This paper documents the atmospheric-moisture-tracking model WAM2layers v3 (Water Accounting Model – 2 layers, version 3). WAM2layers may be used to gain an understanding of atmospheric dynamics and to study rainfall patterns and extremes by mapping their sources or sinks, often in the context of climate and land-use changes. To this end, WAM2layers solves a prognostic equation for tagged moisture in gridded atmospheric datasets such as reanalysis data or climate model output. WAM2layers can be used in forward mode, to determine where evaporated water eventually precipitates, or in backward mode, to determine where precipitation originally evaporated.

WAM2layers v3 represents a complete rewrite of the WAM2layers model originally introduced in 2010 and subsequently used in more than 60 academic studies. This latest version incorporates performance optimizations to cope with the increased resolution of input data and introduces various best practices aimed at improved user-friendliness and software sustainability. As an increasing number of researchers are using the code, this paper is intended as an updated description and reference in the academic literature. After describing the history, model formulation, and numerical implementation, we present and evaluate two example cases to illustrate the use and skill of WAM2layers v3. We then discuss best practices, some important assumptions, and directions for future development.

Quantification of precipitation partitioning into evaporation and runoff is crucial for predicting future water availability. Within the widely used Budyko framework, which relates the long-term aridity index to the long-term evaporative index, curvilinear relationships between these indices (i.e. parametric Budyko curves) allow for the quantification of precipitation partitioning under prevailing climatic conditions. A common assumption is that movement along a specific Budyko curve with changes in the aridity index over time can be used as a predictor for catchment responses to changing climatic conditions. However, various studies have reported deviations around these curves, which raises questions about the usefulness of the method for future predictions. To investigate whether parametric Budyko curves still have predictive power, we quantified the global, regional, and local evolution of deviations of catchments from their parametric Budyko curves over multiple subsequent 20-year periods throughout the last century based on historical long-term water balance data from over 2000 river catchments worldwide. This process resulted in up to four 20-year distributions of annual deviations from the long-term mean parametric curve for each catchment. To use these distributions of deviations to predict future deviations, the temporal stability of these four distributions of deviations was evaluated between subsequent periods of time. On average, it was found that the majority (62 %) of study catchments did not significantly deviate from their expected parametric Budyko curves. Out of the remaining 38 % of catchments that deviated from their expected curves, the long-term magnitude of median deviations remains minor, with 70 % of catchments falling within the range of ±0.025 of the expected evaporative index. When these median deviations were expressed as relative changes in discharge, catchments in arid regions showed higher susceptibility to larger discharge shifts compared to those in humid regions. Furthermore, a significant majority of catchments, constituting around the same percentage, was found to have stable distributions of deviations across multiple time periods, making them well suited to statistically predict future deviations with high predictive power. These findings suggest that while trajectories of change in catchments do not strictly follow the expected long-term mean parametric Budyko curves, the deviations are minor and quantifiable. Consequently, taking into account these deviations, the parametric formulations of the Budyko framework remain a valuable tool for predicting future evaporation and runoff under changing climatic conditions within quantifiable margins of error.

Human actions compromise the many life-supporting functions provided by the freshwater cycle. Yet, scientific understanding of anthropogenic freshwater change and its long-term evolution is limited. Here, using a multi-model ensemble of global hydrological models, we estimate how, over a 145-year industrial period (1861–2005), streamflow and soil moisture have deviated from pre-industrial baseline conditions (defined by 5th–95th percentiles, at 0.5° grid level and monthly timestep over 1661–1860). Comparing the two periods, we find an increased frequency of local deviations on ~45% of land area, mainly in regions under heavy direct or indirect human pressures. To estimate humanity’s aggregate impact on these two important elements of the freshwater cycle, we present the evolution of deviation occurrence at regional to global scales. Annually, local streamflow and soil moisture deviations now occur on 18.2% and 15.8% of global land area, respectively, which is 8.0 and 4.7 percentage points beyond the ~3 percentage point wide pre-industrial variability envelope. Our results signify a substantial shift from pre-industrial streamflow and soil moisture reference conditions to persistently increasing change. This indicates a transgression of the new planetary boundary for freshwater change, which is defined and quantified using our approach, calling for urgent actions to reduce human disturbance of the freshwater cycle.

This manuscript documents the atmospheric moisture tracking model WAM2layers v3 (Water Accounting Model – 2 layers – version 3). WAM2layers may be used to gain understanding of atmospheric dynamics and to study rainfall patterns and extremes by mapping their sources or sinks, often in the context of climate and land-use changes. To this end, WAM2layers solves a prognostic equation for tagged moisture in gridded atmospheric datasets such as reanalysis data or climate model output. WAM2layers can be used in forward mode to determine where evaporated water eventually precipitates, or in backward mode to determine where precipitation originally evaporated.

WAM2layers v3 represents a complete rewrite of the WAM2layers model originally introduced in 2010 and subsequently used in more than sixty academic studies. This latest version incorporates performance optimisations to cope with the increased resolution of input data, and introduces various best practices aimed at improved user-friendliness and software sustainability. Since an increasing number of researchers is using the code, this manuscript is intended as an updated description and reference in the academic literature. After describing the history, model formulation, and numerical implementation, we present and evaluate two example cases to illustrate the use and skill of WAM2layers v3. We then discuss best practices, some important assumptions, and directions for future development.

Land cover and land management changes (LCLMCs) play an important role in achieving low-end warming scenarios through land-based mitigation. However, their effects on moisture fluxes and recycling remain uncertain, although they have important implications for the future viability of such strategies. Here, we analyse the impact of idealized LCLMC scenarios on atmospheric moisture transport in three different Earth system model (ESMs): the Community Earth System Model (CESM), the Max Planck Institute Earth System Model (MPI-ESM), and the European Consortium Earth System Model (EC-EARTH). The LCLMC scenarios comprise of a full cropland world, a fully afforested world, and a cropland world with unlimited irrigation expansion. The effects of these LCLMC in the different ESMs are analysed for precipitation, evaporation, and vertically integrated moisture flux convergence to understand the LCLMC-induced changes in the atmospheric moisture cycle. Then, a moisture tracking algorithm is applied to assess the effects of LCLMC on moisture recycling at the local (grid cell level) and the global scale (continental moisture recycling). By applying a moisture tracking algorithm on fully coupled ESM simulations we are able to quantify the complete effects of LCLMC on moisture recycling (including circulation changes), which are generally not considered in moisture recycling studies. Our results indicate that cropland expansion is generally causing a drying and reduced local moisture recycling, while afforestation and irrigation expansion generally cause wetting and increased local moisture recycling. However, the strength of this effect varies across ESMs and shows a large dependency on the dominant driver. Some ESMs show a dominance of large-scale atmospheric circulation changes while other ESMs show a dominance of local to regional changes in the atmospheric water cycle only within the vicinity of the LCLMC. Overall, these results corroborate that LCLMC can induce substantial effects on the atmospheric water cycle and moisture recycling, both through local effects and changes in atmospheric circulation. However, more research is needed to constrain the uncertainty of these effects within ESMs to better inform future land-based mitigation strategies.

In July 2021, extreme precipitation caused devastating flooding in Germany, Belgium and the Netherlands, particularly in the Geul River catchment. Such precipitation extremes had not been previously recorded and were not expected to occur in summer. This contributed to poor flood forecasting and, hence, extensive damage. Climate change was mentioned as a potential explanation for these unprecedented events. However, before such a statement can be made, we need a better understanding of the drivers of floods in the Geul and their long-term variability, which are poorly understood and have not been recently examined. In this paper, we use an event-based approach to identify the dominant flood drivers in the Geul. We also employ (1) a multi-temporal trend analysis to investigate their temporal variability and (2) a novel methodology to detect the dominant direction of any trend. Results suggest that extreme 24 h precipitation alone is typically insufficient to cause floods. The joint probability of extreme and prolonged rainfall combined with wet initial conditions (compound event) determines the chances of flooding. Flood-producing precipitation shows a consistent increase in the winter half-year, a period in which more than 70 % of extremely high flows have historically occurred. While no consistent trend patterns are evident in the majority of precipitation and extreme flow trends in the summer half-year, an increasing direction is visible in the recent past.

Tropical rainforests rely on their root systems to access moisture stored in soil during wet periods for use during dry periods. When this root zone soil moisture is inadequate to sustain a forest ecosystem, they transition to a savanna-like state, losing their native structure and functions. Yet the influence of climate change on ecosystem's root zone soil moisture storage and the impact on rainforest ecosystems remain uncertain. This study assesses the future state of rainforests and the risk of forest-to-savanna transitions in South America and Africa under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Using a mass-balance-based empirical understanding of root zone storage capacity (Sr), defined as the maximum volume of root zone soil moisture per unit area accessible to vegetation's roots for transpiration, we project how rainforest ecosystems will respond to future climate changes. We find that under the end-of-the-21st-century climate, nearly one-third of the total forest area will be influenced by climate change. As the climate warms, forests will require a larger Sr than they do under the current climate to sustain their ecosystem structure and functions, making them more susceptible to water limitations. Furthermore, warming beyond 1.5–2 °C will significantly elevate the risk of a forest–savanna transition. In the Amazon, the forest area at risk of such a transition grows by about 1.7–5.8 times in size compared to the immediate lower-warming scenario (e.g. SSP2-4.5 compared to SSP1-2.6). In contrast, the risk growth in the Congo is less substantial, ranging from 0.7–1.7 times. These insights underscore the urgent need to limit the rise in global surface temperature below the Paris Agreement to conserve rainforest ecosystems and associated ecosystem services.

Large-sample hydrology datasets have become increasingly available, contributing to significant scientific advances. However, in Europe, only a few such datasets have been published, capturing only a fraction of the wealth of information from national data providers in terms of available spatial density and temporal extent. We present “EStreams”, an extensive dataset of hydro-climatic variables and landscape descriptors and a catalogue of openly available stream records for 17,130 European catchments. Spanning up to 120 years, the dataset includes streamflow indices, catchment-aggregated hydro-climatic signatures and landscape attributes (topography, soils, geology, vegetation and landcover). The catalogue provides detailed descriptions that allow users to directly access streamflow data sources, overcoming challenges related to data redistribution policies, language barriers and varied data portal structures. EStreams also provides Python scripts for data retrieval, aggregation and processing, making it dynamic in contrast to static datasets. This approach enables users to update their data as new records become available. Our goal is to extend current large-sample datasets and further integrate hydro-climatic and landscape data across Europe.

Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity (Sr) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified Sr and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale Sr with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of Sr globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with Sr globally, the relation between precipitation and Sr varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and Sr for precipitation of up to 3 mm d−1, above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and Sr. Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of Sr, which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate Sr on a global scale has the potential to be readily and easily integrated into the parameterization of Sr in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.

Vegetation plays a crucial role in regulating the water cycle through transpiration, which is the water flux from the subsurface to the atmosphere via roots. The amount and timing of transpiration is controlled by the interplay of seasonal energy and water supply. The latter strongly depends on the size of the root zone storage capacity (Sr), which represents the maximum accessible volume of water that vegetation can use for transpiration. Sr is primarily influenced by hydroclimatic conditions, as vegetation optimizes its root system in such a way that it guarantees water uptake and overcomes dry periods. Sr estimates are commonly derived from root zone water deficits that result from the phase shift between the seasonal signals of root zone water inflow (i.e., precipitation) and outflow (i.e., evaporation). In irrigated croplands, irrigation water serves as an additional input into the root zone. However, this aspect has been ignored in many studies, and the extent to which irrigation influences Sr estimates has never been comprehensively quantified. In this study, our objective is to quantify the influence of irrigation on Sr and identify the regional differences therein. To this end, we integrated two irrigation methods, based on the respective irrigation water use and irrigated area fractions, into the Sr estimation. We evaluated the effects compared with Sr estimates that do not consider irrigation for a sample of 4856 catchments globally with varying degrees of irrigation activity. Our results show that Sr consistently decreased when considering irrigation, with a larger effect in catchments with a larger irrigated area. For catchments with an irrigated area fraction exceeding 10 %, the median decrease in Sr was 19 and 23 mm for the two methods, corresponding to decreases of 12 % and 15 %, respectively. Sr decreased the most for catchments in tropical climates. However, the relative decrease was the largest in catchments in temperate climates. Our results demonstrate, for the first time, that irrigation has a considerable influence on Sr estimates over irrigated croplands. This effect is as strong as the effects of snowmelt that have previously been documented in catchments that have a considerable amount of precipitation falling as snow.

Global warming impacts the hydrological cycle, affecting the seasonality and timing of extreme precipitation. Understanding historical changes in extreme precipitation occurrence is crucial for assessing their impacts. This study uses relative entropy to analyze historical changes in seasonality and timing of extreme daily precipitation occurrences on the global domain for 63 years of fifth generation of the European Reanalysis reanalysis data. Our analysis reveals distinct regional patterns of change. During the second half of the 20th century, Africa and Asia experienced high clustering of precipitation extremes. Over the past 60 years, clustering increased in Africa while becoming more spread out in Asia. North America and Australia had initially lower clustering and showed slight increases over time. Extreme events in extra-tropical land regions mainly occurred in summer, with modest shifts in timing. These findings have implications for risk assessments of natural hazard like flash floods and landslides, emphasizing the necessity for region-specific adaptation strategies.

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 from satellite observations on modeled evaporation and soil moisture in the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Specifically, we integrated interannually varying land cover from the European Space Agency Climate Change Initiative and interannually varying leaf area index (LAI) from the Copernicus Global Land Services (CGLS). Additionally, satellite data on the fraction of green vegetation cover (FCover) from CGLS were 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 analyzed 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 interannually varying land cover consistently altered the evaporation and soil moisture in regions with major land cover changes. The interannually varying LAI considerably improved the correlation of SMs and E with respect to the reference data, with the 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 interannually varying LAI in combination with interannually 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.

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.

Root zone soil moisture is a key variable representing water cycle dynamics that strongly interact 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 Earth system functions critical to an agriculture-based civilization to destabilization. However, the global extent and severity of root zone soil moisture changes under future scenarios remain to be scrutinized. Here, we analysed 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 (SSPs) 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 south-eastern South America, northern Africa, and southern Asia. In the Mediterranean region, dry permanent departure may have already happened according to some models. By 2100, there are dry permanent departures in the Mediterranean in 70 % of the ESMs in SSP1–2.6, the most mitigated situation, and more than 90 % in SSP3–7.0 and SSP5–8.5, the medium–high and worst-case scenarios. North-eastern Africa is projected to experience wet permanent departures in 64 % of the ESMs under SSP1–2.6 and 93 % under SSP5–8.5. The percentage of ice-free land area with departures increases in all SSP scenarios as time goes by. Wet departures are more widespread than dry departures throughout the studied time frame, 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 about 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, to 45 %. Our findings underscore the importance of mitigation to avoid further degrading the Earth system functions upheld by soil moisture.

Several safe boundaries of critical Earth system processes have already been crossed due to human perturbations; not accounting for their interactions may further narrow the safe operating space for humanity. Using expert knowledge elicitation, we explored interactions among seven variables representing Earth system processes relevant to food production, identifying many interactions little explored in Earth system literature. We found that green water and land system change affect other Earth system processes strongly, while land, freshwater and ocean components of biosphere integrity are the most impacted by other Earth system processes, most notably blue water and biogeochemical flows. We also mapped a complex network of mechanisms mediating these interactions and created a future research prioritization scheme based on interaction strengths and existing knowledge gaps. Our study improves the understanding of Earth system interactions, with sustainability implications including improved Earth system modelling and more explicit biophysical limits for future food production.

Green water — terrestrial precipitation, evaporation and soil moisture — is fundamental to Earth system dynamics and is now extensively perturbed by human pressures at continental to planetary scales. However, green water lacks explicit consideration in the existing planetary boundaries framework that demarcates a global safe operating space for humanity. In this Perspective, we propose a green water planetary boundary and estimate its current status. The green water planetary boundary can be represented by the percentage of ice-free land area on which root-zone soil moisture deviates from Holocene variability for any month of the year. Provisional estimates of departures from Holocene-like conditions, alongside evidence of widespread deterioration in Earth system functioning, indicate that the green water planetary boundary is already transgressed. Moving forward, research needs to address and account for the role of root-zone soil moisture for Earth system resilience in view of ecohydrological, hydroclimatic and sociohydrological interactions.

Forest and savanna ecosystems naturally exist as alternative stable states. The maximum capacity of these ecosystems to absorb perturbations without transitioning to the other alternative stable state is referred to as ‘resilience’. Previous studies have determined the resilience of terrestrial ecosystems to hydroclimatic changes predominantly based on space-for-time substitution. This substitution assumes that the contemporary spatial frequency distribution of ecosystems’ tree cover structure holds across time. However, this assumption is problematic since ecosystem adaptation over time is ignored. Here we empirically study tropical forests’ stability and hydroclimatic adaptation dynamics by examining remotely sensed tree cover change (ΔTC; aboveground ecosystem structural change) and root zone storage capacity (S_r; buffer capacity towards water-stress) over the last two decades. We find that ecosystems at high (>75%) and low (<10%) tree cover adapt by instigating considerable subsoil investment, and therefore experience limited ΔTC—signifying stability. In contrast, unstable ecosystems at intermediate (30%–60%) tree cover are unable to exploit the same level of adaptation as stable ecosystems, thus showing considerable ΔTC. Ignoring this adaptive mechanism can underestimate the resilience of the forest ecosystems, which we find is largely underestimated in the case of the Congo rainforests. The results from this study emphasise the importance of the ecosystem's temporal dynamics and adaptation in inferring and assessing the risk of forest-savannah transitions under rapid hydroclimatic change.

Future rainfall extremes are projected to increase with global warming according to theory and climate models, but common (annual) and rare (decennial or centennial) extremes could be affected differently. Here, using 25 models from the Coupled Model Intercomparison Project Phase 6 driven by a range of plausible scenarios of future greenhouse gas emissions, we show that the rarer the event, the more likely it is to increase in a future climate. By the end of this century, daily land rainfall extremes could increase in magnitude between 10.5% and 28.2% for annual events, and between 13.5% and 38.3% for centennial events, for low and high emission scenarios respectively. The results are consistent across models though with regional variation, but the underlying mechanisms remain to be determined.

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.