Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO.

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Farmers’ climate perceptions are responsible for shaping their adaptive responses and are thus essential to consider for the design of strategies to reduce vulnerability and increase resilience. In this study, we collected social data in four communities in the central Ethiopian Highlands on farmers’ climate perceptions and adaptations using group discussions and PRA tools. We related these to climate data spanning 30 years (1981 to 2010), consisting of daily minimum temperature, maximum temperature and precipitation, modelled for the four communities using global databases and regional meteorological data. We found that farmers’ climate perceptions showed considerable spatial and gender differences. Perceptions matched well with records describing climate variability, particularly in terms of the shortening and the increased variability of the rainy season, as well as the occurrence of extreme drought in recent years. Climate change, described by long-term average increases in temperature and decreases in precipitation, was perceived, but with subordinate priority. Perceived climate impacts included reduced crop yield, increased occurrence of pests and diseases and increased crop damage by extreme events and poverty. Adaptations were mainly land based and included agronomic measures, land management and ecosystem restoration. Furthermore, important gender differences in adaptation could be traced back to typical gender roles. Results highlight the risk of broadcast adaptation programs, such as the government-propagated combination of mineral fertilizers and early maturing crop varieties. Most importantly, they point to the need to consider climate variability, site- and gender-specific perceptions and priorities.

Sustainable forest management requires a continuous assessment of the forest conditions covering the species distribution, standing tree volume as well as volume increment rates. Forest inventories are designed to record this information. They, in combination with ecosystem models, are the conceptual framework for sustainable forest management. While such management systems are common in many countries, no forest inventory system and/or modeling tools for deriving forest growth information are available in Ethiopia. This study assesses, for the first time, timber volume, carbon, and Net Primary Production (NPP) of forested areas in the Amhara region of northwestern Ethiopia by combining (i) terrestrial inventory data, and (ii) land cover classification information. The inventory data were collected from five sites across the Amhara region (Ambober, Gelawdiwos, Katassi, Mahiberesilasse and Taragedam) covering three forest types: (i) forests, (ii) shrublands (exclosures) and (ii) woodlands. The data were recorded on 198 sample plots and cover diameter at breast height, tree height, and increment information. In order to extrapolate the local terrestrial inventory data to the whole Amhara region, a digital land cover map from the Amhara's Bureau of Agriculture was simplified into (i) forest, (ii) shrubland, and (iii) woodland. In addition, the forest area is further stratified in five elevation classes. Our results suggest that the forest area in the Amhara region covers 2% of the total land area with an average volume stock of 65.7 m 3 •ha −1 ; the shrubland covers 27% and a volume stock of 3.7 m 3 •ha −1 ; and the woodland covers 6% and an average volume stock of 27.6 m 3 •ha −1. The corresponding annual volume increment rates are 3.0 m 3 •ha −1 , for the forest area; 1.0 m 3 •ha −1 , for the shrubland; and 1.2 m 3 •ha −1 , for the woodland. The estimated current total volume stock in the Amhara region is 59 million m 3 .

Many spatial vegetative physiological and environmental impact studies demand consistent as well as fine resolution daily meteorological data, which is often unavailable. This problem is evident when studies and applications focus at the regional and local scale. Meteorological stations cannot provide the needed data due to their irregular locations and several associated shortcomings such as missing data. Climate data from global sources like the National Centers for Environmental Prediction (NCEP) and WorldClim are provided at a coarse resolution which makes them inappropriate to use directly at regional and local scales. In order to circumvent this problem, we downscaled daily WorldClim and NCEP global climate grids to produce weather data sets using a delta method to 0.0083 (approximately 1 × 1 km) spatial resolution for the Amhara region of northwestern Ethiopia. The downscaled data set includes daily precipitation, and minimum and maximum temperature from 1979 to 2010. The drizzle effect in the downscaled data set was first eliminated by using a rain threshold that removed values of <1 mm per day. We compared the downscaled values with our calibration data from 56 meteorological stations in the Amhara region. The comparison between the downscaled and the calibrated data from weather stations showed biases in the downscaled values. A delta-change bias correction technique on a 10-day average basis was used to correct the biases associated with the downscaled data. Simple additive and multiplicative bias correction methods were used for temperature and precipitation, respectively. We then validated the corrected downscaled daily weather data using ten independent weather stations from the region. We found that the downscaling and the bias correction methods have improved the NCEP values. The validation exhibited no bias. The full data set can be accessed freely under the following link: ftp://palantir.boku.ac.at/ Public/ClimateDataEthiopia/.