This study proposes a drought indicator that combines the Standardized Precipitation Index (SPI), the anomalies of soil moisture and the anomalies of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). Computed at the European level, the Combined Drought Indicator (CDI) gives a synthetic and synoptic overview of the drought situation using a classification scheme. Derived from the integration of the three individual indices, this classification scheme is composed of three warning levels: "watch" when a relevant precipitation shortage is observed, "warning" when this precipitation shortage translates into a soil moisture anomaly, and "alert" when these two conditions are accompanied by an anomaly in the vegetation condition. The design of the CDI includes the study of the relationship between the three individual indices. To achieve this, the SPI-3 (3-month SPI) was computed using the precipitation data obtained from a set of weather stations located in different agricultural areas of Europe, while the soil moisture and fAPAR data were extracted from the pixels of the respective grids surrounding these stations. The CDI is assessed for the main drought episodes of Europe between 2000 and 2011, using reported data from different sources, such as the EM-DAT Emergency Events Database and Eurostat annual yield estimates. The capability of the CDI to serve for drought early warning is evaluated as well as its robustness against false alarms. The indicator has been spatially implemented for the entire continent using different information layers of the European Drought Observatory. These layers correspond to SPI-3 grids derived from interpolated weather station precipitation data, anomalies of fAPAR from the MERIS Global Vegetation Index and anomalies of soil moisture obtained using the LISFLOOD distributed hydrological model. Maps of the CDI obtained for the European drought event in spring 2011 are shown and discussed, evaluating its operational applicability. To conclude, the main limitations of the indicator are presented and possible avenues for improvement are discussed.

The ‘rain use efficiency’ (RUE) may be defined as the ratio of above-ground net primary productivity (ANPP) to annual precipitation, and it is claimed to be a conservative property of the vegetation cover in drylands, if the vegetation cover is not subject to non-precipitation related land degradation. Consequently, RUE may be regarded as means of normalizing ANPP for the impact of annual precipitation, and as an indicator of non-precipitation related land degradation. Large scale and long term identification and monitoring of land degradation in drylands, such as the Sahel, can only be achieved by use of Earth Observation (EO) data. This paper demonstrates that the use of the standard EO-based proxy for ANPP, summed normalized difference vegetation index (NDVI) (National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies 3rd generation (GIMMS3g)) over the year (ΣNDVI), and the blended EO/rain gauge based data-set for annual precipitation (Climate Prediction Center Merged Analysis of Precipitation, CMAP) results in RUE-estimates which are highly correlated with precipitation, rendering RUE useless as a means of normalizing for the impact of annual precipitation on ANPP. By replacing ΣNDVI by a ‘small NDVI integral’, covering only the rainy season and counting only the increase of NDVI relative to some reference level, this problem is solved. Using this approach, RUE is calculated for the period 1982–2010. The result is that positive RUE-trends dominate in most of the Sahel, indicating that non-precipitation related land degradation is not a widespread phenomenon. Furthermore, it is argued that two preconditions need to be fulfilled in order to obtain meaningful results from the RUE temporal trend analysis: First, there must be a significant positive linear correlation between annual precipitation and the ANPP proxy applied. Second, there must be a near-zero correlation between RUE and annual precipitation. Thirty-seven percent of the pixels in Sahel satisfy these requirements and the paper points to a range of different reasons why this may be the case.