Delineation of dew formation zones in Iran using long-term model simulations and cluster analysis

Abstract

Abstract. Dew is a non-conventional source of water that has been gaining interest over the last two decades, especially in arid and semi-arid regions. In this study, we performed a long-term (1979–2018) energy balance model simulation to estimate dew formation potential in Iran aiming to identify dew formation zones and to investigate the impacts of long-term variation in meteorological parameters on dew formation. The annual average of dew occurrence in Iran was ∼102 d, with the lowest number of dewy days in summer (∼7 d) and the highest in winter (∼45 d). The average daily dew yield was in the range of 0.03–0.14 L m−2 and the maximum was in the range of 0.29–0.52 L m−2. Six dew formation zones were identified based on cluster analysis of the time series of the simulated dew yield. The distribution of dew formation zones in Iran was closely aligned with topography and sources of moisture. Therefore, the coastal zones in the north and south of Iran (i.e., Caspian Sea and Oman Sea), showed the highest dew formation potential, with 53 and 34 L m−2 yr−1, whereas the dry interior regions (i.e., central Iran and the Lut Desert), with the average of 12–18 L m−2 yr−1, had the lowest potential for dew formation. Dew yield estimation is very sensitive to the choice of the heat transfer coefficient. The uncertainty analysis of the heat transfer coefficient using eight different parameterizations revealed that the parameterization used in this study – the Richards (2004) formulation – gives estimates that are similar to the average of all methods and are neither much lower nor much higher than the majority of other parameterizations and the largest differences occur for the very low values of daily dew yield. Trend analysis results revealed a significant (p<0.05) negative trend in the yearly dew yield in most parts of Iran during the last 4 decades (1979–2018). Such a negative trend in dew formation is likely due to an increase in air temperature and a decrease in relative humidity and cloudiness over the 40 years.

Organizational learning and innovation: A bibliometric analysis and future research agenda

“…Cluster analysis is a powerful statistical tool and technique that aggregates similar data points so that points in the same cluster are more similar than points in other clusters [ 79 ]. According to the cluster analysis, the top 10 references are divided into three groups of distinct references cited together, as a certain number of references were found within each group.…”

“…Below are the possible reasons 8 keyword topics became common in research on organizational learning and innovation during the interest period. Knowledge management is one of the most important conditions for an organization's competitiveness in the business environment of the present day [ 80 ], where organizations take it as a strategic resource that enables them to outperform their competitors and become more innovative and effective [ [79] , [80] , [81] , [82] , [83] , [84] ].…”

A Bibliometric Analysis of Trends on Organizational Learning and Organizational Innovation: Future Research and Agenda

“…Cluster analysis (CA) is a powerful statistical tool and technique that aggregates similar data points so that points in the same cluster are more similar than points in other clusters (Atashi et al, 2021). According to the cluster analysis, the top ten references are divided into three groups of distinct references cited together, as a certain number of references were found within each group.…”