This session is therefore open to observational and modeling contributions advancing the understanding and representation of hydrological, biophysical and biogeochemical processes and couplings in land surface models, including the simulation and predictability of compound extreme events across time scales. Particular attention will be dedicated to the representation of the interaction between hydrological processes and the biosphere (including the human component) to properly characterize the carbon-water nexus, human-water feedbacks, and the effects of land-based mitigation/adaptation options to climate change.
The session also welcomes contributions on high-resolution ESMs, advanced observation systems, and emerging data-driven and Artificial Intelligence approaches that enhance early warning capabilities and support resilience to compound hydrometeorological hazards.
The overarching aim of this session is to provide an open and collaborative space to bridge disciplinary gaps within and across communities involved in land surface modeling, and to strengthen links between land surface process representation and downstream applications in climate prediction and climate-change studies, with particular relevance for compound extreme events and transitions, while highlighting priorities and emerging opportunities for the development of next-generation ESMs.
Orals: Thu, 7 May, 08:30–10:15 | Room 0.31/32
Land hydrology is a fundamental part of the global water cycle, and as such, of Earth’s climate system, including the biosphere. Yet, this basic component is still poorly represented in current models, partly because the structure of the land features scales much smaller than what those models can resolve, but also due to a lack of understanding of processes occurring below ground that are not readily at sight. Here we will examine from the perspective of what is important to the atmosphere from seasonal to centennial timescales, questions such as what groundwater and surface water do in shaping water availability and how vegetation and ecosystems adapt to it, ultimately modulating land-surface fluxes and climate. How relevant are these processes and what are we missing in current land-surface models?
How to cite: Miguez-Macho, G. and Fan, Y.: Land hydrology, water availability for ecosystems and land surface models: what are we missing? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15491, https://doi.org/10.5194/egusphere-egu26-15491, 2026.
Land–atmosphere coupling is a central driver of climate variability and extremes, yet Earth System Models (ESMs) struggle to capture the complex interplay between hydrology, vegetation, and surface energy fluxes. In particular, the evapotranspiration–temperature (ET–T) feedback—a key mechanism linking soil moisture, vegetation water use, and near-surface climate—is poorly constrained, limiting confidence in projections of heat extremes and ecosystem stress. Here, we first assess ET–T feedback across a suite of post-CMIP6 ESMs for the historical period (1980–2014) as compared with available GLEAM observations; thereafter the ET-T feedback is investigated in a set of future idealized warming scenarios spanning multiple global temperature targets. To identify the physical and ecohydrological regimes controlling feedback strength, we apply the Ecosystem Limitation Index (ELI), which distinguishes energy-limited from water-limited conditions. Our results reveal a strong negative ET–T feedback in energy-limited regions, where evapotranspiration efficiently cools the surface and stabilizes temperature. In contrast, the feedback reverses in water-limited and transitional regions: here, worsening soil-moisture deficits suppress evaporation and reduce evaporative cooling, thereby amplifying surface warming. Comparison with GLEAM observations highlights regions where models succeed and fail in capturing these feedbacks, particularly in semi-arid ecosystems where land–atmosphere coupling is strongest. Future warming scenarios indicate an expansion of water-limited regimes, weakening negative ET–T feedbacks and reducing the ability of land surface to buffer temperature variability. This shift implies an increased risk of persistent heat extremes, stronger land-surface amplification of warming, and eco-hydrological transitions in sensitive regions. The findings of this study suggest priorities for next-generation ESMs: better representation of soil moisture dynamics, vegetation water-use strategies, and hydrological constraints.
How to cite: Possega, M., Di Carlo, E., Cherchi, A., and Alessandri, A.: Evaluating Divergent Evapotranspiration Feedbacks to Warming Across Water- and Energy-Limited Regimes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1232, https://doi.org/10.5194/egusphere-egu26-1232, 2026.
Land–atmosphere exchange in tall canopies is strongly controlled by turbulence within and above the canopy and in the roughness sublayer (RSL), where classical Monin–Obukhov similarity theory (MOST) is known to be imperfect. Recent developments in the Noah‑MP land surface model (LSM) include a unified turbulence parameterization that aims to provide a consistent treatment of turbulence from within the canopy, through the RSL, to the surface layer (Abolafia‑Rosenzweig et al., 2021). While this scheme has been tested primarily under snow‑dominated conditions, its performance for non‑snow, multi‑canopy environments over long time periods remains largely unexplored.
Here, we evaluate the unified canopy–RSL turbulence parameterization in Noah‑MP (version 5.1.1) using multi‑year, multi‑level observations from the Lindenberg observatory of the German Meteorological Service (DWD). We focus on two contrasting sites: (i) Kehrigk, a tall evergreen needleleaf forest canopy where RSL effects are expected to be strong, and (ii) Falkenberg, a short grassland site that more closely conforms to MOST assumptions. Both sites provide continuous 30‑min data since 2005, including eddy‑covariance fluxes of sensible and latent heat, radiation components, soil heat flux at 5 cm depth, skin temperature, and multi‑level profiles of air temperature, humidity, and wind speed up to 30 m (forest) and 10 m (grassland). All forcing and flux data undergo standard DWD quality control procedures.
Noah‑MP is run offline at both sites with identical land and soil parameterizations, driven by observed meteorology. We compare a standard configuration (MOST‑based surface‑layer and canopy treatment) with the unified canopy–RSL turbulence configuration. Beyond standard flux evaluation, we will diagnose friction velocity, Monin–Obukhov length, bulk transfer coefficients for heat and moisture, and the vertical structure of wind and temperature in the surface and roughness sublayers. Model performance will be analysed as a function of season, canopy type, and atmospheric stability.
By linking detailed, long‑term observations to alternative turbulence representations in a widely used LSM, this study aims to clarify under which conditions enhanced canopy–RSL formulations improve land–atmosphere coupling in next‑generation Earth System Models.
How to cite: Warrach-Sagi, K., Beyrich, F., He, C., and Abolafia-Rosenzweig, R.: Assessing Canopy and Roughness‑Sublayer Turbulence Representation in Noah‑MP over Forest and Grassland at Lindenberg (Germany), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3979, https://doi.org/10.5194/egusphere-egu26-3979, 2026.
Ecosystem water use efficiency (WUE), an indicator of the trade-off between carbon uptake and water loss, is widely used to assess ecosystem responses to climate change. However, large-scale studies of WUE typically assume a single, fixed lag or accumulation period of climatic drivers across regions. This static assumption neglects spatially heterogeneous temporal responses of WUE to climate, potentially biasing attribution analyses and reducing predictive skill. Here, we developed a pixel-level model to quantify the temporal effects of climatic drivers on WUE by explicitly accounting for no-effect, lagged, cumulative, and combined effects and allowing effect timescales to vary spatially. We found that more than 80% of pixels across China exhibited lagged and/or cumulative effects for each driver, with distinct temporal effect patterns among vegetation types and drivers. In herbaceous cover croplands, precipitation exhibited the shortest lag (0.31 ± 0.56 months) and the longest accumulation time (1.71 ± 0.96 months). Accounting for these spatially heterogeneous temporal effects increased the explanatory power of climatic drivers for WUE variation by 17.7% compared with models without temporal effects. We further showed that for most vegetation types, precipitation and air temperature were more strongly associated with temporal variation in WUE, whereas solar radiation contributed more to spatial variability. These findings indicate that location-specific temporal effects can modulate the climatic controls on WUE. Our framework is readily applicable beyond China and can support a shift toward dynamic climate responses in climate–ecosystem interaction modeling, thereby improving forecasts of ecosystem dynamics and informing climate-adaptive vegetation management.
How to cite: Jiao, X.: Widespread Time-Lagged and Cumulative Effects Modulate Climatic Controls on Ecosystem Water Use Efficiency , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6580, https://doi.org/10.5194/egusphere-egu26-6580, 2026.
Land surface models still lack a realistic representation of groundwater, often relying on a free drainage condition at the bottom of the unsaturated soil column as in the current version of ECLand. This unrealistic assumption places the groundwater infinite depth below the surface, thus limiting the model’s ability to simulate realistic soil–vegetation-groundwater interaction.
To address this limitation, we implemented a Dirichlet boundary condition at the bottom of the unsaturated soil to enable a fully implicit numerical scheme for coupling with groundwater. First, we prescribed the water table depth (WTD) using global scale estimates to allow for the computation of realistic water fluxes between the unsaturated zone and the underlying aquifer. In a second step, a dynamic WTD (hereafter the DYN configuration) was developed by defining the water stored in the unconfined aquifer, which evolves prognostically according to drainage (groundwater recharge) and subsurface runoff (groundwater discharge).
The effects of these developments were preliminarily evaluated through offline land-only simulations forced by station data from the PLUMBER2 project, which includes observational networks such as FLUXNET2015, La Thuile, and OzFlux. We validated the DYN configuration against the model setup with free-drainage conditions (CTRL). Our results show a systematic improvement in both latent and sensible heat fluxes, as quantified by the reductions in the error metrics across most stations, with runoff scoring the best performances.
The results of the global simulations largely corroborate and expand upon those of the station-based evaluation experiments conducted using PLUMBER2. The DYN configuration provides a more accurate representation of WTD, both spatially and temporally. This is evident in global climatological maps and independent observational datasets. Additionally, latent and sensible heat fluxes are consistently better represented in DYN than in CTRL, showing closer agreement with DOLCE and GLEAM products. Improvements are also evident in runoff simulations, with DYN exhibiting greater consistency with GLOFAS observations. Model performance was further evaluated against multiple observational datasets, such as GRACE/GRACE-FO to verify temporal variability in total water storage and to assess long-term mean conditions.
This work demonstrates that incorporating groundwater dynamics significantly improves the realism of land-surface processes, particularly in the representation of the flux exchange of water and energy with other components. These results provide a foundation for the enhancement of the representation of land-climate interactions and hydroclimatological behaviour in next generation of reanalysis and climate predictions.
How to cite: Senigalliesi, V., Alessandri, A., Kollet, S., and Gelsinari, S.: Introducing Groundwater Dynamics into the ECLand Land Surface Model: Implementation and Effects, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19214, https://doi.org/10.5194/egusphere-egu26-19214, 2026.
The plant litter layer, a critical interface between the atmosphere and soil, regulates energy, water, and carbon exchanges, yet its thermal insulation effects are poorly represented in Earth System Models (ESMs). This omission hampers our ability to accurately simulate the climate-hydrology-ecosystem nexus, particularly in cold regions where soil thermal regimes control freeze-thaw processes, hydrology, and biogeochemical cycles. To address this gap, we integrated a dynamic litter layer with explicit thermal properties into the Noah-MP land surface model. Validation against global flux tower sites confirms significant improvements in simulating soil temperature and moisture.
Our results reveal that litter insulation creates a strong seasonal asymmetry in soil temperatures, inducing a net annual cooling (up to –0.69 °C) by providing stronger summer cooling than winter warming. Furthermore, it fundamentally alters soil freeze-thaw processes (FTP), but with divergent impacts: it delays the freezing end date in permafrost regions while advancing it in seasonally frozen ground, with shifts up to 40 days. The strongest modulation of freezing duration (~100 days) occurs in regions with a mean annual temperature near 10°C. We identify six distinct FTP response modes, controlled by the non-linear interplay between climate, litter thickness, and snow depth. The altered thermal and hydrological states feedback to ecosystem processes, offsetting the greening-driven gains in gross primary productivity by 20.57 ± 3.65 g C m⁻² yr⁻¹ while enhancing forest soil organic carbon stocks by 2.08 ± 0.24 kg C m⁻².
These findings demonstrate that the litter layer is a key biogeophysical mediator, directly coupling vegetation dynamics with soil thermal-hydrological states. Explicitly representing this process in ESMs is therefore essential for advancing the simulation of the carbon-water-energy nexus, improving projections of permafrost thaw, ecosystem feedbacks, and hydrological changes under vegetation greening and climate warming.
How to cite: Huang, P., Wang, G., and Valentini, R.: Representing Plant Litter Insulation in Land Surface Models: A Critical Process for Simulating the Soil Thermal-Hydrological-Ecological Nexus in Cold Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22297, https://doi.org/10.5194/egusphere-egu26-22297, 2026.
Flash droughts are rapid-onset events that develop within weeks, imposing severe and often unexpected impacts on agriculture. Their monitoring remains challenging due to several factors, including the scarcity of root-zone soil moisture (RZSM) observations and the lack of methodological consensus. This study has two main objectives: (1) to evaluate the applicability of the European Space Agency Climate Change Initiative Combined Root-Zone Soil Moisture product (ESA CCI COM RZSM) for detecting agricultural flash droughts (AFDs) across southeastern South America (SESA), and (2) to assess how satellite-based indicators obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) capture their physical evolution and agricultural impacts.
We apply two complementary AFD detection frameworks to ESA CCI COM and ERA5 RZSM data for 1979–2022: a statistical percentile-based approach and a physically based formulation derived from the Soil Water Deficit Index (SWDI). The percentile method detects AFDs as rapid transitions from above-normal to below-normal soil moisture. The SWDI identifies events through shifts from near-optimal water availability to physiological stress based on soil hydraulic properties. To evaluate agricultural impacts, we analyze satellite-derived evapotranspiration (EVT) and vegetation indicators from MODIS for two representative events in central-eastern and northern SESA. Vegetation indicators include the Land Surface Water Index (LSWI), fraction of absorbed Photosynthetically Active Radiation (fPAR), and Gross Primary Productivity (GPP).
Our results suggest that AFD detection is strongly conditioned by both methodological framework and dataset characteristics. The percentile-based approach tends to overestimate AFD occurrence in persistently wet or dry regimes, where small fluctuations are amplified after percentile transformation. In contrast, the SWDI-based approach preserves regional hydroclimatic gradients and provides a physically consistent representation of plant water stress. Regarding the dataset, ESA CCI COM RZSM captures the main spatial patterns and seasonal cycles of soil moisture depicted by ERA5 across SESA. However, it exhibits smoother short-term variability, delayed drying, and lower absolute soil moisture than ERA5, which could be attributed to the empirical filtering used to propagate surface signals into deeper layers.
Satellite-derived indicators effectively capture the evolution of AFDs across SESA. Soil moisture depletion is followed by reductions in EVT as ecosystems transition from energy- to water-limited conditions. Vegetation indicators respond shortly thereafter: LSWI reveals declining canopy water content, fPAR shows reduced photosynthetic activity, and GPP reflects suppressed ecosystem productivity. The magnitude and spatial extent of these impacts depend on antecedent soil moisture and land-cover type, highlighting the importance of background conditions in modulating drought severity.
Overall, the results demonstrate that ESA CCI COM RZSM provides valuable information for regional AFD monitoring when its physical limitations are considered. The coherence among soil moisture, surface fluxes, and biological responses highlights the potential of satellite observations to track the onset, intensification, and agricultural consequences of AFDs. These results strengthen the use of multi-sensor satellite systems for operational early-warning applications and impact assessment across climate-sensitive agricultural regions such as SESA.
How to cite: Masaro, L., Lovino, M. A., Pierrestegui, M. J., Müller, G. V., and Dorigo, W.: Satellite-based detection of agricultural flash droughts and their ecosystem impacts in southeastern South America, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-511, https://doi.org/10.5194/egusphere-egu26-511, 2026.
How to cite: Zhu, E. and Wang, Y.: An AI-Based GRACE Terrestrial Water Storage Data Assimilation Improves Hydrological Simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4437, https://doi.org/10.5194/egusphere-egu26-4437, 2026.
Abstract：To address the challenge of simulating runoff in ungauged regions, a hybrid physical–data-driven framework was developed by coupling Soil and Water Assessment Tool (SWAT) with an LSTM–Transformer. SWAT-derived process variables were fused with meteorological forcing to form a physically informed feature set for the Transformer-enhanced LSTM. The framework was first calibrated at a gauged station and then transferred to ungauged basins to evaluate its spatial generalizability. At the gauged station, the SWAT–LSTM–Transformer achieved the highest accuracy among all tested models, yielding an NSE of 0.587 and an R² of 0.728 on the validation dataset. It also maintained a better balance between calibration fit and validation robustness than SWAT–LSTM, SWAT–RF, SWAT–SVM, and stand-alone SWAT. SHAP-based interpretation revealed stable and hydrologically coherent predictor dependencies: temperature, lateral flow, and evaporation emerged as dominant drivers of the model’s runoff simulations, whereas precipitation and soil moisture exerted shorter-term and event-focused influences. When transferred to ungauged stations in the same watershed, the model reproduced seasonal runoff variations and event-scale fluctuations with high accuracy, with NSE ranging from 0.80 to 0.94 and R² from 0.83 to 0.92. Under cross-watershed transfer, the model continued to capture the main temporal patterns, with NSE and R² ranging from 0.62 to 0.83 and 0.60 to 0.84, respectively, although performance declined during extreme events. Overall, the coupled SWAT–LSTM–Transformer framework provides a robust and transferable approach for daily runoff simulation in data-scarce watersheds.
Key words: SWAT; LSTM-Transformer; runoff simulation; ungauged watersheds
How to cite: Peng, Z., Li, Y., and Liu, D.: An interpretable daily runoff simulation method in data-scarce watersheds by coupling SWAT and LSTM-Transformer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7092, https://doi.org/10.5194/egusphere-egu26-7092, 2026.
The rapid development of numerical weather prediction (NWP) models offers new opportunities for improving quantitative precipitation forecasting, while raising challenges in objectively integrating multi-model forecasts. This study presents recent advances in an operational multi-model integration precipitation forecasting method based on the generalized Three-Cornered Hat (TCH) theory.Seven NWP models routinely operated at the National Meteorological Center of the China Meteorological Administration are considered, including ECMWF, GERMAN, NCEP, GRAPES_3KM, BEIJING_MR, GUANGZHOU_MR, and SHANGHAI_MR. The method applies TCH theory to estimate the relative error characteristics of precipitation forecasts from different models. A Bayesian framework is then used to derive objective, model-dependent weighting coefficients, enabling short-range multi-model integration forecasts.The integration performance is evaluated using Threat Score (TS) metrics for 2025. Results show that the TCH-based integration consistently outperforms the single ECMWF model across all precipitation categories. The 24-hour heavy rainfall TS reaches 0.2357, a 48% improvement, while the TS for extreme rainfall events reaches 0.1354, a 141% improvement relative to ECMWF.The multi-model integration products have been operationally implemented at the National Meteorological Center, providing critical support during high-impact weather events, highlighting both recent advances and remaining challenges in operational multi-model precipitation forecasting.
How to cite: chen, S.: Multi-model Integration Precipitation Forecasting Based on TCH Theory: Recent Advances and Challenges, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6074, https://doi.org/10.5194/egusphere-egu26-6074, 2026.
Posters on site: Thu, 7 May, 10:45–12:30 | Hall X4
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.
