Joël Arnault

As climate change progresses, the role of interactions between land and atmosphere (L-A) is becoming increasingly important for weather and climate development. Since the resulting feedback mechanisms depend on the water pathways between land and atmosphere, a sound understanding of the associated processes is necessary in order to represent them realistically in models. Stable water isotopes can be used for this purpose. Since the isotope signature changes constantly due to various influences along the water pathways, they act as natural tracers in the water cycle. By comparing observed isotope data with modeled data within the DFG-project “LAFI” (Land-Atmosphere-Feedback-Initiative) the origin of the modeled behavior can be traced back and the models can be further improved. To this purpose, we utilize the appropriate, fully coupled atmospheric-hydrological modeling system WRF-Hydro-Iso with its innovative “-Iso” implementation. By tracing water pathways with WRF-Hydro-Iso, we aim to improve our understanding of weather- and season-dependent interactions between groundwater, soil moisture, plant transpiration, soil evaporation, isotope signature, and L-A feedback in agricultural areas at the convection permitting scale.Comparing a fully coupled model with all these compartments of the water cycle requires an observatory with multiple instruments—such as the Land-Atmosphere Feedback Observatory (LAFO) near Hohenheim, Stuttgart. During the 2025 growing season, various variables were measured within defined observation periods, which are used to evaluate WRF-Hydro-Iso. Therefore, an innovative multi-month setup with a resolution of 1.25 km was chosen, based on ERA5 reanalysis forcing data and iCESM isotope climatology. In the upcoming work within this project it is planned to reduce the resolution down to 250 m and 50 m.In this session, we evaluate the model results using lysimeter, sap flow, isotope chamber, CRNS (Cosmic Ray Neutron Sensing) soil moisture, and precipitation measurements by displaying the correlation coefficient and RMSE (Root Mean Square Error) to provide an educated overview of model performance. With its rather high spatial and temporal resolution, this study provides detailed insights into L-A interactions, with a particular focus on evapotranspiration (ET) partitioning and isotopic signature herein. First results of a comparison of observation period in June 20.- 27. show that the observed transpiration ratio (T / ET) is 74 %, while the modeled transpiration ratio 86 % overestimates T a bit. However, comparing the diurnal course and peak values it gets clear, that the model is able to capture the overall dynamics of evaporation (E), T and ET already with a resolution of 1,25 km.

Process-based crop models embedded within land surface schemes provide a physically consistent framework for assessing crop–climate interactions. However, their application in semi-arid regions is often constrained by limited field data and simplified management assumptions. In particular, fixed planting dates remain a major source of uncertainty for rainfed systems where sowing decisions are strongly controlled by rainfall timing and intra-seasonal variability.We develop and evaluate a framework for simulating rainfed sorghum growth using the Noah-MP-Crop model, with dynamic planting dates derived from satellite observations. Sowing timing is inferred from temporal trajectories of the GLASS Leaf Area Index (LAI) product, enabling spatially and interannually varying planting information to be incorporated. The approach is applied over the semi-arid eastern Nile basin, where sorghum production is highly sensitive to seasonal rainfall variability.The model is implemented within the WRF-Hydro modeling system and driven by ERA5-Land atmospheric forcing and IMERG satellite-based precipitation. A stepwise calibration strategy is adopted, targeting crop phenology, leaf area development, and carbon allocation processes. Model performance is evaluated against satellite-derived LAI, independent energy flux estimates, and observed yield data, with comparisons between simulations using fixed and dynamic planting assumptions.Results show that dynamic planting dates substantially improve the timing and magnitude of simulated LAI, particularly during early growth stages. In contrast, energy fluxes exhibit weaker sensitivity to planting date representation, reflecting the dominant control of atmospheric demand and radiation on surface energy partitioning in semi-arid conditions. Furthermore, simulations using dynamic planting dates show improved agreement with observed yields, indicating that a realistic representation of sowing variability translates into better seasonal productivity estimates. The findings highlight the importance of representing realistic sowing variability for crop growth simulation, while also illustrating the potential of combining open satellite products with process-based models in data-limited regions.This work demonstrates a practical methodology for integrating dynamic planting information into land surface crop models, providing a transferable approach for improved crop–climate assessments and future seasonal yield prediction applications.

Under the context of global climate change and the intensifying droughts, current understanding of hydrological responses to vegetation cover changes in the Huaihe River Basin remains insufficient. To address this knowledge gap, this study developed a fully coupled WRF/WRF-Hydro-Tag modelling system specifically configured for the basin and conducted an integrated analysis of terrestrial water cycle dynamics, atmospheric water cycle processes, and land–atmosphere interactions. Results reveal that land cover changes exert their strongest influence on the hydrological cycle during the rainy season—particularly in summer. Among the evaluated scenarios, forest expansion induces the most pronounced and spatially extensive alterations to the regional water cycle, followed by grassland expansion; cropland expansion exhibits the weakest effect. Under the forest expansion scenario, the precipitation recycling ratio increases significantly, with annual increments ranging from 0.11% to 0.62%. In contrast, the grassland expansion scenario weakens internal hydrological coupling within the basin, reducing the annual precipitation recycling ratio by 0.03% to 0.17%. Collectively, these findings advance our understanding of how land cover changes modulate hydrological cycle in the Huaihe River Basin under drought conditions, thereby supporting physically consistent interpretations of drought genesis and evidence-based land management policies.

Typhoon‐induced Compound Flood (TCF), driven by the combined impact of extreme rainfall and increasing coastal water level (CWL), poses a substantial threat to urban safety. This study presents a framework for assessing the future compound flood hazard profiles in a coastal megacity in the Delta region of southern China. A coupled hydrology‐hydrodynamic model is applied to simulate the flooding processes of 7 typhoon events. Scenarios are constructed using all possible pairwise combinations of three rainfall and three CWL conditions. These inputs are derived from statistical and dynamical downscaling of climate projections from the Coupled Model Intercomparison Project (CMIP6) ensemble under the SSP5‐8.5 pathway. The results show that future CWL rise contributes more to future inundation than increasing rainfall, whereas rainfall contributions exhibit considerable uncertainties due to regional rainfall downscaling. Under extreme warming scenarios, future typhoons may produce increases of up to 230 mm in total rainfall and 28 mm per hour in rainfall intensity, which in turn increase the average urban inundation depth and area by 1.2 cm and 24.7 , respectively. Given an average CWL of 170 cm and a maximum CWL of 440 cm in the future, the inundation depth and area could increase by up to 8.4 cm and 29 , respectively. Within the 7 typhoons in this study, Hagupit (2014) exhibits the most notable compound effect, potentially expanding the medium‐to‐high risk area (inundation depth above 27 cm) by over 5%. This study demonstrates that climate change may intensify TCF, requiring flood‐mitigating measures to consider rainfall‐CWL interactions.

Land‐based mitigation strategies, such as afforestation and avoided deforestation, are critical to achieving the Paris Agreement's goal of limiting global warming to 1.5°C or 2°C. However, the biophysical impacts of anthropogenic land use and land cover change (LULCC), particularly deforestation and afforestation, on extreme weather events in West Africa remain poorly understood at the regional scale. In this study, we present the first high‐resolution LULCC experiments (at 3 km resolution, covering 2012–2022) using the advanced fully coupled atmosphere‐hydrology WRF‐Hydro model system to assess the potential impacts of idealized land use and land management scenarios on extreme events in the West African savannah region. By analyzing 18 extreme weather indices, we show that deforestation significantly affects temperature extremes (up to 0.45 ± 0.04°C), with effects on regional rainfall extremes being approximately twice as pronounced as those on mean rainfall conditions, along with a significant increase in the number of dry days. Conversely, afforestation generally leads to increases in both mean and extreme precipitation, along with fewer dry days and shorter drought durations. Notably, afforestation produces contrasting responses in temperature extremes depending on vegetation type: converting grassland to mixed or evergreen forest reduces extreme heat via increased transpiration, while conversion to savanna or woody savanna may intensify heat extremes due to albedo‐induced warming effects.

East Africa frequently experiences extreme hydrological events, such as droughts and floods, underscoring the urgent need for improved hydrological simulations to enhance prediction accuracy and mitigate losses. A major challenge lies in the limited quality of precipitation data and constraints on model capabilities. To address these challenges, the upper and middle Tana River basin, characterized by its sensitivity to drought, vulnerability to flooding, and data availability, was selected as a case study. We performed convection-permitting (CP) regional climate simulations using the Weather Research and Forecasting (WRF) model and conducted hydrological simulations with a lake–reservoir-integrated WRF Hydrological modeling system (WRF-Hydro) driven by the CPWRF outputs. Our results show that the CPWRF-simulated precipitation outperforms ERA5 when benchmarked against Integrated Multi-satellite Retrievals for GPM (Global Precipitation Measurement) (IMERG), with evident bias reduction in seasonal precipitation mainly over the Mount Kenya region and with a probability of light rainfall (1–15 mm d⁻¹) during the dry season. Improved precipitation enhances the hydrological simulation, significantly reducing false peak occurrences and increasing the Nash–Sutcliffe efficiency (NSE) by 0.53 in the calibrated lake-integrated WRF-Hydro model (LakeCal) driven by CPWRF output compared to ERA5-driven simulations. Additionally, the lake–reservoir module increases the sensitivity of river discharge to spin-up time and affects discharge through lake–reservoir-related parameters, although adjustments to the parameters (i.e., the runoff infiltration rate, Manning's roughness coefficient, and the groundwater component) have minimal effects on discharge, particularly during the dry season. The inclusion of the lake–reservoir module effectively reduces the model-data bias in WRF-Hydro simulations, particularly for the dry-season flow and peak flow, resulting in an NSE increase of 1.67 between LakeCal and LakeNan (model without the lake–reservoir module). Notably, 24 % of the NSE improvement is attributed to CPWRF and 76 % is attributed to the lake–reservoir module. These findings highlight the enhanced capability of hydrological modeling when combining CPWRF simulations with the lake–reservoir module, providing a valuable tool for improving flood and drought predictability in data-scarce regions like East Africa.

Global warming is accelerating the global water cycle. However, quantification of the acceleration and regional analyses remain open. Accordingly, in this study we address the fundamental hydrological question: Is the water cycle regionally accelerating/decelerating under global warming? For our investigation we have implemented the age-weighted regional water tagging approach into the Weather Research and Forecasting WRF model, namely WRF-age, to follow the atmospheric water pathways and to derive atmospheric water residence times defined as the age of tagged water since its source. We apply a three-dimensional online budget analysis of the total, tagged, and aged atmospheric water into WRF-age to provide a prognostic equation of the atmospheric water residence times and to derive atmospheric water transit times defined as the age of tagged water since its source originating from a particular physical or dynamical process. The newly developed, physics-based WRF-age model is used to regionally downscale the reanalysis of ERA-Interim and the MPI-ESM Representative Concentration Pathway 8.5 scenario exemplarily for an East Asian monsoon region, i.e., the Poyang Lake basin (the tagged water source area), for historical (1980-1989) and future (2040-2049) times. In the warmer (+1.9 °C for temperature and +2% for evaporation) and drier (-21% for precipitation) future, the residence time for the tagged water vapor will regionally decrease by 1.8 hours (from 14.3 hours) due to enhanced local evaporation contributions, but the transit time for the tagged precipitation will increase by 1.8 hours (from 12.9 hours) partly due to slower fallout of precipitating moisture components. These findings reveal the physical mechanisms behind dry-getting-dryer at regional scales.

Climate change has significant impacts on water resources, making the study of hydrological cycle alterations essential for understanding regional climate dynamics. Hydrological models are crucial tools for quantifying these changes and assessing their implications.The Weather Research and Forecasting Hydrological Model (WRF-Hydro), is widely used to simulate regional hydrological processes. This study evaluates the performance of an enhanced version of WRF-Hydro, incorporating an overbank flow module, in simulating runoff for the Myanmar region from 2010 to 2012. The model was driven by offline forcing datasets, specifically Integrated Multi-satellite Retrievals for GPM (IMERG) and ECMWF Reanalysis 5th Generation (ERA5) precipitation products, and the simulated results were compared with observed runoff data. The findings indicate that simulations driven by IMERG precipitation data outperformed those driven by ERA5 in terms of accuracy, likely due to IMERG’s superior representation of regional precipitation patterns. Model performance was assessed by comparing simulated runoff with measurements from seven hydrological stations, where the modified model showed consistent improvements over the default model. NSE improved from −0.27 to 0.49 (default) to 0.51 to 0.62 (modified), indicating enhanced accuracy and reliability. A more detailed analysis of the water cycle reveals that the incorporation of the overbank flow module initially reduces surface runoff, which is offset by an increase in soil moisture storage, accompanied by a slight rise in underground runoff and evapotranspiration. Toward the end of the season, surface runoff increases, which can be attributed to the higher soil storage at the start of the season. These results highlight the significant impact of the overbank flow module on hydrological processes, particularly in flood-prone areas, and suggest that the modified model enhances hydrological forecasting capabilities.

The skill of regional climate models partly relies on their ability to represent land–atmosphere feedbacks in a realistic manner, through the coupling with a land surface model. However, these models often suffer from insufficient or erroneous information on soil hydraulic parameters. In this study, the fully coupled land–atmosphere model WRF-Hydro driven with ERA5 reanalysis is employed to reproduce the regional atmospheric conditions over Central Europe with a horizontal resolution of 4 km for the period 2017–2020. Simulated soil moisture is compared with data from cosmic-ray neutron sensors (CRNS) at three terrestrial environmental observatories of the TERENO network. Soil hydraulic parameters from the European digital soil dataset EU-SoilHydroGrids, together with hydraulic conductivity functions from the Campbell and van Genuchten–Mualem models, are used to test the impact of different representations of soil infiltration on modeled land–atmosphere feedbacks. An updated method to disentangle the proportion of convective precipitation being favored over wet, dry and mixed soils is provided, in order to shed more light on the soil moisture–precipitation feedback mechanism. It is found that WRF-Hydro with van Genuchten–Mualem and EU-SoilHydroGrids best reproduces CRNS soil moisture daily variations, in association with enhanced soil moisture in the root zone and a larger proportion of convective precipitation favored over wet soils. This study demonstrates the importance of adequately considering infiltration processes to realistically reproduce land–atmosphere feedbacks.

The Blue Nile Basin, shared by Ethiopia and Sudan, is a region of significant agricultural importance, supporting the livelihoods of millions who rely on its resources for farming. However, this area faces critical challenges linked to climate change, including rising temperatures and changes in rainfall patterns, which lead to increased crop yield variability. These factors have increased the unpredictability of farming, making it difficult for farmers to plan planting, irrigation, and harvesting schedules effectively. Moreover, the growing population in the Blue Nile region further intensifies the pressure on agricultural systems to produce sufficient food. These challenges highlight the pressing need for crop yield forecasts to enhance agricultural planning, ensure resource efficiency, and strengthen food security in this vulnerable region.This research aims to address these challenges by adopting crop yield forecasts at a subseasonal timescale, integrating process-based crop models into a high-resolution atmospheric simulation framework. Specifically, the Weather Research and Forecasting (WRF) model, coupled with the Noah-MP land surface model, is used to simulate land-atmosphere interactions, including soil moisture, temperature, rainfall, and solar radiation. The output from the WRF-NoahMP system is then fed into a process-based crop model to simulate crop growth and estimate yields.The study seeks to produce an accurate model capable of reproducing water availability and crop yields in the Blue Nile region, considering the limited availability of observational data, and to compare the performance of the process-based crop model against traditional statistical approaches. By providing early warning signals for potential yield fluctuations, this research offers practical tools for improving agricultural decision-making. The findings have implications not only for farmers and policymakers in the Blue Nile Basin but also for regions facing similar climate-induced challenges, paving the way for adaptive strategies in a rapidly changing global environment.