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floodsimilarity - a python module to compute the similarity between multiple flood events

floodsimilarity provides classes and methods to conduct a similarity analysis between multiple flood events. The library mainly consists of two parts: (1) algorithms to compute indices and other statistics based on pandas and xarray (2) well-defined data structures for data exchange (e.g. through the Similarity Backend Module) floodsimilarity is used by the Digital Earth Similarity Backend Module (Eggert, 2021) as part of the Digital Earth Flood Event Explorer. It is developed at the GFZ German Research Centre for Geosciences and funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project.

LPJmL4 Model Code

LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145). We here present the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018a and b, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146). The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. Additionally, LPJmL4 can be download from the Gitlab repository: https://gitlab.pik-potsdam.de/lpjml/LPJmL. Further developments of LPJmL will be published through this Gitlab repository regularly.

LPJmL4 model code and model output for: Global cotton production under climate change - Implications for yield and water consumption

LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2018, http://doi.org/10.5194/gmd-2017-145). We here present an extended version of the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146). Additional features of this version, including agricultural trees as a new cultivation type in LPJmL4, are described and used in Jans et al. (2020, HESS) The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. The model data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2018 part I). Additionally, these results include agricultural trees (olives, non-citrus orchards, and cotton) implemented as a new cultivation type into LPJmL4. Standard results are evaluated in Schaphoff et al. (2018 part II). Results of cotton as a newly implemented agricultural tree are evaluated in Jans et al. (2020), HESSD. The data collection includes some key output variables made with the model setup described by Jans et al. (2020, HESS). Overall, data sets are resulting from 40 different simulations, where we combined 5 different GCMs (GFDL, HadGEM, IPSL, MIROC, NorESM) with 4 different RCPs (2p6, 4p5, 6p0, 8p5) without and with CO2 fertilization, respectively. The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for crop yields, absorbed photosynthetically active radiation and the water fluxes (irrigation, transpiration, evaporation,interception, blue and green evapotranspiration). Crop yields, and water fluxes are given for each crop functional type (CFT), respectively. Monthly data are provided for one carbon flux (net primary production) and the water fluxes transpiration, evaporation, interception, and runoff. The data are provided in one binary file for each variable and simulation. An overview of all variables and information on how data are stored within the binary files are given in the file inventory.

HILLSCAPE Project - Data on moraine soil properties and on overland flow and subsurface flow characteristics

The German-Swiss Hillscape project focuses on the vertical and lateral redistribution of water and matter along hillslopes and how this redistribution is affected by soil, vegetation and landscape development. The overall research question of the project is: How does the hillslope feedback cycle evolve in the first 10,000 years and how is this related to the evolution of hillslope structure? In order to tackle this research question, chronosequences in alpine glacier forelands were selected and artificial rainfall experiments were conducted. These datasets specifically contain data at the interface of sediment transport and hillslope hydrology. Specifically, they contain data about changes in soil surface characteristics (saturated hydraulic conductivity for three soil depths, soil aggregate stability for the surface soil layer), overland and shallow subsurface flow (runoff characteristics as peak flow rates, duration of flow, runoff ratios, event water fractions) and sediment yield values for overland flow along the moraine chronosequence. We measured the near-surface hydrological characteristics of four moraines with different age on a carbonate glacier foreland (forefield of the Griessfirn, close to the Klausenpass alpine road) and silicate glacier foreland (glacier forefield of the Steingletscher, close to the Sustenpass alpine road). The ages of the four moraines were ~30, ~160, ~3000 and ~10000 years (Sustenpass) and ~80, ~160, 4900 and 13500 years (Klausenpass). We selected 3 plots (dimensions: 4m x 6m) on each moraine, based on the vegetation complexity (low, medium and high), to cover as much of the potential variability within each moraine as possible. The structural vegetation complexity was based on the vegetation cover, number of different species, and functional diversity (based on stem growth form, root type, clonal growth organ, seed mass, Raunkiaer’s life form, leaf dry matter content, nitrogen content and specific leaf area (Garnier et al., 2016). We measured the near-surface hydrological properties of each plot (the saturated hydraulic conductivity and the soil aggregate stability) because the properties are essential for the runoff response on each plot. The runoff response and its characteristics for each plot was determined for sprinkling experiments of different intensities and during natural rainfall events (only at Klausenpass). We used tracers (Deuteriumoxid and NaCl) that we added to the sprinkling water and took samples of the soil water, then rainfall and the runoff to perform a 3-end-member hydrograph separation, using the method of Gibson et al. (2000). With that, we were able to identify the mixing (e.g. event water fraction), storage and flow pathways of the overland flow and subsurface flow. We filtered the overland flow samples to define the total sediment flux per experiment.

LPJmL4 model output for the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation

LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145). The data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2017a,). Additionally, these results are evaluated in the companion paper of Schaphoff et al. (2017b, http://doi.org/10.5194/gmd-2017-146). The data collection includes some key output variables made with different model setups described by Schaphoff et al. (2017b). The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for soil, vegetation, aboveground and litter carbon as well as for vegetation distribution, crop yields, sowing dates, maximum thawing depth, and fire carbon emissions. Vegetation distribution is given for each plant functional type (PFT), crop yields, and sowing dates are given for each crop functional type (CFT), respectively. Monthly data are provided for the carbon fluxes (net primary production, gross primary production, soil respiration) and the water fluxes (transpiration, evaporation, interception, runoff, and discharge) and for absorbed photosynthetically active radiation (FAPAR) and albedo. The data are provided in one netcdf file for each variable and experiment described by Schaphoff et al. (2017b). Crop yields and sowing dates are not provided for the LPJmL4-GSI-GlobFIRE-PNV experiment as this represents natural vegetation only. An overview of all variables and the number of bands are given in the file inventory.

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