Department Systems Analysis, Integrated Assessment and Modelling

Dam construction is mostly aimed for multiple functions, including irrigation water provision, hydropower, and some others that bring substantial social benefits. However, global warming impacts on the interaction of the positive outcomes of damming remain little known, particularly in terms of the sustainability of their co-benefits, whereby investigating the different impacts of global warming scenarios of 1.5 °C and 2 °C has been a hotspot in water resources and energy research worldwide. This study used an integrative analysis based on a hydrological, techno-economic and agricultural modeling framework to evaluate the effects of global warming scenarios of 1.5 °C and 2 °C on the co-benefits between hydropower and irrigation in the Mekong River basin. The results show the declined hydropower generation and irrigation water supply in the Mekong River basin under 1.5 °C and 2 °C warming scenarios. The co-benefits between the hydropower and the irrigation is more undermined by the global warming of 2 °C relative to 1.5 °C in the Mekong River basin. Moreover, the changes of co-benefits are sensitive to the consideration of the protected areas in the basin. With the consideration of the protected areas, the co-benefits would be enhanced by 2 °C global warming compared to 1.5 °C global warming. Therefore, it is critical for decision-makers to consider the tradeoffs between the environment and dam construction for ensuring energy and food security under global warming scenarios.

A wide knowledge base regarding the ecological preferences of benthic macroinvertebrates is synthesized in public databases. This knowledge can assist in disentangling the influence of multiple environmental factors on the probability of occurrence of macroinvertebrates and in identifying anthropogenic impacts on the macroinvertebrate assemblage. We aimed to examine and extend current knowledge on ecological preferences by confronting it with independent biomonitoring datasets and to assess how the taxonomic resolution of datasets and the prevalence of taxa affects our ability to do so. We used a habitat suitability-based multi-species distribution model (HS-MSDM) and applied Bayesian inference to confront current knowledge (formalized as prior probability distributions) against independent biomonitoring data across rivers in Switzerland. Shifts in the resulting posterior probability distributions relative to the priors indicate a disagreement with the current knowledge of ecological preferences. Ecological preferences for temperature and organic matter had the highest influence on the predicted occurrence of macroinvertebrates in the model, followed by flow velocity, insecticide pollution, and substratum. Three-fold cross-validation tests demonstrated that the HS-MSDM predicted the distribution of taxa with a relative frequency of occurrence between 0.2 and 0.8 considerably better than a model without consideration of environmental factors. However, it was less able to predict the distribution of taxa with a frequency of occurrence <0.1 or >0.9. Nine taxa with a frequency of occurrence between 0.4 and 0.8 were identified as potentially useful bioindicators, given their strong association with the environmental factors in the model. We also identified 29 taxa for which part of the ecological preference data, particularly temperature and flow-velocity preferences, should be re-examined. For river morphology, 18 sensitive and 10 insensitive taxa were identified, although direct and uniquely linked prior knowledge regarding morphology was lacking for all taxa. Phylogenetically derived information on ecological preferences could be integrated and updated to fill gaps in ecological preference databases. However, the taxonomic resolution of the biomonitoring and ecological preference data plays an important role, as we show by identifying families comprising species that respond differently to environmental factors. These results demonstrate the value of conducting biomonitoring at the most detailed taxonomic level possible.

Uncertainty quantification is very important in environmental management to allow decision makers to consider the reliability of predictions of the consequences of decision alternatives and relate them to their risk attitudes and the uncertainty about their preferences. Nevertheless, uncertainty quantification in environmental decision support is often incomplete and the robustness of the results regarding assumptions made for uncertainty quantification is often not investigated. In this article, an attempt is made to demonstrate how uncertainty can be considered more comprehensively in environmental research and decision support by combining well-established with rarely applied statistical techniques. In particular, the following elements of uncertainty quantification are discussed: (i) using stochastic, mechanistic models that consider and propagate uncertainties from their origin to the output; (ii) profiting from the support of modern techniques of data science to increase the diversity of the exploration process, to benchmark mechanistic models, and to find new relationships; (iii) analysing structural alternatives by multi-model and non-parametric approaches; (iv) quantitatively formulating and using societal preferences in decision support; (v) explicitly considering the uncertainty of elicited preferences in addition to the uncertainty of predictions in decision support; and (vi) explicitly considering the ambiguity about prior distributions for predictions and preferences by using imprecise probabilities. In particular, (v) and (vi) have mostly been ignored in the past and a guideline is provided on how these uncertainties can be considered without significantly increasing the computational burden. The methodological approach to (v) and (vi) is based on expected expected utility theory, which extends expected utility theory to the consideration of uncertain preferences, and on imprecise, intersubjective Bayesian probabilities.

This study documents the development of a semi-distributed hydrological model aimed at reflecting the dominant controls on observed streamflow spatial variability. The process is presented through the case study of the Thur catchment (Switzerland, 1702 km²), an alpine and pre-alpine catchment where streamflow (measured at 10 subcatchments) has different spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on streamflow spatial variability and build a model that reflects them, we follow a two-stage approach. In a first stage, we identify the main climatic or landscape properties that control the spatial variability of streamflow signatures. This stage is based on correlation analysis, complemented by expert judgement to identify the most plausible cause–effect relationships. In a second stage, the results of the previous analysis are used to develop a set of model experiments aimed at determining an appropriate model representation of the Thur catchment. These experiments confirm that only a hydrological model that accounts for the heterogeneity of precipitation, snow-related processes, and landscape features such as geology produces hydrographs that have signatures similar to the observed ones. This model provides consistent results in space–time validation, which is promising for predictions in ungauged basins. The presented methodology for model building can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology, and geology maps) are available in numerous regions around the globe.

Key decisions in the design of biomonitoring programs include taxonomic resolution, geographic extent, and site selection, each of which can affect our ability to infer human impacts on biodiversity from biomonitoring data. These decisions are constrained by monitoring goals and budget limitations, which may require trade-offs between them. In this study, we use species distribution models (SDMs) to assess the effects of key decisions in biomonitoring design on our ability to infer the effects of natural and anthropogenic environmental conditions on the occurrence of benthic macroinvertebrates in streams. We compared 4 datasets that differ in their site-selection strategy, geographic extent, and taxonomic resolution using data from Swiss federal and cantonal biomonitoring programs. We used individual SDMs with 3-fold cross validation to identify the environmental variables that best predict the probability of taxa occurrence across the datasets. We then used a hierarchical multi-species distribution model (hmSDM) to identify how key aspects of biomonitoring design influence the relative importance of the selected explanatory (predictor) variables in the model as well as the model’s predictive performance. The relative importance of the explanatory variables in the hmSDMs was lowest for the dataset with a grid-based site-selection approach and family-level resolution. An increase in predictive performance was achieved by either using a species-level taxonomic resolution for Ephemeroptera, Plecoptera, and Trichoptera or by combining different biomonitoring programs at the family level to increase the number of sites and improve the coverage of environmental conditions. Selecting monitoring sites to provide a good coverage of environmental conditions, while also targeting sites with rare combinations of environmental conditions, could further improve biomonitoring program data. Models based on finer taxonomic resolution revealed that widespread families consist of species and genera with different and stronger responses to environmental conditions. However, many families include species that are too rare to allow inference of significant responses to environmental conditions. We show that hmSDMs of stream invertebrates can contribute to the selection of specific taxa for identification at finer taxonomic resolution. This strategy could facilitate the standardization and combination of multiple biomonitoring datasets and improve the identifiability of stream invertebrate responses to environmental conditions in biomonitoring programs.