Design and implementation of the land surface model NaturalEnvironment within the generic framework OpenDanubia for integrative, distributed environmental modelling

The project GLOWA-Danube (http://www.glowa-danube.de) aimed at investigating the manifold consequences of Global Change on regional water resources in the Upper Danube Basin. In order to achieve this task, an interdisciplinary, university-based network of experts developed the integrative Decision Support System OpenDanubia (OD). The common base for implementing and coupling the various scientific model components is a generic framework, which provides the coordination of the coupled models that run in parallel exchanging iteratively data via their interfaces. The OD framework takes care of technical aspects, such as ordered data exchange between sub-models, data aggregation, data output, model parallelization and data distribution over the network, which means that model developers do not have to be concerned about complexities evolving from coupling their models. Within this framework the sub-model NaturalEnvironment, representing a land surface model, was developed and implemented. The object-oriented design of this sub-model facilitates a plain, logical representation of the actual physical processes simulated by the sub-model. Physical processes to be modelled are organized in naturally ordered, exchangeable lists that are executed on each spatial computation unit for each modelling time step, depending on their land cover. The type of land cover to be simulated on each freely defined spatial unit is distinguished by one of the three types aquatic, terrestrial and glacier. Additionally, the type terrestrial is influenced by dynamic land use changes which can be triggered e.g. by the socio-economic OD sub-model Farming. This paper presents the basic design of the open source (GPL'ed) OD framework and highlights the implementation of the sub-model NaturalEnvironment within this framework, as well as its interactions with other components included in OD

Impact of potential climate change on plant available soil water and percolation in the Upper Danube basin

The soil root zone of the land surface provides plants with water for transpiration and therefore biomass production and its excess water percolates downwards and ultimately recharges the groundwater aquifers. Within the project GLOWA-Danube regional scale impacts of climate change on the water cycle are investigated. Potential changes in the water cycle based on climate scenarios for 2011 to 2060 are simulated with the decision support system DANUBIA that integrates models of natural as well as social sciences. This article presents the results of DANUBIA driven by an ensemble of 12 climates scenarios generated with a stochastic climate simulator regarding the future state of soil moisture and groundwater recharge in the Upper Danube basin

Evaluation of ERA5 and WFDE5 forcing data for hydrological modelling and the impact of bias correction with regional climatologies: A case study in the Danube River Basin

Study region: The Danube River Basin. Study focus: Hydrological modelling of large, heterogeneous watersheds requires appropriate meteorological forcing data. The global meteorological reanalysis ERA5 and the global forcing dataset WFDE5 were evaluated for driving an uncalibrated setup of the mechanistic hydrological model PROMET (0.00833333 degrees/1 h resolution) for the period 1980-2016. Different climatologies were used for linear bias correction of ERA5: the global WorldClim 2 temperature and precipitation climatologies and the regional GLOWA and PRISM Alpine precipitation climatologies. Simulations driven with the uncorrected ERA5 reanalysis, the WFDE5 forcing dataset, ERA5 biascorrected with WorldClim 2 and ERA5 bias-corrected with a GLOWA-PRISM-WorldClim 2 mosaic were evaluated regarding percent bias of discharge and model efficiency. New hydrological insights for the region: Simulations yielded good model efficiencies and low percent biases of discharge at selected gauges. Uncalibrated model efficiencies corresponded with previous hydrological modelling studies. ERA5 and WFDE5 were well suited to drive PROMET in the hydrologically complex Danube basin, but bias correction of precipitation was essential for ERA5. The ERA5-driven simulation bias-corrected with a GLOWA-PRISM-WorldClim 2 mosaic performed best. Bias correction with GLOWA and PRISM outperformed WorldClim 2 in the Alps due to more realistic small-scale Alpine precipitation patterns resulting from higher station densities. In mountainous terrain, we emphasize the need for regional high-resolution precipitation climatologies and recommend them for bias correction of precipitation rather than global datasets

Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTN₅₀) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTN₅₀ approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications

Karl Hillebrand Revolutionär, Grenzgänger, Europäer

Vor einhundertvierzig Jahren wurde der Rhein, unweit von Straßburg, Zeuge dramatischen Geschehens. Unter dem Beschuß aufgescheuchter Wachsoldaten schwammen zwei junge Deutsche zum westlichen Ufer, um dort Sicherheit zu finden. Gewiß, über die Jahrhunderte war es immer wieder geschehen, daß Verfolgte jeweils auf der anderen Seite des Stromes Schutz suchten. Der vorliegende Fall verdient es, im Rahmen dieses Kolloquiums in Erinnerung gerufen zu werden. Denn der eine der Männer, die hier die Gre..

Global Agricultural Land Resources - A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions

Changing natural conditions determine the land's suitability for agriculture. The growing demand for food, feed, fiber and bioenergy increases pressure on land and causes trade-offs between different uses of land and ecosystem services. Accordingly, an inventory is required on the changing potentially suitable areas for agriculture under changing climate conditions. We applied a fuzzy logic approach to compute global agricultural suitability to grow the 16 most important food and energy crops according to the climatic, soil and topographic conditions at a spatial resolution of 30 arc seconds. We present our results for current climate conditions (1981-2010), considering today's irrigated areas and separately investigate the suitability of densely forested as well as protected areas, in order to investigate their potentials for agriculture. The impact of climate change under SRES A1B conditions, as simulated by the global climate model ECHAM5, on agricultural suitability is shown by comparing the time-period 2071-2100 with 1981-2010. Our results show that climate change will expand suitable cropland by additionally 5.6 million km(2), particularly in the Northern high latitudes (mainly in Canada, China and Russia). Most sensitive regions with decreasing suitability are found in the Global South, mainly in tropical regions, where also the suitability for multiple cropping decreases

Global Agricultural Land Resources - A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions

Changing natural conditions determine the land's suitability for agriculture. The growing demand for food, feed, fiber and bioenergy increases pressure on land and causes trade-offs between different uses of land and ecosystem services. Accordingly, an inventory is required on the changing potentially suitable areas for agriculture under changing climate conditions. We applied a fuzzy logic approach to compute global agricultural suitability to grow the 16 most important food and energy crops according to the climatic, soil and topographic conditions at a spatial resolution of 30 arc seconds. We present our results for current climate conditions (1981-2010), considering today's irrigated areas and separately investigate the suitability of densely forested as well as protected areas, in order to investigate their potentials for agriculture. The impact of climate change under SRES A1B conditions, as simulated by the global climate model ECHAM5, on agricultural suitability is shown by comparing the time-period 2071-2100 with 1981-2010. Our results show that climate change will expand suitable cropland by additionally 5.6 million km(2), particularly in the Northern high latitudes (mainly in Canada, China and Russia). Most sensitive regions with decreasing suitability are found in the Global South, mainly in tropical regions, where also the suitability for multiple cropping decreases

Global inventory of suitable, cultivable and available cropland under different scenarios and policies

Where land-use change and particularly the expansion of cropland could potentially take place in the future is a central research question to investigate emerging trade-offs between food security, climate protection and biodiversity conservation. We provide consistent global datasets of land potentially suitable, cultivable and available for agricultural use for historic and future time periods from 1980 until 2100 under RCP2.6 and RCP8.5, available at 30 arc-seconds spatial resolution and aggregated at country level. Based on the agricultural suitability of land for 23 globally important food, feed, fiber and bioenergy crops, and high resolution land cover data, our dataset indicates where cultivation is possible and how much land could potentially be used as cropland when biophysical constraints and different assumptions on land-use regulations are taken into account. By serving as an input for land-use models, the produced data could improve the comparability of the models and their output, and thereby contribute to a better understanding of potential land-use trade-offs