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Vegetation largely controls land surface-atmosphere interactions. Although vegetation is highly dynamic across spatial and temporal scales, most land surface models currently used for reanalyses and near-term climate predictions do not adequately represent these dynamics. This causes deficiencies in the variability of modeled water and energy states and fluxes from the land surface. In this study we evaluated the effects of integrating spatially and temporally varying land cover and vegetation characteristics derived 5 from satellite observations on modelled evaporation and soil moisture in the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Specifically, we integrated inter-annually varying land cover from the European Space Agency Climate Change Initiative, and inter-annually varying Leaf Area Index (LAI) from the Copernicus Global Land Services (CGLS). Additionally, satellite data of the Fraction of green vegetation Cover (FCover) from CGLS was used to formulate and integrate a spatially and temporally varying effective vegetation cover parameterization. The effects of these three implementations on model evaporation fluxes and soil moisture were analysed using historical offline (land-only) model experiments at the global scale, and model performances were quantified with global observational products of evaporation (E) and near-surface soil moisture (SMs). The inter-annually varying land cover consistently altered the evaporation and soil moisture in regions with major land-cover changes. The inter-annually varying LAI considerably improved the correlation of SMs and E with respect to the reference data, with largest improvements in semiarid regions with predominantly low vegetation during the dry season. These improvements are related to the activation of soil moisture-evaporation feedbacks during vegetation-water-stressed periods with inter-annually varying LAI in combination with inter-annually varying effective vegetation cover, defined as an exponential function of LAI. The further improved effective vegetation cover parameterization consistently reduced the errors of model effective vegetation cover, and it regionally improved SMs and E. Overall, our study demonstrated that the enhanced vegetation variability consistently improved the near-surface soil moisture and evaporation variability, but the availability of reliable global observational data remains a limitation for complete understanding of the model response. To further explain the improvements found, we developed an interpretation framework for how the model development activates feedbacks between soil moisture, vegetation, and evaporation during vegetation-water-stress periods.

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.

Root-zone soil moisture is a key variable representing water cycle dynamics that strongly interacts with ecohydrological, atmospheric, and biogeochemical processes. Recently, it was proposed as the control variable for the green water planetary boundary, suggesting that widespread and considerable deviations from baseline variability now predispose destabilization of Earth System functions critical to an agriculture-based civilisation. However, the global extent and severity of root-zone soil moisture changes under future scenarios remains to be scrutinized. Here, we analyzed root-zone soil moisture departures from the pre-industrial climate variability for a multi-model ensemble of 14 Earth System Models (ESMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in four climate scenarios as defined by the Shared Socioeconomic Pathways (SSP), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, between 2021 and 2100. The analyses were done for 43 ice-free climate reference regions used by the Intergovernmental Panel on Climate Change (IPCC).We defined ‘permanent departures’ when a region’s soil moisture exits the regional variability envelope of the pre-industrial climate and does not fall back into the range covered by the baseline envelope until 2100. Permanent dry departures (i.e. lower soil moisture than pre-industrial variability) were found to be most pronounced in Central America, southern Africa, the Mediterranean region, and most of South America, whereas permanent wet departures are most pronounced in southeastern South America, northern Africa, and southern Asia. In the Mediterranean region, permanent departure may have already happened according to some models. By 2100, there is permanent departure in the Mediterranean in 60 % of the ESMs in SSP1-2.6, the most mitigated situation, and 100 % in SSP3-7.0 and SSP5-8.5, the medium-high and worst-case scenarios. North-Eastern Africa is projected to experience permanent departures in 50 % of the ESMs under SSP1-2.6, and 86 % under SSP5-8.5. The percentage of ice-free land area with departures increases in all SSP scenarios as time goes by.Wet departure are more widespread than dry departures throughout the studied timeframe, except in SSP1-2.6. In most regions, the severity of the departures increases with the severity of global warming. In 2050, permanent departures (ensemble median) occur in 10 % of global ice-free land areas in SSP1-2.6, and in 25 % in SSP3-7.0. By the end of the 21st century, the occurrence of permanent departures in SSP1-2.6 increases to 34 %, and in SSP3-7.0, 45 %. Our findings underscore the importance of mitigation to avoid further degrading the Earth System functions upheld by soil moisture.

Achievement of the United Nations Sustainable Development Goals (SDGs) is contingent on understanding the potential interactions among human and natural systems. In Kenya, the goal of conserving and expanding forest cover to achieve SDG 15 “Life on Land” may be related to other SDGs because it plays a role in regulating some aspects of Kenyan precipitation. We present a 40-yr analysis of the sources of precipitation in Kenya and the fate of the evaporation that arises from within Kenya. Using MERRA-2 climate reanalysis and the Water Accounting Model 2 layers, we examine the annual and seasonal changes in moisture sources and sinks. We find that most of Kenya’s precipitation originates as oceanic evaporation but that 10% of its precipitation originates as evaporation within Kenya. This internal recycling is concentrated in the mountainous and forested Kenyan highlands, with some locations recycling more than 15% of evaporation to Kenyan precipitation. We also find that 75% of Kenyan evaporation falls as precipitation elsewhere over land, including 10% in Kenya, 25% in the Democratic Republic of the Congo, and around 5% falling in Tanzania and Uganda. Further, we find a positive relationship between increasing rates of moisture recycling and fractional forest cover within Kenya. By beginning to understand both the seasonal and biophysical interactions taking place, we may begin to understand the types of leverage points that exist for integrated atmospheric water cycle management. These findings have broader implications for disentangling environmental management and conservation and have relevance for large-scale discussions about sustainable development.

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 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. For durations of 5 and 10 days, however, there are less events per year to fit the distribution (37 and 22 on average, respectively), leading to larger inter-annual variability and possible overestimation of the extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the MEV method predicts a relatively constant heaviness of the tail for any precipitation duration, opening up an important research question on what is the ‘correct’ tail behavior of extreme precipitation for different durations. 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.

We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster-Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arc-minute resolution, but a version parameterized at 30 arc-minute resolution is also available. Both versions are available as open source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model. PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1D-2D-hydrodynamic models, respectively. Here, we describe the main components of the model, compare results of the 30 arcminute and the 5 arc-minute versions and evaluate their model performance using GRDC discharge data. Results show that model performance of the 5 arc-minute version is notably better than that of the 30 arc-minute version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arc-minute model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated water withdrawal, by source and sector, matches reasonably well with reported water withdrawal from AQUASTAT.