Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity (Sr) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified Sr and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale Sr with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of Sr globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with Sr globally, the relation between precipitation and Sr varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and Sr for precipitation of up to 3 mm d−1, above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and Sr. Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of Sr, which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate Sr on a global scale has the potential to be readily and easily integrated into the parameterization of Sr in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.@en

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.@en

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.

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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.

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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.@en

Climatic variability can considerably affect catchment-scale root zone storage capacity (S _umax), which is a critical factor regulating latent heat fluxes and thus the moisture exchange between land and atmosphere as well as the hydrological response and biogeochemical processes in terrestrial hydrological systems. However, direct quantification of changes in S _umax over long time periods and the mechanistic drivers thereof at the catchment scale are missing so far. As a consequence, it remains unclear how climatic variability, such as precipitation regime or canopy water demand, affects S _umax and how fluctuations in S _umax may influence the partitioning of water fluxes and therefore also affect the hydrological response at the catchment scale. Based on long-term daily hydrological records (1953-2022) in the upper Neckar River basin in Germany, we found that variability in hydro-climatic conditions, with an aridity index I _A (i.e. E _P/P) ranging between ∼ 0.9 and 1.1 over multiple consecutive 20-year periods, was accompanied by deviations ΔI _E between -0.02 and 0.01 from the expected I _E inferred from the long-term parametric Budyko curve. Similarly, fluctuations in S _umax, ranging between ∼ 95 and 115 mm or ∼ 20 %, were observed over the same time period. While uncorrelated with long-term mean precipitation and potential evaporation, it was shown that the magnitude of S _umax is controlled by the ratio of winter precipitation to summer precipitation (p < 0.05). In other words, S _umax in the study region does not depend on the overall wetness condition as for example expressed by I _A, but rather on how water supply by precipitation is distributed over the year. However, fluctuations in S _umax were found to be uncorrelated with observed changes in ΔIE. Consequently, replacing a long-term average, time-invariant estimate of S _umax with a time-variable, dynamically changing formulation of that parameter in a hydrological model did not result in an improved representation of the long-term partitioning of water fluxes, as expressed by I _E (and fluctuations ΔIE thereof), or in an improved representation of the shorter-term response dynamics. Overall, this study provides quantitative mechanistic evidence that S _umax changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of S _umax cannot explain long-term fluctuations in the partitioning of water (and thus latent heat) fluxes as expressed by deviations ΔIE from the parametric Budyko curve over multiple time periods with different climatic conditions. Similarly, it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment. This further suggests that accounting for the temporal evolution of S _umax with a time-variable formulation of that parameter in a hydrological model does not improve its ability to reproduce the hydrological response and may therefore be of minor importance for predicting the effects of a changing climate on the hydrological response in the study region over the next decades to come.

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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.@en

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.

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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.

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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.@en

Debris-flow activity is strongly controlled by hydro-meteorological trigger conditions, which are expected to change in a future climate. In this study we connect a regional hydro-meteorological susceptibility model for debris flows with climate projections until 2100 to assess changes of the frequency of critical trigger conditions for different trigger types (long-lasting rainfall, short-duration storm, snow-melt, rain-on-snow) in six regions in the Austrian Alps. We find limited annual changes of the number of days critical for debris-flow initiation when averaged over all regions, but distinct changes when separating between hydro-meteorological trigger types and study region. Changes become more evident at the monthly/seasonal scale, with a general trend of critical debris-flow trigger conditions earlier in the year. The outcomes of this study serve as a basis for the development of adaption strategies for future risk management.

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Stable isotopes (I18O) and tritium (3H) are frequently used as tracers in environmental sciences to estimate age distributions of water. However, it has previously been argued that seasonally variable tracers, such as I18O, generally and systematically fail to detect the tails of water age distributions and therefore substantially underestimate water ages as compared to radioactive tracers such as 3H. In this study for the Neckar River basin in central Europe and based on a >20-year record of hydrological, I18O and 3H data, we systematically scrutinized the above postulate together with the potential role of spatial aggregation effects in exacerbating the underestimation of water ages. This was done by comparing water age distributions inferred from I18O and 3H with a total of 21 different model implementations, including time-invariant, lumped-parameter sine-wave (SW) and convolution integral (CO) models as well as StorAge Selection (SAS)-function models (P-SAS) and integrated hydrological models in combination with SAS functions (IM-SAS). We found that, indeed, water ages inferred from I18O with commonly used SW and CO models are with mean transit times (MTTs) of g1/4g1-2 years substantially lower than those obtained from 3H with the same models, reaching MTTs of g1/410 years. In contrast, several implementations of P-SAS and IM-SAS models not only allowed simultaneous representations of storage variations and streamflow as well as I18O and 3H stream signals, but water ages inferred from I18O with these models were, with MTTs of g1/4g11-17 years, also much higher and similar to those inferred from 3H, which suggested MTTs of g1/4g11-13 years. Characterized by similar parameter posterior distributions, in particular for parameters that control water age, P-SAS and IM-SAS model implementations individually constrained with I18O or 3H observations exhibited only limited differences in the magnitudes of water ages in different parts of the models and in the temporal variability of transit time distributions (TTDs) in response to changing wetness conditions. This suggests that both tracers lead to comparable descriptions of how water is routed through the system. These findings provide evidence that allowed us to reject the hypothesis that I18O as a tracer generally and systematically "cannot see water older than about 4 years"and that it truncates the corresponding tails in water age distributions, leading to underestimations of water ages. Instead, our results provide evidence for a broad equivalence of I18O and 3H as age tracers for systems characterized by MTTs of at least 15-20 years. The question to which degree aggregation of spatial heterogeneity can further adversely affect estimates of water ages remains unresolved as the lumped and distributed implementations of the IM-SAS model provided inconclusive results. Overall, this study demonstrates that previously reported underestimations of water ages are most likely not a result of the use of I18O or other seasonally variable tracers per se. Rather, these underestimations can largely be attributed to choices of model approaches and complexity not considering transient hydrological conditions next to tracer aspects. Given the additional vulnerability of time-invariant, lumped SW and CO model approaches in combination with I18O to substantially underestimate water ages due to spatial aggregation and potentially other still unknown effects, we therefore advocate avoiding the use of this model type in combination with seasonally variable tracers if possible and instead adopting SAS-based models or time-variant formulations of CO models.

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Vegetation largely controls land surface–atmosphere interactions. Although vegetation is highly dynamic across spatial and temporal scales, most land surface models currently used for reanalyses and near-term climate predictions do not adequately represent these dynamics. This causes deficiencies in the variability of modeled water and energy states and fluxes from the land surface. In this study we evaluated the effects of integrating spatially and temporally varying land cover and vegetation characteristics derived from satellite observations on modeled evaporation and soil moisture in the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Specifically, we integrated interannually varying land cover from the European Space Agency Climate Change Initiative and interannually varying leaf area index (LAI) from the Copernicus Global Land Services (CGLS). Additionally, satellite data on the fraction of green vegetation cover (FCover) from CGLS were used to formulate and integrate a spatially and temporally varying effective vegetation cover parameterization. The effects of these three implementations on model evaporation fluxes and soil moisture were analyzed using historical offline (land-only) model experiments at the global scale, and model performances were quantified with global observational products of evaporation (E) and near-surface soil moisture (SMs). The interannually varying land cover consistently altered the evaporation and soil moisture in regions with major land cover changes. The interannually varying LAI considerably improved the correlation of SMs and E with respect to the reference data, with the largest improvements in semiarid regions with predominantly low vegetation during the dry season. These improvements are related to the activation of soil moisture–evaporation feedbacks during vegetation-water-stressed periods with interannually varying LAI in combination with interannually varying effective vegetation cover, defined as an exponential function of LAI. The further improved effective vegetation cover parameterization consistently reduced the errors of model effective vegetation cover, and it regionally improved SMs and E. Overall, our study demonstrated that the enhanced vegetation variability consistently improved the near-surface soil moisture and evaporation variability, but the availability of reliable global observational data remains a limitation for complete understanding of the model response. To further explain the improvements found, we developed an interpretation framework for how the model development activates feedbacks between soil moisture, vegetation, and evaporation during vegetation water stress periods.@en

Debris-flow activity is expected to change in a future climate. In this study we connect a susceptibility model for debris-flows on a regional scale with climate projections until 2100. We use this to assess changes of hydro-meteorological trigger conditions for debris flows in six regions in the Austrian Alps. We find limited changes on an annual basis, but distinct changes when separating between hydro-meteorological trigger types and regions. While regions in the east and in the south of Austria may experience less days susceptible to debris flows in summer, there is a general trend of increasing susceptibility earlier in the year for both, rainfall-related and snow-related trigger conditions. The outcomes of this study serve as a basis for the development of adaption strategies for future risk management from this debris-flow hazard.

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Tracing and quantifying water fluxes in the hydrological cycle is crucial for understanding the current state of ecohydrological systems and their vulnerability to environmental change. Especially the interface between ecosystems and the atmosphere that is strongly mediated by plants is important to meaningfully describe ecohydrological system functioning. Many of the dynamic interactions generated by water fluxes between soil, plant and the atmosphere are not well understood, which is partly due to a lack of interdisciplinary research. This opinion paper reflects the outcome of a discussion among hydrologists, plant ecophysiologists and soil scientists on open questions and new opportunities for collaborative research on the topic “water fluxes in the soil-plant-atmosphere continuum” especially focusing on environmental and artificial tracers. We emphasize the need for a multi-scale experimental approach, where a hypothesis is tested at multiple spatial scales and under diverse environmental conditions to better describe the small-scale processes (i.e., causes) that lead to large-scale patterns of ecosystem functioning (i.e., consequences). Novel in-situ, high-frequency measurement techniques offer the opportunity to sample data at a high spatial and temporal resolution needed to understand the underlying processes. We advocate for a combination of long-term natural abundance measurements and event-based approaches. Multiple environmental and artificial tracers, such as stable isotopes, and a suite of experimental and analytical approaches should be combined to complement information gained by different methods. Virtual experiments using process-based models should be used to inform sampling campaigns and field experiments, e.g., to improve experimental designs and to simulate experimental outcomes. On the other hand, experimental data are a pre-requisite to improve our currently incomplete models. Interdisciplinary collaboration will help to overcome research gaps that overlap across different earth system science fields and help to generate a more holistic view of water fluxes between soil, plant and atmosphere in diverse ecosystems.

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Deep seated landslides are common phenomena in Alpine areas. In case of a direct connectivity with the channel system, the catchment' s sediment yield and the probability of other forms of mass wasting processes such as debris flows may be increased significantly. Up to now, sediment dynamics related to deep-seated landslides and debris flows have not been quantified. The Lattenbach catchment (basin area 5,3 km², relief 2134 m) in Grins (Tyrol, Austria), is an example for an active DF-site, where there is geomorphological evidence of deep-seated landslide activity. In this study we shed light (1) on the location and size of active landslides in the catchment, that may deliver sediment to the channel system. Furthermore, we want to (2) quantify the contributed sediment volumes by these landslides (3) and estimate the exported sediment by debris flow. We apply an image correlation algorithm to high resolution ALS and TLS terrain models of derived over a period of 14 years to calculate surface movement rates within the catchment and locate deep seated landslide activity. We further assess the sediment yield of these landslides to the channel system and relate that with DF-volumes measured by a monitoring station at the catchment outlet. We find that there are five deep-seated landslide bodies directly connected to the channel system in the catchment. These are the largest source of sediment and significantly increases the overall sediment yield of the catchment. Our study shall contribute to the limited knowledge about the importance of deep-seated landslides for sediment dynamics and debris-flow activity, as their presence is predicted to be more frequent in the wake of global warming.

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Catchment modelling has undergone tremendous developments during the past decades. In the 1970s, the focus was on simulation of catchment runoff with process descriptions and data inputs being lumped to the catchment scale. Later developments included spatially distributed models allowing data inputs and hydrological processes to be simulated at model grid scale, that is, much finer than catchment scale. These models were able to explicitly simulate various processes such as soil moisture, evapotranspiration, groundwater and surface runoff. With the advancements in remote sensing technology and availability of high-resolution data, increased attention has in recent years been given to enhancing the capability of catchment models to reproduce spatial patterns and in this way improve our understanding of hydrological processes and the physical realism of catchment models. This development process has involved a wide spectrum of different aspects in the modelling process, reaching from an improved understanding of uncertainties in data, model parameters and model structures to new protocols for good modelling practices in water management. Recognizing the important role of biodiversity and social aspects, hydrologists are now extending the scope of their models to capture the interactions between water, biota and human social systems.

This special issue (SI) of hydrological processes is the result of an open call for abstracts announced in October 2020. The SI comprises a collection of 14 papers authored and co-authored by 77 scientists from 37 research institutions in 16 countries. Based on the key focus for each of the papers we have grouped them into five thematic topics: (i) review papers; (ii) papers developing and testing new process descriptions; (iii) papers focusing on how model calibration can improve process descriptions; (iv) papers exploring how the use of multiple model structures can improve model performance and process descriptions; and (v) papers focusing on modelling uncertainties. The grouping of the papers into the five topics should be considered as indicative only, because all papers address more than one of the five themes. The key findings in the papers of this Special Issue are summarized in the following five topic sections.@en

Identifying the vegetation and topographic variables influencing the isotopic variability of xylem water of forest vegetation remains crucial to interpret and predict ecohydrological processes in landscapes. In this study, we used temporally and spatially distributed xylem stable water isotopes measurements from two growing seasons to examine the temporal and spatial variations of xylem stable water isotopes and their relationships with vegetation and topographic variables in a Luxembourgish temperate mixed forest. Species-specific temporal variations of xylem stable water isotopes were observed during both growing seasons with a higher variability for beeches than oaks. Principal component regressions revealed that tree diameter at breast height explains up to 55% of the spatial variability of xylem stable water isotopes, while tree species explains up to 24% of the variability. Topographic variables had a marginal role in explaining the spatial variability of xylem stable water isotopes (up to 6% for elevation). During the drier growing season (2020), we detected a higher influence of vegetation variables on xylem stable water isotopes and a lower temporal variability of the xylem water isotopic signatures than during the wetter growing season (2019). Our results reveal the dominant influence of vegetation on xylem stable water isotopes across a forested area and suggest that their spatial patterns arise mainly from size- and species-specific as well as water availability-dependent water use strategies rather than from topographic heterogeneity. The identification of the key role of vegetation on xylem stable water isotopes has critical implications for the representativity of isotopes-based ecohydrological and catchments studies.

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Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually feature local datasets for model development, while predictions in ungauged basins at a global scale require training on global datasets. In this study, we develop LSTM models for over 500 catchments from the CAMELS-US data base using global ERA5 meteorological forcing and global catchment characteristics retrieved with the HydroMT tool. Comparison against an LSTM trained with local datasets shows that, while the latter generally yields superior performances due to the higher spatial resolution meteorological forcing (overall median daily NSE 0.54 vs. 0.71), training with ERA5 results in higher NSE in most catchments of Western and North-Western US (median daily NSE of 0.83 vs. 0.78). No significant changes in performance occur when substituting local with global data sources for deriving the catchment characteristics. These results encourage further research to develop LSTM models for worldwide predictions of streamflow in ungauged basins using available global datasets. Promising directions include training the models with streamflow data from different regions of the world and with higher quality meteorological forcing.@en

Satellite and hydrological model-based technologies provide estimates of rainfall and soil moisture over larger spatial scales and now cover multiple decades, sufficient to explore their value for the development of landslide early warning systems in data-scarce regions. In this study, we used statistical metrics to compare gauge-based and satellite-based precipitation products and assess their performance in landslide hazard assessment and warning in Rwanda. Similarly, the value of high-resolution satellite and hydrological model-derived soil moisture was compared to in situ soil moisture observations at Rwandan weather station sites. Based on statistical indicators, rainfall data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM_IMERG) showed the highest skill in reproducing the main spatiotemporal precipitation patterns at the study sites in Rwanda. Similarly, the satellite- and model-derived soil moisture time series broadly reproduce the most important trends of in situ soil moisture observations. We evaluated two categories of landslide meteorological triggering conditions from IMERG satellite precipitation: first, the maximum rainfall amount during a multi-day rainfall event, and second, the cumulative rainfall over the past few day(s). For each category, the antecedent soil moisture recorded at three levels of soil depth, the top 5 cm by satellite-based technologies as well as the top 50 cm and 2 m by modelling approaches, was included in the statistical models to assess its potential for landslide hazard assessment and warning capabilities. The results reveal the cumulative 3 d rainfall to be the most effective predictor for landslide triggering. This was indicated not only by its highest discriminatory power to distinguish landslide from no-landslide conditions (AUC ∼ 0.72), but also the resulting true positive alarms (TPRs) of ∼80 %. The modelled antecedent soil moisture in the 50 cm root zone Seroot(t−3) was the most informative hydrological variable for landslide hazard assessment (AUC ∼ 0.74 and TPR 84 %). The hydro-meteorological threshold models that incorporate the Seroot(t−3) and following the cause–trigger concept in a bilinear framework reveal promising results with improved landslide warning capabilities in terms of reduced rate of false alarms by ∼20 % at the expense of a minor reduction in true alarms by ∼8 %.@en