This session presents studies that advance our understanding of ecosystem dynamics in drylands, their role in carbon, water, and nutrient cycling, and the implications for ecosystem resilience under current and future global change. Topics of this session include (i) novel remote sensing approaches and applications for drylands, focusing on surface component mapping and monitoring, (ii) investigations on interactions among dryland ecology, hydrology, and climatology; (iii) presentation of the development or application of novel approaches to quantify and characterize dryland carbon-water-ecosystem interactions across space and time; and (iv) addressing challenges such as temporal and spatial variability and heterogeneity, pulse-driven dynamics, and measurement and modeling needs specific to drylands.
Orals: Wed, 6 May, 10:45–12:30 | Room 2.23
Australia’s semi-arid ecosystems can exert a disproportionate influence on interannual carbon cycling, yet their resilience to increasingly hot and prolonged drought remains poorly constrained. Here, we combine long-term eddy covariance measurements with stand inventory data from paired flux tower sites in Australia’s semi-arid zone to examine ecosystem responses to the Tinderbox drought that preceded the 2019/2020 Black Summer bushfires.
The complete omission of two consecutive wet seasons led to substantial reductions in ecosystem productivity, accompanied by marked declines in understorey grass cover and increased overstorey tree mortality. Despite this widespread drought impact, productivity was reduced rather than halted, highlighting the capacity of woody semi-arid systems to persist under extreme water limitation.
Following the drought, an unusual triple La Niña delivered multiple years of above-average rainfall. Despite experiencing the same climatic forcing, adjacent Acacia woodlands exhibited contrasting recovery trajectories: a stand characterised by lower tree density and higher drought-induced mortality recovered slowly, whereas a denser stand with lower mortality showed a rapid rebound in carbon uptake.
Together, these results demonstrate that local differences in vegetation structure, mortality, and access to soil water strongly shape ecosystem recovery following extreme climate events, complicating efforts to predict the future resilience of semi-arid woody ecosystems under a warming climate.
How to cite: Griebel, A., Wang, T., Lieff, N., Russell, B., Luo, M., Ewenz, C., and Northwood, M.: Same climate, different recovery: Carbon and water dynamics of semi-arid woodlands through drought and deluge, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15033, https://doi.org/10.5194/egusphere-egu26-15033, 2026.
Dryland ecosystems play a key role in regulating both the trend and interannual variability (IAV) of the terrestrial carbon sink and provide key ecosystem services to over 2 billion people. Recent work has highlighted the sensitivity of dryland ecosystems to changing climate, yet uncertainties from satellite data, reliance on land-surface models, and analytical techniques have led to mixed conclusions. Here, we use a unique set of long-term satellite-derived leaf area index (LAI) datasets that include novel machine learning algorithms to remove artifacts that have hindered greening trend analyses in the past. From 1982 to 2020, we find a persistent increase in LAI over 54.7% of drylands, but surprisingly this trend is accompanied by an increase in its interannual variability ( ). Increasing LAIcv is found over 81.8% of drylands, indicating a decline in stability despite long term greening trends. This increasing variability is driven by a divergence between increasing annual growing-season maximum LAI and simultaneously a declining minimum. The observed rise in also correlates well with an increasing vegetation sensitivity over time to rainfall and to shifts in intra- and interannual rainfall variability. While current dynamic global vegetation models (DGVMs) can reproduce long-term greening, they fail to capture increases in , instead simulating declining variability and convergent LAI trends. Given their disproportionate role in driving the interannual growth rate of atmospheric CO₂, declining stability of global dryland systems is indicative of a potential transition to alternative states that will further impact the carbon cycle and critical ecosystem services that drylands provide.
How to cite: Zhang, W., Moore, D., Li, Y., Macbean, N., Feldman, A., Kang, Y., Green, J., Schwalm, C., Poulter, B., Reed, S., and Scott, R.: Four decades of declining stability in global dryland ecosystems despite widespread greening, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2214, https://doi.org/10.5194/egusphere-egu26-2214, 2026.
Change detection in vegetation index time series can identify abrupt responses to disturbances and assess ecosystem stability. Breakpoints in vegetation greening trends are being used for this purpose, with more breakpoints indicating lower resistance. However, breakpoints can be positive or negative, reflecting improving or degrading trends. Therefore, areas with similar breakpoint counts may differ in stability depending on the balance of positive and negative changes. This study investigates how incorporating breakpoint sign improves the assessment of vegetation dynamics and develops an improved typology based on the sign and significance of trend slopes before and after breakpoints. We applied the new breakpoint typology to 35 years of Landsat NDVI data from a pastoral catchment in Morocco’s High Atlas. We derived the total, positive and negative number of breakpoints in NDVI trends accumulated during the study period and the type of breakpoint in response to the most severe drought within that period. Regions with smaller NDVI changes over time exhibited a higher number of breakpoints with a similar share of positive and negative, compared to areas with stronger greening/ browning, which a higher share of positive/negative breakpoint. During the drought, positive breakpoints (positive reversals) were most common, followed by negative breakpoints (interrupted decreases). Areas with positive reversals experienced fewer total breakpoints over the study period and had a greater share of positive breakpoints than areas with interrupted decreases. These findings highlight the importance of analysing the balance of positive and negative breakpoints alongside their total count for understanding ecological change.
How to cite: G. Mayor, Á., Vermeer, A., and Foerster, S.: New ecological change indicators using breakpoints in vegetation trends applied to a dryland catchment in Morocco’s High Atlas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20942, https://doi.org/10.5194/egusphere-egu26-20942, 2026.
Climate change effects (i.e., rising temperatures and reduced rainfall) and land use changes are expected to affect dryland ecosystems worldwide, particularly those that rely on groundwater. These effects are already detected in southeastern Spain where we find one of the few groundwater-dependent ecosystems (GDEs) in European drylands. This GDE is dominated by the phreatophyte Ziziphus lotus (L.) Lam., a winter-deciduous arborescens shrub native to the Mediterranean region. We monitored spatio-temporal variation in groundwater depth and salinity, as well as climate (daily temperature, precipitation, and vapor pressure deficit) for 8 years (2018-2025) to assess the ecophysiological responses of Z. lotus to climate change effects. Hourly groundwater monitoring in 8 boreholes along a natural depth-to-groundwater gradient (2-25 m) was coupled with mid-summer (peak of plant physiological activity) measurements of water potential, photosynthetic capacity, transpiration rate, and water-use efficiency (WUE) of Z. lotus. Overall, annual trends indicated increased water stress and decreased photosynthetic activity of Z. lotus. Lower photosynthetic activity and stable transpiration rates reduced its WUE. Despite the absence of a clear long-term decline in groundwater levels, a sustained increase in air temperature without compensatory precipitation has led to increased atmospheric water demand, driving physiological stress and earlier defoliation in Z. lotus. Elevated groundwater salinity likely imposed additional osmotic stress. This decoupling between carbon assimilation and water loss suggests a progressive weakening of the ecosystem buffering capacity under combined atmospheric and osmotic stress. This long-term study shows that increasing climatic aridity reduces the productivity and weakens the resilience of GDEs, highlighting the vulnerability of these key dryland ecosystems.
How to cite: Torres-García, M. T., Salinas-Bonillo, M. J., Escudero-Clares, M., and Cabello, J.: Groundwater access does not ensure resilience: climate change effects in a groundwater-dependent ecosystem in the Mediterranean region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16936, https://doi.org/10.5194/egusphere-egu26-16936, 2026.
Drylands strongly modulate interannual variability of the global land carbon sink, yet photosynthetic seasonality is often inferred from vegetation greenness and canopy water content under the assumption that canopy dynamics and carbon uptake remain tightly coupled. Here we show that this assumption is widely violated. Across global drylands during 2001–2020, satellite-derived photosynthesis becomes increasingly decoupled from both greenness and canopy water content in the timing of growing-season onset and senescence. Approximately 35% of drylands exhibit significant decoupling at both the start and end of the growing season, with pronounced hotspots in the Sahel, Central Asia and Australia, where the correlation between photosynthetic phenology (SIF-derived) and canopy dynamic phenology (EVI2/VOD) declined by 0.42–0.48 from 2001–2010 to 2011–2020. Spatial attribution indicates that higher precipitation seasonality drives start-of-season decoupling, whereas higher temperature seasonality drives end-of-season decoupling, with both strengthened under elevated CO₂. However, state-of-the-art process-based models fail to reproduce either the emergent decoupling patterns or their inferred controls, suggesting that key nonlinear responses of dryland vegetation to hydroclimatic variability and CO₂ are misrepresented. This widespread decoupling suggests that changes in canopy condition no longer provide a consistent proxy for when carbon uptake begins or ends, potentially biasing estimates of terrestrial carbon sequestration under ongoing climate change. By pinpointing where and why dryland productivity decouples from canopy dynamics, our analysis reveals key model limitations and provides new constraints for predicting dryland carbon uptake and carbon–climate feedbacks under ongoing climate change.
How to cite: Dong, Y., Zhou, Y., Zhang, L., Xiao, J., Grünzweig, J. M., and Li, X.: Climate Variability Drives Phenological Decoupling of Dryland Photosynthesis from Canopy Greenness and Water Content, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8552, https://doi.org/10.5194/egusphere-egu26-8552, 2026.
Dryland ecosystems play a pivotal role in terrestrial biogeochemical cycles and are highly sensitive to climate variability and land-use change. In this study, we investigate coupled carbon (C) and water flux dynamics in two distinct East African dryland systems—a savanna rangeland supporting mixed livestock and wildlife grazing, and a rainfed cropland under minimal tillage—over 185 days encompassing variable moisture conditions.
Although cumulative C emissions were of similar magnitude in both systems, they exhibited markedly different temporal dynamics. The rangeland displayed highly pulsed C exchange patterns, with rapid shifts from net ecosystem C loss to uptake following rainfall events, underscoring the strong influence of precipitation pulses on dryland carbon cycling. In contrast, the cropland functioned as a net C sink during the peak growing season; however, inclusion of lateral C exports via chickpea harvest revealed an overall C source at the ecosystem scale over the observation period. These findings emphasize the need to account for non-vertical C fluxes when assessing land-use impacts in drylands.
We observed higher carbon use efficiency (CUE) in the cropland, linked to effective allocation of assimilated C to biomass facilitated by agronomic inputs and conservation tillage. Peak-season water use efficiency (WUE) was also elevated in the cropland, reflecting optimized management under sufficient soil moisture; yet when averaged across the full period, rangeland WUE exceeded that of the cropland, likely due to persistent vegetation cover and drought-adapted plant traits that promote conservative water use. Notably, the cropland exhibited a complex interplay between WUE and CUE, wherein gains in productivity were accompanied by increased respiration, illustrating nonlinear responses of dryland systems to management and environmental drivers.
Both ecosystems were co-limited by water and nitrogen, and plant physiological adaptations to dry spells—such as maintenance of photosynthesis under moisture stress—were key to sustaining CUE. Our results contribute to improved process-level understanding of carbon–water interactions, pulse-driven variability, and resilience in dryland biogeochemical cycles. They highlight the importance of integrating temporal variability, lateral fluxes, and land-use intensity into dryland carbon and water budget assessments under global change.
How to cite: Leitner, S., Odongo, V., Dowling, T., Gluecks, I., Jackowicz-Korczynski, M., Rinne, J., Wooster, M., and Merbold, L.: Carbon and water dynamics in contrasting East African drylands: implications for ecosystem function and resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20848, https://doi.org/10.5194/egusphere-egu26-20848, 2026.
Grazing by livestock is the most extensive land use on Earth, covering nearly 40% of the terrestrial surface, and is commonly portrayed as a major driver of global land degradation in drylands through overgrazing. Yet, the role of grazing livestock as a driver of global environmental change remains poorly addressed in Earth-system research.
In the first part of the presentation, we synthesize emerging global evidence to document the widespread and largely overlooked process of extensive livestock destocking and discuss its implications for ecosystem functioning. We show that regions containing 42% of grazing livestock species are experiencing reductions in stocking rates, while stocking rates continue to increase in other regions. This duality of increasing and decreasing stocking rates challenges the prevailing focus on overgrazing in research and calls for a more nuanced understanding of extensive livestock systems and their role in global environmental change.
Because grazing livestock is the dominant consumer of terrestrial primary productivity, global destocking can affect biodiversity, fire regimes, carbon sequestration, and land–atmosphere fluxes at large scales. In the second part, we present a regional case study from peninsular Spain showing that recent changes in extensive stocking rates have modulated both greening and browning patterns at large scales. In the most common situation, declines in extensive livestock have produced measurable increases in ecosystem productivity. In medium-to-highly destocked rangelands, destocking accounts for approximately 6% of the observed increase in net primary productivity over the last two decades.
Together, these findings demonstrate that extensive destocking is a relevant and underappreciated land-use driver of global change in drylands and highlight the need to rethink research and policy priorities around global grazing systems.
How to cite: Anadon, J., Sala, O., and Cruz-Amo, L.: Global Destocking Trends and Their Consequences for Ecosystem Primary Productivity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19744, https://doi.org/10.5194/egusphere-egu26-19744, 2026.
The way vegetation is spatially arranged is a major concern for understanding ecosystem structure and functioning, and it is particularly relevant in drylands due to its control on surface water redistribution. As a result, vegetation spatial configurations derived from Earth Observation (EO) data are widely used as the basis for ecological indicators. However, the transition from pixel-based vegetation mapping to pattern-based interpretation is often implicitly assumed to be straightforward.
In patchy dryland landscapes, where vegetation and bare soil coexist at fine spatial scales, this assumption may be particularly problematic. Spatial structures extracted from EO products are not direct observations of ecological organization, but outcomes of classification and mapping choices operating across multiple spatial scales. Sensor characteristics, spatial resolution and spatial support, together with algorithmic choices, jointly shape how vegetation configurations are inferred and subsequently interpreted.
In this contribution, we examine how different EO-derived estimates of vegetation influence the spatial patterns retrieved in dryland ecosystems. Using aerial and drone imagery across varying spatial and spectral resolutions, we assess the sensitivity of commonly used spatial pattern descriptors to mapping and classification choices, without restricting the analysis to a single methodological approach. Particular attention is given to how pixel-level decisions propagate to landscape-scale pattern characterizations, affecting the apparent configuration of vegetation.
Our results show that spatial pattern metrics vary substantially across EO-derived vegetation products, and that apparent differences in configuration may arise from classification artefacts as much as from genuine ecological structure. This has important implications for the use of spatial patterns as empirical proxies for ecosystem functioning, highlighting the need for more scale-aware mapping workflows and more cautious use of EO-derived spatial patterns in dryland environments.
How to cite: Arnau-Rosalén, E., Marqués-Mateu, Á., Martínez-Sánchez, J. F., Molada-Tébar, A., Rodríguez-Lozano, B., Rodríguez-Caballero, E., Pons-Crespo, R., Lázaro-Suau, R., Castillo-Sánchez, V., Cantón-Castilla, Y., Calvo-Cases, A., and Symeonakis, E.: Patchiness configuration fidelity in dryland vegetation: the EO scale-issue and challenge for spatial pattern surveys, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16557, https://doi.org/10.5194/egusphere-egu26-16557, 2026.
Covering 40–50% of the Earth’s surface drylands make an important contribution to the terrestrial carbon sink and the global carbon cycle. However, in addition to extended dry periods, drylands also experience extreme flood events. We will present carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions from Kati-Thanda-Lake-Eyre basin in central Australia during flooding in 2019. The low basin slope resulted in a large wet of inundation (up to 33,547 km2), that remained wet for an extended period (89 to 325 days). Up-scaling the daily measured fluxes for the changing wet surface area, for the period it was wet, has the potential to result in around 115 Tg of CO2 and 21 Gg of CH4 emitted, and 2 Gg of N2O consumed (Total= 117 Tg CO2e). The low gradient and associated low volume of water transported and large wet area also resulted in the vertical flux of carbon being up to about 800 times the river transported carbon. This first-order estimate of GHG emissions from the Kati-Thanda-Lake-Eyre basin suggests that when flooded, dryland systems globally have the potential to make a significant but currently unaccounted for, contribution to global GHG emissions.
How to cite: Eyre, B., Rosentreter, J., and Erler, D.: Greenhouse gas emissions from flooded drylands (Kati Thanda Lake Eyre basin, Australia), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4565, https://doi.org/10.5194/egusphere-egu26-4565, 2026.
Dryland ecosystems, covering 40–50% of global land surface, experience long dry periods interrupted by episodic floods from extreme rainfall events. These highly variable hydrological conditions strongly influence carbon cycling, yet they remain challenging to monitor due to the remoteness and harsh environmental conditions. Satellite remote sensing, process-based models, such as Lund Potsdam Jena (LPJ) offers a basin wide perspective to track carbon dynamics, complementing the field observations.
In this study, we use monthly satellite observations from OCO-2/OCO-3 and TROPOMI between 2019 and 2024 to estimate CO₂ and CH₄ fluxes from the Kati Thanda Lake Eyre Basin (KTLEB), one of the world’s largest endorheic basins and a significant dryland in Australia. The satellite derived CO₂ and CH₄ fluxes were compared with LPJ model simulations, to evaluate spatial, seasonal, and interannual variability, and further compared against field measurements from flooded and non-flooded sites in 2019, 2022, and 2024. In addition, fluxes were compared against multi sensor water and vegetation indices from Landsat, Sentinel, and MODIS to investigate influence of flooding, and vegetation on carbon fluxes.
We find that satellite derived atmospheric CO₂ (XCO₂) concentrations over KTLEB ranged from 394 to 429 ppm (mean 414 ± 0.3 ppm), showing a significant increase from 2019 to 2024 (τ = 0.84). CH₄ (XCH₄) concentrations ranged from 1.776 to 1.812 ppm (mean 1.794 ± 3.6 ppm), also with a significant increase over time (τ = 0.61). Annual CO₂ fluxes exhibited substantial interannual variability, alternating between net uptake and net emission, whereas CH₄ fluxes were predominantly a net sink. Both satellite and LPJ model fluxes showed similar seasonal trends, with higher CO₂ uptake and CH₄ emissions during wet season, although satellite derived CO2 estimates showed a stronger seasonal swing and greater variability, and CH4 estimates were generally higher. Spatially, both datasets showed similar patterns, with CO₂ uptake concentrated in upper catchment and CH₄ emissions prominent along the basin’s major rivers.
Comparison with field measurements showed that CH₄ emissions were higher during wet season, consistent with satellite observations and LPJ model, and annual CH₄ estimates were broadly comparable across field, satellite, and model data during 2019, 2022, and 2024. CO₂ fluxes, however, varied more among the approaches. This is likely because model and satellite may have missed the initial rapid increase in CO2 fluxes after flooding, and field measurements may have missed some CO2 uptake by vegetation as the floodplain dried out. This underscores the need to improve models by including flood effects and incorporate vegetation carbon fluxes while upscaling field observations to better reconcile carbon estimates across datasets. Correlation analyses further supported this, as CO₂ emissions were significantly correlated with water and vegetation indices, with consistent NDWI, NDVI, and EVI alignment, while CH₄ was predominantly driven by NDWI.
Overall, combining satellite, model, and field measurements provides complementary insights into dryland carbon dynamics, showing that both CO₂ and CH₄ fluxes are driven by flooding, with CO₂ also influenced by post-flood vegetation activity. This highlights the value of integrated data for understanding carbon fluxes in drylands.
How to cite: Rana, A., Poulter, B., Colligan, T., Samberg, S., Rosentreter, J., and Eyre, B.: Satellite, model and field observation CO2 and CH4 emissions in the Kati Thanda Lake Eyre basin dryland, Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-318, https://doi.org/10.5194/egusphere-egu26-318, 2026.
Drylands and dry inland waterways are increasingly recognized as important contributors to global carbon cycling, but little is known about their response to flooding. Using soil chambers and the headspace equilibrium method, methane concentrations were measured in the Kati Thanda-Lake Eyre Basin (KTLEB) from 2024 through 2025 to establish a seasonal baseline for methane flux across upland, floodplain, and river channels (dry and inundated). A subsequent rainfall event in 2025 induced significant flooding in the KTLEB which allowed us to characterize key conditions before, during, and after an episodic flood pulse.
Under dry conditions methane uptake occurred across all terrestrial sites during the winter (avg: -0.107 ± 0.056 mg/m-2/d-1) but many switched to producing methane in the summer (avg: 0.278 ± 0.124 mg/ m-2/d-1). Methane flux from inundated river channels averaged 2.5 ± 0.4 mg/ m-2/d-1 and 6.4 ± 1.4 mg/ m-2/d-1 during the winter and summer, respectively. Water-air methane fluxes from the inundated river channels were significantly higher than sediment-air methane fluxes, regardless of landscape position (upland, floodplain or dry river channel).
Wetting from rain just prior to the flood had little effect on methane fluxes. However, methane fluxes during the flooding event initially reached 40 ± 3.9 mg/m m-2/d-1 before rapidly decreasing to 5.6 ± 0.5 mg/ m-2/d-1. As floodwater receded, re-exposed floodplain soil produced the highest methane fluxes (max: 172, avg: 42.3 ± 22.7 mg/ m-2/d-1). Three months later, methane fluxes from the soil and water returned to near their pre-flood rate. This work advances our understanding of seasonal dynamics, including episodic flooding, on methane fluxes in drylands.
How to cite: Samberg, S., Yeo, J., Rosentreter, J., and Eyre, B.: Extreme Temperatures, Soil Moisture, and Flooding Drive Methane Fluxes in the Kati Thanda-Lake Eyre Basin, Central Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-962, https://doi.org/10.5194/egusphere-egu26-962, 2026.
Semi-arid ecosystems cover around one-third of the Earth's terrestrial surface and support over 1.2 billion people. These are water-limited landscapes that are vulnerable to climate variability and various anthropogenic pressures. An important feature of these ecosystems is the emergence of distinct spatial patterns. Such patterns result from processes of self-organisation that are driven by local plant-plant interactions and water redistribution.
Predictions from theoretical models and computer simulations show that these vegetation patterns exhibit specific statistical properties, notably in their cluster-size distributions. Healthy ecosystems are predicted to have underlying power-law distributions (having the form: 𝑝(𝑥) ~ 𝑥−𝛽, where 𝛽 is the power-law exponent), characterised by the presence of vegetation clusters of all sizes, including large, connected patches. As ecosystems degrade, theory predicts a progressive truncation of the power-law distribution, with large clusters fragmenting into smaller ones. This has been proposed as a potential early warning signal of ecosystem collapse.
Although extensive theoretical work on self-organised vegetation patterns has been conducted, empirical validation remains critically limited. Most studies examine spatial gradients at a single moment using a space-for-time approach, but only a few have repeatedly monitored the same ecosystem over many years to determine whether the way clusters change over time aligns with what theory predicts. There are no studies at high spatial resolution (~1 m) that also cover landscape-level scales (>1 km²) that have been conducted so far. Our study addresses this gap by analysing multi-year, high-resolution satellite data (~ 1 m) from the study sites in the African semi-arid region. We have examined six sites in the African drylands, utilising high-resolution data from the WorldView-2 satellite, to derive NDVI, binarize vegetation via Otsu thresholding, and extract underlying vegetation cluster-size distributions. Using the spatialwarnings and poweRlaw packages, we fit power-law, truncated power-law, and exponential models to quantify spatial structure and evaluate the power-law exponent β as an indicator of fragmentation and resilience. Temporal analysis examines how vegetation clusters have changed at each location over a few years.
Looking at the underlying vegetation cluster-size distributions, we found that some sites have 2 < β < 3 (where β is the exponent of the power-law fit to the cluster-size distribution), indicating little fragmentation and a balanced mix of small & large clusters, while one site showed a highly fragmented system. Additional insights can be gained by examining how the clusters have evolved over time at a landscape level. Together, these analyses provide a novel approach to studying semi-arid ecosystems from anywhere in the world, utilising satellite imagery, and to make a quantitative assessment of their health in terms of fragmentation and resilience. This approach provides a scalable framework that can be applied globally to identify vulnerable dryland sites and prioritise those that require conservation and management interventions.
How to cite: Biswas, U. and Guttal, V.: Vegetation cluster-size distribution, dynamics and resilience indicators in African semi-arid ecosystems from high-resolution satellite data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1016, https://doi.org/10.5194/egusphere-egu26-1016, 2026.
Capturing long-term dynamics and the potential under climate change of woody aboveground biomass (AGB) is imperative for calculating and raising carbon sequestration of afforestation in dryland. It has always been a great challenge to accurately capture AGB dynamics of sparse woody vegetation mixed with grassland using only Landsat time-series, resulting in changing trajectories of woody AGB estimates that cannot accurately reflect woody vegetation growth regularity in dryland. In this study, surface reflectance (SR) sensitive to woody AGB was first selected, and interannual time-series of composited SR were smoothed using an S–G filter for each pixel; then, the optimal machine learning algorithm was selected to estimate woody AGB time-series. Pixels that have reached AGB potential were detected based on the AGB changing trajectory, and the potential was spatially and temporally extended using a random forest model combining environmental variables under current climate conditions and CMIP6 climate models. Results show that: (1) minimum value composites based on NIRv during July–September are more capable of explaining woody AGB variation in dryland (R = 0.87, p < 0.01), and the random forest (RF) model has the best performance in estimating woody AGB (R² = 0.75, RMSE = 4.74 t·ha⁻¹) among commonly used machine learning models; (2) annual woody AGB estimates can be perfectly fitted with a logistic growth curve (R² = 0.97, p < 0.001), indicating explicit growth regularity of woody vegetation, which provides a physiological foundation for determining woody AGB potential; and (3) woody AGB potential can be accurately simulated by RF combining environmental variables (R² = 0.95, RMSE = 2.89 t·ha⁻¹), and current woody AGB still has a small potential for increase, whereas overall losses of woody AGB potential are projected for 2030, 2040, and 2050 under CMIP6 SSP-RCP scenarios.
How to cite: Wang, Z.: Capturing woody aboveground biomass historical change and potentialunder climate change using Landsat time-series for afforestation in dryland of China , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2388, https://doi.org/10.5194/egusphere-egu26-2388, 2026.
Trees play a vital role in structuring processes of dryland ecosystems, and China’s drylands have experienced significant vegetation greening in recent decades because of large-scale forestation. However, the contributions of tree cover (TC) expansion to increases in vegetation greenness (leaf area index, LAI), productivity (gross primary production, GPP), and biomass (vegetation optical depth, VOD), along with their incremental differences, remain unclear. This study indicated that the China’s drylands revealed a significant TC increase (2.3% ± 0.3% decade⁻¹, p < 0.05) from 2001 to 2018, whereas non-tree vegetation cover (NTC, i.e., shrubs, grasses, and crops) exhibited a nonlinear shift—rising before 2010 but declining afterward. Forestation-driven TC expansion accounted for more than 75% of LAI increase throughout the study period, as well as over 70% of GPP and VOD increases pre-2010; however, TC expansion contributed to 42.6% of increase in GPP but little to VOD post-2010. Furthermore, rising GPP/LAI ratios coupled with declining VOD/LAI ratios indicated vegetation carbon sequestration enhanced but moisture content reduced per unit leaf area, and TC gains explained over half of the observed divergence between productivity enhancement and biomass accumulation. The results highlight the leading role of tree restoration in the greening of China’s drylands and the subsequent increased incremental differences between productivity and biomass, characterized by “trading water for carbon” at the leaf and canopy scales. The findings underscore the critical need to monitor both biomass distribution and moisture dynamics within the vertical structure of dryland ecosystems, particularly given the carbon–water imbalance driven by large-scale forestation efforts.
How to cite: Sun, G., Gao, G., Liu, X., Fu, Z., Li, C., Feng, X., and Fu, B.: Tree cover gains dominate vegetation greening and incremental differences between productivity and biomass in China’s drylands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3281, https://doi.org/10.5194/egusphere-egu26-3281, 2026.
The northern and central coast of Chile exhibits a pronounced aridity gradient extending over 1,000 km, from the hyperarid Atacama Desert (~18°S) to the Mediterranean shrublands of central Chile (~34°S). This gradient is controlled by the interaction between the South Pacific Anticyclone, the Andean rain shadow, and the Humboldt Current. Despite these extreme arid conditions, the coastal zone is characterized by the recurrent presence of fog associated with marine stratocumulus clouds, which, upon interacting with the coastal range, generate persistent fog banks over land. Marine fog constitutes the main water source for highly specialized ecosystems such as fog oases and fog-dependent forests distributed along this gradient. In addition to fog and rainfall, dew has also been recognized as an important water source for biological communities. While the meteorology of these water inputs is well documented, their combined influence on surface soil moisture and temperature dynamics remains poorly understood.
This study evaluated the ecohydrological response of surface soil (2 cm depth) to atmospheric water regimes at four fog-dependent ecosystems (20°S-32°S): Alto Patache Research Station (hyperarid), Pan de Azúcar National Park (arid), Bosque Fray Jorge National Park (semiarid), and El Boldo Private Park (Mediterranean), during winter and spring 2025.
During the winter-spring study period, results reveal a marked north-south increase in rainfall, from 0.2 mm at Alto Patache to 155 mm at El Boldo, whereas fog exhibited an inverse pattern, peaking at the hyperarid site (>1,100 L m⁻²) and reaching minimum values in the Mediterranean zone (41 L m⁻²). Dew emerged as a relevant water source in arid and hyperarid sites (≈31 L m⁻²), exceeding values in semiarid and Mediterranean environments (10 and 26 L m⁻², respectively). Soil moisture dynamics indicate that Mediterranean and semiarid sites exhibit high temporal variability driven by rainfall pulses (mean ≈0.14 m³ m⁻³, SD = 0.055, and 0.06 m³ m⁻³, SD = 0.018, respectively), whereas hyperarid and arid sites maintain relatively stable moisture regimes (SD ≈ 0.0075) closely associated with fog and dew at weekly timescales. At daily scales, soil temperature showed significant negative correlations with non-rainfall water inputs across all sites, highlighting fog and dew as dominant thermal regulators that buffer soil heating.
We conclude that soil moisture and temperature regimes along this aridity gradient are governed by distinct hydrological drivers. A hydrological compensation mechanism emerges, whereby fog and dew sustain soil moisture and regulate soil temperature during rainless periods, particularly under hyperarid conditions. These findings underscore the critical role of non-rainfall water inputs in maintaining the ecohydrological resilience of drylands soils under future climate change.
How to cite: Ríos-Silva, F., Alfaro, F. D., Fuentealba, M., and del Río, C.: Ecohydrological controls on soil moisture and temperature along an aridity gradient in the Atacama Desert: the role of fog, dew, and rainfall in the maintenance of fog-dependent ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8357, https://doi.org/10.5194/egusphere-egu26-8357, 2026.
Soil organic carbon (SOC) is an important carbon pool in terrestrial ecosystems that plays a key role in global carbon cycling and climate regulation. However, the patterns of SOC variation along aridity gradients remain poorly characterized, particularly under varying aridity conditions. To address this gap, we surveyed 192 sites across nine provinces in China, spanning environmental gradients from humid to extremely arid regions. Our findings revealed a distinct threshold at an aridity level of 0.77, beyond which SOC accumulation and stability became more closely associated with microbial diversity and soil enzyme activity, while the influence of vegetation and nutrient inputs declined significantly. Notably, β-glucosidase activity showed strong correlations with SOC dynamics under severe aridity. These results indicate that intensified aridity conditions shift the biotic and abiotic correlates of SOC, emphasizing the increasing relevance of microbial and enzymatic characteristics under arid environments. This study highlights the nonlinear response of SOC to aridity and identifies a critical threshold at an aridity level of 0.77, underscoring the potential value of microbial and enzymatic indicators in monitoring soil carbon changes. These findings provide important insights into the vulnerability of grassland soil carbon stocks and offer a scientific basis for developing adaptive management strategies to conserve carbon sinks under future climate scenarios.
How to cite: Cheng, Y. and Fu, T.: Aridity-induced nonlinear shifts in soil organic carbon across Chinese grasslands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8834, https://doi.org/10.5194/egusphere-egu26-8834, 2026.
Grasslands are a dominant global ecosystem, yet they face an uncertain future due to the accelerating threat of aridification, particularly in drylands. Species diversity and functional diversity can enhance the resilience of grasslands to aridity and help to maintain multiple ecosystem functions (multifunctionality) to face these challenges. Yet, the relative roles of species diversity and functional diversity in promoting this resilience are not well understood. We examined how diversity-multifunctionality relationships varied with increasing aridity, and explored the underlying mechanisms at 104 grassland sites spanning an extensive aridity gradient in China. Our results indicate that positive diversity-ecosystem multifunctionality relationships strengthened with increasing aridity but waned at aridity levels exceeding 0.83, a region that corresponds to the transition between semiarid and arid climates. This threshold also coincided with a shift in the relative importance of functional diversity and species diversity. Specifically, functional diversity was more strongly associated with multifunctionality under wetter conditions, but under drier conditions, species diversity, particularly plant diversity, played a more dominant role. Predicted drier conditions would promote a more diverse grassland community, but not necessarily species with more diverse traits. Our results suggest that enhancing species diversity can mitigate the impacts of intensified aridification in grasslands under a drier climate. These finding demonstrate the importance of protecting species-rich grasslands as the planet becomes hotter and drier. By prioritizing climate-specific biodiversity management and matching actions to particular dimensions of diversity, grassland managers can ensure that diversity benefits translate into tangible gains in ecosystem services and climate resilience.
How to cite: Niu, W., Ding, J., Zhao, W., Liu, Y., Wang, S., Li, C., Li, Y., Wu, X., Eldridge, D., and Fu, B.: Diversity-multifunctionality relationships shift with increasing aridity in grasslands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9228, https://doi.org/10.5194/egusphere-egu26-9228, 2026.
Mapping dynamic soil properties (DSPs), such as pH and nutrient levels, that fluctuate under management practices (fertiliser application, liming, irrigation, etc.), seasonal cycles, and environmental factors, is essential for precision agriculture, yet reliably quantifying them from satellite imagery remains a challenge. In this study, conducted in a semi-arid agricultural region of Tunisia covering 480 km², we challenge the standard composite-first paradigm by systematically evaluating the relationship between specific satellite acquisition dates and the predictability of DSPs. Using a dense time series of Sentinel-2 imagery (2019-2023) and 215 soil sampling points, we modelled key DSPs (pH, K, P₂O₅, EC, and Na) and soil moisture suctions (pF2.8 and pF4.2) using both temporal mosaic and date-specific approaches, with the latter being applied only on dates with at least 50 valid samples after cloud and vegetation filtering. Our results reveal a crucial limitation of the temporal mosaic approach, which yielded poor predictive performance, validated by low RPIQ values, for all properties. In contrast, date-specific analysis showed that certain DSPs, notably pH, K, and P₂O₅, could be predicted with high accuracy on specific optimal dates. At the same time, EC and Na remained poorly predicted, likely due to a low proportion of saline points in the dataset. We conclude that in such semi-arid agricultural environment, the temporal context of image acquisition is a decisive factor for successfully mapping specific DSPs, mandating a strategic shift from universal composites toward date-specific modelling for operational soil mapping.
How to cite: Abubakar, M., Fouad, Y., Michot, D., Aïchi, H., Martin, L., Zayani, H., and Vaudour, E.: Improved mapping of dynamic soil properties by comparing date-specific vs. temporal mosaicking strategies on bare agricultural soils using Sentinel-2, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10255, https://doi.org/10.5194/egusphere-egu26-10255, 2026.
Global warming leads to increased precipitation variability, impacting vegetation and the terrestrial carbon sink. While the impact of mean annual precipitation on vegetation and the carbon cycle is well-studied, recent research emphasizes the importance of intra-annual precipitation variability in precipitation-productivity relationships. In drylands the effects of changed precipitation variability on ecosystem functioning are still not fully understood, and therefore also not captured well in Earth system models. Available studies cover either multiple sites, but focus on the effects of changing inter-annual precipitation variability on productivity, or focus on single dryland sites to assess the effect of changing seasonal precipitation variability on carbon fluxes. A multi-site analysis of the effect of intra-annual precipitation variability on carbon fluxes across global semi-arid savannas is still missing.
Here, we contribute to closing this gap by incorporating data from multiple eddy covariance measurement stations located in semi-arid savanna ecosystems around the globe. We examined the impacts of precipitation variability on ecosystem carbon fluxes by: i) assessing the sensitivity of ecosystem CO2 fluxes to precipitation amount, frequency and intensity on the annual scale, ii) evaluating the importance of each metric in explaining seasonal CO2 fluxes, and iii) understanding direct and indirect effects of precipitation metrics on ecosystem CO2 fluxes in different seasons.
On the annual scale precipitation variability has a positive effect on both gross primary productivity and ecosystem respiration. However, these effects partly cancel out, with different ecosystem processes dominating in different seasons. On the seasonal scale, both precipitation frequency and intensity explain more variance in net CO2 fluxes than precipitation amount. Linear mixed effect models show that models containing all three metrics together have the most explanatory power. Structural equation models show that across seasons soil water content is the main mediator of precipitation impacts on CO2 fluxes. In the next steps we will investigate how other site properties, such as canopy cover and height, mean annual precipitation or soil composition, modulate the effects of precipitation variability on the ecosystem CO2 fluxes.
How to cite: Nadolski, L., Wesselkamp, M., Lange, M., El Madany, T., Nelson, J., Carrara, A., Wieckowski, A., Hildebrandt, A., Reichstein, M., and Lee, S.-C.: Impact of changing precipitation variability on carbon budgets of global semi-arid savannas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12338, https://doi.org/10.5194/egusphere-egu26-12338, 2026.
In numerous dryland regions, the vegetation is not distributed uniformly in space. Rather, it is organized into patterns with varying degrees of regularity. These patterns may be explained as the result of a self-organization process driven by water scarcity. In this framework, the ability to form patterns represents an important resource for dryland resilience, as it allows the ecosystem to circumvent a “tipping point” transition from uniform vegetation to desert. Reaction-diffusion vegetation models are often employed to reproduce highly regular patterns. The factors determining the emergence of patterns with lower regularities remain unclear.
Recent studies in the field of network theory have shown that similar reaction-diffusion mathematical models generate patterns on idealized networks. Taking inspiration from these theorethical works, we studied the formation of vegetation patterns in relation to the topologies of the networks through which water and biomass diffuse. To do so, we employed a physical reaction-diffusion vegetation model, and gradually modified the topology of the diffusion networks by adding random shortcuts over a 2-dimensional grid (representing either soil heterogeneities or seed dispersal mechanisms), thus interpolating between a regular lattice and a random network.
We found that network topology strongly shapes both the resulting vegetation patterns and the precipitation range that supports them. Three behavioral regimes emerge. On a regular lattice, highly regular patterns develop reflecting local diffusion processes. On a random network, the system is dominated by global pressure towards homogenization yielding either a uniform state or a single patch. In the intermediate shortcut density range, as the network topology resembles a small world network, the interaction between the two scales of diffusion generates two kinds of disordered patterns: low-regularity patterns with a well-defined characteristic wavelength, and irregular patterns characterized by a broad patch size distribution. These disordered patterns resemble real-world observations and, in our model, they show different responses to changing precipitation. Although we focused on dryland vegetation, we suggest that network-mediated diffusion could lead to similar mechanisms in a wide variety of pattern-forming systems.
How to cite: Filippini, S., Ridolfi, L., and von Hardenberg, J.: Dryland vegetation patterns: the impact of diffusion network topology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12786, https://doi.org/10.5194/egusphere-egu26-12786, 2026.
Open woody formations provide critical dryland ecosystem services—fuelwood, charcoal, construction materials, and livestock fodder—yet remain systematically underestimated by global monitoring systems prioritizing dense forests. Conventional binary forest/non-forest classifications fail to effectively detect these formations, creating a disconnect between forest cover assessments and ground reality. This limitation highlights fundamental challenges in detecting sparse woody vegetation where deciduous phenology, spectral confusion with herbaceous vegetation, and shadows compromise traditional remote sensing.
We mapped continuous woody cover (0-100%) across 36 years (1989-2025) using Landsat imagery and Random Forest regression calibrated on 505 photo-interpreted plots and validated on 41 field plots. Images acquired during rainy season/early dry season maximized detection when deciduous species retain foliage. We analyzed fire regimes (2000-2024) and human pressure via distance gradients from habitations (1-10 km) to identify degradation drivers.
Intact woody thickets declined dramatically from 60% (1989) to 35% (2025), with accelerating loss after 2010. Fire is the primary degradation driver: burned areas lose four times more woody cover than unburned zones. Crucially, fire frequency—not single events—determines degradation severity. Natural recovery is very limited even after 10 years, insufficient to offset immediate post-fire losses. Human proximity also shows significant impacts: woody cover is approximately 20% lower near habitations than in remote areas.
Continuous woody cover mapping successfully quantifies sparse woody vegetation dynamics in heterogeneous dryland environments. This approach is transferable to similar dryland ecosystems globally facing comparable detection challenges. Results reveal that fire frequency exceeds natural recovery capacity, providing scientific evidence to inform conservation and restoration strategies in drylands under increasing anthropogenic pressure.
How to cite: Ramalason, F. N., Rakotondrasoa, O. L., Vander Linden, A., Renard, G., and Bastin, J.-F.: Quantifying woody cover dynamics in heterogeneous dryland ecosystems: a 36-year Landsat assessment (1989-2025), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16823, https://doi.org/10.5194/egusphere-egu26-16823, 2026.
Tropical dryland–forest mosaics host hundreds of millions of people and are among the world’s most climate-sensitive landscapes. Yet these heterogeneous regions remain difficult to monitor consistently with Earth observation due to persistent cloud cover, sparse and variable vegetation with asynchronous phenological responses to irregular rainfall pulses, and limited ground reference data. These constraints have historically capped land-use and land-cover (LULC) mapping accuracy and hindered the detection of subtle but consequential transitions in dryland ecosystems. We developed a 21-year (2000–2020) time-series remote-sensing framework that overcomes these barriers by integrating climate-driven compositing optimized for rainfall gradients, region-specific classification, and machine learning. Our approach generates multiclass, annual, cloud-free mosaics with <0.5% pixel gaps from moderate-resolution satellite imagery and maps LULC using a Random Forest model with dozens of spectral, temporal, and fraction-based predictors. External validation demonstrates a step-change in performance for heterogeneous dryland–forest environments, achieving unprecedented >90% overall accuracy and enabling reliable tracking of both forest and non-forest formations at regional scale. Applying this dataset to Northeast Brazil reveals dramatic transformations over two decades: forest cover declined by 22%, grasslands by 68%, and agriculture expanded by 140%, equivalent to roughly 10 million soccer fields, while encroachment around protected Amazon areas intensified. Building on these maps, we apply interval-level intensity analysis and spatial driver diagnostics to examine how land transformation propagates through coupled human–environment systems. Results reveal sustained periods of rapid change in the early 2000s, followed by a partial slowdown after 2013, with distinct spatial pathways of expansion for farming and non-vegetated land. Vegetation losses are strongly correlated with demographic growth, economic activity, and energy use. Critically, severe multi-year droughts affecting ~60% of the study area amplify degradation in seasonally dry tropical forests. Over 70% of forest conversion occurs within 30 km of roads, with sharp decay beyond 50 km, highlighting infrastructure as a dominant organizing force of landscape change. By linking high-accuracy mapping with change intensity, climate stress, and accessibility gradients, this work moves beyond describing where change happens to explaining how and why it propagates. Our approach demonstrates significant improvements over existing datasets, showing 29–70% spatial concordance with alternative products while achieving superior class discrimination. This open-access product (http://www.dsr.inpe.br/DSR/laboratorios/LAF) provides a transferable blueprint for monitoring land transformation and assessing socio-environmental risk across dryland–forest mosaics worldwide.
How to cite: Ali Ganem, K., Xue, Y., Cerqueira Dutra, A., Gillespie, T., Germain Corneel Pareyn, F., and Edemir Shimabukuro, Y.: From Mapping to Risk: Two Decades of Land-Use and Land-Cover Change in Tropical Dryland–Forest Mosaics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18299, https://doi.org/10.5194/egusphere-egu26-18299, 2026.
In this study, we compare two common frameworks to assess ecosystem resilience by looking at temporal changes. One framework is based on the slowing down in the rate of ecosystem recovery from small disturbances, which implies a loss in resilience signalled by higher temporal correlation and variance. The other method applies time-series segmentation to detect breaks in the trend component of the time series, which are interpreted as (positive or negative) shifts in ecosystem functioning. Using remote-sensing vegetation greenness time series for a dryland catchment and a period including a severe drought, we hypothesised that an increased temporal correlation and variance, representing resilience loss, preceded negative drought breakpoints and vice versa. We however found more support for the opposite. Catchment areas responding with the most frequent positive breakpoint to drought (positive reversal) showed higher temporal correlation and variance than areas with the most frequent negative drought breakpoint (interrupted decrease). Further, the lowest temporal correlation and variance were observed in areas with a positive breakpoint in response to drought or without a significant trend in greenness. These results question the robustness of the indicatory potential of temporal early warnings and highlight the need for studies cross-validating resilience indicators.
How to cite: Moreno de las Heras, M., Grootoonk, W., Staal, A., Génin, A., Vermeer, A., and G. Mayor, Á.: Unexpected relations between temporal resilience indicators and trend breakpoints in a dryland catchment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19005, https://doi.org/10.5194/egusphere-egu26-19005, 2026.
Drylands cover more than 41% of the Earth’s terrestrial surface and provide ecosystem services to approximately 2 to 2.5 billion people. Drylands store roughly 30% of the world’s soil organic carbon (C) and exhibit high spatiotemporal variability in biogeochemical cycling, making them a critical component for accurately quantifying terrestrial carbon and nitrogen budgets. This, in turn, requires an improved understanding of the underlying biogeochemical processes. One key challenge in advancing our understanding of dryland carbon cycling is capturing processes occurring in both surface and subsurface soils. Empirical studies have provided valuable insights into how climate, vegetation, and land management shape the distribution of surface SOC, but few have explored the importance of these drivers in explaining deep SOC across diverse dryland systems.
Here, we leverage the Rapid Carbon Assessment (RaCA), a USDA-NRCS initiative launched in 2010 to quantify SOC stocks by genetic horizon to 100 cm depth across the conterminous United States, including more than 2,400 dryland sites spanning multiple land use/land cover (LULC) types, including Rangelands (shrublands and grasslands) and Forest (deciduous, evergreen, and mixed forests). Using a multi-objective Random Forest model to predict SOC stocks at 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, and 60–100 cm depths, we examine the role of vegetation in explaining SOC distribution with depth, including metrics such as NDVI, net primary productivity (NPP), and plant functional cover derived from the Rangeland Analysis Platform. We additionally assess the influence of climate (e.g., aridity index) and soil properties, including texture, pH, rock fragment content, calcium carbonate concentration, and sodium adsorption ratio, obtained from the Soil Survey Geographic Database and complementary data-driven products.
Model performance decreases with depth, with Rangelands sites performing better in the topsoil layers (R2 = 0.45, RMSE = 8.8) than Forest sites (R2 = 0.28, RMSE = 11.56). In forest systems, the highest performance was observed in the 0–5 cm layer (R² = 0.38). In contrast, rangeland systems showed their highest model performance at the 5–15 cm (R² = 0.53) and 15–30 cm (R² = 0.46) intervals, evidence of the strong link between aboveground and belowground plant production on Rangeland systems. Accumulated Local Effects (ALE) and Shapley Additive Explanations (SHAP) were used to characterize the functional form and relative contribution of individual predictors learned by the model across land-cover types and soil depths. In Rangelands, SOC predictions increase monotonically with increasing aridity and net primary productivity (NPP), whereas Forest systems exhibit saturation at high values of these predictors. This contrast highlights that the non-saturating vegetation response to water availability in dryland rangelands, and the saturation of forest productivity under high precipitation regimes, are also reflected in modeled SOC stocks.
Addressing these questions will advance understanding of dryland biogeochemical processes and support more accurate representation of these systems in terrestrial biosphere models.
How to cite: Arteaga, J., K. Smith, W., Reed, S., W. Nauman, T., C. Duniway, M., and B. Osborne, B.: Vegetation and Soil Drivers of Depth-Resolved Soil Carbon Stocks in U.S. Drylands , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21424, https://doi.org/10.5194/egusphere-egu26-21424, 2026.
Cross-border migration of Calliptamus italicus (C. italicus) and Locusta migratoria migratoria (L. migratoria migratoria) threatens agricultural security along the China-Kazakhstan border, yet their migration pathways remain poorly understood. This study integrates geospatial techniques (optimized Maximum Entropy (MaxEnt) and weighted overlay analysis) with multi-source habitat variables (climate, soil, vegetation) to map current and future habitat suitability and migration pathways. Future climate projections were generated using Globle climate models. Key findings: (1) The MaxEnt model achieved robust performance marked with goodlooking values of AUC and TSS, with precipitation seasonality, isothermality, and elevation as dominant drivers. (2) Projections indicate climate change will expand overall suitable habitats for both Italian and L. migratoria migratoria, moderate-high suitability areas in Asian locusts and low-moderate zones in C. italicus will progressively shrink under future climates. (3) Priority migration pathways were identified for L. migratoria migratoria and C. italicus respectively, concentrated along the Irtysh/Ili Rivers, Balkhash/Alakol Lakes, and Tianshan northern slopes. (4) Future scenarios predict corridor shortening and southward shifts, with SSP585 intensifying L. migratoria migratoria habitat fragmentation. Spatial overlap occurs in Irtysh River and Alakol Lake regions, highlighting cross-border monitoring priorities. These results provide geospatial evidence for optimizing early-warning systems and transboundary pest management strategies under climate change scenarios.
How to cite: Guo, X., Zheng, J., Zhang, F., Li, X., and Liu, L.: Risk Assessment of Transboundary Locust Habitat Distribution and Migration Pathways in Kazakhstan and Xinjiang, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22064, https://doi.org/10.5194/egusphere-egu26-22064, 2026.
The plant-water-atmosphere interaction is partly acknowledged as “Green Water” (GW) in the planetary boundaries concept (Wang-Erlandsson et al. 2022). There, the soil moisture in the root-zone is estimated with global models and assumptions about the root depth, plant cover, drought indices and other low-resolution proxies. This study aims to evaluate Green Water with remote sensing data from Sentinel-2 by combining the Normalized Difference Moisture Index (NDMI), vegetation indices, drought indices and detailed information about the LULC down to the species level.
The study’s focus is on seasonal characteristics like moisture retention in the dry season, recovery/water uptake after rainfall and stand vulnerability to drought events. The moisture index (NDMI) and Soil adjusted Vegetation Index (SAVI) will show these seasonal differences of GW and plant health on the aridity gradient in Tunisia.
We expect that
- there are differences in water retention capacities between the land cover types (agriculture – agroforestry – forest plantations)
- species (olive and carob in agroforestry, eucalyptus and pine in forests) show different water retention capabilities and vigor in the dry season.
- these differences will lead to unequal microclimatic effects like surface temperature and latent heat flux.
The results can be used to estimate other ecosystem services related to GW and living plant matter as well as the improvement of model inputs to go beyond mere plant functional types.
How to cite: Fröhlich, N., Kindu, M., Touhami, I., Khorchani, A., and Menzel, A.: Remote Sensing Indicators of Green Water in Mediterranean Croplands, Forests, and Agroforestry Systems: A Multi-Year Study from Tunisia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22946, https://doi.org/10.5194/egusphere-egu26-22946, 2026.
