Xinghua Wu

Temperature and precipitation variations during the freeze–thaw period affect snowmelt and accompanying nitrogen export in a complex manner. These influences can be long-lasting, superimposed, and strengthened. Daily discharge and nitrate-nitrogen NO₃⁻-N concentrations were monitored during the snowmelt periods of 2015 and 2016 in an agricultural watershed in northeastern China. The SWAT model was used to simulate the water and NO₃⁻-N export during the snowmelt period of 1951–2014 to identify the controlling climate factors and the combinations associated with enhanced snowmelt water and NO₃⁻-N export. Our results show that the SWAT model performs well for Re values in simulating the daily snowmelt runoff and NO₃⁻-N export, but poorly for NSE and R² values in simulating NO₃⁻-N export. This is attributed to the absence of snowmelt water refreezing and hysteresis modules. The number of days and precipitation of the stable freezing period and the starting day of the snowmelt period were the factors most strongly associated with daily snowmelt runoff, while daily NO₃⁻-N export is mostly affected by precipitation during the snowmelt period. The combinations of climatic factors favored by snowmelt runoff and NO₃⁻-N export were different. Years with longer stable freezing periods, later snowmelt period starting days, and higher rainfall during snowmelt more readily generated high snowmelt runoff. Correspondingly, under the present model configuration, later-appearing, higher-magnitude and more concentrated rainfall events, and higher temperatures between these rainfall and snowmelt events were linked to elevated NO₃⁻-N export. Finally, this study is of great importance for the prevention of spring floods and water pollution during snowmelt periods.

It is necessary to review changes in the interactions of indicators following the construction of the Three Gorges Dam (TGD) in order to explore the impact of the dam on ecology. Research on changes in interactions among indicators of the comprehensive social–economic–ecology system in the Yangtze River Basin is limited, and the objective of this study was to investigate how this system changed after the operation of the TGD, as well as how the indicators contributed to this change. Here, the correlational network approach using 38 data point indicators from 1949 to 2018 of the Yangtze River Basin was applied to analyze the changes in indicator interactions before and after the TGD operation. The TGD impoundment altered networks of the social–economic–ecological system in the Yangtze River Basin. Indicators are both less positively and less negatively connected. The number of synergy and trade-off networks clusters changed from two (Modularity = 0.33) to -six (Modularity = 0.23) and from two (Modularity = 0.015) to four (Modularity = 0.34) after the TGD operation, indicating that the sustainable development of the Yangtze River Basin might be at a middle level after the TGD operation. Further analysis revealed that the mean annual discharge, downstream fry runoff, and downstream counts of the eggs and larvae of four carp, diatom abundance index, breeding population of Chinese sturgeon, and annual precipitation contributed more to the changes in the networks after 2003.

Sediment pollution caused by potentially harmful trace elements (PHTEs) has attracted a great deal of attention, due to the persistent risks it poses to human beings and ecosystems. However, the pollution status and source identification of PHTEs from the middle and lower reaches of Huaihe River remains unclear. In this study, arsenic, cadmium, chromium, copper, nickel, zinc, lead and isotopic ratios in the lacustrine sediments of this river are investigated to determine the source of, and ecological risk posed by, these PHTEs. The results show that the concentrations of chromium, copper, nickel and zinc are generally close to the background values in the lacustrine sediments, and are assessed as representing a low degree of contamination. By contrast, significantly higher concentrations of arsenic and cadmium are commonly measured in the upper sediments, which are mainly derived from the agricultural application of fertilizers, pesticides and wastewater. Meanwhile, possible atmospheric lead deposition is identified by the isotopic analysis. Among these PHTEs, adverse biological effects of arsenic, chromium, and nickel may occur in lakes according to consensus-based sediment quality guidelines, but cadmium is selected as a PHTE for priority control according to potential ecological risk index. Our study highlights that developing regional sediment quality guidelines and soil restoration in the catchment are crucial for the freshwater ecosystem in the middle and lower reaches of Huaihe River.

Global climate change and human activities have resulted in immense changes in the Earth’s ecosystem, and the interaction between the land surface and the atmosphere is one of the most important processes. Wind is a reference for studying atmospheric dynamics and climate change, analyzing the wind speed change characteristics in historical periods, and studying the influence of wind on the Earth-atmosphere interaction; additionally, studying the wind, contributes to analyzing and alleviating a series of problems, such as the energy crisis, environmental pollution, and ecological deterioration facing human beings. In this study, data from 697 meteorological stations in China from 2000 to 2019 were used to study the distribution and trend of wind speed over the past two decades. The relationships between wind speed and climate factors were explored using statistical methods; furthermore, combined with terrain, climate change, and human activities, we quantified the contribution of environmental factors to wind speed. The results show that a downward trend was recorded before 2011, but overall, there was an increasing trend that was not significant; moreover, the wind speed changes showed obvious seasonality and were more complicated on the monthly scale. The wind speed trend mainly increased in the western region, decreased in the eastern region, was higher in the northeastern, northwestern, and coastal areas, and was lower in the central area. Temperature, bright sunshine duration, evaporation, and precipitation had a strong influence, in which wind speed showed a significant negative correlation with temperature and precipitation and vice versa for sunshine and evapotranspiration. The influence of environmental factors is diverse, and these results could help to develop environmental management strategies across ecologically fragile areas and improve the design of wind power plants to make better use of wind energy.

Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.