Search

Precipitation is considered one of the most important forcing data in scientific investigations involving agriculture, water management, and climate variability. Knowledge of precipitation variation during rainy seasons is the key to the understanding of the precipitation variability under the effect of climate change. This study evaluated some rainy season features (e.g., onset, retreat, and rainy-season precipitation) over the Yellow River basin (YRB), China, and the influence of the El Niño-Southern Oscillation (ENSO). The multi-scale moving t-test was applied to capture the onset and retreat of rainy season. The possible linkage between ENSO-induced precipitation and monsoon and atmospheric circulation was also explored. From the analysis, four conclusions can be drawn: (1) Rainy season began the earliest (latest) and ended the latest (earliest) in the southern (northern) YRB, with rainy-season precipitation increasing from northwest to southeast. (2) Rainy-season precipitation showed strong correlation to SST in Niño regions. Precipitation can reach up to 20% above the average precipitation for decaying central Pacific warming (CPW) and down to 35% below the average precipitation for developing eastern Pacific warming (EPW) in most areas of the YRB. (3) Developing El Niño showed the strongest dry signals among developing ENSO and ENSO Modoki phases. Decaying El Niño and El Niño Modoki indicated overall increasing precipitation, with La Niña and La Niña Modoki during the corresponding period showing the opposite tendency. (4) Different performances of ENSO-induced precipitation attributed to the combined influence of the monsoon from the India Ocean and the atmospheric circulation in the Western North Pacific (WNP). Stronger anti-cyclone and monsoon are related to increasing rainy-season precipitation. These findings can improve the predictability of rainy season features and ENSO-induced precipitation. [ABSTRACT FROM AUTHOR]

The water ecological environment problem in water resources management of river basins mainly lies in the incoordination between the utilization and protection of water resources among different regions. Ecological compensation, therefore, is needed in order to solve this problem. The measurement of reasonable compensation standard plays a vitally important role in the successful establishment of ecological compensation. In this study, a four‐level compensation standard called 'minimum‐basic‐complete‐maximum' (MBCM) is proposed, which is based on the calculation of direct cost, the equalization of essential public service for water, opportunity loss and the comprehensive benefit of water ecological environment protection in the source region. MBCM method provides a multi‐level platform for the establishment of the compensation mechanism, which enables different departments to know their respective costs and benefits clearly. The Sanjiangyuan region (SR) in China is chosen as the study area. The results show that (1) the minimum compensation standard for the SR is 300 million RMB per year; (2) the basic compensation standard is 1.7 billion RMB per year; (3) the complete compensation standard is about 3.75 billion RMB per year; (4) the maximum compensation standard creates the total economic value of 181.6 billion RMB for the downstream water output. The proposed MBCM four‐level compensation standard can also be applied to other river source regions. [ABSTRACT FROM AUTHOR]

China's water stress and its pressure on food security is a widely recognized crisis. A framework is proposed and applied at the provincial level to evaluate China's water security for food production. It is based on a water stress index that accounts for the deficit between water supply and aggregate demand of 23 major field crops over the study period (1961–2015). The water demand is estimated based on method recommended by the Food and Agriculture Organization of the United Nations. The water supply is composed of rainfall and irrigation. Consequently, the water security of both rainfed and irrigated agriculture is evaluated in our research, and the respective contribution of rainfall and irrigation is distinguished. Suggestions regarding adjustment in irrigation investment and planting area are proposed for practical application. The framework also holds great potential to investigate the impact of climate change and the virtual water flow of crops. Plain Language Summary: With 7% of the world's arable land and 6% of its water resources, China has to feed 20% of the world's population. Consequently, China's water stress and its pressure on food security is a widely recognized crisis. A framework is proposed in our research to evaluate China's water security for food production. It is mainly designed to answer the following three questions critical to China's food security but have not been systematically addressed in the literature. How much water is required under China's current agriculture planting structure? What is the respective role of rainfall and irrigation in meeting this requirement? What is the status of water security for China's rainfed and irrigated agriculture? The above three questions are answered based on a water stress index that accounts for the deficit between water supply and aggregate demand of 23 major field crops over the study period (1961–2015). We have also proposed suggestions regarding water security for food production from the framework. For future use, our proposed framework lays the groundwork to investigate the virtual water flow of crops. Climate change signals can also be incorporated into this framework to inform climate‐driven water risk for agriculture. Key Points: A new framework is proposed to evaluate water security for food productionRespective contribution of rainfall and irrigation to water security is distinguishedSpatial patterns of China's water security for food production are investigated [ABSTRACT FROM AUTHOR]

Streamflow simulation of the headwater catchment of the Yellow River basin (HCYRB) in China is important for water resources management of the Yellow River basin. A statistical‐dynamical model, combining regular vine copulas with an optimization method for structure estimation, is presented with an application for simulating the monthly streamflow with local climate drivers at HCYRB. Local climate drivers for streamflow in every month are analyzed using rank‐based correlation. Precipitation, evaporation, and temperature generally show strong associations with streamflow. Winter streamflows relate to total precipitation of the wet season and total evaporation of October and November, while unfrozen‐month streamflows are correlated with evaporation and precipitation of current month and previous 1 month in the wet season. Both canonical vine and D‐vine copulas are applied to develop different conditional quantile functions for streamflows in different months with their dynamical covariates. The covariates are selected from historical streamflows and climate drivers with appropriate lags using partial correlations. The optimal vine trees are selected using the sequential maximum spanning tree algorithm with the weight based on both dependence and goodness of fit. The model demonstrates higher skill than existing vine‐based models and the seasonal autoregressive integrated moving average model. The enhanced skill of the hybrid statistical‐dynamical model comes from an improved capability of capturing nonlinear correlation and tail dependence of streamflow and climate drivers with the optimization of vine structure selection. The model provides an effective advance to enhance water resources planning and management for HCYRB and the whole basin. Key Points: The associations of streamflow in every month and different climate indices in Headwater Catchment of the Yellow River Basin are analyzedThe sequential maximum spanning tree optimization method is proposed for vine‐based monthly streamflow simulation with multiple predictorsThe model is verified skillful for its capability of capturing nonlinearity and tail dependence with the optimization of vine structures [ABSTRACT FROM AUTHOR]

ABSTRACT Climate is a primary driver for extreme rainfall and flood events. In this paper, the temporally changing flood risk associated with the annual maximum 30-day rainfall (30-day AMR) and the annual maximum daily inflow ( AMDI) related to the Xidayang Reservoir catchment is analysed from a climatic context. This is the largest catchment in Daqing River Basin in North China and is highly prone to floods related to the East Asian summer monsoon. Two climate factors, the average May-June-July sea surface temperature anomalies in areas of the northern Indian Ocean and western Pacific Ocean, are identified. They show a high negative correlation with the AMDI and 30-day AMR. Bayesian nonstationary models are then developed for the AMDI and 30-day AMR using the climate predictors identified as covariates. We compared three types of models of AMDI and 30-day AMR: (1) time-invariant, (2) linear temporal trend and (3) climate informed, and found that the climate-informed models exhibit the best performance according to Deviance Information Criterion (DIC) and 90th percentile Bayesian coverage rate for both AMDI and 30-day AMR. A significant decreasing trend is identified in the AMDI and the 30-day AMR, which is found to be associated with the climate predictors. Leave one out cross validation (LOOCV) is used to demonstrate that these models have decent skill in predicting year-to-year variability in flood risk. This can help to provide flood/rainfall dynamic management measures for reservoirs in Daqing River Basin using information from before the beginning of monsoon season, thus facilitating adaptation to a changing climate. [ABSTRACT FROM AUTHOR]

China has a large economic and demographic exposure to extreme events that is increasing rapidly due to its fast development, and climate change may further aggravate the situation. This paper investigates China's socioeconomic risk from extreme events under climate change over the next few decades with a focus on sub-national heterogeneity. The empirical relationships between socioeconomic damages and their determinants are identified using a hierarchical Bayesian approach, and are used to estimate future damages as well as associated uncertainty bounds given specified climate and development scenarios. Considering projected changes in exposure, we find that the southwest and central regions and Hainan Island of China are likely to have a larger percentage of population at risk, while most of the southwest and central regions could generally have higher economic losses. Finally, the analysis suggests that increasing income can significantly decrease the number of people affected by extremes. [ABSTRACT FROM AUTHOR]