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s u m m a r y Accurate rainfall data are of prime importance for many environmental applications. To provide spatially distributed rainfall data, point measurements are interpolated. However, in low density measurement networks, the use of different interpolation methods may result in large differences and hence in deviations from the actual spatial distribution of rainfall. Our study aims at analyzing different rainfall interpolation schemes with regard to their suitability to produce spatial rainfall estimates in a monsoon dominated region with scarce rainfall measurements. The study was carried out in the meso-scale catchment of the Mula and the Mutha Rivers (2036 km 2 ) upstream of the city of Pune, India. Rainfall data from 16 rain gauges were spatially interpolated using seven different methods, including Thiessen polygons, statistical, and geostatistical approaches. The two most suitable covariates for rainfall interpolation were identified as (i) distance in wind direction from the main orographic barrier and as (ii) a 0.05°pattern of mean annual rainfall derived from satellite data acquired by the Tropical Rainfall Measuring Mission (TRMM). Consequently, these two covariates were used in the regression-based interpolation approaches. The quality of the different methods was assessed using a two step validation approach: (i) Cross-validation was used to evaluate the capability to reproduce measured data. (ii) Spatially integrated interpolation performance was assessed by using a hydrologic model to calculate runoff and compare modeled to measured runoff. By this assessment, the regression-based methods showed the best performance. We found that the choice of the covariate had a significant impact on precipitation and runoff amounts, as well as on the temporal course of runoff events. Our results show, that the decision on the suitable interpolation scheme should not only be based on the comparison with point measurements, but should also take the representativeness of the given measurement network as well as of the interpolated spatial rainfall distribution into account. The successful application of regression-based interpolation methods using a high resolution TRMM pattern as covariate is very promising as it is transferable to other data scarce regions.

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named ‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’, includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

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