Eddy covariance (EC) measurements are a backbone of ecological research and have provided valuable insights into the variability of carbon and water fluxes in different ecosystems and under varying environmental conditions. Since these measurements are integrative and weighted over changing areas (footprint), species-specific information cannot be easily derived except for homogenous monocultures. However, EC sites are increasingly established in mixed forest stands which are considered to be more resilient under changing environmental conditions. This leads to the question of how species-specific responses can be determined, and whether the magnitude of fluxes derived from temporally varying flux footprint predictions (FFPs) can provide insight into these responses. At a site in southwestern Germany's Black Forest, primarily composed of mature beech and Douglas fir trees, we investigate the dependence of EC flux measurements on different FFP areas and explore how species-specific contributions to gas exchange can be disentangled using a combined measurement and modeling framework. We applied an ecosystem model that has been calibrated from EC measurements at various sites with beech- and Douglas fir monocultures, and evaluated it with data of soil water content and soil respiration taken at homogeneous parts of the investigated mixed forest site. Then we compared hourly aggregated measurements of net carbon exchange (NEE) and evapotranspiration (ET) with model simulations under four configurations: (i) pure beech, (ii) pure Douglas fir, (iii) a static weighted average of both species, and (iv) a dynamic weighted average based on FFP variations. The results show that weighted combinations of the two species generally provide a better match with hourly EC measurements than single-species simulations, while differences between static and dynamic weighting approaches remain relatively small. However, species-specific flux responses can be significantly different during transitional periods such as autumn and spring when physiological differences between Douglas fir and beeches are most pronounced. We demonstrate that accounting for seasonal differences is particularly important for gap-filling EC measurements in mixed forests and, consequently, for determining annual carbon and water budgets. Furthermore, EC measurements over mixed forests provide valuable information for detailed model evaluation, while species-specific modeling helps disentangle and attribute underlying ecosystem dynamics to individual species.

Subsurface flow in preferential pathways in soils may transport water more rapidly than the soil matrix, may be quickly activated during precipitation events and enhance infiltration or interflow. Vertical pathways are particularly important for runoff generation. However, identifying these pathways is challenging because traditional methods such as piezometers, soil moisture sensors, or hillslope trenches do not adequately capture the spatial scale and frequency of preferential flow features, while other experimental techniques like dye tracing are labor-intensive and invasive. In this study, we introduce a novel method to identify the locations of preferential flow by analysing vertical soil profiles of stable water isotopes. Across four catchments, we drilled 100 soil cores (1-3 m deep) per catchment and analyzed the stable isotope composition of the soil water in 10-20 cm depth intervals to construct depth profiles. We employed clustering techniques to group soil-water isotope profiles and selected those that matched to a seasonal sampling date to establish a reference profile for each catchment using LOESS regression, representing profiles influenced solely by matrix infiltration. Deviations from these reference profiles were then used as indicators of being influenced by vertical or lateral preferential flow. Our results revealed evidence of preferential flow in all studied catchments. Especially in the alpine catchment with highly heterogeneous soils, many profiles showed distinct preferential flow features, including multiple, vertically independent pathways occurring at variable depths, even among adjacent profiles. These findings demonstrate the feasibility of using soil water isotope profiles to assess preferential flow pathways and highlight the substantial spatial and vertical variability of preferential flowpaths at hillslope and catchment scales.

Throughfall in forests is spatially highly heterogeneous creating distinct patterns that persist over time and propagate into the soil. Despite its importance for forest ecohydrological processes, experimentally derived high-quality datasets describing spatio-temporal throughfall dynamics at fine temporal and spatial resolution are still scarce. The majority of studies were unable to measure throughfall at high temporal and/or spatial resolution because of extensive sampling efforts, especially in forests with complex structures. We present a novel, innovative and modular throughfall monitoring system for continuous, automated measurement of throughfall either as isolated canopy throughfall and as integrated throughfall (total throughfall reduced by litter interception). Without removing the water, the system allows to quantify the spatio-temporal throughfall variability at both intra-event and intra-stand levels. The network captures spatial throughfall patterns and their temporal persistence across rainfall events of varying size during leafed and non-leafed periods. The throughfall monitoring network features 60 self-built, cost effective throughfall samplers, with four throughfall collection compartments and tipping bucket units each connected to a newly developed microcontroller board enabling fully automated, low-maintenance operation during rainfall events. The network, collecting data since the winter of 2024/2025, is setup in a stratified sampling pattern among four forest plots of Beech, Douglas fir, Silver fir, and mixed trees in a mature temperate forest in Germany. Data from a four-week observation period in the spring of 2025 are included in this study to showcase the potential of this approach. The data support the networks' ability to capture small-range spatio-temporal throughfall patterns across the study area.

The forest floor (FF) plays a key role in carbon, nutrient, and water cycling. It is the biologically most active compartment of forest soils, highly responsive to environmental conditions. Yet, its response to current changes in environmental conditions and forest management is understudied. Temperate forests are among the best studied ecosystems globally, providing the necessary ecological and biogeochemical background information to assess FF changes. Here, we focus on identifying existing knowledge and gaps in our understanding of the functioning of the FF. Interactions between FF biota and abiotic FF components show multifactorial dependencies with environmental conditions and drive FF turnover. Vice versa, the turnover of FF regulates carbon, nutrient, and water cycling. With slow litter decomposition and limited bioturbation, organic matter accumulates, nutrients cycle tightly within the FF, and water passes through this layer partly along preferential pathways. With rapid litter decomposition and intense bioturbation, FF accumulation is little, the mineral soil is the main nexus for plant nutrient uptake and organic matter transformation, and water infiltrates the mineral soil more homogeneously. The interconnectedness with the adjacent ecosystem compartments is a crucial feature of the FF, feeding back to its functioning and making it a central hub of forest processes. The FF morphology reflects these processes and therefore has untapped potential as an indicator of soil and ecosystem health. Under forest change, the FF might lose its functionality, with negative impacts on nutrient provision, water storage, and carbon sequestration. Consequences for forest growth could be strong and even detrimental. Hence, improved knowledge of FF characteristics and their linkages to mineral soils and aboveground ecosystem compartments is crucial for assessing forest resilience to progressing environmental changes.

The forest floor (FF) possesses a significant water retention capacity, facilitating the transfer of water between the atmosphere and the soil. However, knowledge on the water retention characteristics and water redistribution effects of the FF remain limited. Due to the dominance of laboratory data regarding the storage capacity of a forest's litter layer, we used a combined FF weighted grid-lysimeter and soil moisture network to directly and in-situ measure the dynamics of water storage of the FF and fluxes from and into the FF. The objective was to quantify storage capacities, retention durations, and resulting water redistribution patterns, as well as evaporation from the FF. We present the results of our network at three mixed temperate forest sites with different altitudes, and therefore diverging climatic conditions, located in the Black Forest, southwest Germany. The three sites have an annual mean temperature gradient from 6.3 to 10.3 degrees C, leading to humus forms that vary from typical F-Mull to typical Moder. Throughout the monitored period in 2024-2025, the storage capacity of the FF ranged between 1.4 and 4.2 g g-1 FF and was not only influenced by the type of litter but also by the rainfall characteristics themselves. With our field setup we could show that longer, low intensity rainfall events fill the FF storage more efficiently than shorter heavy rainfall events (- 24 %). Our gridded lysimeter design revealed small-scale spatio-temporal infiltration patterns, caused by a redistribution of rainfall along the passage through the FF. The findings of the lysimeter network provide a comprehensive understanding of how not only the thickness of the FF but rather characteristics like the share of organic fine material define the water cycle within forest ecosystems.

Societal Impact Statement As forests are important for biodiversity and recreation and provide vital resources globally, it is essential that their health can be accurately monitored, for example, by using sap flow probes to measure tree water uptake. We investigated whether sap flow measurements might differ at various locations within the same tree due to variation in wood properties. We found high variability in sap flow measurements across one tree, meaning that accurate measurements might require a large number of probes to be used per tree. This information is critical for ensuring accurate measurements and uncertainty estimation in future research. Summary To derive the transpiration flux of a tree from the measurements of a sap flow probe several parameters, including the thermal properties of the wood, the conducting sapwood area, and radial sap flow velocity profiles, have to be determined. Uncertainties in these parameters can be reduced by repeated sampling, which conflicts with the intention of minimally invasive investigations and efficient use of limited resources. In this study, we instrumented a single tree with 23 sap flow sensors, sampled 30 wood cores from it, and finally cut the whole tree to acquire stems disks for additional measurements. We assessed the heterogeneity within the tree stem and quantified the uncertainties and their propagation into the final transpiration flux estimate. We found that estimated transpiration fluxes based on one single sap flow probe deviated from the pooled average value by more than 15% in one of three cases. The main source of this uncertainty was the spatial heterogeneity of sap flux densities, followed by the uncertainty of the conducting sapwood area, which could effectively be reduced by including additional nondestructive observations of the stem cross section. This study's unusually high number of sap flow sensor installations and wood core samples for one single tree exemplarily shows, that even four sensors installed around a tree stem may still leave a considerable amount of uncertainty for tree level transpiration flux estimates.

Abstract. Soil moisture (SM) is a key variable in terrestrial ecosystems, regulating water and energy exchange at the soil-atmosphere interface. In forest ecosystems, SM exhibits pronounced spatiotemporal variability at the tree-scale in the topsoil of the active root zone due to complex interactions among multiple factors such as soil properties, topography, climate, and vegetation. Existing research on spatiotemporal SM characteristics in forests is limited, impeding our understanding of SM-vegetation interactions. A detailed understanding of when and to what extent vegetation influence topsoil moisture spatiotemporal variability in forests is required to quantify its consequences for (eco‑)hydrological processes and to test hypotheses about the dominating influences on spatiotemporal patterns across different ecosystems. This study characterizes spatiotemporal dynamics of topsoil SM. Our objectives are to quantify plot-scale spatial variability and its temporal evolution from event- to seasonal scale, thereby improving our understanding of SM dynamics across wet and dry states in pure- and mixed-species forest stands. We recorded SM with a dense monitoring network of 400 sensors (SMT100, Truebner GmbH, Germany) installed in the topsoil in a tree-centered design across four plots of mixed and pure Douglas fir, Beech and Silver fir trees in the ECOSENSE forest, southwestern Germany. Statistical and Empirical Orthogonal Function (EOF) analysis together with temporal stability (TS) analysis were combined to quantify spatial SM dynamics from a 2.5-year dataset (2023–2025). Spatial SM follows a consistent annual cycle across all plots transitioning between wet- and dry-preferential states. SM shows high variability for all plots and most plots display a characteristic convex upward curve for the spatial mean soil moisture to coefficient of variation (SM̄–CV) relationship. The CV peaks at intermediate wetness and declines toward the driest and wettest states. Only the Mixed plot shows a linear, negative SM̄–CV relationship. Daily SM skewness follows the same convex relation with negative skew for wet conditions (SM̄ > 20 %) and positive skew for dry conditions. The EOF analysis identified two to three statistically significant modes per plot, together explaining > 80 % of spatial variance. The first EOF explains > 60 % of the variance at each plot and represents the dominant, stable, time-invariant spatial pattern. Seasonal wetting and drying cycles show that spatial variability differs between wet and dry phases, suggesting distinct local and non-local controls dominate the SM patterns according to the wetness state and seasonal phase of the system, increasing or decreasing spatial SM heterogeneity. The temporal stability analysis identified persistently wetter and drier locations. Mean relative difference (MRD) within each plot ranges from –75 % (dry) to +50 % (wet), with the widest range in the Silver fir plot and the narrowest range in the Beech plot. Within each plot, a subset of representative locations could be identified, but the exact location in the plot is random. Spearman rank correlations of spatial SM between the Douglas fir and Beech plot show high spatial stability (ρ ≥ 0.8) for wet phases (winter) whereas lower correlation (ρ ≈ 0.45–0.60) during dry phases. The findings of this study can help to inform sampling strategies and modelling approaches. The dataset presented here is appropriate for detailed analyses of event-based wetting-drying dynamics, and when combined with additional data, it can disentangle the relative contributions of local and non-local controls to the observed spatial patterns and pattern stability.

Water uptake and distribution are critical for drought recovery, yet previous drought conditions have been shown to impair water transport by affecting soil–root contact and xylem conductivity. In order to investigate these dynamics, the approach of applying δ2H-labeled water as a controlled irrigation was adopted, with this irrigation being administered to a mixed stand of mature European beech (Fagus sylvatica (L.)) and Norway spruce (Picea abies Karst. (L)) trees in control (CO) and throughfall exclusion (TE) plots following 5 years of experimental summer drought. The δ2H concentrations were measured in soil, stem, twig and leaf water before and after rewetting to assess water pool turnover. The labeled water infiltrated the upper 70 cm of soil in both treatments within 48 h. However, a notable delay in water uptake and distribution was exhibited by TE trees in comparison with CO trees, where the label was detected in stems and leaves within 24 h. The TE beech demonstrated water uptake after 4 days, while TE spruce exhibited a more pronounced delay of 7 days. Despite this delay, TE trees exhibited a higher turnover of stem water pools (>75%) compared with CO trees (<50%), while leaf water turnover remained similar between treatments. The delayed uptake in TE trees may be attributed to fine root loss in both species and the suberization of surviving fine roots in spruce, which likely reduced water absorption efficiency. Additionally, the depleted stem water reserves in TE spruce may have delayed internal redistribution. These findings underscore the importance of considering species-specific recovery dynamics and provide valuable insights into the long-term impacts of drought on tree water relations.

Soil moisture (SM) in the topsoil horizon is a key variable in terrestrial ecosystems, regulating water and energy exchange at the interface between the soil and the atmosphere. In forest ecosystems, SM exhibits pronounced spatio-temporal variability within the active root zone as a result of complex interactions among multiple factors including soil properties, topography, climate and vegetation. Formulating broadly reliable statements about spatio-temporal soil moisture characteristics and its effects remains challenging. Existing research is limited and sometimes contradictory regarding when and to what extent controls such as tree species influence topsoil SM variability across space and time.This study investigates spatio-temporal dynamics of topsoil SM and their controls in different forest ecosystems. The objectives are to quantify the spatial variability at the plot scale and its temporal evolution from event to seasonal scale, thereby improving our understanding of SM dynamics between wet and dry states and across different mono- and mixed-species forest stands. We investigate the variables governing spatial soil moisture variability and how they modulate SM patterns over time, with a focus on how ecohydrological processes amplify or mitigate SM variability.SM was recorded in four stands in the ECOSENSE forest in southwestern Germany, using 400 time domain transmissometry sensors (SMT100, Truebner GmbH, Germany) installed at 12 cm depth in a tree-centered design across stands of mixed and pure Douglas fir, Beech and Silver fir. A continuous 2.5-year dataset (2023 – 2025) was analysed using statistical and geostatistical approaches to identify dominating spatial SM patterns during wet and dry periods. Temporal stability was evaluated to determine the pattern persistence. Spatial SM differed significantly among plots during most of the observation period. Mean SM followed a similar annual cycle throughout all plots, with typical maxima in late winter and minima in early fall. Despite comparable soil properties and topography, the pure Beech and Douglas fir plots revealed significant seasonal differences in mean SM. Beech has more prominent autumn wetting, while Douglas fir has stronger spring drying, likely reflecting changes in evapotranspiration dynamics. The individual probability density functions of spatial soil moisture distribution transitioned between wet- or dry-preferential unimodal states and intermediate bimodal states. Across plots, spatial mean SM and the coefficient of variation exhibited an upward-convex relationship: variability was low under dry and wet ( 30% mean SM) and high under intermediate moisture conditions. The geostatistical variogram analyses showed short autocorrelation lengths, and pronounced spatial variability at few meters’ distance. Temporal stability of SM varied across plots with a range of persistently wet and dry spots. Individual locations deviated by up to 50% from temporally averaged SM at the Beech, Douglas fir and mixed plot, and up to 75% for the Silver fir plot.Combined with LiDAR derived canopy structure metrics, micro topographic maps, soil properties, and continuous ecohydrological- and meteorological observation, the presented soil moisture dataset provides a unique framework to investigate jointly modulating factors of soil moisture. It enables detailed analysis of wetting-drying cycles, including seasonal and species-specific differences.

Abstract Nitrate continues to be a major threat to drinking water resources, but rapid changes in concentrations cannot be addressed by standard laboratory approaches. This study introduces a low-cost optical sensor for real-time, in-situ monitoring of nitrate (NO3 −) and dissolved organic carbon (DOC) concentrations in natural water samples (soil water, groundwater and river water). Utilizing absorbance and fluorescence at specific wavelengths with LEDs and photodiodes, this sensor system offers an alternative to expensive and complex laboratory or in-situ spectrometer methods and is suited to be paired with flux measurements (e.g., lysimeters) to assess trends and dynamics. Rather than relying on costly xenon lamps and spectrometers, and therefore external power supply, our system consists of three modules that use only LEDs and photodiodes and are optimized for detection at the specific UVA, UVC and red wavelengths. This configuration enables measurements on a broad variety of samples including laboratory standards, groundwater, stream water, and soil water extracts. Initial tests with laboratory nitrate standard solutions up to 100 mg/l achieved high accuracy, with a linear model exhibiting an R2 of 0.99 and mean absolute error (MAE—average magnitude of errors in a set of predictions) of 2.63 mg/l. Although the sensor's accuracy does not fully match that of traditional laboratory analyses like ion chromatography or photogrammetric approaches, it maintains good predictive capabilities with R2 values exceeding 0.9 and MAE of 4.2 mg/l NO3 − for a sample mixture of groundwater, stream water and soil water with concentrations up to 66 mg/l NO3 −. DOC can be predicted with a MAE of 2.2 mg/l. Challenges such as the interference of DOC and turbidity with the nitrate absorbance signal, intense calibration procedures and site-specific variability remain, necessitating further refinement. Nevertheless, this sensor system provides a significant step toward accessible, continuous water quality monitoring and lays the foundation for linking nitrate concentrations to in-situ fluxes. These advancements are crucial for enhancing nutrient management and environmental protection practices.

The sensitivity of streamflow to changes in driving variables such as precipitation and potential evaporation is a key signature of catchment behaviour. Due to increasing interest in climate change impacts, streamflow sensitivities derived from observations have become a widely used metric for catchment characterization, model evaluation, and observation-constrained projections. However, there remain open questions regarding the robustness and temporal variability of empirically-derived sensitivities. In this paper, we revisit theoretical and empirical approaches to estimate streamflow sensitivities to precipitation and potential evaporation. First, we compare different estimation methods, primarily based on linear regression, using a synthetic dataset for which the sensitivities are known. Second, we extend this comparison and use two methods selected based on the previous analysis to estimate sensitivities for >1000 near-natural catchments. Third, we investigate how sensitivities change over time due to changes in the ratio between potential evaporation and precipitation (i.e., aridity index). Our results confirm that multiple regression is preferable to single regression, but that in presence of noise and correlation between precipitation and potential evaporation, even multiple regression methods can lead to high uncertainty, especially for potential evaporation. When analysing real catchments, sensitivity to precipitation is estimated consistently across methods, while sensitivity to potential evaporation is highly uncertain and often yields unrealistic values. Further, as the aridity index increases over time - a trend found in observational data - sensitivities decrease (by 15 %-70 % over 50 years) and can thus not be viewed as static. Empirical sensitivities, as well as their trends, relate strongly to the aridity index, but are also influenced by other factors, especially those related to catchment storage processes, as well as data uncertainty. Our results should urge caution in the use of empirical streamflow sensitivities for climate change impact assessments and call for further investigation.

Glacial retreat, volcanic eruptions, and erosion by landslides, rivers, and humans create barren landscapes where soils and vegetation develop over decadal to millennial timescales. Previous studies have documented the development of individual physical, chemical, and biological parameters in post-disturbance landscapes. However, the rates governing the co-evolution of soils, vegetation, and biogeochemical cycling remain obscure. Here, we present a multi-disciplinary analysis of 39 variables that reflect the storage and release of energy and matter on chronosequences of morainal hillslopes ranging in age from 30 to 14,000 years in two glacier forelands. We find a remarkable similarity in the timescales of physical and biological changes on these moraines. Based on this finding, we propose a ‘hillslope development index’ (HDI) that represents and quantifies the integrated hydrological, biogeochemical, and ecological state of development. In our glacier forelands, the HDI increases rapidly in the first 1000 years and then starts to asymptote, achieving a quasi-steady state at 5000 years. We find that a few key variables closely follow the HDI. These proxies allow us to assess the integrated hydrological, biogeochemical, and ecological state of hillslope development with limited experimental effort.

Knowledge about spatially distributed inundation depth and overland flow quantities, as well as related flow velocities, is critical information for establishing a pluvial flood forecasting system and the related disaster management. This kind of information is often derived from computationally demanding simulations with 2-dimensional hydrodynamic models, limiting the number of scenarios for which information can be provided and challenging real-time forecasting. To address this gap, we developed the model AccRo (Accumulation-based Runoff and Flooding), which is a computationally efficient method to derive maximum inundation depth, maximum flow velocity and maximum specific discharge of a flood event at larger spatial scales, based on an improved flow accumulation method to better represent the spatial extent of inundated areas. To assess the quality of AccRo, we compare the results from the AccRo model with the results of two different state-of-the-art 2-dimensional hydrodynamic models for design cases as well as real-world pluvial flood examples. We find that AccRo is able to represent both, the analytical solution for the design cases and the simulations of the hydrodynamic models in the real-world example in high quality, well within the range of the two hydrodynamic models. In combination with the low computational requirements, we conclude that AccRo is a valuable tool for assessing pluvial flood hazards, typically based on peak conditions of water depths, discharges and flow velocities as well as the maximum spatial extend of flooded area.

Abstract. The forest floor (FF) possesses a significant water retention capacity, facilitating the transfer of water between the atmosphere and the soil. However, knowledge on the water retention characteristics and water redistribution effects of the FF remain limited. Due to the dominance of laboratory data regarding the storage capacity of a forest’s litter layer, we used a combined FF weighted grid-lysimeter and soil moisture network to directly and in-situ measure the dynamics of water storage of the FF and fluxes from and into the FF. The objective was to quantify storage capacities, retention durations, and resulting water redistribution patterns, as well as evaporation from the FF. We present the results of our network at three sites with different altitudes located in the Black Forest, southwest Germany. The three sites have an annual mean temperature gradient from 6.3 °C to 10.3 °C, leading to humus forms that vary from typical F-Mull to typical Moder. Throughout the monitored period in 2024–2025, the storage capacity of the FF ranged between 1.4 and 4.2 g/g FF and was not only influenced by the type of litter but also by the rainfall characteristics themselves. With our field setup we could show that longer, low intensity rainfall events fill the FF storage more efficiently than shorter heavy rainfall events (−24 %). Our gridded lysimeter design revealed small-scale spatio-temporal infiltration patterns, caused by a redistribution of rainfall along the passage through the FF. The findings of the lysimeter network provide a comprehensive understanding of the influence of the FF mass on the water cycle within forest ecosystems.

Subsurface storm flow (SSF) is an essential runoff generation process in headwater catchments, yet its mechanisms remain poorly understood. Observations from natural rainfall events provide valuable insights but suffer from input uncertainties, particularly in forests where interception alters precipitation inputs and isotopic composition. To reduce these uncertainties and ensure comparable rainfall area, amount, and intensity across sites, we conducted large-scale (200 m²) sprinkling experiments on eleven trenched hillslopes in Germany and Austria, thereby facilitating cross-site comparison. Irrigation was applied at ~16 mm/h for 3–4 h, with an initial tracer-free wetting phase followed by the application of deuterated water as a tracer. SSF was monitored continuously and sampled for isotopic composition. Results show that SSF was mainly produced by the rise of the water table within permeable zones. At some sites, observations suggested the occurrence of transmissivity feedback. Initial water table depth strongly governed response time and magnitude, whereas surface slope and land cover showed no clear influence. Tracer results indicate a dual-flow-domain system: preferential flow rapidly delivered traced water, while matrix flow was dominated by pre-event water.

Pluvial (flash) floods frequently cause damage in rural and urban watersheds as a result of short-term, intense local precipitation events that cause infiltration excess runoff and overland flow. Unlike fluvial floods, pluvial floods are primarily characterized by surface runoff and flow in small ditches and creeks, making them unsuitable for evaluation using common extreme value statistics based on long-term river discharge data. Precipitation statistics alone are insufficient for predicting pluvial floods because these floods are also influenced by hydrological and hydrodynamic processes. We propose a new regional-scale pluvial flood index (PFI) that considers precipitation as well as hydrological and hydrodynamic processes to assess the hazard of surface flooding. The PFI is based on local pluvial flood hazard areas (PFHA), which are defined as areas where water depth, flow velocity, or both exceed thresholds that endanger pedestrians and vehicles. We defined four PFI classes based on historical and design events, ranging from no hazard to very large flood hazard. The PFI serves as a simple, dimensionless measure and information tool to support regional to local scale pluvial flood management. PFHA and PFI were calculated for various events using radar-based precipitation input, dynamic simulations of infiltration and saturation excess, and hydrodynamic simulations of surface runoff. PFI forecasting requires quantitative precipitation data as well as appropriate processed-based distributed hydrodynamic and hydrological models at large temporal and spatial scales. We demonstrate the PFI's applicability and utility by creating large-scale flash flood hazard maps and hindcasting an extreme historical event. Furthermore, the PFI can link to detailed local flash flood hazard information, assisting municipal decision-making. It can also be a key component in operational pluvial flood warning systems, providing information on the occurrence and severity of floods on a scale of several hectares to square kilometres. This educates stakeholders and the community, improving real-time warning systems, preparedness, and planning decisions.

Subsurface stormflow (SSF) is a critical runoff-producing mechanism in many upland and mountainous environments, yet the complex relationships between antecedent conditions, rainfall characteristics and SSF response are still not fully understood. Worldwide, the small number of SSF collection systems (trenches), as well as the generally small number of investigated SSF events limit our ability to generalize the findings and explore the influence of a broader range of storm sizes, intensities, antecedent wetness conditions and different hydrogeologic settings. In this study, we present a comprehensive analysis of SSF event characteristics in combination with rainfall event characteristics (depth and intensity), and antecedent conditions. The analysis is based on data collected over a 2-year period at two forested hillslope sites. Our results show that SSF volume is primarily controlled by total rainfall (Ptot) and antecedent wetness, with volumes being up to three orders of magnitude larger under wet initial conditions. The peak SSF flow rates of smaller events were correlated with Ptot and antecedent conditions, but for larger events (Ptot>ca. 20 mm), rainfall intensity and rainfall amount preceding peak rainfall intensity were more influential predictors than antecedent conditions. The steepness of the rising limb of the SSF hydrograph was correlated with Ptot and rainfall intensity. The antecedent soil moisture index (ASI) together with Ptot showed a high correlation with most SSF characteristics. The seasonal analysis revealed that, statistically, the largest SSF volumes occurred in winter and spring, while the highest peak flows were observed in spring and summer. Our results highlight the complex interactions among SSF responses, rainfall characteristics, and antecedent wetness conditions, underscoring the value of long-term monitoring across different seasons.

Abstract Agricultural droughts in many humid regions such as Southern Germany are projected to become more frequent and severe, leading to a substantial rise in irrigation demand to secure crop production. As a result, irrigation is becoming an increasingly important factor in agricultural production, and operational and regulatory decision-makers are relying more on data on site-specific yield responses to severe drought, especially as spatial soil heterogeneity leads to pronounced yield variability. Statistical crop yield models are commonly used to predict yield responses to drought; however, their calibration is often based on aggregated yield data, which can bias predictions of site-specific yield responses. Reported aggregated yield data for drought years are difficult to interpret because it is unclear whether crops were irrigated and to what extent. The aim of this study is to introduce a methodological approach to forecast agro-economic impacts of increasing drought frequency, using site-specific soil properties, meteorological data and yield observations from past drought years to assess potential impacts of future consecutive drought events. High drought frequencies are represented by repeatedly applying the severe drought conditions of 2003 over time. The process-based hydrological model RoGeR was coupled with a linear mixed-effects model, combining simulated soil moisture data with spatially corresponding observed yield data from post-registration variety trials (Landessortenversuche) in the German state of Baden-Württemberg. Yield data from the variety trials are characterized by standardized management practices, particularly with respect to irrigation. We demonstrate the proposed approach in a case study in Southern Germany by forecasting yields under rainfed and irrigation scenarios as well as irrigation volumes for major arable crops and representative soils for the period 2000–2024. The results showed that both yield losses and irrigation-induced yield gains during drought vary significantly between crops and across sites and are associated with substantial differences in irrigation volumes and the number of irrigation events, which in turn affect the profitability of irrigation investments. The findings confirm that site-specific modelling of yield effects is essential for evaluating the profitability of irrigation investments. Our approach provides the necessary data basis and can be applied across spatial scales.

Leaf gas exchange is the key driver of forest carbon uptake and directly determines forest carbon sink activity. Additionally, plants release a variety of biogenic volatile organic compounds (VOCs) acting as stress signals of trees. However, continuous hourly resolved measurements of leaf gas exchange and VOC emissions in tall tree canopies are challenging and remain scarce. To this end, we developed a complex in-situ leaf gas exchange measurement system with 24 cuvettes deployed on mature Fagus sylvatica (n=3) and Pseudotsuga menziesii (n=3) individuals in a mixed temperate forest. We additionally measured sap flux density (Js), radial growth and tree water deficit (TWD) to gain a holistic picture of seasonal leaf and stem water and carbon flux dynamics during the summer of 2024. During midsummer, we found a gradual reduction of stomatal conductance (gs) and VOC emissions of sun, but not shade branchlets of P. menziesii in response to moderate atmospheric and edaphic drying. Decreased gs led to a downregulation of transpiration, Js, and carbon isotope discrimination accompanied by an increase in TWD and intrinsic water use efficiency. Leaf gas exchange of shade branchlets remained unaffected due to microclimatic buffering effects. Contrarily, sun leaves of F. sylvatica profited from sunny midsummer conditions and increased leaf gas exchange, whereas shade leaves benefitted from more diffuse light during early summer exhibiting similar carbon assimilation, transpiration and VOC emissions as sun leaves. For both species we found a clear time lag of four to five hours between maximum leaf and stem water fluxes and a delay of up to 20 hours for the recovery of TWD, highlighting the role of stem water reserves. Pronounced seasonal and diurnal differences of leaf gas exchange, stem water fluxes and VOC emissions showed, that continuous data are essential to better understand the variability of ecosystem flux dynamics.