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With global climate warming, the variability of climate and weather tends to increase driving the water resources available for vegetation become more uncertain. Therefore, there is still a debate as to how vegetation response to, and to which extent it is constrained to water availability. In this study, we analyzed the correlation between vegetation growth and water availability in the Northern Hemisphere from 1982 to 2018. The condition of vegetation growth was characterized by Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI), while the Standardized Precipitation Evapotranspiration Index (SPEI) represented water condition. Our results suggest that the water deficit stress on vegetation growth in the Northern Hemisphere caused by global warming may not be as severe as described in previous studies. Although there was an increasing trend in areas where vegetation growth was constrained by water deficit in the Northern Hemisphere, such trend was not significant; in contrast, there was a significant decreasing trend in areas where vegetation growth was constrained by surplus water. In addition, there was a spatial variability in water availability for vegetation growth at mid and high latitudes. We further found that the response time of vegetation to both water deficit and water surplus was increasing, indicating that the cumulative effect of water availability on vegetation growth was delaying, and that vegetation tends to be affected by climatic extreme events such as drought with longer time scales. Our results highlight the need to reconsider the response of vegetation activities to water availability, and shed light on a more divers reaction pattern of vegetation to water in the context of climate change.

Rainfall and wind speed are two important meteorological variables that have a significant impact on agriculture, human health, and socio-economic development. While individual rainfall or wind events have been widely studied, little attention has been devoted to studying the lead–lag relationship between rainfall and wind speed, particularly in coastal regions where strong dependence between rainfall and wind speed is expected. Taking China's coastline as the case study, this paper aims to explore the variation trends of wind speed and rainfall and reveal the relationships between rainfall events and wind speeds on days before and after rainfall occurrence, by using meteorological station data from 1960 to 2018. The results show that wind speed tended to decrease while rainfall showed a slight increase for most stations. The daily wind speed increased 2 days before rainfall occurrence and decreased after then, with the highest wind speed observed during rainfall onset regardless of rainfall amount. Moreover, heavier rainfall events are more likely to occur with higher wind speeds. The findings of this study potentially improve the understanding of the dependence of rainfall and wind speed, which could help rainfall or wind-related disaster mitigation. HIGHLIGHTS Dependence of daily rainfall and wind speed over China's coastline is investigated.; Daily wind speed increased 2 days before rainfall occurrence and decreased after then.; Heavier rainfall events are more likely to occur with higher wind speeds.;

The direct and seasonally-lagged effects of compound weather and climate events in spring on vegetation productivity vary with latitude and can amplify the effects of individual weather events, according to observationally-constrained estimates and process-based terrestrial ecosystem models.

With the rapid development of urbanization and a population surge, the drawback of water pollution, especially eutrophication, poses a severe threat to ecosystem as well as human well-being. Timely monitoring the variations of water quality is a precedent to preventing the occurrence of eutrophication. Traditional monitoring methods (station monitoring or satellite remote sensing), however, fail to real-time obtain water quality in an accurate and economical way. In this study, an unmanned aerial vehicle (UAV) with a multispectral camera is used to acquire the refined remote sensing data of water bodies. Meanwhile, in situ measurement and sampling in-lab testing are carried out to obtain the observed values of four water quality parameters; subsequently, the comprehensive trophic level index (TLI) is calculated. Then three machine learning algorithms (i.e., Extreme Gradient Boosting (XGB), Random Forest (RF) and Artificial Neural Network (ANN)) are applied to construct the inversion model for water quality estimation. The measured values of water quality showed that the trophic status of the study area was mesotrophic or light eutrophic, which was consistent with the government’s water-control ambition. Among the four water quality parameters, TN had the highest correlation (r = 0.81, p = 0.001) with TLI, indicating that the variation in TLI was inextricably linked to TN. The performances of the three models were satisfactory, among which XGB was considered the optimal model with the best accuracy validation metrics (R2 = 0.83, RMSE = 0.52). The spatial distribution map of water quality drawn by the XGB model was in good agreement with the actual situation, manifesting the spatial applicability of the XGB model inversion. The research helps guide effective monitoring and the development of timely warning for eutrophication.

Availability of precipitation data at high spatial and temporal resolution is crucial for the understanding of precipitation behaviors that are determinant for environmental aspects such as hydrology, ecology, and social aspects like agriculture, food security, or health issues. This study evaluates the performance of 3B42-V7 satellite-based precipitation product on extreme precipitation estimates in China, by using the Fuzzy C-Means algorithm and L-moment-based regional frequency analysis method. The China Gauge-based Daily Precipitation Analysis (CGDPA) product is employed to measure the estimation biases of 3B42-V7. Results show that: (1) for most regions of China, the Generalized Extreme Value and Generalized Normal distributions are preferable for extreme precipitation estimates; (2) the extreme precipitation estimations of 3B42-V7 for different return periods have a high correlation with those of CGDPA, with biases within 25% for a majority of China on extreme precipitation estimates.

Anthropogenic activities have a tremendous impact on water ecosystems worldwide, especially in China. To quantitatively evaluate the hydrological alteration connected with aquatic lives and river ecological risks, we took the Beijiang River located in South China as the case study and used ecosurplus (defined as ecological carrying capacity exceeding ecological consumption)/ecodeficit (defined as ecological consumption exceeding carrying capacity) and Indicators of Hydrological Alterations to evaluate hydrological changes. The Ecologically Relevant Hydrologic Indicators were employed to select the key indices of Indicators of Hydrological Alterations, and the eco-environmental water demand calculation provide an effective way for the reservoir operation. Results showed that: (1) High flows contributed more to the ecodeficit, while low flows contributed more to the ecosurplus; (2) the ecodeficit in some parts of the river basin might exceed the ecosurplus after reservoir construction, especially along the main stream; and (3) the determination of eco-environmental water demand is a feasible way for improving the environment by controlling reservoirs. The current study can help guide the optimization of hydrological operation in the basin toward making the ecosystem healthier and has potential to further provide a reference for other basins in terms of hydrological alterations driven by anthropogenic activities.

Extreme precipitation can cause disasters such as floods, landslides and crop destruction. A further study on extreme precipitation is essential for enabling reliable projections of future changes. In this study, the trends and frequency distribution changes in extreme precipitation across different major river basins around the world during 1960–2011 were examined based on two of the latest observational data sets respectively collected from 110,000 and 26,592 global meteorological stations. The results showed that approximately a quarter of basins have experienced statistically significant increase in maximum consecutive one-day, three-day and five-day precipitation (RX1day, RX3day and RX5day, respectively). In particular, dramatic increases were found in the recent decade for the Syr Darya River basin (SDR) and Amu Darya River basin (ADR) in the Middle East, while a decrease in RX3day and RX5day were seen over the Amur River basin in East Asia. One third of basins showed remarkable changes in frequency distributions of the three indices, and in most cases the distributions shifted toward larger amounts of extreme precipitation. Relative to the subperiod of 1960–1984, wider range of the three indices over SDR and ADR were detected for 1985–2011, indicating intensification along with larger fluctuations of extreme precipitation. However, some basins have frequency distributions shifting toward smaller amounts of RX3day and RX5day, such as the Columbia River basin and the Yellow River basin. The study has potential to provide the most up-to-date and comprehensive global picture of extreme precipitation, which help guide wiser public policies in future to mitigate the effects of these changes across global river basins.

While rapid urbanization promotes social and economic development, it poses a serious threat to the health of rivers, especially the small and medium-scale rivers. Flood control for small and medium-scale rivers in highly urbanized areas is particularly important. The purpose of this study is to explore the most effective flood control strategy for small and medium-scale rivers in highly urbanized areas. MIKE 11 and MIKE 21 were coupled with MIKE FLOOD model to simulate flooding with the flood control standard, after which the best flooding control scheme was determined from a whole region perspective (both the mainstream and tributary conditions were considered). The SheGong River basin located near the Guangzhou Baiyun international airport in Guangzhou city over south China was selected for the case study. The results showed that the flooding area in the basin of interest accounts for 42% of the total, with maximum inundation depth up to 0.93 m under the 20-year return period of the designed flood. The flood-prone areas are the midstream and downstream where urbanization is high; however the downstream of the adjacent TieShan River is still able to bear more flooding. Therefore, the probable cost-effective flood control scheme is to construct two new tributaries transferring floodwater in the mid- and downstream of the SheGong River into the downstream of the TieShan River. This infers that flood control for small and medium-scale rivers in highly urbanized areas should not simply consider tributary flood regimes but, rather, involve both tributary and mainstream flood characters from a whole region perspective.