Research on the Growth Mechanism of PM2.5 in Central and Eastern China during Autumn and Winter from 2013–2020

Haze is a majorly disastrous type of weather in China, especially central and eastern of China. The development of haze is mainly caused by highly concentrated fine particles (PM2.5) on a regional scale. Here, we present the results from an autumn and winter study conducted from 2013 to 2020 in seven highly polluted areas (27 representative stations) in central and eastern China to analyze the growth mechanism of PM2.5. At the same time, taking Beijing Station as an example, the characteristics of aerosol composition and particle size in the growth phase are analyzed. Taking into account the regional and inter-annual differences of fine particles (PM2.5) distribution, the local average PM2.5 growth value of the year is used as the boundary value for dividing slow, rapid, and explosive growth (only focuses on the hourly growth rate greater than 0). The average value of PM2.5 in the autumn and winter of each regional representative station shows a decreasing trend as a whole, especially after 2017, whereby the decreasing trend was significant. The distribution value of +ΔPM2.5 (PM2.5 hourly growth rate) in the north of the Huai River is lower than that in the south of the Huai River, and both of the +ΔPM2.5 after 2017 showed a significant decreasing trend. The average PM2.5 threshold before the explosive growth is 70.8 µg m−3, and the threshold that is extremely prone to explosive growth is 156 µg m−3 to 277 µg m−3 in north of the Huai River. For the area south of the Huai River, the threshold for PM2.5 explosive growth is relatively low, as a more stringent threshold also puts forward stricter requirements on atmospheric environmental governance. For example, in Beijing, the peak diameters gradually shift to larger sizes when the growth rate increases. The number concentration increasing mainly distributed in Aitken mode (AIM) and Accumulation mode (ACM) during explosive growth. Among the various components of submicron particulate matter (PM1), organic aerosol (OA), especially primary OA (POA), have become one of the most critical components for the PM2.5 explosive growth in Beijing. During the growth period, the contribution of secondary particulate matter (SPM) to the accumulated pollutants is significantly higher than that of primary particulate matter (PPM). However, the proportion of SPM gradually decreases when the growth rate increases. The contribution of the PPM can reach 48% in explosive growth. Compared to slow and rapid growth, explosive growth mainly occurs in the stable atmosphere of higher humidity, lower pressure, lower temperature, small winds, and low mixed layers.

Evolving Threshold of Flood-Leading Precipitation in a User-Oriented Forecast System Based on the TIGGE Dataset

Specific users play a key role in interactive forecast systems through user-oriented information (UOI). For hydrological users, a key component of the user-oriented forecast system (UOFS) is to determine the threshold of flood-leading precipitation (TFLP) as a target of the forecast by considering the decision-making information at the user end. This study demonstrates a novel way of simulating TFLP via the inverse simulation of a hydrological model, combined with the flood hazard assessment in the upper reaches of the Huai River Basin controlled by the Wang Jiaba (WJB) hydrological station. The flood hazard, defined as the probability of precipitation beyond the daily evolving TFLP for the next day, was evaluated by using the THORPEX Interactive Global Grand Ensemble (TIGGE) datasets, including 162 members retrieved from 5 TIGGE archive centers. Having integrated the real-time monitored water level (as the UOI) into the UOFS, we applied it to the flood season of 2008 as a case study to evaluate the flood hazard generated by the UOFS for the WJB sub-basin. The simulated TFLP corresponded well with the gap between the monitored and warning water level. The predicted flood hazard probability showed good agreement with the first two flood peaks at 100% accuracy, while exceeding 60% accuracy for the third flood event in that season. Thus, the flood hazard could be better quantified via integration of the forecasted flood-leading precipitation. Overall, this study highlights the usefulness of a UOFS coupled with interactive UOI of real-time water level to determine the dynamical TFLP for flood hazard evaluation with ensemble precipitation forecast. The early flood warning which resulted from such integrated UOFS is directly applicable to operational flood prevention and mitigation.

Projecting Hydrological Responses to Climate Change Using CMIP6 Climate Scenarios for the Upper Huai River Basin, China

The influence of climate change on the regional hydrological cycle has been an international scientific issue that has attracted more attention in recent decades due to its huge effects on drought and flood. It is essential to investigate the change of regional hydrological characteristics in the context of global warming for developing flood mitigation and water utilization strategies in the future. The purpose of this study is to carry out a comprehensive analysis of changes in future runoff and flood for the upper Huai River basin by combining future climate scenarios, hydrological model, and flood frequency analysis. The daily bias correction (DBC) statistical downscaling method is used to downscale the global climate model (GCM) outputs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and to generate future daily temperature and precipitation series. The Xinanjiang (XAJ) hydrological model is driven to project changes in future seasonal runoff under SSP245 and SSP585 scenarios for two future periods: 2050s (2031–2060) and 2080s (2071–2100) based on model calibration and validation. Finally, the peaks over threshold (POT) method and generalized Pareto (GP) distribution are combined to evaluate the changes of flood frequency for the upper Huai River basin. The results show that 1) GCMs project that there has been an insignificant increasing trend in future precipitation series, while an obvious increasing trend is detected in future temperature series; 2) average monthly runoffs in low-flow season have seen decreasing trends under SSP245 and SSP585 scenarios during the 2050s, while there has been an obvious increasing trend of average monthly runoff in high-flow season during the 2080s; 3) there is a decreasing trend in design floods below the 50-year return period under two future scenarios during the 2050s, while there has been an significant increasing trend in design flood during the 2080s in most cases and the amplitude of increase becomes larger for a larger return period. The study suggests that future flood will probably occur more frequently and an urgent need to develop appropriate adaptation measures to increase social resilience to warming climate over the upper Huai River basin.

Assessment of gridded precipitation products in the hydrological modeling of a flood-prone mesoscale basin

Precipitation plays a critical role in hydrometeorological studies. A predictive analysis of gridded rainfall datasets may provide a cost-effective alternative to conventional rain gauge observations. Here, our objective is to evaluate the performance of satellite and reanalysis precipitation products in the hydrological modeling of a mesoscale watershed. The research also examines the accuracy of hydrological simulations in a sizeable flood-prone watershed in the absence of observed data associated with the myriad water retaining structures present in the catchment. We use three precipitation products, namely Tropical Rainfall Measurement Missions (TRMM) 3B42 Version 7, Climate Forecast System Reanalysis (CFSR), and daily precipitation data recorded at multiple rain gauges in the upper Huai River Basin to simulate streamflow. The Soil & Water Assessment Tool (SWAT) is utilized for runoff modeling, while SWAT-CUP is used to perform sensitivity analysis and to calibrate and validate the simulation results. Nash–Sutcliffe efficiency, percent bias, and Kling-Gupta efficiency (KGE) are employed to evaluate modeling efficiency for three precipitation datasets on different temporal scales. The results indicate that TRMM and CFSR datasets provide satisfactory results on both daily and monthly scales. Specifically, the SWAT model performs better at monthly simulations than daily simulations for all precipitation datasets used.

Inconsistent variation of return periods of temperature extremum in China and its projection based on CMIP6 results

Increasingly extreme temperature events under global warming can have considerable impacts on sectors such as industrial activities, health, and transportation, suggesting that risk for these kinds of events under climate change and its regional sensitivity should be reassessed. In this study, the observation and multi-model simulations from CMIP6 are comprehensively used to explore the regional differences of the extreme temperature response to climate change from the perspective of return period (RP). The Gumbel model of generalized extremum distribution is applied to estimate the RP for the annual extremum of temperature based on Gaussian distribution of daily temperature. The analysis on the observation in selected three sites indicates that the regional inconsistency of RP variation is not only existed in extreme high temperature (HTx) but also in low temperature (LTn) during the past several decades. The annual amplitude of temperature extremum in the Northeast China is enlarged with summer becoming hotter and winter becoming colder while the opposite situation is detected in Huang-Huai River Basin with cooler summer and relatively stable winter, and South China is characterized by hotter summer and slight warmer winter. From the spatial distribution of the HTx and LTn variations of fix RP, it is found that the Northeast China and Jiang-Huai River Basin is the most sensitive areas, respectively, in the response of extreme low temperature and high temperature to global warming. However, the regional inconsistency of the extreme temperature change is only observed under SSP1-2.6 scenario in the CMIP6 simulation but gradually disappeared from SSP2-4.5 to SSP5-8.5.

Factors Affecting the Long-Term Development of Specialized Agricultural Villages North and South of Huai River

Village-level agricultural specialization in China is becoming increasingly important for rural development. However, existing knowledge of specialized agricultural villages (SAVs) based on singular assessment criteria and data describing static time points becomes insufficient in addressing multifaceted developmental questions today. We examined the long-term development patterns of SAVs in Anhui, China, with attributes from multiple angles, and explored how local factors affected SAV development across space and time using random forest regression. We found that as time elapsed, economic rationality drove specialized farmers closer to sale dependency and made SAVs more susceptible to market and economic factors, which builds upon previous findings analyzing SAVs at specific time points and consolidates the importance of market factors in the long-term development of SAVs. However, this susceptibility manifests differently in these two geographically contrasting regions north and south of Huai River. The northern SAVs received increased influences from market and economic factors, while the southern SAVs were continuously controlled by market and location factors. The dynamic spatial and temporal patterns of the two regions point to different dependencies, which emphasized local sales in the north and distant sales in the south. We propose that policies and strategies regarding SAV development accommodate these dynamics and address appropriate influencing factors accordingly.

Lag impacts of the anomalous July soil moisture over Southern China on the August rainfall over the Huang–Huai River Basin

The effect of soil moisture (SM) on precipitation is an important issue in the land–atmosphere interaction and shows largely regional differences. In this study, the SM of the ERA-Interim reanalysis and precipitation data of the weather stations were used to investigate their relationship over eastern China during July and August. Moreover, the WRF model was applied to further validate the effect of SM on rainfall. In the observations, a significantly negative relationship was found that, when the soil over southern China is wet (dry) in July, the rainfall decreases (increases) over the Huang–Huai–River basin (hereafter HHR) in August. In the model results, the soil can “memorize” its wet anomaly over southern China from July to August. In August, the wet soil increases the latent heat flux at surface and the air moisture at lower levels of the atmosphere, which is generally unstable due to the summer monsoon. Thus, upward motion is prevailing over southern China in August, and the increased surface air moisture is transported upwards. After that, the condensation of water vapor is enhanced at the middle and upper levels, increasing the release of latent heat in the atmosphere. The heat release forms a cyclonic circulation at the lower levels over eastern China, and induces the transport and convergence of water vapor increased over southern China in August. This further strengthens the upward motion over southern China and the cyclonic circulation at the lower levels. Therefore, positive feedback appears between water vapor transport and atmospheric circulation. Meanwhile, the cyclonic circulation over southern China results in a response of water vapor divergence and a downward motion over HHR. Consequently, the negative anomalies of precipitation occur over HHR in August. When the July soil is dry over southern China, the opposite results can be found through the similar mechanism.