Department of Plant Systematics, University of Bayreuth, Bayreuth, Germany.
Waldkunde-Institut Eberswalde, Eberswalde, Germany.
PLoS One. 2024 Mar 13;19(3):e0299363. doi: 10.1371/journal.pone.0299363. eCollection 2024.
Global, spatially interpolated climate datasets such as WorldClim and CHELSA, widely used in research, are based on station data, which are rare in tropical mountains. However, such biodiversity hotspots are of high ecological interest and require accurate data. Therefore, the quality of such gridded datasets needs to be assessed. This poses a kind of dilemma, as proving the reliability of these potentially weakly modelled data is usually not possible due to the lack of stations. Using a unique climate dataset with 170 stations, mainly from the montane and alpine zones of sixteen mountains in Tanzania including Kilimanjaro, we show that the accuracy of such datasets is very poor. Not only is the maximum amount of mean annual precipitation drastically underestimated (partly more than 50%), but also the elevation of the precipitation maximum deviates up to 850m. Our results show that, at least in tropical regions, they should be used with greater caution than before.
全球空间插值气候数据集，如 WorldClim 和 CHELSA，广泛应用于研究中，它们基于站点数据，但在热带山区，站点数据非常稀缺。然而，这些生物多样性热点地区具有很高的生态意义，需要准确的数据。因此，需要评估这些网格化数据集的质量。这就形成了一种困境，因为由于缺乏站点，通常不可能证明这些潜在模型较弱的数据的可靠性。我们使用一个独特的气候数据集，其中包含 170 个站点，主要来自坦桑尼亚的 16 座山脉的山地和高山地区，包括乞力马扎罗山，结果表明这些数据集的准确性非常差。不仅年平均降水量的最大值被严重低估（部分地区超过 50%），而且降水最大值的海拔高度也偏差了 850 米。我们的研究结果表明，至少在热带地区，它们的使用应该比以前更加谨慎。
