The 2303 Wetlands of International Importance distribute unevenly in different continents. Europe owns the largest number of sites, while Africa has the largest area of sites. More than half of the sites are affected by three or four impact factors (55%). The most significant impact factors are pollution (54%), biological resources use (53%), natural system modification (53%), and agriculture and aquaculture (42%). The main affected objects are land area and environment of the wetlands, occurred in 75% and 69% of the sites, respectively. The types most affected by land area occupation are river wetlands and lake wetlands, the types with the greatest impact on environment are marine/coastal wetlands and river wetlands, the type with the greatest impact on biodiversity is river wetlands, the types most affected by water resources regulation are marsh wetlands and river wetlands, and the types most affected by climate change are lake wetlands and marine/coastal wetlands. About one-third of the wetland sites have been artificially reconstructed. However, it is found that the proportions of natural wetland sites not affected or affected by only one factor are generally higher than that of wetland sites both containing natural wetlands and human-made wetlands, while the proportions of wetland sites both containing natural wetlands and human-made wetlands affected by three or four factors are generally higher than that of natural wetland sites. Wetland sites in the UK and Ireland are least affected among all countries. Wetland management plans in different regions still have large space for improvement, especially in Africa and Asia. The protection and restoration of global wetlands can be carried out in five aspects, including management and policy, monitoring, restoration, knowledge, and funding.

Machine learning algorithms are becoming more and more popular in natural disaster assessment. Although the technology has been tested in flood susceptibility analysis of several watersheds, research on global flood disaster risk assessment based on machine learning methods is still rare. Considering that the watershed is the basic unit of water management, the purpose of this study was to conduct a risk assessment of floods in the global fourth-level watersheds. Thirteen conditioning factors were selected, including: maximum daily precipitation, precipitation concentration degree, altitude, slope, relief degree of land surface, soil type, Manning coefficient, proportion of forest and shrubland, proportion of artificial surface, proportion of cropland, drainage density, population, and gross domestic product. Four machine learning algorithms were selected in this study: logistic regression, naive Bayes, AdaBoost, and random forest. The global susceptibility assessment model was constructed based on four machine learning algorithms, thirteen conditioning factors, and global flood inventories. The evaluation results of the model show that the random forest performed better in the test, and is an efficient and reliable tool in flood susceptibility assessment. Sensitivity analysis of the conditioning factors showed that precipitation concentration degree and Manning coefficient were the main factors affecting flood risk in the watersheds. The susceptibility map showed that fourth-level watersheds in the global high-risk area accounted for a large proportion of the total watersheds. With the increase of extreme hydrological events caused by climate change, global flood disasters are still one of the most threatening natural disasters. The global flood susceptibility map from this study can provide a reference for global flood management.

The resource allocation of different component organs of crops under drought stress is a strategy for the coordinated growth of crops, which also reflects the adaptability of crops to drought condition. In this study, maize variety namely ‘Denghai 618’, under the ventilation shed, two treatment groups of light drought (LD) and moderate drought (MD), and the same rehydration after drought are set, as well as the normal water supply for control in shed (CS). The drought experiment was conducted in the jointing–tasseling stage in 2021. The effects of different drought stress on the water content and biomass allocation of each component organ were analyzed. The results showed that (1) during the drought period, the water content of each component organ of summer maize decreased in general, but the Water content distribution ratio (WCDR) of the root increased by 1.83%– 2.35%. The WCDR of stem increased by 0.52%– 1.40%. (2) Under different drought treatments, the root biomass (RB) increased 33.94% ~ 46.09%, and fruit biomass (FB) increased 1.46% ~ 2.49%, the leaf biomass (LB) decreased by 8.2% and 1.46% respectively under LD and MD. (3) The allometric growth model constructed under sufficient water is not suitable for drought stress; the allometric exponent α under drought stress is lower than that of the CS: CS (α=1.175) > MD (α = 1.136) > LD (α = 1.048), which also indicates that the impact of existing climate change on grain yield may be underestimated. This study is helpful to understand the adaptive strategies of the coordinated growth of maize component organs under drought stress and provide a reference for the prediction of grain yield under climate change.

Drought-flood abrupt alternation (DFAA) is an extreme hydrological phenomenon caused by meteorological anomalies. To combat the climate change, the watershed integrated management model—Soil and Water Assessment Tool model (SWAT)—was used to simulate DFAA, total nitrogen (TN) and total phosphorus (TP) from 1961 to 2050, based on measured precipitation data in the Hetao area and the downscaled Representative Concentration Pathways (RCPs) climate scenarios. In the future, the increase in temperature and the increase in extreme precipitation will aggravate the pollution of water bodies. Results indicate that the risk of water quality exceeding the standard will increase when DFAA happens, and the risk of water quality exceeding the standard was the greatest in the case of drought-to-flood events. Results also indicate that, against the backdrop of increasing temperature and increasing precipitation in the future, the frequency of long-cycle and short-cycle drought-flood abrupt alternation index (LDFAI, SDFAI) in the Hetao area will continue to decrease, and the number of DFAA situations will decrease. However, the zone of high-frequency DFAA situations will move westward from the eastern Ulansuhai Nur Lake, continuing to pose a risk of water quality deterioration in that region. These results could provide a basis for flood control, drought resistance and pollution control in the Hetao and other areas.

Under the influence of global climate change, the discharges of major global rivers directed into the ocean have undergone significant changes. To study the trends and causes in discharge variation, we selected 40 large rivers and analyzed their annual discharges near their estuaries from 1960 to 2010. The method of runoff variation attribution analysis based on the Budyko hypothesis for large-scale basins was developed, in which influencing factors of human activities and glacial melting factors were added to the formula. The contribution rate of climate factors and human activities to changes in discharge were quantitatively identified. Climatic factors include precipitation, evapotranspiration and glacial melting. Human activity factors include underlying surface and artificial water transfer. The contribution rate is determined by the elastic coefficient, which is obtained by the ratio of change rate of each factor and the change rate of runoff. The results indicated that the discharges predominantly showed downward trends with a few upward trends. Rivers in North America and Africa showed downward trends, and those in Europe principally showed upward trends. Climate was the main influencing factor of discharges changes, and only approximately 25% of river discharges were greatly affected by human activities. River discharges in 75% of the basins which mainly contains subtropical monsoon humid climate and savanna climate zones showed upward trends. In the four basins which are mainly contains tropical rainforest climate and tropical monsoon climate, they all showed downward trends. The trend of discharges in the temperate monsoon climate, temperate continental climate, and temperate maritime climate cannot be accurately judged because of irregular variation. The discharges in the mid-high latitudinal zones predominantly showed upward trends, while those in the mid-low latitudinal zones with the influence of human activities showed downward trends.