Systems Analysis, Integrated Assessment and Modelling
In SIAM, we develop and apply models and formal techniques in order to understand, demonstrate, and predict the behavior of natural, technical, social and economical systems that pertain to water and other natural resources. Read more
Prieto, C., Kavetski, D., Le Vine, N., Álvarez, C., & Medina, R. (2021). Identification of dominant hydrological mechanisms using Bayesian inference, multiple statistical hypothesis testing, and flexible models. Water Resources Research, 57(8), e2020WR028338 (32 pp.). doi:10.1029/2020WR028338, Institutional Repository
In hydrological modeling, the identification of model mechanisms best suited for representing individual hydrological (physical) processes is of major scientific and operational interest. We present a statistical hypothesis-testing perspective on this model identification challenge and contribute a mechanism identification framework that combines: (i) Bayesian estimation of posterior probabilities of individual mechanisms from a given ensemble of model structures; (ii) a test statistic that defines a “dominant” mechanism as a mechanism more probable than all its alternatives given observed data; and (iii) a flexible modeling framework to generate model structures using combinations of available mechanisms. The uncertainty in the test statistic is approximated using bootstrap sampling from the model ensemble. Synthetic experiments (with varying error magnitude and multiple replicates) and real data experiments are conducted using the hydrological modeling system FUSE (7 processes and 2–4 mechanisms per process yielding 624 feasible model structures) and data from the Leizarán catchment in northern Spain. The mechanism identification method is reliable: it identifies the correct mechanism as dominant in all synthetic trials where an identification is made. As data/model errors increase, statistical power (identifiability) decreases, manifesting as trials where no mechanism is identified as dominant. The real data case study results are broadly consistent with the synthetic analysis, with dominant mechanisms identified for 4 of 7 processes. Insights on which processes are most/least identifiable are also reported. The mechanism identification method is expected to contribute to broader community efforts on improving model identification and process representation in hydrology.
Fenicia, F., & Kavetski, D. (2021). Behind every robust result is a robust method: perspectives from a case study and publication process in hydrological modelling. Hydrological Processes, 35(8), e14266 (9 pp.). doi:10.1002/hyp.14266, Institutional Repository
Socioeconomic development has led to increased consumption of both blue and green water. Consequently, China is facing serious water scarcity issue. However, few studies have investigated interactions of blue and green water footprints, as well as driving forces underlying the changes in water footprints across provinces and sectors. To fill in this knowledge gap, we quantified the spatial-temporal dynamics of the blue and green water footprint (BWF and GWF, respectively), and analyzed the key factors that drive the provincial-level changes in BWF and GWF from 2002 to 2012. The analysis is facilitated by the approaches of multi-region input-output analysis and structural decomposition analysis, and we developed one decoupling index to quantify the water-economy relation and substitution between green and blue water. The results show that China's BWF averaged at 161 billion m3/yr, about one-third the size of the GWF. In addition, water scarce provinces in Northern China were moving towards decoupling between economic growth and blue water consumption, with GWF playing an increasingly important role. The changes in the WFs were mainly influenced by changes in affluence (final demand per capita), technological improvements (decreased direct water consumption intensity), and consumption pattern (composition of the final demand) rather than changes in the population and export. Technology improvement, consumption pattern shift and industrial structure adjustment contribute to WF reductions, thus help improve water security and sustainability in China. This study provides a new approach to analyze water-economy relations for water scarce countries.
The Dual Scripps Plankton Camera (DSPC) is a new approach for automated in-situ monitoring of phyto- and zooplankton communities based on a dual magnification dark-field imaging microscope. Here, we present the DSPC and its associated image processing while evaluating its capabilities in i) detecting and characterizing plankton species of different size and taxonomic categories and ii) measuring their abundance in both laboratory and field applications. In the laboratory, body size and abundance estimates by the DSPC significantly and robustly scaled with measurements derived by microscopy. In the field, a DSPC installed permanently at 3 m depth in Lake Greifensee (Switzerland) delivered images of plankton individuals, colonies, and heterospecific aggregates at hourly timescales without disrupting natural arrangements of interacting organisms, their microenvironment or their behavior. The DSPC was able to track the dynamics of taxa, mostly at the genus level, in the size range between ∼10 μm to ∼ 1 cm, covering many components of the planktonic food web (including parasites and potentially toxic cyanobacteria). Comparing data from the field-deployed DSPC to traditional sampling and microscopy revealed a general overall agreement in estimates of plankton diversity and abundances. The most significant disagreements between traditional methods and the DSPC resided in the measurements of zooplankton community properties. Our data suggest that the DSPC is better equipped to study the dynamics and demography of heterogeneously distributed organisms such as zooplankton, because high temporal resolution and continuous sampling offer more information and less variability in taxa detection and quantification than traditional sampling. Time series collected by the DSPC depicted ecological succession patterns, algal bloom dynamics and diel fluctuations with a temporal frequency and morphological resolution that was never observed by traditional methods. Access to high frequency, reproducible and real-time data of a large spectrum of the planktonic ecosystem expands our understanding of both applied and fundamental plankton ecology. We conclude the DSPC is robust for both research and water quality monitoring and suitable for stable long-term deployments.
Statistical species distribution models (SDMs) are widely used to quantify how taxa respond to environmental conditions and to predict their distribution. However, the application of SDMs to freshwater fish taxa is complicated by the active dispersal of fish taxa through river networks, and the species- and habitat-dependent observation process (i.e., the sampling method and effort) required to accurately sample their distributions. Many studies have applied presence-absence models (PAMs) to fish taxa, while more recent studies have proposed zero-inflated models (ZIMs) to account for count observations with many zeroes. However, relatively few studies have incorporated the observation process into the model structure, which would facilitate the combination of data from various monitoring programs that differ in their observation process. In this study, we use conceptual models to identify potentially dominant natural and anthropogenic environmental conditions with a direct, mechanistic effect on the distributions of freshwater fish taxa in Switzerland, a region with a large range of environmental conditions, from alpine streams that are mainly affected by hydromorphological alterations to lowland streams in densely populated areas with intensive agricultural land use. Moreover, numerous barriers impede fish migration along the entire river network. Using combined data from two fish monitoring programs in Switzerland, we applied an exhaustive cross-validation procedure to select a set of environmental variables with the highest (out-of-sample) predictive performance for the PAM and ZIM for fish density (individuals/m2) of the seven most prevalent fish taxa (Salmo spp., Cottus spp., Squalius spp., Barbatula spp., Barbus spp., Phoxinus spp., Gobio spp.). We used these variables to develop a PAM and ZIM for each taxon that accounts for differences in sampling methods and sampling effort. We quantified the quality of fit during calibration using all samples and predictive performance during 5-fold cross-validation of each model. Results show that stream temperature and stream morphology within the accessible habitat commonly appear among the best predictive presence-absence models for multiple taxa. Spatial variables that account for migration barriers and quantify morphological conditions within the accessible habitat were selected for 6 out of 7 taxa. The selected PAMs performed well for all taxa with an intermediate prevalence (10–40%), with an explanatory power () of between 0.32 - 0.37 during calibration using all samples and only minor decreases in explanatory power during cross-validation (= 0.34 – 0.44). As expected, the PAM for the highly prevalent Salmo spp. (91%) failed to predict the few absence data points. By contrast, the ZIM model performed best for Salmo spp., with a standardized likelihood ratio of 1.56. For all other taxa besides Barbus spp. the ZIM models also had likelihood ratios above one, indicating a better predictive performance than the null model. We hope this study stimulates the development and application of fish species distribution models based on prior knowledge of causally linked environmental variables and incorporating observation errors to improve their predictive performance. This can facilitate learning from biomonitoring data to support management.
Bridging the gap between data science and mechanistic modelling to gain knowledge about community assembly
Heterogeneous data platform for operational modeling and forecasting of Swiss lakes in collaboration with the Swiss Data Science Center.
Deep Neural Networks (DNNs) have shown empirical performance but they are still nevertheless a black-box function modeling data
Scalable Bayesian inference framework for uncertainty quantification in stochastic models using thousands of processors in parallel at the Swiss Supercomputing Center and ETH Zurich.
Mechanistic modelling of the macroinvertebrate community composition in rivers.
We compare invasions in aquatic and terrestrial ecosystems primarily at large (national) spatial scales and among several higher-level taxa (insects, molluscs, crustaceans, all major vertebrate classes, and plants).
We use machine learning methods to predict the effects of chemicals on aquatic species.
Development of a semi-distributed hydrological model with a “flexible” approach. Testing and comparing of different model structures to combine modeling and experimenting into a learning process.