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Quantifying flood risk depends on accurate probability estimation, which is challenging due to non‐stationarity and the combined effects of multiple factors in a changing environment. The threat of compound flood risks may spread from coastal areas to inland basins, which have received less attention. In this study, a framework based on time‐varying copulas was introduced for the treatment of compound flood risk and bivariate design in non‐stationary environments. Archimedean copulas were developed to diagnose the non‐stationary trends of flood risk. Return periods, average annual reliabilities, and bivariate designs were estimated. Model uncertainty was analyzed by comparing the results for stationary and non‐stationary conditions. The case study investigated the extreme rainfall and water level series from the Qinhuai River Basin and the Yangtze River in China. The results showed that marginal distributions and correlations are non‐stationary in all bivariate combinations. Ignoring composite effects may lead to inappropriate quantification of flood risk. Excluding non‐stationarity may lead to risk over or underestimation. It showed the limitations of the 1‐day scale and quantified the uncertainty of non‐stationary models. This study provided a flood risk assessment framework in a changing environment and a risk‐based design technique, which is essential for climate change adaptation and water management. [ABSTRACT FROM AUTHOR]

The addition of crop straw is considered an important measure for sustainable agricultural production. Crop straw when incorporated into the soil affects crop growth and development by changing the hydrothermal conditions of the soil. However, quantitative studies that inform on the mechanisms by which added straw or straw biochar (SSB) affects hydrothermal response of soil and thereby crop productivity in lime concretion black soil (LCBS) are lacking. Moreover, the sustained effects during continuous cropping are less well understood. The impact of SSB on soil properties and crop yields in a typical LCBS area was systematically investigated through field experiments with maize and wheat rotations. Four treatments were set: straw (S), fertilizer (F), straw with fertilizer (SF) and straw biochar with fertilizer (BF). The results indicated that soil water‐holding capacity and rainfall storage efficiency were improved in BF and SF treatments. Compared with the F treatment, the soil water storage increased by 80%–98% and the response time of soil water to rainfall was advanced by approximately 4 h in the other three treatments during the maize season. BF and SF treatments only increased soil water storage by 6.8% due to the lack of rainfall during the wheat season. Soil inorganic nitrogen and available phosphorus were significantly increased in SF treatment by 101.1% and 32.9% compared with BF treatment in the wheat season. Considering crop plant height, leaf area index and yield, SF and BF treatments were beneficial to crop growth and improved water use efficiency. They did not increase maize yield significantly, with a maximum increase of 2.1% in BF treatment compared with F treatment. But, SF treatment significantly increased wheat yield by 11.8% and BF treatment increased wheat yield by 6.7%. Overall, this study illustrated the positive effects of SSB additions on the production of LCBS from multiple perspectives. This will provide reference for improving the soil hydrothermal conditions of LCBS and ensure food security. [ABSTRACT FROM AUTHOR]

The interaction between soil moisture (SM) and vegetation dynamics has been proven in previous studies. In situ measurements have provided reliable data to investigate and validate the time effect in different zones, which is important in the hydrology and agriculture fields. There were 845 SM in situ monitoring measurements utilized with the correlation between SM and vegetation across various soil depths and climate zones in China. The impact of climate and teleconnection factors on SM and the leaf area index (LAI) are also discussed. The results indicate that SM increases from northwest to southeast in China. The time lag responses of SM to temperature, precipitation, relative humidity, and sunshine duration are 0–3 days, 3–7 days, 1–3 days, and 3–15 days, respectively. The LAI is most strongly correlated with the climate of the current month. When the LAI leads SM, a negative correlation is observed, whereas a positive correlation is observed when SM leads the LAI. This proves that vegetation growth restricts the increase in SM, and soil drying further restricts the growth of vegetation. There was a response time of 2–4 months between the LAI and SM. The effect of vegetation and deeper SM was significant in the arid zone, while they were coupled with shallow SM in the humid zone. Additionally, the El Niño–Southern Oscillation (ENSO) showed a significant positive correlation with SM in 2015–2016 with signals of 9–14 months. The results provide support for balancing the contradiction between future vegetation restoration and water resource scarcity. [ABSTRACT FROM AUTHOR]