Eco‐meteorological characteristics of the southern slopes of Kilimanjaro, Tanzania. (7th December 2015)

• Record Type: Journal Article
• Authors: Appelhans, Tim; Mwangomo, Ephraim; Otte, Insa; Detsch, Florian; Nauss, Thomas; Hemp, Andreas
• Is Part Of: International journal of climatology. Volume 36:Number 9(2016)
• Page Start: 3245
• Page End: 3258
• Publication Date: 2015-12-07
• Subjects: meteorology -- climatology -- Tanzania -- Kilimanjaro -- kriging -- machine learning; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.4552
• Languages: English
• ISSNs: 0899-8418

Abstract

ABSTRACT: This study introduces the set‐up of a new meteorological station network on the southern slopes of Kilimanjaro, Tanzania, since 2010 and presents the recorded characteristics of air temperature, air humidity and precipitation in both a plot‐based and area‐wide perspectives. The station set‐up follows a hierarchical approach covering an elevational as well as a land‐use disturbance gradient. It consists of 52 basic stations measuring ambient air temperature and above‐ground air humidity and 11 precipitation measurement sites, with recording intervals of 5 min. With respect to precipitation observations, the network extends the long‐term recordings of A. Hemp who has installed and maintained up to 117 multi‐month accumulating rainfall buckets in the region since 1997. The meteorological characteristics of the study region based on the derived data since 2010 are mostly in line with previous studies, although we see increased precipitation amounts at higher elevations during these years when compared with long‐term means. We furthermore identify a mean annual condensation level at about 2300 m a.s.l. which has not been reported before. Finally, this is the first study to provide high resolution maps of mean monthly and mean annual temperature, humidity and precipitation for Kilimanjaro, which are of great value for geographically oriented meteorological or ecological investigations. Detailed performance statistics of the geo‐statistical and machine learning techniques used for the gap filling of the recorded meteorological time series and their regionalization to the Kilimanjaro region indicate that the presented data sets provide reliable measurements of the meteorological reality at Kilimanjaro.