Numerous studies have explored the role of vegetation in controlling and mediating hydrological states and fluxes at the level of individual processes, which has led to improvements in our understanding of plot‐scale dynamics. Relatively less effort has been directed toward spatially‐explicit studies of vegetation‐hydrology interactions at larger scales of a landscape. Only few continuous, process‐oriented ecohydrological models had been proposed with structures of varying complexity. This study contributes to their further evolution and presents a novel ecohydrological model, Tethys‐Chloris. The model synthesizes the state‐of‐the‐art knowledge on individual processes and coupling mechanisms drawn from the disciplines of hydrology, plant physiology, and ecology. Specifically, the model reproduces all essential components of the hydrological cycle: it resolves the mass and energy budgets in the atmospheric surface layer at the hourly scale, while representing up to two layers of vegetation; it includes a module of snowpack evolution; it describes the saturated and unsaturated soil water dynamics, processes of runoff generation and flow routing. The component of vegetation dynamics parameterizes life cycle processes of different plant functional types, including photosynthesis, phenology, carbon allocation, and tissue turnover. This study presents a confirmation of the long‐term, plot‐scale model performance by simulating two types of ecosystems corresponding to different climate conditions. A consistent and highly satisfactory model skill in reproducing the energy and water budgets as well as physiological cycles of plants with minimum calibration overhead is demonstrated. Furthermore, these applications demonstrate that the model permits the identification of data types and observation frequencies crucial for appropriate evaluation of modeled dynamics. More importantly, through a synthesis of a wide array of process representations, the model ensures that climate, soil, vegetation, and topography collectively identify essential modes controlling ecohydrological systems, i.e., that satisfactory performance is a result of appropriate mimicking of internal processes.

Decision makers and consultants are particularly interested in “detailed” information on future climate to prepare adaptation strategies and adjust design criteria. Projections of future climate at local spatial scales and fine temporal resolutions are subject to the same uncertainties as those at the global scale but the partition among uncertainty sources (emission scenarios, climate models, and internal climate variability) remains largely unquantified. At the local scale, the uncertainty of the mean and extremes of precipitation is shown to be irreducible for mid and end‐of‐century projections because it is almost entirely caused by internal climate variability (stochasticity). Conversely, projected changes in mean air temperature and other meteorological variables can be largely constrained, even at local scales, if more accurate emission scenarios can be developed. The results were obtained by applying a comprehensive stochastic downscaling technique to climate model outputs for three exemplary locations. In contrast with earlier studies, the three sources of uncertainty are considered as dependent and, therefore, non‐additive. The evidence of the predominant role of internal climate variability leaves little room for uncertainty reduction in precipitation projections; however, the inference is not necessarily negative, because the uncertainty of historic observations is almost as large as that for future projections with direct implications for climate change adaptation measures.

Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real‐time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high‐fidelity modeling in real‐time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time‐critical decision‐making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high‐fidelity computations in real‐time.

Interannual variability of precipitation can directly or indirectly affect many hydrological, ecological, and biogeochemical processes that, in turn, influence climate. Despite the significant importance of the phenomenon, few studies have attempted to elucidate spatial patterns of this variability at the global scale. This study uses land gauge precipitation records of the Global Historical Climatology Network, version 2, as well as reanalysis data to provide an assessment of the spatial organization of characteristics of precipitation interannual variability. The coefficient of variation, skewness, and short- and long-range dependence of the precipitation variability are analyzed. Among the major inferences is that the coefficient of variation of annual precipitation shows a significant correlation with intra-annual seasonality. Specifically, subyearly precipitation anomalies occurring in locations with pronounced seasonality affect the total yearly amount, imposing a higher variability in the annual precipitation fluctuations. Furthermore, the study illustrates that a positive skewness of the distribution of annual precipitation is a robust property worldwide and its magnitude is related to the coefficient of variation. Additionally, annual precipitation exhibits very weak small-lag autocorrelation. Conversely, the intensity of long-memory–long-range dependence is significantly larger than zero, hinting that organized long-term variations are an important feature of the interannual variability of precipitation.