Freely available global digital elevation models (DEMs) are important inputs for many research fields and applications. During the last decade, several global DEMs have been released based on satellite data. ASTER and SRTM are the most widely used DEMs, but the more recently released, AW3D30, TanDEM-X and MERIT, are being increasingly used. Many researchers have studied the quality of these DEM products in recent years. However, there has been no comprehensive and systematic evaluation of their quality over areas with variable topography and land cover conditions. To provide this comparison, we examined the accuracy of six freely available global DEMs (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM) in four geographic regions with different topographic and land use conditions. We used local high-precision elevation models (Light Detection and Ranging (LiDAR), Pleiades-1A) as reference models and all global models were resampled to reference model resolution (1m). In total, 608 million 1x1 m pixels were analyzed. To estimate the accuracy, we generated error rasters by subtracting each reference model from the corresponding global DEM and calculated descriptive statistics for this difference (e.g., median, mean, root-mean-square error (RMSE)). We also assessed the vertical accuracy as a function of the slope, slope aspect, and land cover. We found that slope had the strongest effect on DEM accuracy, with no relationship for slope aspect. The AW3D30 was the most robust and had the most stable performance in most of the tests and is therefore the best choice for an analysis of multiple geographic regions. SRTM and NASADEM also performed well where available, whereas NASADEM, as a successor of SRTM, showed only slight improvement in comparison to SRTM. MERIT and TanDEM-X also performed well despite their lower spatial resolution.

cluster. Points with a high positive Z-score (≥1.645) and a significant P value (i.e., hot spots) represent clusters of large forest loss areas, whereas points with a low negative Z-score (≤-1.645) and a significant P value (i.e., cold spots) represent clusters of small forest loss areas. We performed the hotspot analysis separately for each year. Active fire data. To estimate the forest loss potentially caused by fire, we used the Fire Information for Resource Management System (FIRMS) active fire product 68 which is derived from the MODIS sensor aboard the NASA Aqua and Terra satellites. We used the MCD14ML (collection 6) product for active fire data from 2001 to 2017. This data is produced based on the daily MODIS middle-infrared and thermal infrared bands, which are compared with reference data in order to identify pixels with an active fire. We used the definition "potentially caused by fire" for two reasons. The first one relates to the difference in spatial resolution between the forest loss data (30 m) and the MODIS-based product (1 km), which is a limitation of our results because a fire could be located in any area within the 1-km MODIS pixel. The second relates to the fact that fire is used as a management tool for purposes such as burning the residual vegetation that remains or begins to grow after deforestation, which means that it was not directly responsible for the forest loss. We combined the annual (FIRMS) active fire points with the forest loss patches (polygons) during the same year in order to identify forest loss patches that contained one or more FIRMS active fire points within their borders. By summarizing the area of these forest loss patches, we obtained the total area of forest loss that had been potentially caused by fire.

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GWML2 is an international standard for the online exchange of groundwater data that addresses the problem of data heterogeneity. This problem makes groundwater data hard to find and use because the data are diversely structured and fragmented into numerous data silos. Overcoming data heterogeneity requires a common data format; however, until the development of GWML2, an appropriate international standard has been lacking. GWML2 represents key hydrogeological entities such as aquifers and water wells, as well as related measurements and groundwater flows. It is developed and tested by an international consortium of groundwater data providers from North America, Europe, and Australasia, and facilitates many forms of data exchange, information representation, and the development of online web portals and tools.

Soil erosion caused by climate and land-use changes is one of the biggest environmental challenges in highland Ethiopia. The aim of this study was to assess the future soil erosion risks and evaluate the potential conservation measures in the Rib watershed, northwestern highland Ethiopia. We used the HadGEM2-ES model with a moderate greenhouse gas (GHG) concentration scenario (RCP4.5) to project the future climate. The future land-use patterns were predicted using the CA-Markov model. We integrated the RUSLE model with GIS to estimate the spatial distribution of soil loss and identify erosion risk areas. We found that the Rib watershed is highly vulnerable to future climate and land-use changes, leading to a high soil erosion risk. Despite slight growth of forest cover during the study period, the total soil loss for the watershed was estimated to be 7.93 × 10 6 t year −1 in 2017 and was predicted to increase to 9.75 × 10 6 t year −1 in 2050, an increase of about 23%. The increase in forest cover was due to the expansion of the area of eucalyptus plantations which are more prone to erosion. Moreover, field survey showed that the residual native forests are sparsely vegetated and mostly used for cattle grazing, increasing the erosion risk even more. In contrast, the combined use of afforestation with native trees and physical soil conservation measures in the upper areas of the catchment could decrease soil loss by 62%. Our results stress the importance of combining soil conservation measures, including converting eucalyptus plantations to native forests, to mitigate the effects of future climate change and increased agricultural production on soil erosion.