Air pollutants can penetrate deep into the lungs, enter the bloodstream, and trigger a cascade of cardiovascular diseases. Elevated pollutant levels in cities are often associated with heavy traffic and industrial emissions, highlighting the need for effective mitigation strategies. Street trees can reduce air pollution through dry deposition, whereby particles are captured by tree canopies in the absence of precipitation. However, city-level models typically assume uniform deposition rates and neglect location-specific variation in tree benefits. Here, we designed a social-ecological systems approach (SES) and revealed substantial spatial disparities in tree-derived air quality benefits within a city. We found that communities with lower urban canopy received fewer air quality benefits. To address these differences, priority tree planting sites were determined using a stepwise framework that takes into account both neighbourhood-level population exposure and social vulnerability. Our findings demonstrate the uneven distribution of urban ecosystem services, emphasizing the importance of integrating environmental justice into urban forestry planning and provide practical guidance on optimizing planting for reducing population exposure to air pollutants. 

How to cite: Cui, S. and Adams, M.: Unequal Canopies, Unequal Benefits: Environmental Justice Implications of Street Tree Air Pollution Mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2092, https://doi.org/10.5194/egusphere-egu26-2092, 2026.

Wetlands play a critical role in terrestrial hydrology and land–atmosphere exchanges, yet they remain poorly represented in many land surface models. Most approaches rely on static wetland maps, preventing models from capturing hydrological variability and associated feedbacks. Here we introduce a new dynamic wetland scheme in the ISBA land surface model, combining explicit hydrological processes with an annually varying diagnostic of wetland extent.

Wetland extent is computed using a TOPMODEL-based approach that links grid-cell saturation deficit with sub-grid topographic indices, and includes a correction for soil organic content to better represent peat-rich areas. Hydrological properties of wetlands and sub-grid runoff redistribution allow water to accumulate and persist in saturated zones, influencing the overall grid-cell water budget.

Simulated wetland extent shows good spatial agreement with multiple satellite-derived wetland datasets across a range of climate zones. Hydrological evaluation against GRACE-based terrestrial water storage and observed river discharge indicates that dynamic wetlands exert a modest but physically consistent influence on ISBA hydrology: they adjust discharge timing and magnitude without degrading model skill, while increasing grid-cell water storage and associated evapotranspiration. However, regional patterns of simulated evapotranspiration reveal a strong sensitivity to the assumed wetland vegetation type, underscoring the need for improved vegetation representation.

In particular, the dynamic wetland extent opens new opportunities for simulating wetland biogeochemistry, including methane emissions, and for exploring the key role of soil oxygen availability in controlling greenhouse gas fluxes.

How to cite: Hardouin, L., Decharme, B., Colin, J., and Delire, C.: A dynamic representation of wetlands for the ISBA land surface model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7919, https://doi.org/10.5194/egusphere-egu26-7919, 2026.

As extreme precipitation events intensify under climate change, understanding changes in precipitation patterns over East Asia has become increasingly important. While most future projections have relied on CMIP6 models, the Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) integrates terrestrial–oceanic carbon cycle feedback including nitrogen deposition and biogeochemical processes to enhance the reliability of climate projection. Despite these advancements, C4MIP has been underutilized in hydrological assessments for East Asia. In this study, we analyze precipitation patterns over East Asia during the historical period (1980–2014) using a C4MIP multi-model ensemble and evaluate model performance through comparison with reanalysis datasets. The C4MIP ensemble demonstrates improved skill in capturing seasonal and interannual patterns of vertically integrated moisture flux convergence (VIMFC), particularly during periods of pronounced moisture convergence and divergence. Under the SSP5–8.5-bgc scenario, projection indicate intensified moisture convergence and increased risks of extreme precipitation over southeastern China and North Korea. These findings provide a diagnostic evaluation of C4MIP's hydrological performance and offer valuable insights for future regional climate projections and adaptation strategies.

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant RS-2024-00404042 and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00343921).

How to cite: jeon, N., Son, R., and Lee, D.: C4MIP Multi-Model Projections of Moisture Convergence and Extreme Precipitation Risks over East Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8456, https://doi.org/10.5194/egusphere-egu26-8456, 2026.

Gross primary production (GPP) is an important indicator of carbon uptake by ecosystems, and plants play a central role in ecosystem carbon sequestration. Understanding how plant-driven GPP fluctuates from year to year and which climate factors control these fluctuations is essential for assessing carbon sequestration. In addition, how carbon sequestration by these plants responds to a warming climate is still not well understood. The lack of high-resolution, well-networked, and long-term stable observations, together with mixed signals from land–atmosphere interactions, makes it difficult to identify and isolate the climate factors influencing plant-driven GPP from an observational perspective. In contrast, land surface models provide an alternative approach to addressing these limitations.

In this study, we conducted 5-km resolution simulations using a land surface model (Community Land Model Version 5, CLM 5, Lawrence et al., 2019) forced with high-resolution atmospheric datasets and updated land surface data covering the Italy and the western Mediterranean region. The high-resolution simulations allow for improved discrimination among different land types, such as urban areas and natural vegetation. We further articulated implementation of Corine land-cover data to better represent current land surface conditions and distribution of Plant Functional Types (PFT). Remarkable progress in the last years has increased representation of more and more complex processes incorporating, among others, plant and soil hydrological and carbon cycles, physiological and phenological processes, land surface heterogeneity and PFT parameterization in LSM. However, large limitations still remain due to uncertainties in representation of spatial and temporal dynamics of model parameters, sub-grid heterogeneity, and ultimately resolving optimal allocation and ecosystem functioning at small scales.  Mediterranean regions were selected as the focus of this study because, as climate change hotspot, they experience strong variability of ecosystem processes and dependencies to changing climate and to increasing severe drought-heatwaves compound events, making vegetation-based mitigation practices particularly urgent. 

We found that both temperature and precipitation play dominant roles in shaping interannual variations in GPP. Under cold or dry regimes, warmer temperatures and higher precipitation are beneficial for higher GPP. In contrast, under warm and wet regimes, further increases in temperature and precipitation are not beneficial for plant GPP production. We further used the model to identify suitable temperature and precipitation ranges for the growth of different plant types, and to examine how global warming is altering these ranges. Our analysis may provide implications for future afforestation practices, particularly in selecting forest types and specific climate/geographic zones that can achieve better carbon sequestration under a warming climate.

How to cite: Xiao, H.-M., Peano, D., Mereu, S., and Trabucco, A.: How do climate factors influence plant-based carbon sequestration in land surface model, and how does this change under global warming?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9964, https://doi.org/10.5194/egusphere-egu26-9964, 2026.

Accurately simulating latent and sensible heat fluxes is a long-standing open challenge in the land modeling community. The recent model intercomparison project PLUMBER 2 over 154 flux towers showed that simple 1-variable linear regression models can outperform process-based models in simulating latent and sensible heat. PLUMBER 2 simulations were run using default model parameters, leaving the potential performance gains from parameter estimation unquantified.

Identifying optimal parameters in land models has several challenges, including high computational cost and the need to identify parameters that can correctly reproduce temporal dynamics (i.e., good performance across different time epochs) and spatial patterns (i.e., good performance across many sites). To evaluate the ability of different calibration methods to handle these challenges, this study compared the performance of traditional and machine-learning emulator-based calibration methods against Long Short-Term Memory (LSTM) benchmarks, with single-objective experiments (latent heat or sensible heat calibrated individually) and multi-objective experiments (latent and sensible heat calibrated simultaneously). We also tested two ways to train emulators and LSTMs: either considering one site at a time or leveraging information from multiple sites and their attributes simultaneously.

Our results show that the calibrated simulations outperformed the default parameters and the simple benchmarks used in PLUMBER 2, demonstrating the potential to improve process-based models. Moreover, we observed that traditional calibration methods have a tendency to overfit: these traditional calibration methods can achieve high performance during calibration but are unable to achieve similar results during validation. The emulator-based methods achieve more consistent results across both calibration and validation time periods. Additionally, we found that parameter estimation methods that incorporate information from multiple sites simultaneously achieve better spatial consistency than methods that only learn from one site at a time. These results suggest that the performance gap between LSTM and process-based models can be significantly narrowed through calibration.

How to cite: Aguirre, I., Knoben, W., Vasquez, N., and Clark, M.: Benchmarking machine learning-based emulators and traditional methods to calibrate land model parameters for 124 global flux tower sites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14172, https://doi.org/10.5194/egusphere-egu26-14172, 2026.

Human interactions with the water cycle are increasingly recognised as critical drivers of land-climate feedbacks, yet they have long been under-represented in climate modelling.  With ongoing climate change, water management strategies and irrigation practices are becoming more important across many parts of the world. Since these activities can significantly alter surface energy and water fluxes, and thus local and regional climate, it is important to study these processes in more detail.

Although some Earth system models and regional climate models have started to incorporate irrigation routines, they still lack a representation of water availability from different sources and the competing demands of other sectors. To address this gap, we are developing the flexible water modelling tool C-CWatM that can be easily coupled with existing (regional) climate models. Based on the socio-hydrological model CWatM, it simulates river discharge, groundwater, reservoirs and lakes, as well as water demand and consumption from industry, households and agriculture.

In this contribution, we present initial results from coupled simulations using C-CWatM and the regional climate model REMO to study the impact of large-scale irrigation on regional climate conditions. The coupling is implemented via the OASIS3-MCT coupler, which manages synchronised data exchange and regridding of coupling fields. REMO provides the forcing fields required by C-CWatM and receives irrigation water amounts from C-CWatM, which are then applied within REMO's irrigation scheme. 

The development and coupling of C-CWatM allows climate models to realistically account for irrigation constraints, which is particularly important in water-scarce regions and under the increasing risk of droughts driven by climate change. Thus, our approach is an important step towards next-generation land surface modelling and promotes collaboration between hydrology and climate modelling communities to advance understanding of land-climate feedbacks and inform future adaptation strategies.

How to cite: Schmitt, A. and Greve, P.: Irrigation–climate feedbacks in coupled climate simulations: First results using an integrated hydrological modelling tool, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17003, https://doi.org/10.5194/egusphere-egu26-17003, 2026.

Soil moisture plays a critical role in water-limited regions through its strong coupling and feedbacks with vegetation. However, state-of-the-art Land Surface Models (LSMs) used in reanalysis and near-term prediction systems still lack a realistic coupling of vegetation, limiting their ability to properly account for the fundamental role of vegetation in modulating the feedback with soil–moisture.
In this study, we incorporate Leaf Area Index (LAI) variability from observations - derived from the latest-generation satellite products provided by the Copernicus Land Monitoring Service - into three different LSMs. The models perform a coordinated set of offline, land-only simulations forced by hourly atmospheric fields from the ERA5 reanalysis. An experiment using interannually varying LAI (SENS) is compared with a control simulation based on climatological LAI (CTRL) in order to quantify vegetation feedbacks and their impact on simulated near-surface soil moisture.
Our results show that interannually varying LAI substantially affects near-surface soil moisture anomalies across all three models and over the same water-limited regions. However, the response differs markedly among models. Compared with ESA-CCI observations, near-surface soil moisture anomalies significantly improve in one model (HTESSEL–LPJ-GUESS), whereas the other two models (ECLand and ISBA–CTRIP) exhibit a significant degradation in anomaly correlation. The improved performance in HTESSEL–LPJ-GUESS is attributed to the activation of a positive soil moisture–vegetation feedback enabled by its effective vegetation cover (EVC) parameterization. In HTESSEL–LPJ-GUESS, EVC varies dynamically with LAI following an exponential relationship constrained by satellite observations. Enhanced (reduced) soil moisture limitation during dry (wet) periods leads to negative (positive) LAI and EVC anomalies, which in turn generate a dominant positive feedback on near-surface soil moisture by increasing (decreasing) bare-soil exposure to direct evaporation from the surface. In contrast, ECLand and ISBA–CTRIP prescribe EVC as a fixed parameter that does not respond to LAI variability, preventing the activation of this positive feedback. In these models, the only active feedback on near-surface soil moisture anomalies is negative and arises from reduced (enhanced) transpiration associated with negative (positive) LAI anomalies.
Our findings demonstrate that simply prescribing observed vegetation properties in LSMs does not guarantee a realistic coupling between vegetation and soil moisture. Instead, it is shown that the explicit representation of the underlying vegetation processes is essential to activate the proper feedback and capture the correct soil moisture response.

How to cite: Alessandri, A., Possega, M., Cherchi, A., Di Carlo, E., Boussetta, S., Balsamo, G., Ardilouze, C., Dayon, G., Catalano, F., Gelsinari, S., Massari, C., and van Oorschot, F.:  Surface Soil Moisture–Vegetation Feedbacks in Water-Limited Regions across Land Surface Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19820, https://doi.org/10.5194/egusphere-egu26-19820, 2026.

The High-Resolution Earth System Modeling, Analysis and Prediction for a Society Resilient to Hydrometeorological Hazards (EarthRes) is a program of the International Decade of Sciences for Sustainable Development (IDSSD), endorsed by UNESCO in 2025. EarthRes aims to build global societal resilience to hydrometeorological hazards through five pillars: (1) establishing cooperative observation networks; (2) advancing process-based understanding of Earth system dynamics; (3) enhancing prediction and early warning capabilities; (4) fostering indigenous and local knowledge and data sharing; and (5) strengthening capacity building among international partners.

This presentation will introduce the program's recent progress, including collaborative observations for understanding Earth system dynamics, the integration of a regional climate model with a coupled land surface-hydrology-ecology model that accounts for human activities (e.g., reservoir regulation, irrigation, urbanization), and the development of a forecasting framework. This framework connects the regional model with an AI model to predict droughts, floods, and compound events at synoptic to sub-seasonal scales.

Other activities under EarthRes will also be introduced, and future plans will be discussed. Through international collaboration and targeted capacity-building, EarthRes seeks to enhance sub-seasonal prediction and early warning capabilities, with particular benefits for vulnerable regions.

How to cite: Yuan, X., Sheffield, J., Pan, M., Kam, J., He, X., Roundy, J., Chaney, N., Wanders, N., Wang, L., Li, C., and Hao, Y.: An introduction to the EarthRes program, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11167, https://doi.org/10.5194/egusphere-egu26-11167, 2026.

The increasing drought onset speed is driving a global transition toward more frequent flash droughts, presenting unprecedented challenges for drought management and adaptation. However, projected changes in future flash drought characteristics show considerable divergence among climate models. Here, using models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we demonstrate that models capable of capturing the land-atmosphere coupling gradient between dry and wet soil conditions tend to project more pronounced global transition from slow to flash droughts in the future. This emergent relationship provides a robust constraint for future projections based on observed land-atmosphere coupling characteristics. Our analysis suggests that the societal and environmental risks posed by future flash droughts could be more severe than previously projected. Given the widespread impacts of flash droughts, this study not only enhances our understanding of uncertainties in drought projections, but also holds promise for supporting socio-economic planning and adaptation strategies through constrained projection.

How to cite: Wang, Y. and Xing, Y.: Constraining Flash Drought Projections Through Land-Atmosphere Coupling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2682, https://doi.org/10.5194/egusphere-egu26-2682, 2026.

In 2024, an exceptionally severe abrupt drought-to-flood transition (ADFT) event occurred over Henan Province in central China, causing substantial economic losses due to its abruptness and limited early warning. Although intraseasonal oscillations (ISOs) can provide precursors for forecasting extremes, previous studies have primarily focused on floods or droughts in isolation, leaving the synergistic impacts of multiple ISO modes on drought-to-flood transitions poorly understood. Here we show that the 2024 ADFT event was jointly modulated by two ISO modes with opposite propagation directions. During the drought stage, Rossby wave train maintained a Ural blocking pattern and displaced the westerly jet southward. This circulation configuration suppressed precipitation while enhancing temperature and sensible heat, leading to persistent drought conditions. During the transition-to-flood stage, both the Rossby wave train and the Western Pacific Subtropical High (WPSH) oscillation acted in concert. The southeastward-propagating Rossby wave train disrupted the blocking, while the WPSH oscillation migrated northwestward. Their combined effects shifted the rain belt northward, strengthened southerly moisture transport, increased latent heating, and ultimately triggered the extreme flood. The synergy between these two ISO modes amplified the transition magnitude by 50%, suggesting that the ADFT event would have been largely suppressed in the absence of their concurrent influence. These results underscore critical role of ISO phase evolution and propagation in ADFT events, and suggest that they may serve as useful precursors for forecasting abrupt transitions.

How to cite: Zhou, S. and Yuan, X.: The impact of intraseasonal oscillations on the 2024 abrupt drought-to-flood transition over central China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2684, https://doi.org/10.5194/egusphere-egu26-2684, 2026.

Floods are the most common natural hazards, and the compound effects of flood events pose severe challenges to flood protection. The lack of flood observation data makes it difficult to identify and analyze compound flood effects. Here, we employed a data-driven approach to reconstruct discharge in ungauged regions. We classified flood events from a compound perspective, quantified the contributions of different drivers, and compared the impacts of compound and non-compound flood events. Our results showed that pronounced compound effects were common in most flood events, with many compound flood events clustered in India and southeastern China. Compound events caused substantially greater impacts than non-compound events in Asia and North America.

How to cite: Zhao, J. and De Michele, C.: Classification and Attribution of Compound Flood Events , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13594, https://doi.org/10.5194/egusphere-egu26-13594, 2026.

With the acceleration of urbanization, complex underlying surfaces, pipe networks, river channels, and hydraulic facilities (gates, sluices, pumps) have significantly increased the number of computational grids and physical processes, making the computational efficiency of physical rainfall-runoff models insufficient to meet the timeliness requirements of emergency management for flood disasters. This necessitates further research on new technologies to enhance the computational efficiency of flood simulation and forecasting models. The development of AI technology provides new approaches for rapid flood disaster simulation and forecasting. This study proposes three innovative methods to address these challenges. First, GPU Accelerated Model for Surface Water Flow and Associated Transport. Second, AI Based Rapid Predicting Method for Flood Process. Third, Model Application for Dam Break Flood Simulation. 

How to cite: Pan, X. and Hou, J.: Rapid Forecasting Method for Flood Process by Using on Physically Based Numerical and AI Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17882, https://doi.org/10.5194/egusphere-egu26-17882, 2026.

Hydroclimate volatility, characterized by abrupt transitions between dry and wet extremes, poses a growing threat to global water security. Yet, current understanding of these transitions largely relies on meteorological metrics, which often fail to capture the full complexity of hydrological processes, land surface memory, and human water management. Here, we present a global assessment of water whiplash through the lens of terrestrial water storage (TWS). By integrating hydrological modeling with data-driven approaches, we reconstructed a comprehensive long-term TWS dataset to identify these events and account for delayed hydrological responses. Our results reveal a widespread intensification of global water whiplash in recent decades, with a substantial further increase projected under high-warming scenarios. Attribution analysis indicates that while climate change acts as the dominant driver of this amplification, human water management plays a critical role in spatially modulating these events, capable of either significantly mitigating or exacerbating local volatilities. We identify key hotspots of intensification in the tropics and high latitudes, encompassing extensive agricultural regions and major river basins. These findings establish TWS as a vital integrative indicator for monitoring abrupt hydrological transitions and underscore the urgent need for adaptive water management strategies to navigate an increasingly volatile hydroclimate.

How to cite: Yang, Y. and Zhao, R.: Global amplification of water whiplash revealed by terrestrial water storage, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18864, https://doi.org/10.5194/egusphere-egu26-18864, 2026.