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Abstract. This study applied the two-dimensional AdH (adaptive hydraulics) hydrodynamic model to a river reach to analyze flood hydraulics on complex floodplains. Using the AdH model combined with bathymetry and topographic data from the United States Geological Survey (USGS) seamless server and the United States Army Corps of Engineers (USACE), we intended to examine the interactions between the channel and floodplain of a 10 km stretch at McCarran Ranch, which is located at the lower Truckee River in Nevada. After calibrating the model, we tested the dependence of the modeling results on mesh density, input parameters, and time steps and compared the modeling results to the existing gauged data (both the discharge and water stage heights). Results show that the accuracy of prediction from the AdH model may decline slightly at higher discharges and water levels. The modeling results are more sensitive to the roughness coefficient of the main channel, which suggests that the model calibration should give priority to the main channel roughness. A detailed analysis of the floodwater dynamics was then conducted using the modeling approach to examine the hydraulic linkage between the main channel and floodplains. We found that large flood events could lead to a significantly higher proportion of total flow being routed through the floodplains. During peak discharges, a river channel diverted as much as 65 % of the total discharge into the floodplain. During the periods of overbank flow, the transboundary flux ratio was approximately 5 to 45 % of the total river discharge, which indicates substantial exchange between the main channel and floodplains. The results also showed that both the relations of the inundation area and volume versus the discharge exhibit an apparent looped curve form, which suggests that flood routing has an areal hysteresis effect on floodplains.

This RISJ factsheet presents an analysis of a sample of 4.26 million news-related tweets from a larger dataset of 28 million tweets collected during the 2017 UK General Election. The document examines the role of digital-born and legacy news media in online political discussions. To do this, the authors Sílvia Majó-Vázquez, Jun Zhao, Jason R. C. Nurse, and Rasmus Kleis Nielsen have mapped Twitter discussions around a range of keywords and hashtags tied to the election as well as the activity of 129 British media outlets, including 105 legacy media and 24 digital-born outlets. Their findings show that legacy media, including most broadcasters – BBC News, Channel 4 News, and Sky News figured very prominently in the political discussion on Twitter. The Economist and the Financial Times as well as a few pure digital-players also drew high levels of engagement and attention on Twitter during the same period of time, though they were much less active in posting news content. Overall, the study shows legacy media generated almost four times as much activity and engagement as digital-born news media during the election. The figure below, also included in the study, plots the evolution of the volume of tweets over time by type of news provider. As shown, broadcasters were responsible for the highest number of tweets related to the election and were the more frequently mentioned during that period. Newspapers reached parity with broadcasters in terms of overall tweet volume during conventional political events like the television debates and the polling day.

Recently, the use of linear features for processing remote sensing images has shown its importance in applications. As one of typical linear targets, road is a hot spot of remote sensing image interpretation. Since extracting road by manual processing is too expensive and time consuming, researches based on automatic and semi-automatic have become more and more popular. Such interest is motivated by the requirements for civilian and military applications, such as road maps, traffic monitoring, navigation applications, and topographic mapping. How to extract road accurately and efficiently from SAR images is a key problem. In this paper, through analyzing characteristics of road, semi-automatic road extraction based on Extend Kalman Filtering (EKF) and Particles Filtering (PF), is presented. These two methods have the same algorithm flow which is an iterative approach based on prediction and update. The specific procedure as follows: at prediction stage, we obtain prior probability density function by the prior stage and prediction model, and through prior probability density function and the new measurement, at update stage we obtain the posterior probability density function which is the optimal estimation of road system state. Both EKF and PF repeat the steps above until the extracting tasks are finished. We use these two methods to extract road respectively. The effectiveness of the proposed method is demonstrated through the experiments from Howland by UAVSAR in L-band. And through contrast experiments, we discover that extracting difference complexity of road based on different methods can improve accuracy and efficiency. The results show that EKF has better performance on road with middle noise and PF has better performance on road with high noise.

Recently, the use of linear features for processing remote sensing images has shown its importance in applications. As one of typical linear targets, road is a hot spot of remote sensing image interpretation. Since extracting road by manual processing is too expensive and time consuming, researches based on automatic and semi-automatic have become more and more popular. Such interest is motivated by the requirements for civilian and military applications, such as road maps, traffic monitoring, navigation applications, and topographic mapping. How to extract road accurately and efficiently from SAR images is a key problem. In this paper, through analyzing characteristics of road, semi-automatic road extraction based on Extend Kalman Filtering (EKF) and Particles Filtering (PF), is presented. These two methods have the same algorithm flow which is an iterative approach based on prediction and update. The specific procedure as follows: at prediction stage, we obtain prior probability density function by the prior stage and prediction model, and through prior probability density function and the new measurement, at update stage we obtain the posterior probability density function which is the optimal estimation of road system state. Both EKF and PF repeat the steps above until the extracting tasks are finished. We use these two methods to extract road respectively. The effectiveness of the proposed method is demonstrated through the experiments from Howland by UAVSAR in L-band. And through contrast experiments, we discover that extracting difference complexity of road based on different methods can improve accuracy and efficiency. The results show that EKF has better performance on road with middle noise and PF has better performance on road with high noise.

This study investigates the relationship between gender diversity on the board and dividend payouts in China using a large sample over the period 2003–2017. Our results provide robust and strong evidence showing that gender diversity on the board is positively associated with cash payments of dividends. The empirical outcomes confirm that gender diversity on the board facilitates corporate governance and subsequently promotes dividend payouts. We demonstrate that gender diversity on the board has the greatest effect when the board has critical mass participation (three or more female directors) compared with only their token participation. However, independent female directors increase dividend payouts, while female executive directors do not have a significant impact. Furthermore, we extend the literature on the relationship between dividend payments and government ownership by providing evidence that gender diversity has a higher impact on dividend payouts for state-owned enterprises than non-state-owned enterprises. After controlling the endogeneity problems, our findings are reliable and robust. JEL classifications: G30, G35