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Plant rooting depth affects ecosystem resilience to environmental stress such as drought. Deep roots connect deep soil/groundwater to the atmosphere, thus influencing the hydrologic cycle and climate. Deep roots enhance bedrock weathering, thus regulating the long-term carbon cycle. However, we know little about how deep roots go and why. Here, we present a global synthesis of 2,200 root observations of >1,000 species along biotic (life form, genus) and abiotic (precipitation, soil, drainage) gradients. Results reveal strong sensitivities of rooting depth to local soil water profiles determined by precipitation infiltration depth from the top (reflecting climate and soil), and groundwater table depth from below (reflecting topography-driven land drainage). In well-drained uplands, rooting depth follows infiltration depth; in waterlogged lowlands, roots stay shallow, avoiding oxygen stress below the water table; in between, high productivity and drought can send roots many meters down to the groundwater capillary fringe. This framework explains the contrasting rooting depths observed under the same climate for the same species but at distinct topographic positions. We assess the global significance of these hydrologic mechanisms by estimating root water-uptake depths using an inverse model, based on observed productivity and atmosphere, at 30″ (∼1-km) global grids to capture the topography critical to soil hydrology. The resulting patterns of plant rooting depth bear a strong topographic and hydrologic signature at landscape to global scales. They underscore a fundamental plant–water feedback pathway that may be critical to understanding plant-mediated global change.

• Root access to bedrock water storage or groundwater is an important trait allowing plant survival in seasonally dry environments. However, the degree of coordination between water uptake depth, leaf-level water-use efficiency (WUEi) and water potential in drought-prone plant communities is not well understood. • We conducted a 135-d rainfall exclusion experiment in a subtropical karst ecosystem with thin skeletal soils to evaluate the responses of 11 co-occurring woody species of contrasting life forms and leaf habits to a severe drought during the wet growing season. • Marked differences in xylem water isotopic composition during drought revealed distinct ecohydrological niche separation among species. The contrasting behaviour of leaf water potential in coexisting species during drought was largely explained by differences in root access to deeper, temporally stable water sources. Smaller-diameter species with shallower water uptake, more negative water potentials and lower WUEi showed extensive drought-induced canopy defoliation and/or mortality. By contrast, larger-diameter species with deeper water uptake, higher leaf-level WUEi and more isohydric behaviour survived drought with only moderate canopy defoliation. • Severe water limitation imposes strong environmental filtering and/or selective pressures resulting in tight coordination between tree diameter, water uptake depth, iso/anisohydric behaviour, WUEi and drought vulnerability in karst plant communities

Computational complexity has been the bottleneck for applying physically based simulations in large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessment. To overcome the issue of long computational time and accelerate the prediction process, this paper proposes that the prediction of maximum water depth can be considered an image‐to‐image translation problem in which water depth rasters are generated using the information learned from data instead of by conducting simulations. The proposed data‐driven urban pluvial flood approach is based on a deep convolutional neural network trained using flood simulation data obtained from three catchments and 18 hyetographs. Multiple tests to assess the accuracy and validity of the proposed approach were conducted with both design and real hyetographs. The results show that flood prediction based on neural networks use only 0.5% of the time compared with that of physically based models, with promising accuracy and generalizability. The proposed neural network can also potentially be applied to different but relevant problems, including flood analysis for flood‐safe urban layout planning.

Knowledge of the precise water depth in shallow areas of the ocean is of great significance to the safe navigation of ships and hydrographic surveying. Compared with traditional bathymetry, satellite remote sensing for water depth determination makes it possible to cover large areas by dynamic observation. In this paper, we conducted an optically shallow water bathymetric inversion study using a Stumpf empirical model, random forest model, neural network model, and support vector machine model based on Sentinel-2 satellite images and Ganquan Dao measured bathymetry data. We compared and analyzed the inversion results based on the empirical model and different machine learning models. The results show that the Stumpf empirical and machine learning models are capable of inverting optically shallow water depth. Moreover, the machine learning models had better fitting ability than the Stumpf empirical model with a sufficient number of samples, especially when the water depth was greater than 15 m. In addition, the random forest model had the highest overall accuracy among these models, with a root mean square error (RMSE) of 1.41 m and a regression coefficient (R2) of 0.96 for the test data.

Global peatlands store more carbon than is naturally present in the atmosphere 1 , 2 . However, many peatlands are under pressure from drainage-based agriculture, plantation development and fire, with the equivalent of around 3 per cent of all anthropogenic greenhouse gases emitted from drained peatland 3 – 5 . Efforts to curb such emissions are intensifying through the conservation of undrained peatlands and re-wetting of drained systems 6 . Here we report eddy covariance data for carbon dioxide from 16 locations and static chamber measurements for methane from 41 locations in the UK and Ireland. We combine these with published data from sites across all major peatland biomes. We find that the mean annual effective water table depth (WTD e ; that is, the average depth of the aerated peat layer) overrides all other ecosystem- and management-related controls on greenhouse gas fluxes. We estimate that every 10 centimetres of reduction in WTD e could reduce the net warming impact of CO 2 and CH 4 emissions (100-year global warming potentials) by the equivalent of at least 3 tonnes of CO 2 per hectare per year, until WTD e is less than 30 centimetres. Raising water levels further would continue to have a net cooling effect until WTD e is within 10 centimetres of the surface. Our results suggest that greenhouse gas emissions from peatlands drained for agriculture could be greatly reduced without necessarily halting their productive use. Halving WTD e in all drained agricultural peatlands, for example, could reduce emissions by the equivalent of over 1 per cent of global anthropogenic emissions. Halving average drainage depths in agricultural peatlands could reduce greenhouse gas emissions by the equivalent of 1 per cent of all anthropogenic emissions.

Aquifers contain the largest store of unfrozen freshwater, making groundwater critical for life on Earth. Surprisingly little is known about how groundwater responds to surface warming across spatial and temporal scales. Focusing on diffusive heat transport, we simulate current and projected groundwater temperatures at the global scale. We show that groundwater at the depth of the water table (excluding permafrost regions) is conservatively projected to warm on average by 2.1 °C between 2000 and 2100 under a medium emissions pathway. However, regional shallow groundwater warming patterns vary substantially due to spatial variability in climate change and water table depth. The lowest rates are projected in mountain regions such as the Andes or the Rocky Mountains. We illustrate that increasing groundwater temperatures influences stream thermal regimes, groundwater-dependent ecosystems, aquatic biogeochemical processes, groundwater quality and the geothermal potential. Results indicate that by 2100 following a medium emissions pathway, between 77 million and 188 million people are projected to live in areas where groundwater exceeds the highest threshold for drinking water temperatures set by any country. Model projections suggest that shallow groundwater temperatures will increase by 2.1 °C by the end of the century, with groundwater expected to exceed drinkable temperatures in a number of populated regions under a medium-emissions pathway.

Groundwater lowering can produce dramatic changes in the physiological performance and survival of plant species. The impact of decreasing water availability due to climate change and anthropogenic groundwater extraction on coastal dune ecosystems has become of increasing concern, with uncertainties about how vegetation will respond in both the short and long terms. We aimed to evaluate the water‐use responses of different plant functional types to increasing groundwater table depth and how this would affect their physiology in Mediterranean coastal dune systems differing in aridity. We modelled water‐table depth, quantified the contribution of different soil layers to plant water through Bayesian isotope mixing models and used a combination of spectral and isotope data to characterize plant ecophysiology. We found that increasing depth to groundwater triggered water uptake adjustments towards deeper soil layers only in the dry season. These adjustments in water source use were made by conifer trees (Pinus pinea, P. pinaster) and hygrophytic shrubs (Erica scoparia, Salix repens) but not by the xerophytic shrub Corema album. Moreover, we observed a greater use of groundwater under semi‐arid conditions. Accompanying the greater use of water from deep soil layers as a response to increasing groundwater depth, the semi‐arid dimorphic‐rooted conifer tree P. pinea and hygrophytic shrub E. scoparia declined their water content (WI), without implications on photosynthetic parameters, such as chlorophyll content (CHL), photochemical index (PRI) and δ13C. Unexpectedly, under semi‐arid conditions, the shallow‐rooted xerophytic shrub C. album, associated with an absence of water source use adjustments, showed a decline in WI, CHL and PRI with groundwater table lowering. We provide insight into how different species, belonging to different functional types, are acclimating to groundwater changes in a region experiencing climatic drought and a scarcity in groundwater due to anthropogenic exploitation. Greater depth to groundwater combined with limited precipitation can have a significant effect on plants’ water source use and ecophysiology in semi‐arid coastal dune ecosystems. A plain language summary is available for this article. Plain Language Summary

Amazonia contains the most extensive tropical forests on Earth, but Amazon carbon sinks of atmospheric CO 2 are declining, as deforestation and climate-change-associated droughts 1 – 4 threaten to push these forests past a tipping point towards collapse 5 – 8 . Forests exhibit complex drought responses, indicating both resilience (photosynthetic greening) and vulnerability (browning and tree mortality), that are difficult to explain by climate variation alone 9 – 17 . Here we combine remotely sensed photosynthetic indices with ground-measured tree demography to identify mechanisms underlying drought resilience/vulnerability in different intact forest ecotopes 18 , 19 (defined by water-table depth, soil fertility and texture, and vegetation characteristics). In higher-fertility southern Amazonia, drought response was structured by water-table depth, with resilient greening in shallow-water-table forests (where greater water availability heightened response to excess sunlight), contrasting with vulnerability (browning and excess tree mortality) over deeper water tables. Notably, the resilience of shallow-water-table forest weakened as drought lengthened. By contrast, lower-fertility northern Amazonia, with slower-growing but hardier trees (or, alternatively, tall forests, with deep-rooted water access), supported more-drought-resilient forests independent of water-table depth. This functional biogeography of drought response provides a framework for conservation decisions and improved predictions of heterogeneous forest responses to future climate changes, warning that Amazonia’s most productive forests are also at greatest risk, and that longer/more frequent droughts are undermining multiple ecohydrological strategies and capacities for Amazon forest resilience. Drought response is structured by water-table depth in higher-fertility Southern Amazonia, whereas lower-fertility Northern Amazonia supports more-drought-resilient forests independent of water-table depth.

Uncertainty is a significant challenge in tsunami hazard analysis. Tsunami heights are affected by complex factors and change constantly during propagation. The heights of tsunami have random characteristics. This study proposes that the water depths (related to seabed topography) are the most important factors that affect tsunami height. But across the globe, a considerable area of seabed topography has not been measured. So it is necessary to use the method of uncertainty to consider the water depth. The Wiener process is utilized to quantify the random changes of the water depth, which can better describe the situation that water depths change in a non-monotonic way. Considering the uncertainty of water depth, a Weiner process-based probabilistic model was established for predicting the maximum tsunami height, which is different from the maximum tsunami height deterministic or stochastic model previously studied with higher prediction efficiency and good prediction accuracy. The probability distribution of maximum tsunami heights was calculated using the stochastic model. The mean value of the maximum tsunami heights was very similar to the average value of 165 actual observations of maximum tsunami heights collected from 1997 to 2017.

The presented study evaluates the effect of water-table depth on groundwater evaporation (Eg) and recharge (Rg) when they occurred alternately. A lysimeter experiment incorporating a 1-year-long bromide tracer test was conducted under conditions with a range of maintained water-table depths. The results revealed that both the Rg and Eg decreased as the water table fell, until it was down to the extinction depth of groundwater evaporation (EDGE, 2.4 m). The annual quantity of Rg started to be stable at 100 mm when the water table was below the EDGE, since the maximum soil-water deficit no longer increased. When the water table was above the EDGE, Rg and Eg restricted each other and thus occurred alternately; in the wet season, >68% of the annual Rg occurred, with only <10% of the annual Eg. The fast response of the soil-water potential to irrigation and soil evaporation tended to make the gradient of the whole potential profile unidirectional when the water table was shallow, which promoted both Rg and Eg. Taking soil evaporation, Rg and Eg into account, the inversely calculated position of the bromide concentration peak was close to the actual position, suggesting that bromide tracer is effective for tracing the complicated processes of the unsaturated zone flow when Rg and Eg occur alternately. The study improved understanding of the way that the water table affects Rg and Eg in the shallow groundwater area and proved that bromide tracer would be an innovative technique to estimate Eg.