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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 heterogeneity of precipitation rates in high-mountain regions is not sufficiently captured by state-of-the-art climate reanalysis products due to their limited spatial resolution. Thus there exists a large gap between the available data sets and the demands of climate impact studies. The presented approach aims to generate spatially high resolution precipitation fields for a target area in central Asia, covering the Tibetan Plateau and the adjacent mountain ranges and lowlands. Based on the assumption that observed local-scale precipitation amounts are triggered by varying large-scale atmospheric situations and modified by local-scale topographic characteristics, the statistical downscaling approach estimates local-scale precipitation rates as a function of large-scale atmospheric conditions, derived from the ERA-Interim reanalysis and high-resolution terrain parameters. Since the relationships of the predictor variables with local-scale observations are rather unknown and highly nonlinear, an artificial neural network (ANN) was utilized for the development of adequate transfer functions. Different ANN architectures were evaluated with regard to their predictive performance. The final downscaling model was used for the cellwise estimation of monthly precipitation sums, the number of rainy days and the maximum daily precipitation amount with a spatial resolution of 1 km2. The model was found to sufficiently capture the temporal and spatial variations in precipitation rates in the highly structured target area and allows for a detailed analysis of the precipitation distribution. A concluding sensitivity analysis of the ANN model reveals the effect of the atmospheric and topographic predictor variables on the precipitation estimations in the climatically diverse subregions.

Ovarian development and maintenance are poorly understood; however, diseases that affect these processes can offer insights into the underlying mechanisms. XX female gonadal dysgenesis (XX-GD) is a rare, genetically heterogeneous disorder that is characterized by underdeveloped, dysfunctional ovaries, with subsequent lack of spontaneous pubertal development, primary amenorrhea, uterine hypoplasia, and hypergonadotropic hypogonadism. Here, we report an extended consanguineous family of Palestinian origin, in which 4 females exhibited XX-GD. Using homozygosity mapping and whole-exome sequencing, we identified a recessive missense mutation in nucleoporin-107 (NUP107, c.1339G>A, p.D447N). This mutation segregated with the XX-GD phenotype and was not present in available databases or in 150 healthy ethnically matched controls. NUP107 is a component of the nuclear pore complex, and the NUP107-associated protein SEH1 is required for oogenesis in Drosophila. In Drosophila, Nup107 knockdown in somatic gonadal cells resulted in female sterility, whereas males were fully fertile. Transgenic rescue of Drosophila females bearing the Nup107D364N mutation, which corresponds to the human NUP107 (p.D447N), resulted in almost complete sterility, with a marked reduction in progeny, morphologically aberrant eggshells, and disintegrating egg chambers, indicating defective oogenesis. These results indicate a pivotal role for NUP107 in ovarian development and suggest that nucleoporin defects may play a role in milder and more common conditions such as premature ovarian failure.

The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan and Pamir and Altai mountains. During the summer months the snow-melt- and glacier-melt-dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims to develop a generic tool for deriving statistical forecast models of seasonal river discharge based solely on observational records. The generic model structure is kept as simple as possible in order to be driven by meteorological and hydrological data readily available at the hydro-meteorological services, and to be applicable for all catchments in the region. As snow melt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite-based snow cover data, and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to four predictors. A user-selectable number of the best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived every month from January until June. The application of the model for several catchments in Central Asia – ranging from small to the largest rivers (240 to 290 000 km2 catchment area) – for the period 2000–2015 provided skilful forecasts for most catchments already in January, with adjusted R2 values of the best model in the range of 0.6–0.8 for most of the catchments. The skill of the prediction increased every following month, i.e. with reduced lead time, with adjusted R2 values usually in the range 0.8–0.9 for the best and 0.7–0.8 on average for the set of models in April just before the prediction period. The later forecasts in May and June improve further due to the high predictive power of the discharge in the first 2 months of the snow melt period. The improved skill of the set of forecast models with decreasing lead time resulted in narrow predictive uncertainty bands at the beginning of the snow melt period. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of operational implementation.

For many ecological processes, near-surface temperature is one of the main controlling parameters. Since meteorological observations in high mountains are sparse, climate model data are frequently used. State of the art climate reanalysis products or general circulation models have a horizontal spatial resolution on the order of 50 × 50 km, i.e. far too coarse to represent the spatial variability of near-surface temperatures in mountain regions. Based on the assumption that elevation is the main factor determining the distribution of temperatures, we present a SAGA-GIS based approach for elevation and bias correction of climate model output data. Modeled temperature and geopotential height at different pressure levels were used to derive local temperature profiles by means of a polynomial regression approach. Atmospheric temperatures at surface level can then be derived from the regression equation. Compared with a simple elevation adjustment of ERA-Interim near-surface temperatures using an invariant lapse rate of 0.65°C per 100 m, the method showed good results, particularly in complex terrain. The method was used to generate high-resolution daily temperature fields for a target area in Central Asia for the period from 1989 onwards. Gridded trends of different temperature indices were calculated. For winter and spring, the highest temperature trends were detected in the high mountain regions. In summer, the calculated trend magnitudes were generally smaller. During the post-monsoon season, trends were more pronounced at low elevations. For the southern slopes of the Himalayan Arc and the valleys of the Tibetan Plateau, a significant decrease in frost days and an increase in growing degree days were detected.