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.

Abstract. 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.

BackgroundThe sodium‐glucose cotransporter 2 (SGLT2) inhibitors are a class of oral hypoglycemic agents. We undertake a systematic review and meta‐analysis of prospective studies to determine the effect of SGLT2 on blood pressure (BP) among individuals with type 2 diabetes mellitus.Methods and ResultsPubMed‐Medline, Web of Science, Cochrane Database, and Google Scholar databases were searched to identify trial registries evaluating the impact of SGLT2 on BP. Random‐effects models meta‐analysis was used for quantitative data synthesis. The meta‐analysis indicated a significant reduction in systolic BP following treatment with SGLT2 (weighted mean difference −2.46 mm Hg [95% CI −2.86 to −2.06]). The weighted mean differences for the effect on diastolic BP was −1.46 mm Hg (95% CI −1.82 to −1.09). In these subjects the weighted mean difference effects on serum triglycerides and total cholesterol were −2.08 mg/dL (95% CI −2.51 to −1.64) and 0.77 mg/dL (95% CI 0.33‐1.21), respectively. The weighted mean differences for the effect of SGLT2 on body weight was −1.88 kg (95% CI −2.11 to −1.66) across all studies. These findings were robust in sensitivity analyses.ConclusionsTreatment with SGLT2 glucose cotransporter inhibitors therefore has beneficial off‐target effects on BP in patients with type 2 diabetes mellitus and may also be of value in improving other cardiometabolic parameters including lipid profile and body weight in addition to their expected effects on glycemic control. However, our findings should be interpreted with consideration for the moderate statistical heterogeneity across the included studies.

Abstract. 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 (FLEX L ) is lumped, and it does not make use of elevation data. The second model (FLEX D ) is semidistributed with different parameter sets for different units. This model uses elevation data indirectly, taking spatially variable drivers into account. The third model (FLEX T0 ), also semi-distributed, makes explicit use of topography information. The structure of FLEX T0 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 (FLEX T ) has the same model structure and parameterization as FLEX T0 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 FLEX T0 and FLEX T perform better than the other models in nested sub-catchment validation and they are therefore better spatially transferable. Among these two models, FLEX T performs better than FLEX T0 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) FLEX T0 and FLEX T 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 FLEX T are useful for improving model transferability. Most of the precipitation on the forested hillslopes evaporates, thus generating relatively little runoff.

Abstract. 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, FLEXA, FLEXB and FLEXC were developed based on these functional units, where FLEXA is a lumped representation of the study catchment, and the semi-distributed formulations FLEXB and FLEXC progressively introduce more complexity. In spite of increased complexity, FLEXB and FLEXC 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., FLEXC, the performance increases even without traditional calibration. The additional introduction of relational constraints further improved the performance of these models.

Abstract. Conceptual environmental systems models, such as rainfall runoff models, generally rely on calibration for parameter identification. Increasing complexity of this type of model for better representation of hydrological process heterogeneity typically makes parameter identification more difficult. Although various, potentially valuable, strategies for better parameter identification were developed in the past, strategies to impose general conceptual understanding regarding how a catchment works into the process of parameterizing a conceptual model has still not been fully explored. In this study we assess the effect of imposing semi-quantitative, relational expert knowledge into the model development and parameter selection, 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 simplicity to complexity, three model set-ups, FLEXA, FLEXB and FLEXC have been developed based on these functional units. While FLEXA is a lumped representation of the study catchment, the semi-distributed formulations FLEXB and FLEXC introduce increasingly more complexity by distinguishing 2 and 3 functional units, respectively. In spite of increased complexity, FLEXB and FLEXC allow modelers to compare parameters as well as states and fluxes of their different functional units to each other. Based on these comparisons, expert knowledge based, semi-quantitative relational constraints have been imposed on three models structures. More complexity of models allows more imposed constraints. It was shown that a constrained but uncalibrated semi-distributed model, FLEXC, can predict runoff with similar performance than a calibrated lumped model, FLEXA. In addition, when constrained and calibrated, the semi-distributed model FLEXC exhibits not only higher performance but also reduced uncertainty for prediction, compared to the calibrated, lumped FLEXA model.

Abstract:The glaciers on Tibetan Plateau play an important role in the catchment hydrology of this region. However, our knowledge with respect to water circulation in this remote area is scarce. In this study, the HBV light model, which adopts the degree-day model for glacial melting, was employed to simulate the total runoff, the glacier runoff and glacier mass balance (GMB) of the Dongkemadi River Basin (DRB) at the headwater of the Yangtze River on the Tibetan Plateau, China. Firstly, the daily temperature and precipitation of the DRB from 1955 to 2008 were obtained by statistical methods, based on daily meteorological data observed in the DRB (2005)(2006)(2007)(2008) and recorded by four national meteorological stations near the DRB . Secondly, we used 4-year daily air temperature, precipitation, runoff depth and monthly evaporation, which were observed in the DRB, as input to obtain a set of proper parameters. Then, the annual runoff, the glacier runoff and GMB were calculated using the HBV model driven by interpolated meteorological data. The calculated GMB fits well with the observed results. At last, using the temperature and precipitation predicted by climate models, we predicted the changes of runoff depth and GMB of the DRB in the next 40 years. Under all climate-change scenarios, annual glacier runoff shows a significant increase due to intensified ice melting.

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