Trend analysis of streamflow provides practical information for better management of water resources on the eve of climate change. Thus, the objective of this study is to evaluate the presence of possible trends in the annual, seasonal, maximum, and minimum flow of Yangtze River at Cuntan and Zhutuo stations in China for the period 1980 to 2015. The assessment was carried out using the Mann–Kendall trend test, and the innovative trend analysis, while Sen’s slope is used to estimate the magnitude of the changes. The results of the study revealed that there were increasing and decreasing trends at Cuntan and Zhutuo stations in different months. The mean annual flow was found to decrease at a rate of −26.76 m³/s and −17.37 m³/s at both stations. The minimum flow was found to significantly increase at a rate of 30.57 m³/s and 16.37 m³/s, at a 95% level of confidence. Maximum annual flows showed an increasing trend in both regions of the Yangtze River. On the seasonal scale, the results showed that stations are more sensitive to seasonal flow variability suggesting a probable flooding aggravation. The winter season showed an increasing flow trend, while summer showed a decreasing trend. The spring flow was found to have an increasing trend by the Mann–Kendall test at both stations, but in the Zhutuo Station, a decreasing trend was found by way of the innovative trend analysis method. However, the autumn flow indicated a decreasing trend over the region by the Mann–Kendall (MK) test at both stations while it had an increasing trend in Cuntan by the innovative trend analysis method. The result showed nonstationary increasing and decreasing flow trends over the region. Innovative trend analysis method has the advantage of detecting the sub-trends in the flow time series because of its ability to present the results in graphical format. The results of the study indicate that decreasing trends may create water scarcity if proper adaptation measures are not taken.

The human influence on the river ecosystem has increased in recent years to feed the growing demand for water to communities by constructing different water structures. It is essential to understand the potential impacts of water structures on river hydrologic regimes. Thus, this study investigates the influence of the cascade dams located upstream of the Three Gorges Dam on the Yangtze River on the river ecosystem. The study was carried out for the period 2003–2015 for both Cuntan and Miaohe stations. The analysis was conducted considering two periods, pre-impact; before the dam construction and post-impact; after the dam construction. The assessment was carried out using “Indicators of Hydrologic Alteration.” The results of this study revealed that the cascade dams built upstream of Three Gorges Dam has both positive and negative impacts at both stations. Flows were found to have positive impacts in July while low in October for both stations. The 1-day minimum flows were found to decrease by 7% over Miaohe Station while the 1-day maximum was decreased up to 2% in Cuntan. Overall, the results of the study indicate that there are undesirable impacts which should be adjusted to maintain the river ecosystem at an acceptable level compared to its natural state. It is expected that the findings of the study can guide water managers to adjust the hydropower operation sustainably.

Biochar is a carbon-based substance made by the pyrolysis of organic waste. The amount of biochar produced is determined by the type of feedstock and pyrolysis conditions. Biochar is frequently added to the soil for various reasons, including carbon sequestration, greenhouse gas mitigation, improved crop production by boosting soil fertility, removing harmful contaminants, and drought mitigation. Biochar may also be used for waste management and wastewater treatment. Biochar’s various advantages make it a potentially appealing instrument material for current science and technology. Although biochar’s impacts on soil chemical qualities and fertility have been extensively researched, little is known about its impact on enhancing soil physical qualities. This review is intended to describe biochar’s influence on some crucial soil physical and hydrological properties, including bulk density of soil, water holding capacity, soil porosity, soil hydraulic conductivity, soil water retention, water repellence–available plant water, water infiltration, soil temperature, soil color, and surface albedo. Therefore, we propose that the application of biochar in soils has considerable advantages, and this is especially true for arable soils with low fertility.

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R²), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m³/s), R², NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.

This study aimed to assess the interrelationship among extreme natural events and their impacts on environments and humans through a systematic and quantitative review based on the up-to-date scientific literature. Namely, the main goal was to add additional knowledge to the existing evidence of the impacts related to floods, droughts, and landslides on humans and the environment in China; this in order to identify knowledge gaps in research and practice to aid in improving the adaptation and mitigation measures against extreme natural events in China. In this study, 110 documents were analyzed in the evaluation of several impacts triggered by extreme events. Records were obtained from Scopus and Web of Science and examined with a text mining instrument to assess the pattern of publications over the years; the problems linked to extreme weather events were investigated, and the study gaps were discussed. This paper extends work by systematically reviewing recent evidence related to floods, droughts, and landslides in China. We listed the critical studies that focused on the impact of extreme events on both humans and the environment described in current reviews. The findings revealed that goods safety, social safety, and financial losses are of significant concern to the scientific community due to extreme natural events, which from our analysis resulted in being more frequent and intense. It is still underdeveloped to implement distant sensing and imaging methods to monitor and detect the impact of severe weather occurrences. There are still significant study gaps in the fields of the effects of extreme weather events. The analysis result shows that extreme events are increased during the time, so more in-depth investigation and efforts on adaptation, mitigation measures, and strategical governance plans are desperately required.