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Central America is a region vulnerable to hydrometeorological threats. Recently, the impacts of droughts caused higher economic losses in comparison to, for example, floods and landslides. This study focuses on the spatio‐temporal behaviour of cumulative rainfall deficits across Central America attempting to provide an historical context to the most recent drought episodes. We developed a long‐term (1950–2014), monthly rainfall data set that merged large‐scale interpolated products with a station observation network to spatially and temporally evaluate the 12‐month Standardized Precipitation Index (SPI12) across the region. We found that El Niño cannot always be associated with drier conditions and that severe droughts are likely to spatially develop from localized phenomena to cover the entire region beyond the Central American drought corridor (CADC). Furthermore, there is not always a clear separation into the Pacific and Caribbean domain in terms of drought behaviour, but generally El Niño episodes can be associated with drier conditions on the Pacific slope and wetter conditions in the Caribbean. We could also show that trends in the SPI series are spatially variable and that more localized significant positive and negative trends exist throughout Central America. For example, central pacific Nicaragua was identified as a hot spot for significant drying conditions related to El Niño. We aim at developing this effort into a near‐real time and publicly available drought monitor in the near future to increase resilience and adaption efforts in the region.

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Abstract:In basins of South-eastern Spain; such as the semiarid Segura River Basin (SRB), a strong decrease in runoff from the end of the 1970s has been observed. However, in the SRB the decreasing trend is not only related with climate variability and change, also with intensive reforestation aimed at halting desertification and erosion, whichever the reason is, the default assumption of stationarity in water resources systems cannot be guaranteed. Therefore there is an important need for improvement in the ability of monitoring and predicting the impacts associated with the change of hydrologic regimes. It is thus necessary to apply non-stationary probabilistic models, which are able to reproduce probability density functions whose parameters vary with time. From a high-resolution daily gridded rainfall dataset of more than five decades (1950−2007), the spatial distribution of lengths of maximum dry spells for several thresholds are assessed, applying Generalized Additive Models for Location Scale and Shape (GAMLSS) models at the grid site. Results reveal an intensification of extreme drought events in some headbasins of the SRB important for water supply. The identification of spatial patterns of drought hazards at basin scale, associated with return periods; contribute to designing strategies of drought OPEN ACCESSWater 2015, 7 5459 contingency preparedness and recovery operations, which are the leading edge of adaptation strategies.