The system uses vegetation and precipitation indices and their anomalies. The indices include the NDVI and the VCI aggregated over 1- and 3-months periods and SPI aggregated over the same time periods. The BOKU system supports a two-pronged approach to drought mitigation in Kenya. First of is the disbursement of social security funds to the population in the four most vulnerable counties of Turkana, Marsabit, Mandera and Wajir as outlined in [PERSON] [23]. These counties comprise our study area. Second is the drought contingency fund (DCF), which is used to provide essential services across the sectors of livestock, water, health and nutrition, education and security. The disbursement of the funds follows a no-regrets approach, using the VCI aggregated over 3 months (VCI3M), in a setting where proactive decisions  
Figure 1: Conceptualization of various drought types and their progression. The impacts on people and livelihoods is a function of the vulnerability of the livelihoods as well as the severity, duration and spatial extent of the drought. are acceptable even when foreseen risks do not materialize. Ultimately, the system thresholds the VCI3M to create five classes of the VCI3M anomaly (Above Normal, Normal, Moderate, Severe and Extreme vegetation deficit conditions). These classes are used as proxy classes for drought conditions. The BOKU system in [PERSON] and [PERSON] [22] together with the extended-MODIS (eMODIS) from the widely used Famine Early Warning Systems Network (FEWSNET) [24] are near real-time (NRT). Other NRT systems are found in [PERSON] et al. [25] and in [PERSON] and [PERSON] [26]. [PERSON] and [PERSON] [26] implemented a combination of real-time and long-term satellite observations that is indicated to have detected the 2010 drought in the Horn of Africa (HA). Increasingly, there is need for predictive EWS. As recognised in [PERSON] et al. [9], there exists a supply deficit for predictive systems at regional, national and local scales. The IGAD [27], for example, documents a drought predictive approach in the GHA that employs a statistical downscaling of five global models to provide a prediction of conditions 1-2 months in advance. The differences between drought prediction approaches are based on the data used and the methods deployed. Most of the approaches are based on the use of single variables of either precipitation or vegetation conditions. Predictions based on SPI are demonstrated in [PERSON] et al. [3], [PERSON] et al. [18] and in [PERSON] [19]. Streamflow indices, for example, are used in [PERSON] et al. [28] to investigate the impact of hydrological drought. Some studies are based on SPEI as either the main indicator, or in addition to SPI as documented in [PERSON] et al. [1], [PERSON] et al. [29] and in [PERSON] and [PERSON] [30]. Other studies define a super index of drought indices as documented in [PERSON] [31], [PERSON] et al. [32] and [PERSON] et al. [33] that define multi-variate standardised dry index (MSDI), drought defining index (DDI) and enhanced combined drought index (ECDI), respectively. The approach in [PERSON] et al. [33] uses a weighted approach on the four datasets used to define ECDI. The use of multiple indices and variables in [PERSON] et al. [34] stands out in the use of eleven variables from oceanic, environmental, climate and satellite data in the prediction of vegetation outlook (VegOut). This approach of using multiple indices in the prediction of vegetation conditions as a proxy of drought effects is also documented in [PERSON] et al. [35] and [PERSON] et al. [36]. The use of multiple indices is as opposed to [PERSON] and [PERSON] [22], who directly assessed vegetation conditions in NRT. In terms of methods and approaches, most of the studies focus on a single technique that is either purely statistical or machine learning (ML). Statistical methods have a range in complexity from simple forecasting to multiple regression tree techniques [34], and to ensemble approaches that employ more than one modelling technique.