Diarrhoea caused by waterborne pathogens still has a large burden of disease. We introduce a modelling and scenario analysis framework that enables better understanding of sources of and possible future changes in the disease burden due to environmental change and management implementation. The state-of-the-art research that can contribute to the development of the framework at the large scale is analysed, together with research gaps and opportunities for future research. Priorities have been identified and these include implementation of Quantitative Microbial Risk Assessment and application of the models in scenario analyses. The credibility of the model outputs should be central in the analysis, for example by developing stochastic models. Implementation of the framework contributes towards achieving the Sustainable Development Goals.

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Several model inter-comparison projects (MIPs) have been carried out recently by the climate, hydrological, agricultural and other modelling communities to quantify modelling uncertainties and improve modelling systems. Here we focus on MIP design for large-scale water quality models. Water quality MIPs can be useful to improve our understanding of pollution problems and facilitate the development of harmonized estimates of current and future water quality. This can provide new opportunities for assessing robustness in estimates of water quality hotspots and trends, improve understanding of processes, pollution sources, water quality model uncertainties, and to identify priorities for water quality data collection and monitoring. Water quality MIP design should harmonize relevant model input datasets, use consistent spatial/temporal domains and resolutions, and similar output variables to improve understanding of water quality modelling uncertainties and provide harmonized water quality data that suit the needs of decision makers and other users.

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Due to the increasing relevance of analyzing water consumption along product life cycles, the water accounting and vulnerability evaluation model (WAVE) has been updated and methodologically enhanced. Recent data from the atmospheric moisture tracking model WAM2-layers is used to update the basin internal evaporation recycling (BIER) ratio, which denotes atmospheric moisture recycling within drainage basins. Potential local impacts resulting from water consumption are quantified by means of the water deprivation index (WDI). Based on the hydrological model WaterGAP3, WDI is updated and methodologically refined to express a basin's vulnerability to freshwater deprivation resulting from the relative scarcity and absolute shortage of water. Compared to the predecessor version, BIER and WDI are provided on an increased spatial and temporal (monthly) resolution. Differences compared to annual averages are relevant in semiarid and arid basins characterized by a high seasonal variation of water consumption and availability. In order to support applicability in water footprinting and life cycle assessment, BIER and WDI are combined to an integrated WAVE+ factor, which is provided on different temporal and spatial resolutions. The applicability of the WAVE+ method is proven in a case study on sugar cane, and results are compared to those obtained by other impact assessment methods.@en