The root zone moisture storage capacity (S_R) of terrestrial ecosystems is a buffer providing vegetation continuous access to water and a critical factor controlling land‐atmospheric moisture exchange, hydrological response, and biogeochemical processes. However, it is impossible to observe directly at catchment scale. Here, using data from 300 diverse catchments, it was tested that, treating the root zone as a reservoir, the mass curve technique (MCT), an engineering method for reservoir design, can be used to estimate catchment‐scale S_R from effective rainfall and plant transpiration. Supporting the initial hypothesis, it was found that MCT‐derived S_R coincided with model‐derived estimates. These estimates of parameter S_R can be used to constrain hydrological, climate, and land surface models. Further, the study provides evidence that ecosystems dynamically design their root systems to bridge droughts with return periods of 10–40 years, controlled by climate and linked to aridity index, inter‐storm duration, seasonality, and runoff ratio.

This study presents an "Earth observation-based" method for estimating root zone storage capacity – a critical, yet uncertain parameter in hydrological and land surface modelling. By assuming that vegetation optimises its root zone storage capacity to bridge critical dry periods, we were able to use state-of-the-art satellite-based evaporation data computed with independent energy balance equations to derive gridded root zone storage capacity at global scale. This approach does not require soil or vegetation information, is model independent, and is in principle scale independent. In contrast to a traditional look-up table approach, our method captures the variability in root zone storage capacity within land cover types, including in rainforests where direct measurements of root depths otherwise are scarce. Implementing the estimated root zone storage capacity in the global hydrological model STEAM (Simple Terrestrial Evaporation to Atmosphere Model) improved evaporation simulation overall, and in particular during the least evaporating months in sub-humid to humid regions with moderate to high seasonality. Our results suggest that several forest types are able to create a large storage to buffer for severe droughts (with a very long return period), in contrast to, for example, savannahs and woody savannahs (medium length return period), as well as grasslands, shrublands, and croplands (very short return period). The presented method to estimate root zone storage capacity eliminates the need for poor resolution soil and rooting depth data that form a limitation for achieving progress in the global land surface modelling community.

Conceptual environmental system models, such as rainfall runoff models, generally rely on calibration for parameter identification. Increasing complexity of this type of models for better representation of hydrological process heterogeneity, typically makes parameter identification more difficult. Although various, potentially valuable, approaches for better parameter estimation have been developed, strategies to impose general conceptual understanding of how a catchment works into the process of parameter estimation has not been fully explored. In this study we assess the effects of imposing semi-quantitative, relational inequality constraints, based on expert-knowledge, for model development and parameter specification, efficiently exploiting the complexity of a semi-distributed model formulation. Making use of a topography driven rainfall-runoff modeling (FLEX-TOPO) approach, a catchment was delineated into three functional units, i.e., wetland, hillslope and plateau. Ranging from simple to complex, three model setups, FLEX^A, FLEX^B and FLEX^C were developed based on these functional units, where FLEX^A is a lumped representation of the study catchment, and the semi-distributed formulations FLEX^B and FLEX^C progressively introduce more complexity. In spite of increased complexity, FLEX^B and FLEX^C allow modelers to compare parameters, as well as states and fluxes, of their different functional units to each other, allowing the formulation of constraints that limit the feasible parameter space. We show that by allowing for more landscape-related process heterogeneity in a model, e.g., FLEX^C, the performance increases even without traditional calibration. The additional introduction of relational constraints further improved the performance of these models.

Permafrost extends 40% of the Qinghai-Tibet Plateau (QTP), a region which contains the headwaters of numerous major rivers in Asia. As an aquiclude, permafrost substantially controls surface runoff and its hydraulic connection with groundwater. The freeze–thaw cycle in the active layer significantly impacts soil water movement direction, velocity, storage capacity, and hydraulic conductivity. Under the accelerating warming on the QTP, permafrost degradation is drastically altering regional and even continental hydrological regimes, attracting the attention of hydrologists, climatologists, ecologists, engineers, and decision-makers. A systematic review of permafrost hydrological processes and modeling on the QTP is still lacking, however, leaving a number of knowledge gaps. In this review, we summarize the current understanding of permafrost hydrological processes and applications of some permafrost hydrological models of varying complexity at different scales on the QTP. We then discuss the current challenges and future opportunities, including observations and data, the understanding of processes, and model realism. The goal of this review is to provide a clear picture of where we are now and to describe future challenges and opportunities. We concluded that more efforts are needed to conduct long-term field measurements, employ more advanced observation technologies, and develop flexible and modular models to deepen our understanding of permafrost hydrological processes and to improve our ability to predict the future responses of permafrost hydrology to climate changes.

Although elevation data are globally available and used in many existing hydrological models, their information content is still underexploited. Topography is closely related to geology, soil, climate and land cover. As a result, it may reflect the dominant hydrological processes in a catchment. In this study, we evaluated this hypothesis through four progressively more complex conceptual rainfall-runoff models. The first model (FLEXL) is lumped, and it does not make use of elevation data. The second model (FLEXD) is semi-distributed with different parameter sets for different units. This model uses elevation data indirectly, taking spatially variable drivers into account. The third model (FLEXT0), also semi-distributed, makes explicit use of topography information. The structure of FLEXT0 consists of four parallel components representing the distinct hydrological function of different landscape elements. These elements were determined based on a topography-based landscape classification approach. The fourth model (FLEXT) has the same model structure and parameterization as FLEXT0 but uses realism constraints on parameters and fluxes. All models have been calibrated and validated at the catchment outlet. Additionally, the models were evaluated at two sub-catchments. It was found that FLEXT0 and FLEXT perform better than the other models in nested sub-catchment validation and they are therefore better spatially transferable. Among these two models, FLEXT performs better than FLEXT0 in transferability. This supports the following hypotheses: (1) topography can be used as an integrated indicator to distinguish between landscape elements with different hydrological functions; (2) FLEXT0 and FLEXT are much better equipped to represent the heterogeneity of hydrological functions than a lumped or semi-distributed model, and hence they have a more realistic model structure and parameterization; (3) the soft data used to constrain the model parameters and fluxes in FLEXT are useful for improving model transferability. Most of the precipitation on the forested hillslopes evaporates, thus generating relatively little runoff.