Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

China’s carbon neutrality: an extensive and profound systemic reform

● China has pledged ambitious carbon peak and neutrality goals for mitigating global climate change. ● Major challenges to achieve carbon neutrality in China are summarized. ● The new opportunities along the pathway of China’s carbon neutrality are discussed from four aspects. ● Five policy suggestions for China are provided. China is the largest developing economy and carbon dioxide emitter in the world, the carbon neutrality goal of which will have a profound influence on the mitigation pathway of global climate change. The transition towards a carbon-neutral society is integrated into the construction of ecological civilization in China, and brings profound implications for China’s socioeconomic development. Here, we not only summarize the major challenges in achieving carbon neutrality in China, but also identify the four potential new opportunities: namely, the acceleration of technology innovations, narrowing regional disparity by reshaping the value of resources, transforming the industrial structure, and co-benefits of pollution and carbon mitigation. Finally, we provide five policy suggestions and highlight the importance of balancing economic growth and carbon mitigation, and the joint efforts among the government, the enterprises, and the residents.     

A multidecadal oscillation in precipitation and temperature series is pronounced in low flow series from Puget Sound streams

In Pacific Northwest streams, summer low flows limit water available to competing instream (salmon) and out-of-stream (human) uses, creating broad interest in how and why low flows are trending. Analyses that assumed linear (monotonic) change over the last ~60 years revealed declining low flow trends in minimally disturbed streams. Here, polynomials were used to model flow trends between 1929 and 2015. A multidecadal oscillation was observed in flows, which increased initially from the 1930s until the 1950s, declined until the 1990s, and then increased again. A similar oscillation was detected in precipitation series, and opposing oscillations in surface temperature, Pacific Decadal Oscillation, and Interdecadal Pacific Oscillation series. Multidecadal oscillations with similar periods to those described here are well known in climate indices. Fitted model terms were consistent with flow trends being influenced by at least two drivers, one oscillating and the other monotonic. Anthropogenic warming is a candidate driver for the monotonic decline, and variation in (internal) climatic circulation for the oscillating trend, but others were not ruled out. The recent upturn in streamflows suggests that anthropogenic warming has not been the dominant factor driving streamflow trends, at least until 2015. Climate projections based on simulations that omit drivers of multidecadal variation are likely to underestimate the range, and rate of change, of future climatic variation.     

Rainfall Interception Based on Indirect Methods: A Case Study in Temperate Forests in Oaxaca,Mexico

Rainfall interception represents the amount of water trapped in natural cover that is not drained directly to the ground. Intercepted rainfall may evaporate after a rain event, making it one of the main drivers of water balance and hydrologic regionalization. This process can be affected by factors such as climate, altitude, vegetation type, and topography. Here is a simple method of calculating rainfall interception in temperate forests using in Santa Maria Yavesia, Oaxaca, and Mexico as an illustrative study area. We used two rain gauges to measure net precipitation (N_p) under the canopy at each study site and one gauge outside the canopy to obtain gross precipitation (G_p). Throughfall (T_h) was indirectly measured using hemispherical photographs. Rainfall interception was obtained through a combination T_h and G_p and N_p. The mean rainfall interception was 50.6% in the Abies forests, 23%–40% in the coniferous‐mixed forests, and 27.4% in the broad‐leaved forests. We classified rainfall events by intensity to determine the effect of canopy structure and precipitation and found that 75% of the events were weak events, 24% were moderate events, and 1% were strong events. In addition, we found that rainfall interception was lower when the intensity of precipitation was higher. Our method can be replicated in different ecosystems worldwide as a tool for assessing the influence of rainfall interception in terms of ecological services.     

三峡水库浮游植物群落特征及水体富营养化评价

基于2003—2017年三峡水库浮游植物群落结构、优势种群的变化和2017年水库干支流水质数据,全面分析浮游植物群落结构和演替特征,并运用综合营养状态指数法对水体富营养化程度进行评价。结果表明,三峡水库浮游植物种类丰富,监测期间共鉴定出浮游植物7门62属,细胞密度在7.5×10~4~2.8×10~7cell/L之间变化,Shannon-Wiener多样性指数为1.0~3.0,在α-中污带和β-中污带之间,说明三峡水库水生态环境健康状况相对较好;三峡水库浮游植物季节性演替特征呈硅藻和甲藻向蓝藻和绿藻演替的趋势,年际变化特征分析发现浮游植物密度在2008年175 m实验蓄水后大量增长,且优势藻类由河道型藻类向湖泊型藻类转化。通过监测数据分析,得出三峡水库干流处于中营养状态,支流在春季主要处于轻度富营养状态,秋季支流比春季支流的富营养化程度低,主要处于中营养状态,总氮(total nitrogen,TN)、总磷(total phosphorus,TP)和透明度(transparency,SD)为水质主要影响因子。     

鄱阳湖平原区农村门塘浮游藻类群落特征及其生物学评价

于2018年—2019年在鄱阳湖区周边选取4个县（区）10个村庄的典型门塘开展浮游藻类采样调查。研究共鉴定出藻类6门83种，以蓝藻门、绿藻门和硅藻门为主；浮游藻类细胞密度全年范围为7.30×104个/L～2.78×1011个/L，年均值为1.4×1010个/L，其中夏季细胞密度最大，冬季细胞密度最小；优势种主要有小环藻、微囊藻、铜绿微囊藻、卵形隐藻等，且具有较为明显的季节演替，全年优势种为小环藻；藻类多样性指数（H′）年均值为1.49，丰富度指数（M）年均值为1.92，全年水体生物学评价结果为中度污染。     

射阳湖浮游动物群落结构特征及其与环境因子相关性研究

2016—2017年对射阳湖开展浮游动物群落结构和水质指标逐月监测。结果表明,射阳湖共鉴定出浮游动物61种,其中原生动物、轮虫、枝角类和桡足类分别为25种、24种、7种和5种。浮游动物种类数呈现春夏季多于秋冬季的变化趋势。浮游动物优势种数量较多、分布广,群落结构相对复杂且稳定。运用Shannon-Wiener指数、均匀度指数和B/T指数对射阳湖水质作评价,结果表明,该湖处于轻度—中度污染状态,生物学指标评价法与常规水质评价结果一致。浮游动物群落结构与水质指标相关性分析表明,水温、透明度、Chl-a和DO是影响射阳湖浮游动物群落结构的主要水质指标。     

屏蔽服冷却系统对人体表面温度影响的研究*

为科学有效地论证多种方式组合的冷却屏蔽服在不同环境条件下对人体表面温度控制的效果，需要对冷却系统及人体敏感部位发热量进行客观评估。通过对5名健康男性的高温测试，探究人体在不同环境温度下体表温度的变化，得出胸部、背部及额头为热量最高部位，并构建以“人体-降温屏蔽服-外界环境”为主体的冷却系统数值模型，对不同环境中的屏蔽服冷却效果展开研究，分析穿戴冷却屏蔽服时人体躯干部分的温度分布及影响。结果表明:在屏蔽服中靠近胸部、背部部位引入相变材料和风扇，均可帮助人体降低体温，提高舒适度。     

Development of machine learning based prediction models for hazardous properties of chemical mixtures

Lower flammability limit (LFL), upper flammability limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and consequence analysis. In this study, a comprehensive prediction model set was constructed by using expanded chemical mixture databases of chemical mixture hazardous properties. Machine learning based gradient boosting quantitative structure-property relationship (GB-QSPR) method is implemented for the first time to improve the model performance and prediction accuracy. The result shows that all developed models have significantly higher accuracy than other regular QSPR models, with the 5-fold cross-validation RMSE of LFL, UFL, AIT, and FP models being 1.06, 1.14, 1.08, and 1.17, respectively. All developed QSPR models can be used to estimate reliable chemical mixture hazardous properties and provide useful guidance in chemical mixture hazard assessment and consequence analysis.     

肥煤自燃全过程热动力学特征参数研究

实验测定了林西矿肥煤样品30~900℃煤自燃全过程热动力学特征参数,得出:TG/DTG曲线显示煤样DTG初始临界温度45℃,干裂温度122℃,活性温度195℃,增速温度265℃,质量极大值温度342℃,着火温度465℃,最大热失重速率温度515℃和燃尽温度690℃;DSC曲线显示,煤样初始放热温度60℃、最大热释放速率温度511℃。结合TG-DTG-DSC曲线综合分析可知,煤温达到510℃左右时煤样反应最剧烈。由煤自燃标志气体测定实验系统得出:煤温130℃后CO,CO 2释放量迅速增加,210℃增加速度下降;CH 4,C 2 H 6含量变化具有规律性且两者变化相近;C 2 H 4出现温度为130℃;C 2 H 4/C 2 H 6比值在190~350℃有较强的规律性,呈上升趋势且上升速度较快;350℃之后,CH 4,C 2 H 6,C 2 H 4体积分数均开始急剧增大;C 2 H 4/CO与C 2 H 4/CO 2变化趋势大致相同,在130~350℃时缓慢增长,达到350℃后比值呈指数形式上升。经拟合曲线,得到活化能的3个突变点温度:70,180,220℃,其中180℃与交叉点温度相吻合。通过以上研究,得到了肥煤自燃全过程的热力学特征参数,为实际生产中防治煤自燃提供了理论依据。