Abstract. The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.

The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C + + in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing. Published by Copernicus Publications on behalf of the European Geosciences Union.Geosci. Model Dev.

Key message The "NFI 2012 environmental data base climate" is part of the environmental database of the German National Forest Inventory. It contains climate information for 26,450 inventory points generated from gridded daily climate data for 1961-2100 at a spatial resolution of 250 m. Grids are based on DWD-Observations and REMO EURO-CORDEX climate projections. Access to the databases is provided via the URL:

The Okavango catchment in southern Africa is subject to environmental as well as socio‐economic transformation processes such as population growth and climate change. The degradation of soil and vegetation by deforestation and overgrazing is one of the downsides of this development, reducing the capacity of the land to provide ecosystem functions and services. In this study, climate simulations are brought together with secondary socioeconomic, pedologic and remote‐sensing data in a GIS‐based assessment of the factors commonly associated with land degradation risk. A high resolution overview is provided for decision‐makers and stakeholders in the region by identifying priority intervention areas where a long‐term decline in ecosystem function and land productivity is most likely to occur. The approach combines 19 risk factors into seven individual ratings for topography, landcover, soil, demography, infrastructure, livestock pressure and climate. These ratings are then weighted and combined into an integrated degradation risk index (DRI). The results show that the land degradation risk is quite heterogeneously distributed in the study area and caused by different factors. Three hot‐spots are identified and compared, one of which is in the far northwestern part of the catchment, one around the local center Rundu and one on the outskirts of the Okavango Delta. We conclude that the approach is suitable to give an overview on degradation risk in the study area, although the classification process is a crucial procedure that should be standardized for further research. Copyright © 2015 John Wiley & Sons, Ltd.

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