral and social aspects known to affect group collaboration and decision outcomes. When testing the resulting pre-/post assessment in a problem structuring intervention with 13 Dutch sewer managers, a surprise happened. Despite their prior agreement, it was met with resistance and social dynamics unfolded that hindered its completion. In hindsight, the pre-assessment revealed issues that if considered for designing rather than evaluating the intervention might have avoided failure. I will present the approach and the insights gained as well as how these might be adapted for better diagnosis of the situation to inform intervention design and evaluation.

Automatic leak localization has been suggested to reduce the time and personnel efforts needed to localize (small) leaks. Yet, the available methods require a detailed demand distribution model for successful calibration and good leak localization performance. The main aim of this work was to analyze whether such a detailed demand distribution is needed. Two demand distributions were used: a factorized distribution that distributes the inflow demand proportionally across the consumption nodes according to individual billing data, and a uniform distribution that equally distributes demand across all consumption nodes. The performance of the automatic leak localization method, using both demand distribution models, was compared. A new measure for leak localization performance that is based on the percentage of false positive nodes is proposed. It was possible to localize the leaks with both demand distribution models, although performance varied depending on the timing and duration of the measurement.