Comprehensive scenario management of sustainable spatial planning and urban water services

Abstract

Adaptations of existing central water supply and wastewater disposal systems to demographic, climatic and socioeconomic changes require a profound knowledge about changing influencing factors. The paper presents a scenario management approach for the identification of future developments of drivers influencing water infrastructures. This method is designed within a research project with the objective of developing an innovative software-based optimisation and decision support system for long-term transformations of existing infrastructures of water supply, wastewater and energy in rural areas. Drivers of water infrastructures comprise engineering and spatial factors and these are predicted by different methods and techniques. The calculated developments of the drivers are illustrated for a model municipality. The developed scenario-manager enables the generation of comprehensive scenarios by combining different drivers. The scenarios are integrated into the optimisation model as input parameters. Furthermore, the result of the optimisation process - an optimal transformation strategy for water infrastructures - can have impacts on the existing fee system. General adaptation possibilities of the present fee system are presented.

Sanitation systems

“…To our knowledge, only two modelling approaches can be found in the literature that covers all the factors mentioned above. One is DAnCE4Water (Dynamic Adaptation for enabling City Evolution for Water, (Rauch et al 2017), (Urich et al 2013)) and the other is SinOptikom (Baron et al 2017, Baron et al 2015. Both simulate in detail the transition of water infrastructures for an entire catchment over long periods considering deep uncertainty and multi-criteria assessment algorithms to evaluate numerous options to meet targets at a variety of spatial scales.…”

A Simplified Sanitary Sewer System Generator for Exploratory Modelling at City-Scale

“…The applicability and sustainability of conventional centralised sewer systems for wastewater management are increasingly questioned (Wong and Brown 2009, Hering et al 2012, Marlow et al 2013, Larsen et al 2016, Bakhshipour et al 2019. Aging or underperforming infrastructure and predicted climatic, demographic, technological, urban and socio-economic developments, call for an adaptation of current urban wastewater management systems, considering future requirements and challenges related to service provision (Baron et al, 2016, Larsen et al, 2016. This adaptation involves not only technical aspects, but also resource efficiency, resilience and sustainability.…”

“…This adaptation involves not only technical aspects, but also resource efficiency, resilience and sustainability. Recent research suggests a transition towards more decentralised schemes (e.g., on-site, cluster or community-level infrastructure for treating, dispersing or reusing wastewater at or near its source) that align better with development scenarios (Kaufmann Alves 2013, Larsen et al 2013, Baron et al 2016, Bakhshipour et al 2019, Hoffmann et al 2020, Elmqvist et al 2021) and changing goals (Hering et al 2012). Their 'degree of centralisation', defined as the ratio of sinks and sources (Eggimann et al 2015) can vary from fully centralised (e.g., all sources are connected to one centralised wastewater treatment plant) to fully decentralised (every source has its own local sink) (Larsen et al 2013, Eggimann et al 2015, Poustie et al 2015.…”

“…Bakhshipour et al (2019) generate multiple infrastructure layouts and explore different degrees of centralization in both green-fields and existing urban areas. Others considered the impact of the urban characteristics on water infrastructure (Kaufmann Alves 2013, Baron et al 2016, Bach et al 2018. Most of these, aim to obtain a detailed representation of the sewer network and are therefore often computationally expensive depending on the size of the network (i.e.…”

“…This approach was applied to several small, rural villages in Germany with 500-800 inhabitants (approximately 4 km network length). SinOptikom is computationally hard to scale and details on the modelling approach and its implementation are not publicly accessible (Baron et al 2016).…”