Abteilung Systemanalyse, Integrated Assessment und Modellierung

Aim: Species distribution models (SDMs) are widely used to study geographic distributions of taxa in response to natural and anthropogenic environmental conditions. For a community, common approaches include fitting individual SDMs (iSDMs) to all taxa or directly modelling community properties such as richness. However, the parameters of iSDMs are difficult to identify for rare taxa, and community properties do not reveal taxon‐specific responses. Individual models can be combined into a hierarchical multispecies distribution model (mSDM) that constrains taxon‐specific parameters according to overarching community parameters, or a joint model (jSDM) in which interdependencies between taxa are jointly inferred. We compare how individual, hierarchical multispecies and joint SDMs differ in quality of fit, explanatory power and predictive performance, and analyse how these properties depend on the prevalence of taxa.Taxa: Presence–absence observations of 245 benthic macroinvertebrate taxa identified at a mixed taxonomic resolution.Location: Four hundred and ninety‐two sites in rivers throughout Switzerland.Methods: Individual, hierarchical and joint hierarchical generalized linear models (GLM) were developed for all taxa. Parameters were estimated using maximum likelihood estimation or Bayesian inference with Hamiltonian Markov chain Monte Carlo simulations. Predictive performance was assessed with cross‐validation. In addition, the predicted family and species richness of the models was compared with a GLM for richness.Results: Individual models show a slightly higher quality of fit largely due to overfitting for rare taxa. The mSDM achieves a similar quality of fit and explanatory power, mitigates overfitting for rare taxa and considerably improves predictive performance over the whole community. The joint models further improve the quality of fit, but decrease predictive performance and increase predictive uncertainty.Main conclusions: We show that even a relatively simple mSDM combines many of the analytical capabilities of iSDMs and improves predictive performance. Increasingly complex mSDMs and jSDMs provide additional analytical possibilities, but depending on the data and research questions, different levels of complexity may be appropriate.

The widespread application of deterministic hydrological models in research and practice calls for suitable methods to describe their uncertainty. The errors of those models are often heteroscedastic, non-Gaussian and correlated due to the memory effect of errors in state variables. Still, residual error models are usually highly simplified, often neglecting some of the mentioned characteristics. This is partly because general approaches to account for all of those characteristics are lacking, and partly because the benefits of more complex error models in terms of achieving better predictions are unclear. For example, the joint inference of autocorrelation of errors and hydrological model parameters has been shown to lead to poor predictions. This study presents a framework for likelihood functions for deterministic hydrological models that considers correlated errors and allows for an arbitrary probability distribution of observed streamflow. The choice of this distribution reflects prior knowledge about non-normality of the errors. The framework was used to evaluate increasingly complex error models with data of varying temporal resolution (daily to hourly) in two catchments. We found that (1) the joint inference of hydrological and error model parameters leads to poor predictions when conventional error models with stationary correlation are used, which confirms previous studies; (2) the quality of these predictions worsens with higher temporal resolution of the data; (3) accounting for a non-stationary autocorrelation of the errors, i.e. allowing it to vary between wet and dry periods, largely alleviates the observed problems; and (4) accounting for autocorrelation leads to more realistic model output, as shown by signatures such as the flashiness index. Overall, this study contributes to a better description of residual errors of deterministic hydrological models.

Human activities, as well as climate change, have had increasing impacts on natural hydrological systems, particularly streamflow. However, quantitative assessments of these impacts are lacking on large scales. In this study, we use the simulations from six global hydrological models driven by three meteorological forcings to investigate direct human impact (DHI) and climate change impact on streamflow in China. Results show that, in the sub-periods of 1971–1990 and 1991–2010, one-fifth to one-third of mean annual streamflow (MAF) reduced due to DHI in northern basins and much smaller (< 4 %) MAF reduced in southern basins. From 1971–1990 to 1991–2010, total MAF changes range from −13 % to 10 % across basins, wherein the relative contributions of DHI change and climate change show distinct spatial patterns. DHI change caused decreases in MAF in 70 % of river segments, but climate change dominated the total MAF changes in 88 % of river segments of China. In most northern basins, climate change results in changes of −9 % to 18 % of MAF, while DHI change results in decreases of 2 % to 8 % in MAF. In contrast with the impacts of climate change that may increase or decrease streamflow, DHI change almost always contributes to decreases in MAF over time, wherein water withdrawals are supposed to be the major impact on streamflow. This quantitative assessment can be a reference for attribution of streamflow changes at large scales despite uncertainty remains. We highlight the significant DHI in northern basins and the necessity to modulate DHI through improved water management towards a better adaptation to future climate change.

Freshwater ecosystems are increasingly under threat as they are confronted with multiple anthropogenic impairments. This calls for comprehensive management strategies to counteract, or even prevent, long-term impacts on habitats and their biodiversity, as well as on their ecological functions and services. The basis for the efficientmanagement and effective conservation of any ecosystem is sufficient knowledge on the state of the systemand its response to external influence factors. In freshwater ecosystems, state information is currently drawnfrom ecological assessments at the reach or site scale. While these assessments are essential, they are not sufficientto assess the expected outcome of different river restoration strategies, because they do not account for importantcharacteristics of the whole river network, such as habitat connectivity or headwater reachability. This isof particular importance for the spatial prioritization of restoration measures. River restoration could be supportedbest by integrative catchment-scale ecological assessments that are sensitive to the spatial arrangementof river reaches and barriers. Assessments at this scale are of increasing interest to environmental managersand conservation practitioners to prioritize restoration measures or to locate areas worth protecting. We presentan approach based on decision support methods that integrates abiotic and biotic ecological assessments at thereach-scale and aggregates them spatially to describe the ecological state of entire catchments. This aggregationis based on spatial criteria that represent important ecological catchment properties, such as fish migration potential,resilience, fragmentation and habitat diversity in a spatially explicit way.We identify the most promisingassessment criteria from different alternatives based on theoretical considerations and a comparison with biologicalindicators. Potential applications are discussed, particularly for supporting the strategic, long-term planningand spatial prioritization of restoration measures.

International food trade entails virtual water flows across trading partners. It has been proposed to attenuate regional water scarcity by importing water‐intensive commodities from water‐abundant regions. In addition to alleviating water scarcity in virtual water importing countries, existing studies have reported that food trade also generates global water savings. However, little is known how these global water savings may alleviate water scarcity, which is more relevant to the sustainable use of water resources than only assessing the savings. In this paper, we conducted a comprehensive review on studies of water savings and losses associated with food trade on different spatial scales. We found that the concept of global water savings is built on the disparities in water productivity across countries, whereas the regional water savings measure the inflows of virtual water trade. The significance of water savings is dimmed by the fact that the savings are often not driven by water scarcity. Meanwhile, lacking policy relevance impairs the usefulness of water saving accounting. Future studies should link water savings to alleviating water scarcity at various levels. The water saving accounting needs to go to finer scale, for example, to subnational and river basin scales, to support real water resource management. In the meantime, interdisciplinary efforts are necessary to enhance the water savings as a holistic measure for addressing water scarcity on regional and global scales.