Selection of Evolutionary Multicriteria Strategies: Application in Designing a Regional Water Restoration Management Plan

Sustainability of water resources has become a challenging problem worldwide, as the pollution levels of natural water resources (particularly of rivers) have increased drastically in the last decades. Nowadays, there are many Waste Water Treatment Plant (WWTP) technologies that provide different levels of efficiency in the removal of water pollutants, leading to a great number of combinations of different measures (PoM) or strategies. The management problem, then, involves finding which of these combinations are efficient, regarding the desired objectives (cost and quality). Therefore, decisions affecting water resources require the application of multi-objective optimization techniques which will lead to a set of tradeoff solutions, none of which is better or worse than the others, but, rather, the final decision must be one particular PoM including representative features of the whole set of solutions. Besides, there is not a universally accepted standard way to assess the water quality of a river. In order to consider simultaneously all these issues, we present in this work a hydroinformatics management tool, designed to help decision makers with the selection of a PoM that satisfies the WFD objectives. Our approach combines: 1) a Water Quality Model (WQM), devised to simulate the effects of each PoM used to reduce pollution pressures on the hydrologic network; 2) a Multi-Objective Evolutionary Algorithm (MOEA), used to identify efficient tradeoffs between PoMs’ costs and water quality; and 3) visualization of the Pareto optimal set, in order to extract knowledge from optimal decisions in a usable form. We have applied our methodology in a real scenario, the inner Catalan watersheds with promising results

Australasia

Observed changes and impacts Ongoing climate trends have exacerbated many extreme events (very high confidence). The Australian trends include further warming and sea level rise sea level rise (SLR), with more hot days and heatwaves, less snow, more rainfall in the north, less April–October rainfall in the southwest and southeast and more extreme fire weather days in the south and east. The New Zealand trends include further warming and sea level rise (SLR), more hot days and heatwaves, less snow, more rainfall in the south, less rainfall in the north and more extreme fire weather in the east. There have been fewer tropical cyclones and cold days in the region. Extreme events include Australia’s hottest and driest year in 2019 with a record-breaking number of days over 39°C, New Zealand’s hottest year in 2016, three widespread marine heatwaves during 2016–2020, Category 4 Cyclone Debbie in 2017, seven major hailstorms over eastern Australia and two over New Zealand from 2014–2020, three major floods in eastern Australia and three over New Zealand during 2019–2021 and major fires in southern and eastern Australia during 2019–2020

Calculating the benefit of conservation actions

The benefit (or additionality) attributable to a conservation action is the difference between the outcomes of two scenarios: (1) the scenario with the conservation action, and (2) the alternative scenario, in which action did not occur. However, many conservation decisions are made using approaches that do not appropriately calculate this benefit. We review recent scientific literature and conservation policies to examine how conservation benefit is calculated in three situations: systematic reserve selection, investment in agri-environment schemes, and biodiversity offset trades. In the examples we considered, the approaches used to calculate conservation benefit often involved assumptions about the alternative scenario that were not explicit, demonstrably wrong or both. We suggest that assumptions about how conservation value changes over time in the alternative scenario can often be substantially refined, and that making these assumptions explicit by calculating directly the expected difference between the two scenarios is likely to improve the quality of conservation decision-making