Multisector Dynamics: Advancing the Science of Complex Adaptive Human-Earth Systems

The field of MultiSector Dynamics (MSD) explores the dynamics and co-evolutionary pathways of human and Earth systems with a focus on critical goods, services, and amenities delivered to people through interdependent sectors. This commentary lays out core definitions and concepts, identifies MSD science questions in the context of the current state of knowledge, and describes ongoing activities to expand capacities for open science, leverage revolutions in data and computing, and grow and diversify the MSD workforce. Central to our vision is the ambition of advancing the next generation of complex adaptive human-Earth systems science to better address interconnected risks, increase resilience, and improve sustainability. This will require convergent research and the integration of ideas and methods from multiple disciplines. Understanding the tradeoffs, synergies, and complexities that exist in coupled human-Earth systems is particularly important in the context of energy transitions and increased future shocks

Decision-support for adaptive and sustainable urban wastewater system management in the face of uncertainty

With sustainable development as the new overarching goal, urban wastewater system (UWS) managers are now being asked to take all social, economic, technical and environmental facets related to their decisions into account. In this complex decision-making environment, uncertainty can be formidable. Uncertainty is present both in the ways the system is interpreted stochastically, but also in its natural ever-shifting behaviour. This inherent uncertainty leads to the conclusion that better decisions will be made if the decision-making process is adaptive and iterative. UWS decision-support frameworks exist in the literature, but none of them effectively addresses all these needs. Hence, there is a need for an adaptive framework that supports UWS management by addressing aspects of sustainability and uncertainty of various types. The development of such a framework is the main outcome of this work, and is supported by two demonstrative applications presented in this thesis.En el marc de desenvolupament sostenible, els gestors dels sistemes de sanejament han d’incloure factors socials, econòmics, tècnics i ambientals durant la presa de decisions. Donada l’elevada complexitat en la presa de decisions, la incertesa relacionada amb aquests factors pot esdevenir rellevant i per tant s’ha d’incorporar de forma adequada. La incertesa està present no només en la forma d’interpretar el sistema sinó també en el seu comportament natural i canviant. Necessitem processos de decisió que siguin adaptatiu i iteratiu per tal d’assegurar que el sistemes de sanejament mantenen la seva eficàcia durant la seva vida útil. Tot i que existeixen en la bibliografia alguns marcs d’ajuda a la presa de decisions cap d’ells inclou de forma efectiva tots aquests aspectes. Per tant, necessitem disposar d’un marc que ajudi en la gestió dels sistemes de sanejament sota el paradigma de gestió adaptativa al mateix temps que consideri aspectes de sostenibilitat i incertesa de diferent origen. Aquest és l’objectiu principal d’aquest treball, conjuntament amb dos aplicacions de demostració

Do machine learning methods used in data mining enhance the potential of decision support systems? A review for the urban water sector

With sustainable development as their overarching goal, Urban Water System (UWS) managers need to take into account all social, economic, technical and environmental facets related to their decisions. Decision support systems (DSS) have been used widely for handling such complexity in water treatment, having a high level of popularity as academic exercises, although little validation and few full-scale implementations reported in practice. The objective of this paper is to review the application of artificial intelligence methods (mainly machine learning) to UWS and to investigate the integration of these methods into DSS. The results of the review suggest that artificial neural networks is the most popular method in the water and wastewater sectors followed by clustering. Bayesian networks and swarm intelligence/optimization have shown a spectacular increase in the water sector in the last 10 years, being the latest techniques to be incorporated but overtaking case-based reasoning. Whereas artificial intelligence applications to the water sector focus on modelling, optimization or data mining for knowledge generation, their encapsulation into functional DSS is not fully explored. Few academic applications have made it into decision making practice. We believe that the reason behind this misuse is not related to the methods themselves but rather to the disassociation between the fields of water and computer engineering, the limited practical experience of academics, and the great complexity inherently presentThe authors would like to acknowledge the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007e2013 under REA agreement 289193 (SANITAS ITN). The authors thank the Spanish Ministry of Economy and Competitiveness (RYC-2013-14595, CTM2015-66892-R). The authors also acknowledge support from the Economy and Knowledge Department of the Catalan Government through the Consolidated Research Group (2014 SGR 291) - Catalan Institute for Water Research and (2014-SGR-1168)-LEQUIA-University of Giron

Assessing Urban Wastewater System Upgrades Using Integrated Modeling, Life Cycle Analysis, and Shadow Pricing

This study assesses the environmental impacts of four measures proposed for upgrading of the urban wastewater system of Eindhoven and the Dommel River in The Netherlands, against the base case, “do-nothing” option. The measures aim to reduce the overall environmental impact of the Eindhoven urban wastewater system (UWS) by targeting river dissolved oxygen depletion and ammonia peaks, reducing combined sewer overflows, and enhancing nutrient removal. The measures are evaluated using a life cycle analysis with the boundaries including the receiving river section by means of an integrated model of the UWS. An uncertainty analysis of the estimated impacts has been performed to support the outcomes. The study also uses the economic concept of shadow prices to assign relative weights of socio-economic importance to the estimated life cycle impacts. This novel integration of tools complements the assessments of this UWS with the inclusion of long-term global environmental impacts and the investigation of trade-offs between different environmental impacts through a single monetary unit. The results support the selection of deeper clarifiers as the most environmentally beneficial measure for upgrade