The rainfall-runoff transformation in catchments usually follows a variety of slower and faster flow paths, leading to a mixture of "younger"and "older"water in streamflow. Previous studies have investigated the time-variable distribution of water ages in streamflow (transit time distribution, TTD) using stable isotopes of water (δ 18O, δ 2H) together with transport models based on Storage Selection (SAS) functions. These functions are traditionally formulated based on soil moisture to mimic the preferential release of younger water as the system becomes wetter. In this study, we hypothesized that, in a heterogeneous catchment with a significant fast-runoff response component, precipitation intensity, in addition to soil moisture, plays a critical role in the preferential release of younger water. To test this hypothesis, we used high-resolution δ 18O data (weekly and event-based streamflow δ18O samples) in a 66 ha agricultural catchment. We tested two scenarios of the SAS function parameterization for the preferential-flow age selection: one as a function of soil moisture only and one as a function of both soil moisture and precipitation intensity. The results showed that accounting for both soil moisture and precipitation intensity to define the shape of SAS functions for preferential flow improved the tracer simulation in streamflow (increasing the Nash-Sutcliffe efficiency from 0.31 to 0.51). This also led to a higher percentage of streamflow (an increase from 2.87 % to 4.38 %) with shorter transit times (TTs younger than 7 d), with the largest differences occurring during the summer and autumn months. This was due to the fact that incorporating both soil wetness and precipitation intensity in the SAS formulation accounts for rapid flow pathways such as infiltration excess overland flow, preferential flow through macropores, and tile drain flow - allowing precipitation water to bypass much of the soil matrix and to reach the stream with minimal storage or mixing, even under dry soil conditions. We showed for the agricultural study catchment that a significant portion of event water bypasses the soil matrix through fast-flow paths, resulting in younger water reaching the stream for both low- and high-intensity precipitation. Thus, in catchments where preferential flows and overland flow are the dominant flow processes, soil-wetness-dependent and precipitation-intensity-conditional SAS functions may be required to better describe the timescale of solute transport in modelling, which has implications for stream water quality and agricultural management practices such as the timing of fertilizer application.

Adaptation of ecosystems’ root zones to climate change critically affects drought resilience and vegetation productivity. However, a global quantitative assessment of this mechanism is missing. In this study, we analyzed high-quality observation-based data to find that the global average root zone water storage capacity (S_R) increased by 11%, from 182 to 202 mm in 1982–2020. The total increase of S_R equals to 1652 billion m³ over the past four decades. S_R increased in 9 out of 12 land cover types, while three relatively dry types experienced decreasing trends, potentially suggesting the crossing of ecosystems’ tipping points. Our results underscore the importance of accounting for root zone dynamics under climate change to assess drought impacts.

The consistency of hydrological models, i.e. their ability to reproduce observed system dynamics, needs to be improved to increase their predictive power. As using streamflow data alone to calibrate models is not sufficient to constrain them and render them consistent, other strategies must be considered, in particular using additional types of data. The aim of this study was to test whether simultaneous calibration of dissolved organic carbon (DOC) and nitrate (NO3-) concentrations along with streamflow improved the hydrological consistency of a parsimonious solute-transport model. A multi-objective approach with four calibration scenarios was used to evaluate the model's predictions for an intensive agricultural headwater catchment. After calibration, the model reasonably simultaneously reproduced the dynamics of discharge and DOC and NO3- concentrations in the stream of the headwater catchment from 2008-2016. Evaluation using independent datasets indicated that the model usually reproduced dynamics of groundwater level and soil moisture in upslope and riparian zones correctly for all calibration scenarios. Using daily stream concentrations of DOC and NO3- along with streamflow to calibrate the model did not improve its ability to predict streamflow for calibration or evaluation periods. The approach significantly improved the representation of groundwater storage and to a lesser extent soil moisture in the upslope zone but not in the riparian zone. Parameter uncertainty decreased when the model was calibrated using solute concentrations, except for parameters related to fast and slow reservoir flow. This study shows the added value of using multiple types of data along with streamflow, in particular DOC and NO3- concentrations, to constrain hydrological models to improve representation of internal hydrological states and flows. With the increasing availability of solute data from catchment monitoring, this approach provides an objective way to improve the consistency of hydrological models that can be used with confidence to evaluate scenarios.

On 29 October 2024, torrential rainfall locally exceeding 300 mm within less than 24 h, caused devastating floods in the province of Valencia in Spain. In this study we quantify and describe the spatial and temporal structure of the rainfall event on this day using rainfall observations from approximately 225 personal weather stations (PWSs), low-cost commercial devices primarily operated by citizens. The network density of PWSs is ∼7 times higher compared to the dedicated rain gauge network operated by the Spanish Meteorological Agency (AEMET) in the province of Valencia, allowing a more detailed analysis of the spatial and temporal rainfall dynamics. In addition, PWS observations are available in near real-time to the public with a temporal resolution of 5 min, whereas the data from AEMET are not available in real time for the public and at a lower publicly available temporal resolution (1 h). Daily rainfall sums recorded by the PWSs showed a high correlation (r=0.94) and low bias (underestimation of 4 %) compared to rainfall reported by AEMET. In the upstream parts of the Magro catchment (1661 km2), a first burst of extreme rainfall, reaching up to 180 mm of rainfall in a few hours, started in the morning, leading to the generation of a first flood wave in the upstream parts of the catchment. While the resulting flood wave was propagating downstream through the channel network, a second rainfall peak occurred, which moved downstream along with the flood wave. This spatial and temporal coincidence has likely exacerbated the devastating power of this event. Based on the PWS data, it could have been anticipated that the extreme rainfall already occurring early in the morning would likely result in flooding in the Magro catchment. Areal rainfall maps based on interpolating PWS data indicated catchment average rainfall exceeding 150 mm d−1 across an area of more than 2500 km2. However, the total accumulated rainfall remains uncertain due to interrupted measurements likely caused by power outage and inherent uncertainty associated with interpolating point measurements. For the Rambla de Poyo catchment, the resulting average discharge was around 900 m3 s−1. The estimated return period of the catchment-average rainfall and resulting discharge from this event exhibits large uncertainties, with on average exceeding 10 000 and 900 years, respectively. This study shows the potential of PWSs for real-time rainfall monitoring and potentially flood early warning systems, by complementing dedicated rain gauge networks in order to reduce the uncertainty from areal rainfall estimates and to localize potential flooding more accurately.

Large-sample hydrology datasets have advanced hydrological research, yet the impact of landscape map details on identifying dominant streamflow generation processes remains underexplored. This study investigates the role of geology using maps of increasing detail – global, continental, and regional – each reclassified into four permeability classes. These geological attributes were used along with topography, soil, land use, and climate attributes to identify dominant controls on streamflow signatures across 4469 European catchments. To distinguish landscape influences from the otherwise dominant influence of climate, we conducted separate analyses on nested basins. Three scales were considered to assess scale-dependent patterns: large (63 nested basins), intermediate (the Moselle nested basin), and small (five nested catchments within the Moselle). The large-scale study used geology information from global and continental maps, while the others also incorporated regional maps. At the large scale, dominant controls varied widely between nested basins, but landscape generally outweighed climate, highlighting the value of our nested basin design. At this scale, continental and global geology maps produced different correlation patterns, with neither consistently superior. At the intermediate scale, increased geological detail led geology to shift from the least to the most correlated variable for certain streamflow signatures. The small-scale experiment reinforced these findings, as the regional map highlighted controls more consistent with process understanding. This study underscores the benefit of integrating detailed, region-specific geological data into large sample hydrology studies, and demonstrates the utility of a nested basins design. These findings have important implications for hydrological regionalization and streamflow prediction in ungauged basins.

While measured streamflow is commonly used for hydrological model evaluation and calibration, an increasing amount of data on additional hydrological variables is available. These data have the potential to improve process consistency in hydrological modeling and consequently for predictions under change, as well as in data-scarce or ungauged regions. Here, we show how these hydrological data beyond streamflow are currently used for model evaluation and calibration. We consider storage and flux variables, namely snow, soil moisture, groundwater level, terrestrial water storage, evapotranspiration, and altimetric water level. We aim at summarizing the state-of-the-art and providing guidance for the use of additional hydrological variables for model evaluation and calibration. Based on a review of the current literature, we summarize observation methods and uncertainties of currently available data sets, challenges regarding their implementation, and benefits for model consistency. The focus is on catchment modeling studies with study areas ranging from a few km² to ~500,000 km². We discuss challenges for implementing alternative variables that are related to differences in the spatio-temporal resolution of observations and models, as well as to variable-specific features, for example, discrepancy between observed and simulated variables. We further discuss advancements required to deal with uncertainties of the hydrological data and to integrate multiple, potentially inconsistent datasets. The increased model consistency and improvement shown by most reviewed studies regarding the additional variables often come at the cost of a slight decrease in streamflow model performance.

Hydrological models play a vital role in projecting future changes in streamflow. Despite the strong awareness of non-stationarity in hydrological system characteristics, model parameters are typically assumed to be stationary and derived through calibration on past conditions. Integrating the dynamics of system change in hydrological models remains challenging due to uncertainties related to future changes in climate and ecosystems.

Nevertheless, there is increasing evidence that vegetation adjusts its root zone storage capacity – considered a critical parameter in hydrological models – to prevailing hydroclimatic conditions. This adaptation of the root zone to moisture deficits can be estimated by the Memory method. When combined with long-term water budget estimates from the Budyko framework, the Memory method offers a promising approach to estimate future climate–vegetation interaction and thus time-variable parameters in process-based hydrological models.

Our study provides an exploratory analysis of non-stationary parameters for root zone storage capacity in hydrological models for projecting streamflow in six catchments in the Austrian Alps, specifically investigating how future changes in root zone storage impact modeled streamflow. Using the Memory method, we derive climate-based parameter estimates of the root zone storage capacity under historical and projected future climate conditions. These climate-based estimates are then implemented in our hydrological model to assess the resultant impact on modeled past and future streamflow.

Our findings indicate that climate-based parameter estimations significantly narrow the parameter ranges linked to root zone storage capacity. This contrasts with the broader ranges obtained solely through calibration. Moreover, using projections from 14 climate models, our findings indicate a substantial increase in the root zone storage capacity parameters across all catchments in the future, ranging from +10 % to +100 %. Despite these alterations, the model performance remains relatively consistent when evaluating past streamflow, independent of using calibrated or climate-based estimations for the root zone storage capacity parameter. Additionally, no significant differences are found when modeling future streamflow when including future climate-induced adaptation of the root zone storage capacity in the hydrological model. Variations in annual mean, maximum and minimum flows remain within a 5 % range, with slight increases found for monthly streamflow and runoff coefficients. Our research shows that although climate-induced changes in root zone storage capacity occur, they do not notably affect future streamflow projections in the Alpine catchments under study. Our findings suggest that incorporating a dynamic representation of the root zone storage capacity parameter may not be crucial for modeling streamflow in humid and energy-limited catchments. However, our observations indicate relatively larger changes in root zone storage capacity within the less humid catchments, corresponding to higher variations in modeled future streamflow. This suggests a potentially higher importance of dynamic representations of root zone characteristics in arid regions and underscores the necessity for further research on non-stationarity in these regions.

The temporal dynamics of water ages provide crucial insights into hydrological processes and transport mechanisms, yet there remains a significant gap in quantifying water age variability across different temporal scales. This study utilizes a comprehensive dataset spanning 70 years of hydrological observations and tritium records (1953–2022) with a semi-distributed hydrological model with integrated tracer routing routine based on StorageAge Selection functions SAS, to explore the temporal evolution of water ages in the 4000 km2 Upper Neckar River basin, Germany. Our findings indicate a systematic convergence of the variability of young water fractions and other metrics of water age in riverflow and evaporation towards stable values when averaging over increasing time scales. While at daily scales exhibiting considerable variability with young water fractions in riverflow Fwy,Q ∼ 0.01–0.91 and in evaporation Fwy,E ∼ 0.02–0.75, the variability of Fwy,Q and Fwy,E gradually reduces with increasing averaging time scales and converge to 0.45–0.47 and 0.96–0.97, respectively, between individual decades. Liquid water input (PL), comprising rainfall and snow melt, emerges as the dominant driver of Fwy,Q across all time scales. In contrast, Fwy,E shows varying controls with time scale: soil moisture content governs daily fluctuations, whereas PL dominates at the decadal scale. Overall, water ages demonstrate remarkable stability with only minor deviations in response to climatic variability: a 20% fluctuation in average decadal PL results in only ∼4% variation in Fwy,Q and ∼1% in Fwy,E over the study period. These findings suggest a lack of major long-term dynamics in water ages. Consequently, the results suggest that the physical transport dynamics in the Upper Neckar River basin, and potentially in comparable river basins with similar water age characteristics, can be considered near-stationary over multiple decades.

Quantification of precipitation partitioning into evaporation and runoff is crucial for predicting future water availability. Within the widely used Budyko framework, which relates the long-term aridity index to the long-term evaporative index, curvilinear relationships between these indices (i.e. parametric Budyko curves) allow for the quantification of precipitation partitioning under prevailing climatic conditions. A common assumption is that movement along a specific Budyko curve with changes in the aridity index over time can be used as a predictor for catchment responses to changing climatic conditions. However, various studies have reported deviations around these curves, which raises questions about the usefulness of the method for future predictions. To investigate whether parametric Budyko curves still have predictive power, we quantified the global, regional, and local evolution of deviations of catchments from their parametric Budyko curves over multiple subsequent 20-year periods throughout the last century based on historical long-term water balance data from over 2000 river catchments worldwide. This process resulted in up to four 20-year distributions of annual deviations from the long-term mean parametric curve for each catchment. To use these distributions of deviations to predict future deviations, the temporal stability of these four distributions of deviations was evaluated between subsequent periods of time. On average, it was found that the majority (62 %) of study catchments did not significantly deviate from their expected parametric Budyko curves. Out of the remaining 38 % of catchments that deviated from their expected curves, the long-term magnitude of median deviations remains minor, with 70 % of catchments falling within the range of ±0.025 of the expected evaporative index. When these median deviations were expressed as relative changes in discharge, catchments in arid regions showed higher susceptibility to larger discharge shifts compared to those in humid regions. Furthermore, a significant majority of catchments, constituting around the same percentage, was found to have stable distributions of deviations across multiple time periods, making them well suited to statistically predict future deviations with high predictive power. These findings suggest that while trajectories of change in catchments do not strictly follow the expected long-term mean parametric Budyko curves, the deviations are minor and quantifiable. Consequently, taking into account these deviations, the parametric formulations of the Budyko framework remain a valuable tool for predicting future evaporation and runoff under changing climatic conditions within quantifiable margins of error.

Accurate rainfall observations with high spatial and temporal resolutions are key for hydrological applications, in particular for reliable flood forecasts. However, rain gauge networks operated by regional or national environmental agencies are often sparse, and weather radars tend to underestimate rainfall. As a complementary source of information, rain gauges from personal weather stations (PWSs), which have a network density 100 times higher than dedicated rain gauge networks in the Netherlands, can be used. However, PWSs are prone to additional sources of error compared to dedicated gauges, because they are generally not installed and maintained according to international guidelines. A systematic long-term analysis involving PWS rainfall observations across different seasons, accumulation intervals, and rainfall intensity classes has been missing so far. Here, we quantitatively compare rainfall estimates obtained from PWSs with rainfall recorded by automatic weather stations (AWSs) from the Royal Netherlands Meteorological Institute (KNMI) over the 2018–2023 period, including a sample of 1760 individual rainfall events in the Netherlands. This sample consists of the 10 highest rainfall accumulations per season and accumulation intervals (1, 3, 6, and 24 h) over a 6-year period. It was found that the average of a cluster of PWSs severely underestimates rainfall (around 36 % and 19 % for 1 h and 24 h intervals, respectively). By adjusting the data with areal reduction factors to account for the spatial variability of rainfall extremes and applying a bias correction factor of 1.22 to compensate for instrumental bias, the average relative bias decreases to −5 % for 1 h intervals or almost zero for intervals of 3 h and longer. The highest correlations (0.85 and 0.86) and lowest coefficients of variation (0.14 and 0.18) were found for 24 h intervals during winter and autumn, respectively. We show that most PWSs are able to capture high rainfall intensities up to around 30 mm h−1, indicating that these can be utilized for applications that require rainfall data with a spatial resolution of the order of kilometres, such as for flood forecasting in small, fast-responding catchments. PWSs did not observe the most intense rainfall events, which were associated with return periods exceeding 10 or 50 years (above approximately 30 mm h−1) and occurred in spring and summer. However, the spatial distribution of rainfall likely played a large role in the observed differences rather than instrumental limitations. This emphasizes the importance of having a dense rain gauge network. In addition, the variation in undercatch is likely partly due to the disproportional underestimation of tipping bucket rain gauges with increasing intensities. Outliers during winter were likely caused by solid precipitation and can potentially be removed using a temperature module from the PWS. We recommend additional research on dynamic calibration of the tipping volumes to improve this further.

The migration of birds through a stopover site may be understood as a physical process of hydrological flow through a reservoir whose water levels rise and fall over a migration period. Hydrological flow models show promise as a way of integrating information on storage (daily counts of birds), inflow/outflow (number of birds arriving/departing each day) and transit time (length of stay (LOS)) over a migration period. We used a hydrological flow model to evaluate the relationship between stopover duration and passage population sizes of migrant birds under variable wetland conditions in two case studies. First, we considered the northward migration of Common Cranes Grus grus at Gallocanta Nature Reserve in Spain. We calibrated the model with daily counts recorded in 1984 and 1985, and then used it to predict transit time distributions based on counts of cranes 30 years later (2015–17). The model was calibrated with a mean transit time of 6.5 days observed in 1984/85, consistent with observed values of LOS, and predicted a mean transit time of 5.2 days for the 2015–17 period. The model also predicted an ~6.2× increase of the total migratory passage population of cranes at Gallocanta, which qualitatively agreed with the large increase in the overall population from the 1980s to 2010s. Second, migration dynamics of Eurasian Spoonbills Platalea leucorodia were considered during southward migration at two intertidal coastal wetlands in northern Spain from 2002 to 2005. The model well captured the observed differences in transit time between Urdaibai Biosphere Reserve (median transit time of 1.1 days) and Santoña Marshes Natural Park (median transit time of 2.1 days). Transit times of both species were negatively correlated with estimates of annual population sizes. These results indicate that hydrological flow models can provide insights into the migration ecology of waterbirds (or species where regular counts during migration are available), and that LOS is a dynamic decision that can depend on the population sizes of migratory birds.

The water balance of catchments will, in many cases, strongly depend on its state in the recent past (e.g. previous days). Processes causing significant hydrological memory may persist at longer timescales (e.g. annual). The presence of such memory could prolong drought and flood risks and affect water resources over long periods, but the global universality, strength, and origin of long memory in the water cycle remain largely unclear. Here, we quantify annual memory in the terrestrial water cycle globally using autocorrelation applied to annual time series of water balance components. These time series of streamflow, global gridded precipitation, and GLEAM potential and actual evaporation, along with a GRACE (Gravity Recovery and Climate Experiment)-informed global terrestrial water storage reconstruction, indicate that, at annual timescales, memory is typically absent in precipitation but strong in terrestrial water stores (root zone moisture and groundwater). Outgoing fluxes (streamflow and evaporation) positively scale with storage, and so they also tend to hold substantial annual memory. As storage mediates flow extremes, such memory often also occurs in annual extreme flows and is especially strong for low flows and in large catchments. Our model experiments show that storage–discharge relationships that are hysteretic and strongly non-linear are consistent with these observed memory behaviours, whereas non-hysteretic and linear drainage fails to reconstruct these signals. Thus, a multi-year slow dance of terrestrial water stores and their outgoing fluxes is common; it is not simply mirroring precipitation memory and appears to be caused by hysteretic storage and drainage mechanisms that are incorporable in hydrological models.

This paper investigates the influence of multi-decadal climatic variability on the temporal evolution of root zone storage capacities (Sr,max) and its implications for streamflow predictions in the Meuse basin. Through a comprehensive analysis of 286 catchments across Europe and the US that are hydro-climatically comparable to the Meuse basin, we construct inter-decadal distributions of past deviations in evaporative ratios (IE) from expected values based on catchment aridity (IA). These distributions of ΔIE were then used to estimate inter-decadal changes in Sr,max and to quantify the associated consequences for streamflow predictions in the Meuse basin. Our findings reveal that, while catchments do not strictly adhere to their specific parametric Budyko curves over time, the deviations in IE are generally very minor, with an average ΔIE=0.01 and an interquartile range (IQR) of −0.01 to 0.03. Consequently, these minor deviations lead to limited inter-decadal changes in Sr,max, mostly ranging between −10 and +21 mm (−5 % to +10 %). When these changes (ΔSr,max) are accounted for in hydrological models, the impact on streamflow predictions in the Meuse basin is found to be marginal, with the most significant shifts in monthly evaporation and streamflow not exceeding 4 % and 12 %, respectively. Our study underscores the utility of parametric Budyko-style equations for first-order estimates of future Sr,max in hydrological models, even in the face of climate change and variability. This research contributes to a more nuanced understanding of hydrological responses to changing climatic conditions and offers valuable insights for future climate impact studies in hydrology.

The new scientific decade (2023-2032) of the International Association of Hydrological Sciences (IAHS) aims at searching for sustainable solutions to undesired water conditions–whether it be too little, too much or too polluted. Many of the current issues originate from global change, while solutions to problems must embrace local understanding and context. The decade will explore the current water crises by searching for actionable knowledge within three themes: global and local interactions, sustainable solutions and innovative cross-cutting methods. We capitalise on previous IAHS Scientific Decades shaping a trilogy; from Hydrological Predictions (PUB) to Change and Interdisciplinarity (Panta Rhei) to Solutions (HELPING). The vision is to solve fundamental water-related environmental and societal problems by engaging with other disciplines and local stakeholders. The decade endorses mutual learning and co-creation to progress towards UN sustainable development goals. Hence, HELPING is a vehicle for putting science in action, driven by scientists working on local hydrology in coordination with local, regional, and global processes.

Large-sample hydrology datasets have become increasingly available, contributing to significant scientific advances. However, in Europe, only a few such datasets have been published, capturing only a fraction of the wealth of information from national data providers in terms of available spatial density and temporal extent. We present “EStreams”, an extensive dataset of hydro-climatic variables and landscape descriptors and a catalogue of openly available stream records for 17,130 European catchments. Spanning up to 120 years, the dataset includes streamflow indices, catchment-aggregated hydro-climatic signatures and landscape attributes (topography, soils, geology, vegetation and landcover). The catalogue provides detailed descriptions that allow users to directly access streamflow data sources, overcoming challenges related to data redistribution policies, language barriers and varied data portal structures. EStreams also provides Python scripts for data retrieval, aggregation and processing, making it dynamic in contrast to static datasets. This approach enables users to update their data as new records become available. Our goal is to extend current large-sample datasets and further integrate hydro-climatic and landscape data across Europe.

Coastal flooding events pose a critical risk in delta areas, since they are characterized by population growth and urban expansion. A better understanding of Extreme Water Levels (EWLs), the mechanisms generating them, and their components, i.e., astronomical tide and storm surge is of great importance as they drive the maintenance and design of flood protection systems. Therefore, a statistical investigation of them can provide new insights for more reliable flood risk mitigation infrastructures. In this study, we analyse these components and compare different probabilistic methods i.e., univariate extreme value analysis, copula functions, and Joint Probability Method (JPM) for the better estimation of EWLs. We use Hoek van Holland (NL) as a representative case study, since the dynamic conditions of this deltaic environment with man-made infrastructures render the area of strategic importance. The results indicate that a more accurate estimate of the declustering time between extreme events can be achieved using correlation of high surges and high wind speeds, taking into consideration also the wind direction. In the Southwest Delta this time estimated to be around 4 days. Furthermore, the EWLs components, i.e., surge and astronomical tide, show negative dependence. From the comparison between statistical approaches to model EWLs, results show that EWLs estimated via EVA and JPM do not vary significantly, while copulas’ seems to outperform the other methods. However, the selection of the proper copula to show the dependence is critical. As a conclusion, the analysis of the dependence between tides and storm surges can lead to more robust inferences of EWLs.

In this study, we propose a probabilistic metric, the formative drought rate (FDR), to quantify drought propagation. It is the probability that a meteorological drought in precipitation forms a hydrological drought in streamflow. Drought events were identified based on the standardized precipitation index and streamflow drought index, respectively, at 1-, 3-, 6- and 12-month timescales. The method was tested in three river basins in Turkey (Kucuk Menderes, Gediz and Ergene). In each river basin, meteorological stations were coupled with streamflow gauging stations to form pairs of stations depending on their distance from each other and the length of their common record periods. The FDR was calculated across all timescales for each pair of stations. It was found capable to describe the river basin-specific spatial and temporal variability of drought propagation. As the FDR is defined in the form of probability, it is expected to be a useful metric for quantifying propagation from meteorological to hydrological drought. Thus, it carries a potential for scientific research and practice in water resources management.

This study quantifies the field hydraulic performance of a dual-functionality landfill cover, combining microbial methane oxidation with water diversion using a capillary barrier. The investigated 500 m2 test field, constructed on a landfill in the Netherlands, consisted of a cover soil optimised for methane oxidation, underlain by a sandy capillary layer and a gravelly capillary block. Outflows from these layers were measured between 2009 and 2023. Average precipitation was 848 mm/a, evapotranspiration, diverted infiltration and breakthrough amounted to 504 (59.4 %), 282 (33.3 %) and 62 (7.3 %) mm/a, respectively. On average, the capillary barrier diverted 82 % of the inflow into the capillary layer. Breakthrough occurred mainly from October to March when evapotranspiration was low and the maximum water storage capacity of the cover soil was reached. During this period, inflow into the capillary barrier exceeded its diversion capacity, caused by the relatively high hydraulic conductivity of the cover soil due to its optimisation for gas transport. The diversion capacity declined drastically in the year after construction and increased again afterwards. This was attributed to suffusion of sand from the capillary layer into the capillary block and subsequent washout to greater depths or the influence of iron precipitates at the bottom of the capillary layer. The effect of a more finely grained methane oxidation layer on the hydraulic and methane oxidation performance should be investigated further. These measures could further improve the combined performance of the dual functionality landfill cover system under the given conditions of a temperate climate.

The root zone is a vital part of the Earth system and a key element in hydrology, ecology, agronomy, and land surface processes. However, its definition varies across disciplines, creating barriers to interdisciplinary understanding. Moreover, characterizing the root zone is challenging due to a lack of consensus on definitions, estimation methods, and their merits and limitations. This opinion paper provides a holistic definition of the root zone from a hydrology perspective, including its moisture storage, deficit, and storage capacity. We demonstrate that the root zone plays a critical role in the biosphere, pedosphere, rhizosphere, lithosphere, atmosphere, and cryosphere of the Earth system. We underscore the limitations of the traditional reductionist approach in modelling this complex and dynamic zone and advocate for a shift towards a holistic, ecosystem-centred approach. We argue that a holistic approach offers a more systematic, simple, dynamic, scalable, and observable way to describe and predict the role of the root zone in Earth system science.

Vegetation plays a crucial role in regulating the water cycle through transpiration, which is the water flux from the subsurface to the atmosphere via roots. The amount and timing of transpiration is controlled by the interplay of seasonal energy and water supply. The latter strongly depends on the size of the root zone storage capacity (Sr), which represents the maximum accessible volume of water that vegetation can use for transpiration. Sr is primarily influenced by hydroclimatic conditions, as vegetation optimizes its root system in such a way that it guarantees water uptake and overcomes dry periods. Sr estimates are commonly derived from root zone water deficits that result from the phase shift between the seasonal signals of root zone water inflow (i.e., precipitation) and outflow (i.e., evaporation). In irrigated croplands, irrigation water serves as an additional input into the root zone. However, this aspect has been ignored in many studies, and the extent to which irrigation influences Sr estimates has never been comprehensively quantified. In this study, our objective is to quantify the influence of irrigation on Sr and identify the regional differences therein. To this end, we integrated two irrigation methods, based on the respective irrigation water use and irrigated area fractions, into the Sr estimation. We evaluated the effects compared with Sr estimates that do not consider irrigation for a sample of 4856 catchments globally with varying degrees of irrigation activity. Our results show that Sr consistently decreased when considering irrigation, with a larger effect in catchments with a larger irrigated area. For catchments with an irrigated area fraction exceeding 10 %, the median decrease in Sr was 19 and 23 mm for the two methods, corresponding to decreases of 12 % and 15 %, respectively. Sr decreased the most for catchments in tropical climates. However, the relative decrease was the largest in catchments in temperate climates. Our results demonstrate, for the first time, that irrigation has a considerable influence on Sr estimates over irrigated croplands. This effect is as strong as the effects of snowmelt that have previously been documented in catchments that have a considerable amount of precipitation falling as snow.