中国大气本底观测发展概述

中国早期的大气本底观测网建设起步于上世纪80年代初，并于10a后基本建成。目前，中国气象局、中国科学院在原有观测站的基础上各自建设和发展着自己下属的大气本底观测网络。全球大气观测网给出了一整套开展大气本底观测的技术标准，各国均予以严格遵循。20多年来，中国大气本底观测工作在基础数据积累以及观测标准制定等方面取得了较大进展，与此相关的科学研究也取得了长足进步。最后对中国大气本底观测发展过程中存在的一些问题进行了探讨。     

Variation characteristics of evaporation in the Gulang River Basin during 1959-2013

Based on monthly evaporation of two meteorological stations in the Gulang River Basin of China, the inter-annual variation of evaporation during 1959-2013 were analyzed using Mann-Kendall and wavelet analysis. The results demonstrated that the annual evaporation show a fluctuating increase over the past 50 years approximately, with an average increase rate of 4.26 mm per decade. The overall trend was decrease-increase-decrease. According to the cumulative anomaly curve,the turning point of the annual evaporation occurred in 1979, in which the evaporation increased in the early stage and decreased in the later stage. Meanwhile, the seasonal variation of the evaporation shows that it decreased in Spring and Autumn, and increased in Summer and Winter, especially obvious for the later. The evaporation abruptly changed in Spring and Summer in 2008 and in Winter in 1994. In addition, all evaporation increased after the changes. However,the evaporation in Autumn abruptly changed in 1986 and 1999, which show a trend of increase-decrease-increase.Wavelet analysis shows that evaporation in Summer and wet season would decrease in the next few years, and in the other seasons would increase. Based on the aforementioned analysis, it can be concluded that increased evaporation is mainly induced by increase of evaporation in dry season, especially in Winter, and this trend to be continued in the future for the Gulang River Basin.     

Effects of land use and cultivation time on soil organic and inorganic carbon storage in deep soils

The vertical distribution and exchange mechanisms of soil organic and inorganic carbon(SOC, SIC) play an important role in assessing carbon(C) cycling and budgets. However, the impact of land use through time for deep soil C(below 100 cm) is not well known. To investigate deep C storage under different land uses and evaluate how it changes with time, we collected soil samples to a depth of 500 cm in a soil profile in the Gutun watershed on the Chinese Loess Plateau(CLP); and determined SOC, SIC, and bulk density. The magnitude of SOC stocks in the 0–500 cm depth range fell into the following ranking: shrubland(17.2 kg m~(-2)) grassland(16.3 kg m~(-2)) forestland(15.2 kg m~(-2)) cropland(14.1 kg m~(-2)) gully land(6.4 kg m~(-2)). The ranking for SIC stocks were: grassland(104.1 kg m~(-2)) forestland(96.2 kg m~(-2)) shrubland(90.6 kg m~(-2)) cropland(82.4 kg m~(-2)) gully land(50.3 kg m~(-2)). Respective SOC and SIC stocks were at least 1.6-and 2.1-fold higher within the 100–500 cm depth range, as compared to the 0–100 cm depth range. Overall SOC and SIC stocks decreased significantly from the 5 th to the 15 th year of cultivation in croplands, and generally increased up to the 70 th year. Both SOC and SIC stocks showed a turning point at 15 years cultivation, which should be considered when evaluating soil C sequestration. Estimates of C stocks greatly depends on soil sampling depth, and understanding the influences of land use and time will improve soil productivity and conservation in regions with deep soils.     

3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling,Anhui, China

Natural Resources Research - In the past few decades, a variety of data-driven predictive modeling techniques has led to a dramatic advancement in mineral prospectivity mapping (MPM). The random...     

柴达木低品位固体钾矿溶解转化率与品位关系

我国是缺钾大国,探明的可溶性钾盐十分有限,柴达木盆地是我国盐湖钾盐的聚集地,含水层骨架中含有数亿吨低品位固体钾矿,开发出这些低品位固体钾盐对增加国内钾肥供给,保障粮食安全具有重大意义。青海盐湖工业股份有限公司历经十年开发出低品位固体钾盐的浸泡式溶解转化方法,试验表明,固体钾盐的溶解转化率随固体钾品位的增加而增加,累计溶解转化率为56%～98%。该技术在柴达木多个盐湖得到推广应用,察尔汗盐湖保有固体钾盐达2.96×10~8 t,平均品位KCl为1.24%,根据试验数据回归分析溶解转化率77%,可溶解转化出钾盐(KCl)约2.28×10~8 t,柴达木盆地盐湖保有3.43×10~8 t固体钾盐,平均品位KCl为1.25%,溶解转化率78%,可溶解转化出钾盐(KCl)2.68×10~8 t,大幅度增加了钾盐储备量。     

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.     

Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis

Natural Resources Research - Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of...     

Integration of InVEST-habitat quality model with landscape pattern indexes to assess mountain plant biodiversity change: A case study of Bailongjiang watershed in Gansu Province

Journal of Geographical Sciences - Mountains in western China, hosted rich biodiversity and millions of people and inhabitant with vital ecosystem services, had experienced the most serious...     

1983—2017年宁夏盐池县生态治理政策的类型与变化——基于政策工具视角

政策工具是政府实施政策的手段。结合盐池县1983-2017年出台的生态治理政策,运用内容分析法,从政策工具视角,对盐池县生态治理政策的类型与变化进行了分析,并深入探讨其原因。结果表明:强制型和混合型政策工具占主导地位,其中,规制、直接提供、信息与劝诫、补贴与奖励4种工具使用频率最高,自愿型工具的使用还很缺乏。政策工具的运用受到中央政策的显著影响。以后应当开发更多可利用的政策工具,并对不同政策工具进行优化组合。通过本研究可深入了解生态脆弱区地方政府生态政策的政策工具使用情况,为生态脆弱区生态保护的政策工具选择与优化组合提供参考。     

气候和人为因素对植被变化影响的评价方法综述

系统评价气候变化和人为因素引起的植被退化,对有效开展生态工程建设、实现生态环境的可持续发展具有重要意义。目前绝大多研究主要分析气候因素对植被变化的影响,分析气候因素和人类活动对植被变化的双重影响少有研究。本文主要介绍这一双重影响对植被变化的贡献程度的主要研究方法——定性-半定量法、回归分析法、残差趋势法、基于净第一生产力的评价方法。综述分析了气候和人为因素对植被变化驱动的主要结论,总结目前各研究方法存在的问题,提出了未来需要重点加强气候和人为因素对植被变化驱动因素的分解研究,加强模型评价和不确定性量化研究等。