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.