A substantial amount of the tropical forests of South America and Africa is generated through moisture recycling (i.e., forest rainfall self-reliance). Thus, deforestation that reduces evaporation and dampens the water cycle can further increase the risk of water-stress-induced forest loss in downwind areas, particularly during water scarce periods. However, few studies have investigated dry period forest rainfall self-reliance over longer records and consistently compared the rainforest moisture recycling in both continents. Here, we analyze dry-season anomalies of moisture recycling for mean-years and dry-years, in the South American (Amazon) and African (Congo) rainforests over the years 1980-2013. We find that, in the dry seasons, the reliance of forest rainfall on their own moisture supply (ρfor) increases by 7% (from a mean annual value of 26% to 28%) in the Amazon and up to 30% (from 28% to 36%) in the Congo. Dry years further amplify dry season ρfor in both regions by 4-5%. In both the Amazon and Congo, dry season amplification of ρfor is strongest in regions with a high mean annual ρfor. In the Amazon, forest rainfall self-reliance has declined over time. At the country scale, dry season ρfor can differ drastically from mean annual ρfor. In for example Bolivia and Gabon, mean annual ρfor is ~30% while dry season ρfor is ~50%. The dry period amplification of forest rainfall self-reliance further highlights the role of forests for sustaining their own resilience, and for maintaining downwind rainfall at both regional and national scales.

Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide an insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the heaviness of the tail obtained with MEV was relatively unaffected by the precipitation duration. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.

Climate change and deforestation influence the rainfall patterns in the tropics, thereby increasing the risk of drought-induced forest-to-savanna transitions. Forest ecosystems respond to these changing environmental conditions by adapting various drought coping strategies driven by different magnitudes of water-stress (i.e., defined here as a deficit in soil water availability inhibiting plant growth due to change in rainfall patterns). A better understanding of forest dynamics in response to the water-stress conditions is, therefore, crucial to determine the rainforest’s present ecohydrological conditions, as well as project a possible rainforest-savanna transition scenario. However, our present understanding of such transitions is entirely based on rainfall, which does not consider the adaptability of vegetation to droughts by utilizing subsoil moisture in a quantifiable metric. Using remote-sensing derived root zone storage capacity (Sr) and tree cover, we analyze the water-stress and drought coping strategies of the rainforest-savanna ecosystems in South America and Africa. The results from our empirical and statistical analysis allows us to classify the ecosystem’s adaptability to droughts into four key classes of drought coping strategies: lowly water-stressed forest (shallow roots, high tree cover), moderately water-stressed forest (investing in Sr, high tree cover), highly water-stressed forest (trade-off between investments in Sr and tree cover) and savanna-grassland regime (competitive rooting strategy, low tree cover). This study concludes that the ecosystems’ responses are primarily focused on allocating carbon in the most efficient way possible to maximize their hydrological benefits. The insights from this study suggest remote sensing-based Sr as an important indicator revealing important subsoil forest dynamics and opens new paths for understanding the ecohydrological state, resilience, and adaptation dynamics of the tropical ecosystems under a rapidly changing climate.