• Energy Research
• Research software close

Supplementary material of Neder C. 2023, Neder et al. 2024a & 2024b. R script for species distribution models for benthic antarctic species. A case study for Potter Cove. 

Source code for EC-CAS (Environment Canada Carbon Assimilation System) v1.0. This code uses GEM-MACH-GHG (https://zenodo.org/record/3246556) as the forward model. EC-CAS uses an Ensemble Kalman Filter (EnKF) as the data assimilation technique. Please see the header/comments in each file. The following files constitute the core of the EC-CAS. sekfeta.ftn90 and sekflib.ftn90 implement the EnKF. trlcma.ftn90 and observations.ftn90 implement the interpolation of model state (Hx). burp_read_mod.ftn90 and burp_functions.ftn90 convert the observations to BURP format. sortcma.ftn90 and regions_mod.ftn90 divide the observations into batches to be assimilated by the EnKF.

Python scripts and a Jupyter notebook for processing water demand and availability data, simulating three allocation methods (non-priority, sequential, and traffic light frameworks), and generating visualizations for the Almeria, Spain case study. These scripts support the analyses presented in the thesis “Exploring a Novel Modelling Framework for Water Management Purposes under Past and Future Conditions”, submitted in partial fulfillment of the requirements for the Master of Science in Water Resources Engineering.

GCM compareR GCM compareR is a web application developed to assist ecologists, conservationists and policy makers at understanding climate change scenarios and differences between Global Circulation Models (GCMs), and at assisting the triage of subsets of models in an objective and informed manner. GCM compareR is written in R and uses the web app development package shiny. The code of this app can be find in the project's github, https://github.com/marquetlab/GCM_compareR. The number of GCMs that are accessible to researchers and practitioners has grown large. Concretely, meteorological research centers worldwide have contributed more than 35 different GCMs for four distinct climate change scenarios as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5; (Taylor, Stouffer, and Meehl 2012)). All these models have shown good performance and skill in predicting historical climatic data, but present differences among them as a result of different sources of uncertainty (including model formulation, resolution and sensitivity to initial conditions, climate noise; (Flato et al. 2013)). GCMs could be ranked by their skill at specific geographic areas, but models that most accurately predict historic data are not necessarily the most useful for making future climate projections (Knutti 2008). In practice, best practices when conducting any evaluation advice for using multi-model approaches where differences in GCMs projections are adequantely understood and assessed as uncertainty (Pierce et al. 2009, Flato et al. (2013)). Also, and even though the ideal case would use all available GCMs, researchers are often forced to work with a few selected models for computational restrictions (Barsugli et al. 2013). However, the choice of some GCMs and not other has the potential to influence results (Synes and Osborne 2011), and thus it should be made following informed and replicable procedures (P. Mote et al. 2011, Snover et al. (2013), Vano et al. (2015)). GCM compareR has been design to serve the purpose of informing about differences and similarities between GCMs and climate change scenarios, and of assisting the triage of models that best suit every used needs.

Microplastics (MP) are recognized as a major pollutant in terrestrial environments, prompting concerns regarding their effects on plant-soil dynamics. Despite evidence of MP altering soil physicochemical properties, impacts on belowground root traits and arbuscular mycorrhizal (AM) fungi remains poorly explored. Existing research has mainly centered on a few model plant species, emphasizing root biomass, and often employs single polymer types and addition rates that surpass realistic scenarios. To investigate how environmentally relevant mixtures and concentrations of MPs impact plant growth, root trait expression and AM fungal colonization, we conducted a greenhouse experiment using six plant species chosen for their contrasting root life strategies; three species in the Amaryllidaceae family represented resource conservation root traits (Allium fistulosum (Onion), Allium tuberosum (Chive), Allium porrum (Leek)), and three from the Solanaceae family, represented plants with resource acquisitive root traits (Solanum lycopersicum (Tomato), Solanum melongena (Eggplant), Capsicum annuum (Pepper). MP treatments consisted of control (0% MP), low (0.1% w/w) and high (1% w/w) MP additions, using an environmentally relevant MP mixture of weathered polymer types and shapes. Above and belowground biomass, average root trait expression (specific root length (SRL), average root diameter (D) and root tissue density (RTD), AM fungal colonization, as well as intraspecific variability across MP addition treatments. We found that the addition of environmentally relevant additions of MPs was species specific and not determined by root life-strategy. MPs increased biomass in Leek, Eggplant and Tomato, while decreasing AM fungal colonization in Tomato. MP additions had no discernible impact on average root functional trait expression across species. However, the addition of MPs resulted in altered intraspecific variability in root traits and AM fungal colonization, indicating a mechanism for plant tolerance to MPs. To address the impacts of MP on plant functioning, our study highlights the need for future research to focus on environmentally relevant mixtures of MPs, considering various plant species' capacities to tolerate soil contamination and the potential for tipping points under real-world conditions. Funding provided by: Natural Sciences and Engineering Research CouncilROR ID: https://ror.org/01h531d29Award Number:

This is the code and data used for the publication: Haest, B., Hüppop, O., & Bairlein, F. (2020). Weather at the winter and stopover areas determines spring migration onset, progress, and advancements in Afro-Palearctic migrant birds. Proceedings of the National Academy of Sciences, 117(29), 17056–17062. https://doi.org/10.1073/pnas.1920448117. The archive consists of: (1) A collection of R scripts and functions that enables repetition of the analyses performed in the study. (2) The dataset, containing the mean spring phenology at Helgoland of six trans-Saharan migrant bird species over the period 1960-2014. Please see the PNAS publication for more details, or contact me at birgen.haest@protonmail.com with any questions regarding the code or data. When using this code or data, please attribute/cite this dataset appropriately using the doi as well as the original publication in PNAS.

Data and code from Daudt et al. (2025) Estuarine, Coastal and Shelf Science [https://doi.org/10.1016/j.ecss.2025.109405]

We analysed the global geographical characteristics of how extreme surface air temperature and rainfall have evolved, based on the recurrence rate of record-breaking events, and found hot spots with anomalously high as well as regions with anomalously low numbers of record-breaking events. The recurrence rate was defined as the proportion of the actual count of record-breaking events over time to the number expected in a hypothetically stable climate. In a stable climate, the data is independent and identically distributed (iid) if the data is sampled at intervals that makes the autocorrelation between data points negligible. Anomalous recurrence rates indicate shifts in the tails of statistical distributions, and our analysis of record-high annual mean surface air temperatures revealed highest recurrence rates in the tropics, as opposed to the polar regions with the fastest warming. We present new evidence for extremely hot years becoming more common and widespread over the 1950-2023 period, based on recurrence rates as well as the global surface area fraction with daily mean surface air temperature exceeding 30°C and 40°C. A similar analysis for annual total precipitation highlights regions with increasingly more extreme annual precipitation as well as record-low annual precipitation typically associated with drought conditions. A multi-model ensemble of 306 runs with global climate models (CMIP6 SSP2-45) reproduced the statistics of record-breaking high annual mean surface air temperatures, but there were some differences with the reanalysis on annual total precipitation record-breaking recurrence rates. The global climate model simulations suggested a slightly altered geographical pattern for record-breaking annual precipitation recurrence rates, especially over parts of the Arctic. Analysis using R and R-markdown script. Data from the ERA5 and NCEP2 reanalyses as well as global glimate models (CMIP6 SSP2-45).

Description: The DGSD-VNE (DiGitization for resource-aware Subgraph Detection in Virtual Network Embedding) algorithm consists of three main phases: Graphize-VNE, GraphDetect-VNE, and ReverseGraphize-VNE. In the Graphize-VNE phase, the algorithm digitizes the edges of the Network Infrastructure and Virtual Network Requests through two steps: RequestPoint Conversion, which converts VNR edges into a digital format compatible with the infrastructure, and EdgePoint Conversion, which digitizes the infrastructure edges into a set of points. The GraphDetect-VNE phase is divided into two subphases: MidpointGraphing, which uses the K-Nearest Neighbors (KNN) algorithm to reduce the search space by treating infrastructure edges as data points and mapping them to a 2D plane through midpoint calculation, and Constraint Satisfaction and Structure Preservation, which ensures that the VNR is mapped onto the infrastructure while respecting structural and resource constraints. If structure preservation and constraint satisfaction are successful, the edges are added to the Selected list, allocated for a specific duration, and deallocated when the allocation period expires. Finally, the ReverseGraphize-VNE phase converts the digitized, embedded VNR back to its original graph form through ReversePointConversion, ensuring the VNR is restored to its graph-oriented structure for further analysis or processing. This simulation environment deploys a medium-sized network infrastructure consisting of 65 virtual nodes distributed across 15 substrate nodes. These virtual nodes interconnect with other virtual nodes with a link probability from0.5 to 0.9, forming the network infrastructure. Consequently, the total number of edges in this network is 1881. The processing capacity of each network infrastructure node, falls within the range of 60 to 80, following a uniform distribution. Additionally, the Link Capacity of network infrastructure edges falls in the range of 60 to 80. In the case of VNRs, their arrival rate follows an exponential distribution with λ= 0.25 to 0.75 as it is used traditionally. The processing capacity of requested VNR nodes, falls in the range of 40 to 60 following a uniform distribution, the Link Capacity of requested VNR links falls in the range of 40 to 60 and the entire system simulates for 400 VNRs with k = 10 as it gives better acceptance rate. Dependencies: 1. matplotlib 3.9.4 2. networkx 3.2.1 3. numpy 2.0.2 Usage: 1. Run the DGSD_VNE.py file to generate the dataset according to the simulation environment setup. 2. Measure the performance metrics by adjusting the values as needed. Note: The dataset used in this study is generated dynamically by running the Python code itself.

With this model, we investigate resource extraction and labor conditions in the Global South as well as implications for climate change originating from industry emissions in the North. The model serves as a testbed for simulation experiments with evolutionary political economic policies addressing these issues. In the model, heterogeneous agents interact in a self-organizing and endogenously developing economy. The economy contains two distinct regions – an abstract Global South and Global North. There are three interlinked sectors, the consumption good–, capital good–, and resource production sector. Each region contains an independent consumption good sector, with domestic demand for final goods. They produce a fictitious consumption good basket, and sell it to the households in the respective region. The other sectors are only present in one region. The capital good sector is only found in the Global North, meaning capital goods (i.e. machines) are exclusively produced there, but are traded to the foreign as well as the domestic market as an intermediary. For the production of machines, the capital good firms need labor, machines themselves and resources. The resource production sector, on the other hand, is only located in the Global South. Mines extract resources and export them to the capital firms in the North. For the extraction of resources, the mines need labor and machines. In all three sectors, prices, wages, number of workers and physical capital of the firms develop independently throughout the simulation. To test policies, an international institution is introduced sanctioning the polluting extractivist sector in the Global South as well as the emitting industrial capital good producers in the North with the aim of subsidizing innovation reducing environmental and social impacts. The DOI pointing to this resource is a `concept version` representing all versions of this computational model and will always redirect to the latest version of this computational model. See https://zenodo.org/help/versioning for more details on the rationale behind a concept version DOI that rolls up all versions of a given computational model or any other digital research object.