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 (S_r), 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 S_r 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.

The hydrological response of watersheds affected by large-scale coal mining activities is complex and difficult to simulate. The present study aims to bridge this gap by simulating the effects of land-use and topographical changes due to coal mining on surface runoff in the Jamunia basin of Jharkhand, India. The derivatives of digital elevation model (DEM) have been used to simulate the changes in topography of the study area and the runoff has been calculated using Soil and Water Assessment Tool (SWAT) hydrological model. The study results revealed significant increase in surface runoff (mm) during the simulation period. The findings of this study established that unplanned mining activities can reduce the water holding capacity of downstream reservoirs and increase the runoff. The outcome of the study will be helpful for mine planners to design sustainable mining operations which will have less adverse impact on the hydrological regime of the watershed. 

Academia has experienced acceleration and expansion in parallel with the Great Acceleration, which has shaped the Anthropocene. Among other pressures, the expectation to be internationally mobile conflicts with many values held by sustainability scholars and results in disillusionment. The changes in the academic system can be seen through the framework of the adaptive cycle, which can help us understand historical parallels and shape the system to better align with sustainability values in future. We hope this piece can contribute to the discussion of the next steps forward to reimagine academia.

Tropical rainforests in the Amazon and Congo River basins and their climate are mutually dependent. Evaporation from these forests help regulate the regional and global water cycle. Furthermore, these rainforests themselves depend on precipitation to sustain their structure and functions. However, the rapid increase in human activities (such as burning fossil fuels and deforestation) has significantly changed the rainforests’ climate. Due to the effect of human-induced perturbations on moisture feedbacks (i.e., precipitation and evaporation patterns), these rainforests risk tipping to a savanna or treeless state.

Understanding how these forests respond to climate change will aid in assessing their resilience to water-induced perturbations as well as in anticipating and preparing for potential tipping risks in the future. However, our understanding of how vegetation responds to climate change is fragmented, which limits our capacity to predict these risks. Previous studies have primarily relied on precipitation data to understand these forest-to-savanna transitions. However, ecosystem transition risks are also associated with water-stress, which depends on the vegetation’s capacity to adapt to drier conditions by storing water in its root zone. This thesis investigates the effect of hydroclimatic changes on root zone adaptation and its implications for forest resilience.

Paper I uses remote sensing data to analyse water-stress and drought coping strategies across the rainforest-savanna transects. Paper II uses the root zone storage capacity to quantify the resilience of forest ecosystems. Using the empirical understanding of root zone forest dynamics and hydroclimatic estimates from Earth System Models, Paper III projects future forest transitions and estimates tipping risks by the end of the 21^st century under four different shared socio-economic pathways. Paper IV uses atmospheric moisture tracking data to investigate the leverage landholders in South America have over precipitation and the resilience of forest ecosystems. 

Papers I and II reveal the non-linear relationship between the ecosystem’s above-ground structure and root zone storage capacity. These studies indicate that, under hydroclimatic changes, the ecosystem’s root zone storage capacity is much more dynamic than its above-ground forest structure and is more representative of the ecosystem’s transient state than precipitation. Ignoring this root zone adaptive capacity can underestimate forest resilience, primarily observed in the Congo rainforest. Paper III projects that the risk of forest-savanna transition will increase with climate change severity, most prominently observed in the Amazon rainforest. Paper IV finds that all landholders have equal leverage over the moisture precipitating locally and over farther-downwind land systems. According to this study, smallholders have a disproportionately larger influence over forest rainfall. However, large landholders have a larger influence on forest resilience as well as over the moisture precipitating on croplands and pastures. These results warrant the need for policies to factor in the impact of deforestation on downwind actors and promote effective ecosystem stewardship. The insights from this thesis highlight the importance of understanding and assessing ecosystem dynamics under a rapidly changing climate for strengthening management and conservation efforts across the globe. 

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.

Climate and deforestation-induced changes in precipitation drive tropical forest-savanna transitions. However, precipitation alone provides a superficial understanding of the underlying mechanism behind these transitions. This is because our knowledge of how vegetation responds to changes in hydroclimate is fragmented. Under a rapidly changing climate, it is increasingly important to understand forest adaptation to predict future forest-savanna transition risks. However, there are two major bottlenecks to achieving this: (i) there is no universal metric that represents forest adaptation, and (ii) at continental scale, empirical evidence to ecosystem response under changing climate is still lacking. This thesis uses remote sensing-derived root zone storage capacity – a novel metric representing the vegetation's capacity to utilise subsoil moisture storage - and above-ground tree cover structure to provide empirical evidence to ecosystems’ response under changing hydroclimate and the influence of hydroclimatic adaptation on the resilience of tropical forests. The results reveal a non-linear relationship between ecosystem’s above-ground structure and subsoil moisture storage capacity. Furthermore, the ecosystem’s capacity to utilise subsoil moisture is much more dynamic and reflective of their transient conditions under changing precipitation than above-ground structure; thereby highlighting its application as an early warning signal. Ignoring this adaptive capacity can undermine forest resilience. The result from this thesis also emphasises the applicability of remote sensing in inferring and assessing ecosystem adaptation under rapid hydroclimatic change and can assist in strengthening management and conservation efforts across the continents.

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. 

Forests play a vital role in maintaining the global carbon balance. However, globally, forest ecosystems are increasingly threatened by climate change and deforestation in recent years. Monitoring forests, specifically forest biomass is essential for tracking changes in carbon stocks and the global carbon cycle. However, developing countries lack the capacity to actively monitor forest carbon stocks, which ultimately adds uncertainties in estimating country specific contribution to the global carbon emissions. In India, authorities use field-based measurements to estimate biomass, which becomes unfeasible to implement at finer scales due to higher costs. To address this, the present study proposed a framework to monitor above-ground biomass (AGB) at finer scales using open-source satellite data. The framework integrated four machine learning (ML) techniques with field surveys and satellite data to provide continuous spatial estimates of AGB at finer resolution. The application of this framework is exemplified as a case study for a dry deciduous tropical forest in India. The results revealed that for wet season Sentinel-2 satellite data, the Random Forest (adjusted R² = 0.91) and Artificial Neural Network (adjusted R² = 0.77) ML models were better-suited for estimating AGB in the study area. For dry season satellite data, all the ML models failed to estimate AGB adequately (adjusted R² between −0.05 – 0.43). Ensemble analysis of ML predictions not only made the results more reliable, but also quantified spatial uncertainty in the predictions as a metric to identify its robustness.

Climate change and deforestation have increased the risk of drought-induced forest-to-savanna transitions across the tropics and subtropics. However, the present understanding of forest-savanna transitions is generally focused on the influence of rainfall and fire regime changes, but does not take into account the adaptability of vegetation to droughts by utilizing subsoil moisture in a quantifiable metric. Using rootzone storage capacity (Sr), which is a novel metric to represent the vegetation's ability to utilize subsoil moisture storage and tree cover (TC), we analyze and quantify the occurrence of these forest-savanna transitions along transects in South America and Africa. We found forest-savanna transition thresholds to occur around a Sr of 550–750 mm for South America and 400–600 mm for Africa in the range of 30%–40% TC. Analysis of empirical and statistical patterns allowed us to classify the ecosystem's adaptability to droughts into four classes of drought coping strategies: lowly water-stressed forest (shallow roots, high TC), moderately water-stressed forest (investing in Sr, high TC), highly water-stressed forest (trade-off between investments in Sr and TC) and savanna-grassland regime (competitive rooting strategy, low TC). The insights from this study are useful for improved understanding of tropical eco-hydrological adaptation, drought coping strategies, and forest ecosystem regime shifts under future climate change.

Extended exposure to change in rainfall patterns and permanent land-use change (LUC) have reduced the capability of the forests to withstand any external stresses, also defined as forest resilience loss. Major parts of the Amazon forest is under threat of tipping towards a treeless savanna state due to these changes in rainfall patterns and LUC. This loss in forest resilience thus also prevents the forest to return to its pre-disturbed state of the natural cycle and makes the forest more prone to tipping. Yet, this change in natural cycle is not sudden and involves a certain time lag for the forest system to respond. Previous studies determined the forest resilience, but have only considered precipitation or climatological drought to be the key influencing factor. However, neither are a direct measure of the water stress of the forest and thus do not fully reflect the hydrological dynamics underlying forest resilience loss. This study addresses the research questions: (i) do change in climatic patterns have a significant effect on forest resilience?, (ii) how does the change in rainfall patterns orLUC affect the environmental dynamics of the forest over time?, (iii) whether the quantification of rainfall, rootzone storage capacity and LUC patterns at a temporal scale better for understanding the resilience loss of the forest?

The present study aims at understanding the complex dynamics of the resilience of the forest system using a time-series approach. Advanced remote sensing resources allow us to determine and understand patterns in the tipping behaviour at a temporal scale as well as to understand the hydrological dynamics and environmental triggers. For this, we combined precipitation data, root zone storage capacity and satellite-based forest cover and LUC data analyzed along a time-series. This is to better represent the resilience loss of the forest towards hydrological interactions and also provide a better understanding of the hydrological process for the forest tipping rather than a statistical relation. Landsat-7 data is ideal for determining the forest change, due to its regional time-series availability from early 2000’s until today. This study provides a better understanding of the hydrological dynamics of the rainforest by utilizing a time-series approach. Root zone storage capacity represents the water stored in the roots of the forest (a.k.a., water available to the forest) and it is a much better representation for assessing water stress of the Amazonian rainforest than precipitation. Thus, also a better parameter for evaluating forest resilience loss over time.