Time-Lagged Correlation between Soil Moisture and Intra-Annual Dynamics of Vegetation on the Mongolian Plateau

Soil moisture is a reliable water resource for plant growth in arid and semi-arid regions. Characterizing the interaction between soil moisture and vegetation is important for assessing the sustainability of terrestrial ecosystems. This study explores the spatiotemporal characteristics of four soil moisture layers (layer 1: 0–7 cm, layer 2: 7–28 cm, layer 3: 28–100 cm, and layer 4: 100–289 cm) and the time-lagged correlation with the normalized difference vegetation index (NDVI) for different vegetation types on an intra-annual scale on the Mongolian Plateau (MP). The most significant results indicated that: (1) the four layers of soil moisture can be roughly divided into rapid change (layers 1 and 2), active (layer 3), and stable (layer 4) layers. The soil moisture content in the different vegetation regions was forest > grassland > desert vegetation. (2) The soil moisture in layer 1 showed the strongest positive correlation with NDVI in the whole area; meanwhile, the soil moisture of layers 2 and 3 showed the strongest negative correlation with the NDVI mainly in grassland and desert, and layer 4 showed the strongest negative correlation with the NDVI in the forest. (3) Mutual responses of NDVI and deep layer soil moisture required a longer time compared with the shallow layer. In the annual time scale, the NDVI was affected by the change in soil moisture in most of the study area, except for coniferous forest and desert vegetation regions. (4) Under the different stages of vegetation change, the soil moisture changes advance than NDVI about 3 months during the greening stage, while the NDVI changes advance than soil moisture by 0.5 months during the browning stage. Regardless of the stage, changes in soil moisture are initiated from the shallow layer and advance to the deep layer. The results of this study provide deep insight into the relationship between soil moisture and vegetation in arid and semi-arid regions.

Time-Domain Correlation Quantitative Analysis Method of Regional Rainfall-Landslide Displacement Responses Based on a Time-Domain Correlation Model

Landslide deformation is the most intuitive and effective characterization of the evolution of landslides and reveals their inherent risk. Considering the inadequacy of existing deformation monitoring data in the early warning of landslide hazards, resulting in insufficient disaster response times, this paper proposes a time-domain correlation model. Based on a regional rainfall-landslide deformation response analysis method, a time-domain correlation measure between regional rainfall and landslide deformation and a calculation method based on impulse response functions are proposed for prevalent rainfall-induced landslide areas, and the correlation with the rainfall-triggered landslide deformation mechanism is quantitatively modeled. Furthermore, using rainfall monitoring data to optimize the indicator system for landslide deformation monitoring and early warning significantly improves the preliminary warning based on landslide deformation. The feasibility of the method proposed in this paper is verified by analyzing the historical monitoring data of rainfall and landslide deformation at 15 typical locations in 5 landslide hidden hazard areas in Fengjie County, Chongqing city. (1) The correlation models for the XP landslide and XSP landslide involve a 5-day lagged correlation under a 56-day cycle and a 18-21-day lagged correlation under a 49-52-day cycle, which means that the deformation in the above areas can be modeled cyclically according to monitoring data, and early landslide warnings can be provided in advance with a lag time. (2) The correlation models for the TMS landslide and OT landslide show consistent correlations under a 48-50-day cycle and a 58-day cycle, which means that the deformation in the above areas can be predicted based on rainfall accumulation, and real-time warnings of future landslide deformation and displacement can be obtained. (3) The HJWC landslide presents a disorderly correlation pattern, which means that a preliminary landslide deformation warning cannot be provided based on rainfall alone; other monitoring data need to be supplemented and analyzed.

Analysis and potential use of Google Trends as a monitoring tool for risk communication during COVID-19 pandemic

COVID-19 Pandemic has become a major problem in various infected countries, including Indonesia. The proper risk communication strategy during this outbreak was important to reduce the impact. Therefore, this research was intended to assess the potential use of Google Trends as a tool to monitor risk communication during COVID-19 pandemic in Indonesia. Search patterns were analyzed using the terminology used to identify COVID-19 in Indonesia, followed by information-finding keywords 'gejala (symptoms)', 'mencegah (preventing)', and 'obat (drug)' keywords compared to the number of newly confirmed COVID-19 cases in Indonesia using time-lagged correlation analysis from December 31th, 2019 to April 20th, 2020. Peaks within respective timelines were qualitatively described according to current COVID-19 related events. ”Corona” was the terminology mostly used in Indonesia to identify COVID-19. There were five spikes observed from “corona” keyword timeline, which each spike was dependent on the media coverage and regulation by the Government. Validation using time-lagged correlation yields significant results between corona, corona symptoms, preventing corona and corona drugs compared to newly confirmed COVID-19 cases in Indonesia. Google Trends can potentially be used to maximize the improvement of risk communication and as a tool to monitor public restlessness during the COVID-19 pandemic in Indonesia by Government.

Analysing inter-media agenda setting influence between Social media and electronic media; a perspective from Pakistan

Inter-media agenda setting is a commonly used phenomenon to investigate the transfer of contents between news media. The recent digitization era challenges the traditional presuppositions. This study investigates the inter-media agenda setting influence between social media and traditional media. To address this question, the present study investigates first level agenda setting between Twitter and ARY news during Farishta murder case 2019. Content analysis method was used to assess agendas present within Twitter and ARY news. By employing cross-lagged correlation, the study investigates the inter-media agenda setting influence between Twitter agendas and of ARY news agendas. Aggregate findings of cross-lagged correlation reveal a clear agenda setting influence of Twitter on ARY news coverage agenda about Farishta murder case. The results of the study suggest that Twitter has the capability to influence broadcast agendas of television in Pakistan

Government frames and their influence on news framing: An analysis of cross-lagged correlations in the Mexican context

Frame building has been described as the flow of frames from political actors to journalists and, thus, to news articles. One influence factor to be considered in the area of framing is media input, which consists of materials that political actors send to newsrooms to facilitate their work while influencing the news. To find out to what extent the government’s frames for the Merida Initiative influenced news frames, we identified the issue-specific frames of the initiative, measured their presence in newspapers and media input, and conducted eight cross-lagged correlation analyses. On seven occasions, the correlation went above the baseline. For this reason, it was concluded that the salience of frames in media input had a significant role in the salience of frames in news.

Seasonal Prediction from Arctic Sea Surface Temperatures: Opportunities and Pitfalls

Arctic sea ice extent and sea surface temperature (SST) anomalies have been shown to be skillful predictors of weather anomalies in the midlatitudes on the seasonal time scale. In particular, below-normal sea ice extent in the Barents Sea in fall has sometimes preceded cold winters in parts of Eurasia. Here we explore the potential for predicting seasonal surface air temperature (SAT) anomalies in Europe from seasonal SST anomalies in the Nordic seas throughout the year. First, we show that fall SST anomalies not just in the Barents Sea but also in the Norwegian Sea have the potential to predict wintertime SAT anomalies in Europe. Norwegian Sea SST anomalies in spring are also significant predictors of European SAT anomalies in summer. Second, we demonstrate that the potential for prediction is sensitive to trends in the data. In particular, the lagged correlation between Norwegian Sea SST anomalies in spring and European SAT anomalies in summer is considerably higher for raw data than linearly detrended data, largely due to warming SST and SAT trends in recent decades. Third, we show that the potential for prediction has not been stationary in time. One key result is that, according to two twentieth-century reanalyses, the strength of the negative lagged correlation between Barents Sea SST anomalies in fall and European SAT anomalies in winter after 1979 is unprecedented since 1900.