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 recently published work by Liu et al. [1] introduced the Stepwise Ecological Restoration (STERE) framework, offering an innovative, systematic and modular methodology for ecological restoration. As ecosystem degradation continues to pose a significant global threat to biodiversity, ecosystem services, and human well-being, traditional restoration methods often fail to account for the complexities and levels of degradation encountered in different ecosystems. The STERE framework represents a groundbreaking solution by providing a systematic theory that adapts restoration efforts to the unique conditions and specific needs of an ecosystem.

To better understand the increasing human impact on the water cycle and the feedbacks between hydrology and society, the International Association of Hydrological Sciences (IAHS) organized the scientific decade “Panta Rhei–Everything Flows: Change in hydrology and society” (2013–2022). A key finding is the need to use integrated approaches to assess the co-evolution of human–water systems in order to avoid unintended consequences of human interventions over long periods of time. Additionally, substantial progress has been made in leveraging new data sources on human behaviour, e.g. through text mining of social media posts. Much has been learned about detecting hydrological changes and attributing them to their drivers, e.g. quantifying climate effects on floods. To achieve further progress, we recommend broadening the understanding, the discipline and training activities, while at the same time pursuing synthesis by focusing on key themes, developing innovative approaches and finding sustainable solutions to the world’s water problems.

Identifying flood-inducing processes remains a challenge in catchment hydrology due to the complex runoff dynamics, particularly in semi-arid regions where surface and subsurface mechanisms alternatively drive streamflow across seasons. Tracer data can help identify hydrograph sources, but they are often unavailable or lack sufficient temporal resolution. To aid process identification at the event-scale, we developed an integrated hydrological-hydrodynamic framework and compared multiple model hypotheses informed by hydrological signatures. We systematically tested these hypotheses through falsification, meta-evaluation, spatial validation, and posterior diagnostics, using the semi-arid Salsola nested catchment in southern Italy as case study. While all model structures performed well on common calibration metrics, differences emerged in spatial transferability tests and alternative diagnostic assessments. Some models, despite strong performance, exhibited inconsistent representations of internal runoff mechanisms, indicating that they achieved good results for the wrong reasons. Furthermore, the choice of routing schemes significantly influenced high-peak estimations and overall model performance, particularly when Horton-type overland flow was considered. This underscores the need to treat routing methods as a key component in event-scale modeling. Our findings reveal that during consecutive storm events in the study catchment, surface processes dominate the initial stages, whereas subsurface processes become more influential in later events, providing valuable insights that may be applicable to similar semi-arid regions. Overall, we emphasize the importance of hypothesis testing in runoff process identification, which can compensate for the absence of hydrochemical data for hydrograph separation. Additionally, our results highlight the value of a landscape-based modeling approach for distinguishing alternative runoff generation processes.

The miombo woodland is the largest dry woodland formation in sub-Saharan Africa, covering an estimated area of 2.7–3.6 million km2. Compared to other global ecosystems, the miombo woodland demonstrates unique interactions between plant phenology and climate. For instance, it experiences an increase in the leaf area index (LAI) during the dry season. However, due to limited surface exchange observations in the miombo region, there is a lack of information regarding the effect of these properties on miombo woodland evaporation. It is crucial to have a better understanding of miombo evaporation for accurate hydrological and climate modelling in this region. Currently, the only available regional evaporation estimates are based on satellite data. However, the accuracy of these estimates is questionable due to the scarcity of field estimates with which to compare. Therefore, this study aims to compare the temporal dynamics and magnitudes of six satellite-based evaporation estimates – the Topography-driven Flux Exchange (FLEX-Topo) model, Global Land Evaporation Amsterdam Model (GLEAM), Moderate-Resolution Imaging Spectrometer (MODIS) MOD16 product, operational Simplified Surface Energy Balance (SSEBop) model, Thornthwaite–Mather climatic Water Balance (TerraClimate) dataset, and Water Productivity through Open access of Remotely sensed derived data (WaPOR) – during different phenophases in the miombo woodland of the Luangwa Basin, a representative river basin in southern Africa. The goal of this comparison is to determine if the temporal dynamics and magnitudes of the satellite-based evaporation estimates align with the documented feedback between miombo woodland and climate. In the absence of basin-scale field observations, actual evaporation estimates based on the multi-annual water balance (Ewb) are used for comparison. The results show significant discrepancies among the satellite-based evaporation estimates during the dormant and green-up and mid-green-up phenophases. These phenophases involve substantial changes in miombo species' canopy phenology, including the co-occurrence of leaf fall and leaf flush, as well as access to deeper moisture stocks to support leaf flush in preparation for the rainy season. The satellite-based evaporation estimates show the highest agreement during the senescence phenophase, which corresponds to the period of high temperature, high soil moisture, high leaf chlorophyll content, and highest LAI (i.e. late rainy season into the cool-dry season). In comparison to basin-scale actual evaporation, all six satellite-based evaporation estimates appear to underestimate evaporation. Satellite-based evaporation estimates do not accurately represent evaporation in this data-sparse region, which has a phenology and seasonality that significantly differ from the typical case in data-rich ground-truth locations. This may also be true for other locations with limited data coverage. Based on this study, it is crucial to conduct field-based observations of evaporation during different miombo species phenophases to improve satellite-based evaporation estimates in miombo woodlands.

Hydrology is the bloodstream of the terrestrial system. The terrestrial system is alive, with the ecosystem as its active agent. The ecosystem optimises its survival within the constraints of energy, water, climate and nutrients. The key variables that the ecosystem can modify are the controls on fluxes and storages in the hydrological system, such as: the capacities of preferential flow paths (preferential infiltration, recharge and subsurface drainage); and the storage capacities in the root zone, wetlands, canopy and ground surface. It can also, through evolution, adjust the efficiency of carbon sequestration and moisture uptake. Some of these adjustments can be made fast, particularly rootzone storage capacity, infiltration capacity, vegetation density and species composition. These system components are important controls on hydrological processes that in hydrological models are generally considered static and are determined by calibration on climatic drivers of the past. This leads to hydrological models that are dead and incapable to react to change, whereas the hydrological system is alive and will adjust. The physical law driving this evolutionary process is the second law of thermodynamics with the Carnot limit as its constraint. This physical limit allows optimisation techniques to explore the reaction of the hydrological system and its components to change in climatic drivers. This implies a new direction in the theory of hydrology, required to deal with change and addressing the Unsolved Problems in Hydrology.

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.

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.

The trend and magnitude of actual evaporation across the phenophases of miombo woodlands are unknown. This is because estimating evaporation in African woodland ecosystems continues to be a challenge, as flux observation towers are scant if not completely lacking in most ecosystems. Furthermore, significant phenophase-based discrepancies in both trend and magnitude exist among the satellitebased evaporation estimates (i.e. Global Land Evaporation Amsterdam Model (GLEAM), moderate resolution imaging spectroradiometer (MODIS), operational simplified surface energy balance (SSEBop), and water productivity through open-access remotely sensed derived data (WaPOR)), making it difficult to ascertain which of the estimates are close to field conditions. Despite the many limitations with estimation of evaporation in woodlands, the development and application of the distributed temperature system (DTS) is providing deepened insights and improved accuracy in woodland energy partitioning for evaporation assessment. In this study, the Bowen ratio distributed temperature sensing (BRDTS) approach is used to partition available energy and estimate actual evaporation across three canopy phenophases of the miombo woodland, covering the entire 2021 dry season (May–October) and early rain season (November– December) at a representative site in Mpika in Zambia, southern Africa. To complement the field experiment, four satellite-based evaporation estimates are compared to the field observations. Our results show that actual evaporation of the miombo woodland appears to follow the trend of the net radiation, with the lowest values observed during the phenophase with the lowest net radiation in the cool dry season and the highest values during the phenophase with peak net radiation in the early rainy season. It appears the continued transpiration during the driest period in the dormant phenophase (with lowest canopy cover and photosynthetic activities) may be influenced by the species-dependent adapted physiological attributes such as access to moisture in deep soils (i.e. due to deep rooting), plant water storage, and the simultaneous leaf fall and leaf flush among miombo plants. Of the four satellite-based evaporation estimates, only the WaPOR has a similar trend to the field observations across the three phenophases. However, all four satellitebased estimates underestimate the actual evaporation during the dormant and green-up phenophases. Large coefficients of variation in actual evaporation estimates among the satellite-based estimates exist in the dormant and green-up phenophases and are indicative of the difficulty in estimating actual evaporation in these phenophases. The differences between field observations and satellite-based evaporation estimates can be attributed to the model structure, processes, and inputs.

A parsimonious semi-distributed rainfall–runoff model has been developed for flow prediction. In distribution, attention is paid to both the timing of the runoff and the heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged upper Ping catchment in Thailand has been divided into 32 sub-catchments, which can be grouped into five gauged sub-catchments at which internal performance is evaluated. To test the effect of timing, first the excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from the storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire upper Ping River basin (UPRB), controlled by station P.1 located at the centre of Chiang Mai province. The outcome of FLEX-SD was compared to the (1) observations at the internal stations, (2) calibrated FLEXL model, and (3) the semi-distributed URBS model – another established semi-distributed rainfall–runoff model. FLEX-SD showed better or similar performance during calibration and especially in validation. Subsequently, we tried to distribute the moisture storage capacity by constraining FLEX-SD on patterns of the NDII (normalised difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone during dry periods. The maximum moisture-holding capacity in the root zone is assumed to be a function of the maximum seasonal range of NDII values and the annual average NDII values to construct two alternative models, namely FLEX-SD-NDIIMaxMin and FLEX-SD-NDIIAvg. The additional constraint on the moisture-holding capacity (Sumax) by NDII, particularly in FLEX-SD-NDIIAvg, improved both the model performance and the realism of its distribution across the UPRB, which corresponds linearly to the percentage of evergreen forests (R2=0.69). To check how well the models represents simulated root zone soil moisture (Sui), the performance of the FLEX-SD-NDII models was compared to the time series of the soil water index (SWI). The correlation between the Sui and the daily SWI appeared to be very good and was even better than the correlation with the NDII, which does not provide good estimates during wet periods. The SWI, which is model-based, was not used for calibration but appeared to be an appropriate index for validation.

Traditional hydrological theories are based on the assumption that soil is key in determining water's fate in the hydrological cycle. According to these theories, soil hydraulic properties determine water movement in both saturated and unsaturated zones, described by matrix flow formulas such as the Darcy–Richards equations. They also determine plant-available moisture and thereby control transpiration. Here we argue that these theories are founded on a wrong assumption. Instead, we advocate the reverse: the terrestrial ecosystem manipulates the soil to satisfy specific water management strategies, which are primarily controlled by the ecosystem's reaction to climatic drivers and by prescribed boundary conditions such as topography and lithology. According to this assumption, soil hydraulic properties are an effect rather than a cause of water movement. We further argue that the integrated hydrological behaviour of an ecosystem can be inferred from considerations about ecosystem survival and growth without relying on internal-process descriptions. An important and favourable consequence of this climate- and ecosystem-driven approach is that it provides a physical justification for catchment models that do not rely on soil information and on the complexity associated with the description of soil water dynamics. Another consequence is that modelling water movement in the soil, if required, can benefit from the constraints that are imposed by the embedding ecosystem. Here we illustrate our ecosystem perspective of hydrological processes and the arguments that support it. We suggest that advancing our understanding of ecosystem water management strategies is key to building more realistic hydrological theories and catchment models that are predictive in the context of environmental change.

Uncrewed aerial vehicles (UAVs), affordable precise global navigation satellite system hardware, multi-beam echo sounders, open-source 3D hydrodynamic modelling software, and freely available satellite data have opened up opportunities for a robust, affordable, physics-based approach to monitoring river flows. Traditional methods of river discharge estimation are based on point measurements, and heterogeneity of the river geometry is not contemplated. In contrast, a UAV-based system which makes use of geotagged images captured and merged through photogrammetry in order to generate a high-resolution digital elevation model (DEM) provides an alternative. This UAV system can capture the spatial variability in the channel shape for the purposes of input to a hydraulic model and hence probably a more accurate flow discharge. In short, the system can be used to produce the river geometry at greater resolution so as to improve the accuracy in discharge estimations. Three-dimensional hydrodynamic modelling offers a framework to establish relationships between river flow and state variables such as width and depth, while satellite images with surface water detection methods or altimetry records can be used to operationally monitor flows through the established rating curve. Uncertainties in the data acquisition may propagate into uncertainties in the relationships found between discharge and state variables. Variations in acquired geometry emanate from the different ground control point (GCP) densities and distributions used during photogrammetry-based terrain reconstruction. In this study, we develop a rating curve using affordable data collection methods and basic principles of physics. The basic principal involves merging a photogrammetry-based dry bathymetry and wet bathymetry measured using an acoustic Doppler current profiler (ADCP). The output is a seamless bathymetry which is fed into the hydraulic model so as to estimate discharge. The impact of uncertainties in the geometry on discharge estimation is investigated. The impact of uncertainties in satellite observation of depth and width is also analysed. The study shows comparable results between the 3D and traditional river rating discharge estimations. The rating curve derived on the basis of 3D hydraulic modelling was within a 95 % confidence interval of the traditional gauging-based rating curve. The 3D-hydraulic-model-based estimation requires determination of the roughness coefficient within the stable bed and the floodplain using field observation at the end of both the dry and wet season. Furthermore, the study demonstrates that variations in the density of GCPs beyond an optimal number have no significant influence on the resultant rating relationships. Finally, the study observes that which state variable approximation (water level and river width) is more accurate depends on the magnitude of the flow. Combining stage-appropriate proxies (water level when the floodplain is entirely filled and width when the floodplain is filling) in data-limited environments yields more accurate discharge estimations. The study was able to successfully apply advanced UAV and real-time kinematic positioning (RTK) technologies for accurate river monitoring through hydraulic modelling. This system may not be cheaper than in situ monitoring; however, it is notably more affordable than other systems such as crewed aircraft with lidar. In this study the calibration of the hydraulic model is based on surface velocity and the water depth. The validation is based on visual inspection of an RTK-based waterline. In future studies, a larger number of in situ gauge readings may be considered so as to optimize the validation process.

Increased attention directed at frozen-soil hydrology has been prompted by climate change. In spite of an increasing number of field measurements and modeling studies, the impact of frozen-soil on hydrological processes at the catchment scale is still unclear. However, frozen-soil hydrology models have mostly been developed based on a “bottom-up” approach, i.e. by aggregating prior knowledge at pixel scale, which is an approach notoriously suffering from equifinality and data scarcity. Therefore, in this study, we explore the impact of frozen-soil at catchment-scale, following a “top-down” approach, implying: expert-driven data analysis → qualitative perceptual model → quantitative conceptual model → testing of model realism. The complex mountainous Hulu catchment, northeast of the Tibetan Plateau, was selected as the study site. Firstly, we diagnosed the impact of frozen-soil on catchment hydrology, based on multi-source field observations, model discrepancy, and our expert knowledge. Two new typical hydrograph properties were identified: the low runoff in the early thawing season (LRET) and the discontinuous baseflow recession (DBR). Secondly, we developed a perceptual frozen-soil hydrological model, to explain the LRET and DBR properties. Thirdly, based on the perceptual model and a landscape-based modeling framework (FLEX-Topo), a semi-distributed conceptual frozen-soil hydrological model (FLEX-Topo-FS) was developed. The results demonstrate that the FLEX-Topo-FS model can represent the effect of soil freeze/thaw processes on hydrologic connectivity and groundwater discharge and significantly improve hydrograph simulation, including the LRET and DBR events. Furthermore, its realism was confirmed by alternative multi-source and multi-scale observations, particularly the freezing and thawing front in the soil, the lower limit of permafrost, and the trends in groundwater level variation. To the best of our knowledge, this study is the first report of LRET and DBR processes in a mountainous frozen-soil catchment. The FLEX-Topo-FS model is a novel conceptual frozen-soil hydrological model, which represents these complex processes and has potential for wider use in the vast Tibetan Plateau and other cold mountainous regions.

Despite the importance of forests in the water and carbon cycles, accurately measuring their contribution remains challenging, especially at night. During clear-sky nights current models and theories fail, as non-turbulent flows and spatial heterogeneity become more important. One of the standing issues is the ‘decoupling’ of the air masses in and above the canopy, where little turbulent exchange takes place, thus preventing proper measurement of atmospheric fluxes. Temperature inversions, where lower air is colder and thus more dense, can be both the cause and result of this decoupling. With Distributed Temperature Sensing (DTS) it is now possible to detect these temperature inversions, and increase our understanding of the decoupling mechanism. With DTS we detected strong inversions within the canopy of a tall Douglas Fir stand. The inversions formed in on clear-sky nights with low turbulence, and preferentially formed in the open understory. A second inversion regularly occurred above the canopy. Oscillations in this upper inversion transferred vertically through the canopy and induced oscillations in the lower inversion. We hypothesize that the inversions could form due to a local suppression of turbulent motions along the height of the canopy. This was supported by a 1-D conceptual model, which showed that a local inversion layer would always form within the canopy if the bulk inversion (over the full canopy) was strong enough. Due to the near-continuous vertical motion and specific height the inversions occur at, a very high measurement density (better than ∼2 m) and measurement frequency (>0.1 Hz) are required to detect them. Consequently, it could be possible that the observed inversions are a regular feature in similarly structured forests, but are generally not directly observed. With DTS it is possible to detect and describe these types of features, which will aid in improving our understanding of atmospheric flows over complex terrain such as forests.

Rapid modern technological advancements have led to significant improvements in river monitoring using unmanned aerial vehicles (UAVs), photogrammetric reconstruction software, and low-cost real-time kinematic Global Navigation Satellite System (RTK GNSS) equipment. UAVs allow for the collection of dry bathymetric data in environments that are difficult to access. Low-cost RTK GNSS equipment facilitates accurate measurement of wet bathymetry when combined with subaqueous measuring tools such as acoustic Doppler current profilers (ADCPs). Hydraulic models may be constructed from these data, which in turn can be used for various applications such as water management, forecasting, early warning and disaster preparedness by responsible water authorities, and construction of river rating curves. We hypothesise that the reconstruction of dry terrain with UAV-based photogrammetry combined with RTK GNSS equipment leads to accurate geometries particularly fit for hydraulic understanding and simulation models. This study sought to (1) compare open-source and commercial photogrammetry packages to verify if water authorities with low resource availability have the option to utilise open-source packages without significant compromise on accuracy; (2) assess the impact of variations in the number of ground control points (GCPs) and the distribution of the GCP markers on the quality of digital elevation models (DEMs), with a particular emphasis on characteristics that impact hydraulics; and (3) investigate the impact of using reconstructions based on different GCP numbers on conveyance and hydraulic slope. A novel method which makes use of a simple RTK tie line along the water edge measured using a low-cost but highly accurate GNSS is presented so as to correct the unwanted effect of lens distortion (“doming effect”) and enable the concatenation of geometric data from different sources. Furthermore, we describe how merging of the dry and wet bathymetry can be achieved through gridding based on linear interpolation. We tested our approach over a section of the Luangwa River in Zambia. Results indicate that the open-source software photogrammetry package is capable of producing results that are comparable to commercially available options. We determined that GCPs are essential for vertical accuracy, but also that an increase in the number of GCPs above a limited number of five only moderately increases the accuracy of results, provided the GCPs are well spaced in both the horizontal and vertical dimension. Furthermore, insignificant differences in hydraulic geometries among the various cross sections are observed, corroborating the fact that a limited well-spaced set of GCPs is enough to establish a hydraulically sound reconstruction. However, it appeared necessary to make an additional observation of the hydraulic slope. A slope derived merely from the UAV survey was shown to be prone to considerable errors caused by lens distortion. Combination of the photogrammetry results with the RTK GNSS tie line was shown to be essential to correct the slope and made the reconstruction suitable for hydraulic model setup.

Increased attention directed at frozen soil hydrology has been prompted by climate change. In spite of an increasing number of field measurements and modeling studies, the impact of frozen soil on hydrological processes at the catchment scale is still unclear. However, frozen soil hydrology models have mostly been developed based on a bottom-up approach, i.e., by aggregating prior knowledge at the pixel scale, which is an approach notoriously suffering from equifinality and data scarcity. Therefore, in this study, we explore the impact of frozen soil at the catchment scale, following a top-down approach, implying the following sequence: expert-driven data analysis → qualitative perceptual model → quantitative conceptual model → testing of model realism. The complex mountainous Hulu catchment, northeast of the Qinghai-Tibet Plateau (QTP), was selected as the study site. First, we diagnosed the impact of frozen soil on catchment hydrology, based on multi-source field observations, model discrepancy, and our expert knowledge. The following two new typical hydrograph properties were identified: The low runoff in the early thawing season (LRET) and the discontinuous baseflow recession (DBR). Second, we developed a perceptual frozen soil hydrological model to explain the LRET and DBR properties. Third, based on the perceptual model and a landscape-based modeling framework (FLEX-Topo), a semi-distributed conceptual frozen soil hydrological model (FLEX-Topo-FS) was developed. The results demonstrate that the FLEX-Topo-FS model can represent the effect of soil freeze-Thaw processes on hydrologic connectivity and groundwater discharge and significantly improve hydrograph simulation, including the LRET and DBR events. Furthermore, its realism was confirmed by alternative multi-source and multi-scale observations, particularly the freezing and thawing front in the soil, the lower limit of permafrost, and the trends in groundwater level variation. To the best of our knowledge, this study is the first report of LRET and DBR processes in a mountainous frozen soil catchment. The FLEX-Topo-FS model is a novel conceptual frozen soil hydrological model which represents these complex processes and has the potential for wider use in the vast QTP and other cold mountainous regions.

Future hydrological behavior in a changing world is typically predicted based on models that are calibrated on past observations, disregarding that hydrological systems and, therefore, model parameters may change as well. In reality, hydrological systems experience almost continuous change over a wide spectrum of temporal and spatial scales. In particular, there is growing evidence that vegetation adapts to changing climatic conditions by adjusting its root zone storage capacity, which is the key parameter of any terrestrial hydrological system. In addition, other species may become dominant, both under natural and anthropogenic influence. In this study, we test the sensitivity of hydrological model predictions to changes in vegetation parameters that reflect ecosystem adaptation to climate and potential land use changes. We propose a top-down approach, which directly uses projected climate data to estimate how vegetation adapts its root zone storage capacity at the catchment scale in response to changes in the magnitude and seasonality of hydro-climatic variables. Additionally, long-term water balance characteristics of different dominant ecosystems are used to predict the hydrological behavior of potential future land use change in a space-for-time exchange. We hypothesize that changes in the predicted hydrological response as a result of 2 K global warming are more pronounced when explicitly considering changes in the subsurface system properties induced by vegetation adaptation to changing environmental conditions. We test our hypothesis in the Meuse basin in four scenarios designed to predict the hydrological response to 2 K global warming in comparison to current-day conditions, using a process-based hydrological model with (a) a stationary system, i.e., no assumed changes in the root zone storage capacity of vegetation and historical land use, (b) an adapted root zone storage capacity in response to a changing climate but with historical land use and (c, d) an adapted root zone storage capacity considering two hypothetical changes in land use. We found that the larger root zone storage capacities (+34 %) in response to a more pronounced climatic seasonality with warmer summers under 2 K global warming result in strong seasonal changes in the hydrological response. More specifically, streamflow and groundwater storage are up to −15 % and −10 % lower in autumn, respectively, due to an up to +14 % higher summer evaporation in the non-stationary scenarios compared to the stationary benchmark scenario. By integrating a time-dynamic representation of changing vegetation properties in hydrological models, we make a potential step towards more reliable hydrological predictions under change.

Forest evaporation exports a vast amount of water vapor from land ecosystems into the atmosphere. Meanwhile, evaporation during rain events is neglected or considered of minor importance in dense ecosystems. Air convection moves the water vapor upwards leading to the formation of large invisible vapor plumes, while the identification of visible vapor plumes has not yet been studied. This work describes the formation process of vapor plumes in a tropical wet forest as evidence of evaporation processes happening during rain events. In the dry season of 2018 at La Selva Biological Station (LSBS) in Costa Rica it was possible to spot visible vapor plumes within the forest canopy. The combination of time-lapse videos at the canopy top with conventional meteorological measurements along the canopy profile allowed us to identify the driver conditions required for this process to happen. This phenomenon happened only during rain events. Visible vapor plumes during the daytime occurred when the following three conditions are accomplished: presence of precipitation (P), air convection, and a lifting condensation level value smaller than 100 m at 43 m height (z lcl.43).

Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.

In the Luangwa basin in Zambia, long-term total water storage variations were observed with Gravity Recovery and Climate Experiment, but not reproduced by a standard conceptual hydrological model that encapsulates our current understanding of the dominant regional hydrological processes. The objective of this study was to identify potential processes underlying these low-frequency variations through combined data analysis and model hypothesis testing. First, we analyzed the effect of data uncertainty by contrasting observed storage variations with multi-annual estimates of precipitation and evaporation from multiple data sources. Second, we analyzed four different combinations of model forcing and evaluated their skill to reproduce the observed long-term storage variations. Third, we formulated alternative model hypotheses for groundwater export to potentially explain low-frequency storage variations. Overall, the results suggest that the initial model's inability to reproduce the observed low-frequency storage variations was partly due to the forcing data used and partly due to the missing representation of regional groundwater export. More specifically, the choice of data source affected the model's ability to reproduce annual maximum storage fluctuations, whereas the annual minima improved by adapting the model structure to allow for groundwater export from a deeper groundwater layer. This suggests that, in contrast to previous research, conceptual models can reproduce long-term storage fluctuations if a suitable model structure is used. Overall, the results highlight the value of alternative data sources and iterative testing of model structural hypotheses to improve runoff predictions in a poorly gauged basin leading to enhanced understanding of its hydrological processes.