Pronounced warming occurring on the Tibetan Plateau is expected to stimulate alpine grassland growth but could also increase atmospheric aridity that limits photosynthesis. But there lacks a systematic assessment of the impact of atmospheric aridity on alpine grassland productivity. Here we combine satellite observations, flux‐tower‐based productivity, and model simulations to quantify the effect of atmospheric aridity on grassland productivity and its temporal change between 1982 and 2011. We found a negative impact of atmospheric vapor pressure deficit on grassland productivity. This negative effect becomes increasingly intensified in terms of the impact severity and extent, suggesting an increasingly important role of atmospheric aridity on productivity. We further demonstrated that this negative effect is mitigated but cannot be overcompensated by the positive effect of rising CO2. Given that vapor pressure deficit is projected to further increase by ~10–38% in the future, Tibetan alpine grasslands will face an increasing stress of atmospheric drought.

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A warmer climate is expected to accelerate global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the accuracy and improvement of global hydrological models (GHMs)-based flood simulation is insufficient for most applications. Here we compared flood simulations from five GHMs under the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) protocol, against those calculated from 1032 gauging stations in the Global Streamflow Indices and Metadata Archive for the historical period 1971-2010. A machine learning approach, namely the long short-term memory units (LSTM) was adopted to improve the GHMs-based flood simulations within a hybrid physics-machine learning approach (using basin-averaged daily mean air temperature, precipitation, wind speed and the simulated daily discharge from GHMs-CaMa-Flood model chain as the inputs of LSTM, and observed daily discharge as the output value). We found that the GHMs perform reasonably well in terms of amplitude of peak discharge but are relatively poor in terms of their timing. The performance indicated great discrepancy under different climate zones. The large difference in performance between GHMs and observations reflected that those simulations require improvements. The LSTM used in combination with those GHMs was then shown to drastically improve the performance of global flood simulations (especially in terms of amplitude of peak discharge), suggesting that the combination of classical flood simulation and machine learning techniques might be a way forward for more robust and confident flood risk assessment.

Global warming is expected to enhance the global hydrological cycle, leading dry regions to become drier and wet regions to become wetter (the DDWW paradigm). However, this hypothesis has been challenged by both observational and modeling studies. One major source of these disagreements originates from the choice of the drought indices used. Hydrological processes are complex, but drought indices are often based on a relatively simple calculation. A single index may, therefore, place undue emphasis on particular processes while ignoring others, with the result that it would not capture the holistic picture of hydrological changes and may even lead to an incorrect interpretation. Six common drought indices were calculated for the global vegetated land areas for the period from 1982 to 2012 and different indices tend to create apparently contradictory results for many regions. To overcome the single-index problem, the six drought indices were integrated into a composite map of global land moisture trends. By using this integrated approach, the majority (55%) of vegetated land areas experienced wetting or drying trends. For the regions with significant changes, supporting evidence was identified for the DDWW paradigm in one-fifth of the area. The opposite pattern to DDWW (dry areas becoming wetter and wet areas drier) occurred over 29% of the area. We also find an asymmetrical pattern with a larger proportion of wet areas getting wetter (12%) than dry areas getting drier (8%). The DDWW theory is more useful when the pure precipitation-driven drought metrics are considered but when evapotranspiration and soil conditions are integrated, the DDWW is not conclusive.

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In the past three decades, China has built more than 87 000 dams with a storage capacity of ≈6560 km3 and the total surface area of inland water has increased by 6672 km2. Leaching of N from fertilized soils to rivers is the main source of N pollution in China, but the exposure of a growing inland water area to direct atmospheric N deposition and N leaching caused by N deposition on the terrestrial ecosystem, together with increased N deposition and decreased N flow, also tends to raise N concentrations in most inland waters. The contribution of this previously ignored source of  N deposition to freshwaters is estimated in this study, as well as mitigation strategies. The results show that the annual amounts of N depositions ranged from 4.9 to 16.6 kg · ha−1 · yr−1 in the 1990s to exceeding 20 kg · ha−1 · yr−1 in the 2010s over most of regions in China, so the total mass of ΔN (the net contribution of N deposition to the increase in N concentration) for lakes, rivers and reservoirs change from 122.26 Gg N · yr−1 in the 1990s to 237.75 Gg N · yr−1 in the 2010s. It is suggested that reducing the N deposition from various sources, shortening the water-retention time in dams and decreasing the degree of regulation for rivers are three main measures for preventing a continuous increase in the N-deposition pollution to inland water in China.