scholarly journals Patterns in Indices of Daily and Seasonal Rainfall Extremes: Southwest Florida Gulf Coastal Zone

Climate ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 83
Author(s):  
Margaret Gitau

Extreme events have the most adverse impacts on society and infrastructure, and present the greatest challenges with respect to impacts. Information on the status and trends of these events is, thus, important for system design, management, and policy decision-making. In this study, variations in daily and seasonal rainfall extremes were explored with a focus on the southwest Florida Gulf coastal zone for the period 1950–2016. Rainfall occurring on very wet days accounted for about 50% of the seasonal rainfall in the area (regardless of the season), while about 25% of the seasonal rainfall came from extremely wet days except in the period between October and December for which this latter value was about 40%. No significant changes were seen in the maximum one-day rainfall at any of the stations regardless of the time scale. However, there was a significant increase in the number of wet days in the rainy season at Myakka River (p = 0.0062) and Naples (p = 0.0027) and during October–December at Myakka River (p = 0.0204). These two stations also experienced significant increases in the number of wet days in a year. Significant increases in the contribution to rainy season rainfall from very wet days (rainfall > 25.4 mm, 1 in) were seen at Arcadia (p = 0.0055). Regional results point to an increasingly wetter climate with increasing contributions from extreme events in some areas, both of which have implications for design and management decision making.

2018 ◽  
Vol 17 (2) ◽  
pp. 55-65 ◽  
Author(s):  
Michael Tekieli ◽  
Marion Festing ◽  
Xavier Baeten

Abstract. Based on responses from 158 reward managers located at the headquarters or subsidiaries of multinational enterprises, the present study examines the relationship between the centralization of reward management decision making and its perceived effectiveness in multinational enterprises. Our results show that headquarters managers perceive a centralized approach as being more effective, while for subsidiary managers this relationship is moderated by the manager’s role identity. Referring to social identity theory, the present study enriches the standardization versus localization debate through a new perspective focusing on psychological processes, thereby indicating the importance of in-group favoritism in headquarters and the influence of subsidiary managers’ role identities on reward management decision making.


1970 ◽  
Vol 15 (2) ◽  
pp. 136, 138
Author(s):  
RICHARD L. MERRITT

2006 ◽  
Author(s):  
Leigh A. Baumgart ◽  
Ellen J. Bass ◽  
Brenda Philips ◽  
Kevin Kloesel

Author(s):  
Glenda H. Eoyang ◽  
Lois Yellowthunder ◽  
Vic Ward

Author(s):  
I. М. Mikhaylenko ◽  
V. N. Timoshin

The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.


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