The Horn of Africa drylands (HAD) are among the most vulnerable regions to hydroclimatic extremes. The two rainfall seasons — long and short rains — exhibit high intraseasonal and interannual variability. Accurately simulating the long and short rains has proven to be a significant challenge for the current generation of weather forecast and climate models, revealing key gaps in our understanding of the drivers of rainfall in the region. In contrast to existing climate modelling and observation-based studies, here we analyze the HAD rainfall from an observationally-constrained Lagrangian perspective. We quantify and map the major oceanic and terrestrial sources of moisture driving the variability in the long and short rains. Specifically, our results show that the Arabian Sea (through its influence on the northeast monsoon circulation) and the southern Indian Ocean (via the Somali low level jet) contribute ~80% of the HAD rainfall. We see that moisture contributions from land sources are very low at the beginning of each season, but supply up to ~20% from the second month onwards, i.e., when the oceanic-origin rainfall has already increased water availability over land. Further, our findings suggest that the interannual variability in the long and short rains is driven by changes in circulation patterns and regional thermodynamic processes rather than changes in ocean evaporation. Our results can be used to better evaluate, and potentially improve, numerical weather prediction and climate models, which has important implications for (sub-)seasonal forecasts and long-term projections of the HAD rainfall.

Understanding the partitioning of runoff into baseflow and quickflow is crucial for informed decision-making in water resources management, guiding the implementation of flood mitigation strategies, and supporting the development of drought resilience measures. Methods that combine the physically-based Budyko framework with machine learning (ML) have shown promise in estimating global runoff. However, such ‘hybrid’ approaches have not been used for baseflow estimation. Here, we develop a Budyko-constrained ML approach for baseflow estimation by incorporating the Budyko-based baseflow coefficient (BFC) curve as a physical constraint. We estimate the parameters of the original Budyko curve and the newly developed BFC curve based on 13 climatic and physiographic characteristics using boosted regression trees (BRT). BRT models are trained and tested in 1226 catchments worldwide and subsequently applied to the entire global land surface at a 0.25° grid scale. The catchment-trained models exhibit strong performance during the testing phase, with R2 values of 0.96 and 0.88 for runoff and baseflow, respectively. Results reveal that, on average, 30.3% (spatial standard deviation std=26.5%) of the continental precipitation is partitioned into runoff, of which 20.6% (std=22.1%) is baseflow and 9.7% (std=10.3%) is quickflow. Among the 13 climatic and physiographic characteristics, topography and soil-related characteristics generally emerge as the most important drivers, although significant regional variability is observed. Comparisons with previous datasets suggest that global runoff partitioning is still highly uncertain and warrants further research.

Groundwater is an important water source for evaporation, especially during dry conditions. Despite this recognition, plant access to groundwater is often neglected in global evaporation models. This study proposes a new, conceptual approach to incorporate plant access to groundwater in existing global evaporation models. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used, and the resulting influence of groundwater on global evaporation is assessed. The new GLEAM-Hydro model relies on the linear reservoir assumption for modelling groundwater flow, and introduces a transpiration partitioning approach to estimate groundwater contributions. Model estimates are validated globally against field observations of evaporation, soil moisture, discharge and groundwater level for the time period 2015-2021, and compared to a regional groundwater model. Results indicate only mild improvements in evaporation estimates, as most eddy-covariance stations are located in energy-limited regions or regions with no plant access to groundwater. The temporal dynamics of the simulated evaporation improves across 75% of the stations where groundwater is a relevant water source. The skill of the model for variables such as soil moisture and runoff remains similar to GLEAM v3. Representing groundwater access influences evaporation in 22% of the continental surface, and it increases evaporation globally by 2.5 mm year-1 (0.5% of terrestrial evaporation). The proposed approach enables a more realistic process representation of evaporation under water-limited conditions in satellite-data driven models such as GLEAM, and sets the ground to assimilate satellite gravimetry data in the future.