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Topographical effects of climate data and their impacts on the estimation of net primary productivity in complex terrain: A case study in Wuling mountainous area, China

机译:气候数据的地形影响及其对复杂地形净初级生产力估算的影响-以武陵山区为例

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Topography has remarkable effects on the local climate, especially in mountainous areas. The accuracy of climate data is pivotal to the estimation of net primary productivity (NPP). Unrealistic simulations of climate data without considering the topography would lead to biased estimation of NPP. In this work, we aim to evaluate quantitatively the NPP difference with and without considering the topographical effects of climate data, and furthermore, to explore the spatio-temporal characteristics of NPP difference and the primary contributing variables to the difference. For these purposes, two different climate datasets were first built and compared with the station observations, one of which considered topographical effects (terrain-based climate dataset) while the other one did not (ordinary climate dataset). We quantified topographical effects of climate data on NPP estimation by inputting two different climate datasets to the same ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS), to evaluate the importance of considering topography during NPP calculations. Then, spatio-temporal characteristics of the NPP difference were explored, and the primary contributing variables were determined through a series of simulation experiments. Results showed that, on average, ordinary climate dataset underestimated NPP by 12.5% compared with terrain-based climate dataset over the whole Wuling mountainous area. Topographical effects of climate data had larger impacts on the NPP estimation in summer time than in winter months. In space, differences between ordinary NPP and terrain-based NPP were negligible below 200 m, and above 200 m, the differences increased first and then steadily decreased; discrepancies between NPPs continually augmented with the slope increasing; and NPP was more likely to be affected in the north and northwest than in the south and southeast The primary climate variables contributing to the NPP difference in Wuling mountainous area were temperatures, followed by global solar radiation. The research methods developed in this case study can also be applied to other study areas and other ecosystem models. (C) 2015 Elsevier B.V. All rights reserved.
机译:地形对当地的气候有显着影响,特别是在山区。气候数据的准确性对于估算净初级生产力(NPP)至关重要。不考虑地形而对气候数据进行不切实际的模拟会导致NPP的估计偏差。在这项工作中,我们的目的是在不考虑气候数据的地形影响的情况下定量评估NPP差异,并探索NPP差异的时空特征和造成该差异的主要因素。为此,首先建立了两个不同的气候数据集,并将其与站点观测值进行了比较,其中一个考虑了地形效应(基于地形的气候数据集),而另一个则没有考虑(常规气候数据集)。我们通过将两个不同的气候数据集输入相同的生态系统模型(北方生态系统生产力模拟器,BEPS)来量化气候数据对NPP估算的地形影响,以评估在NPP计算过程中考虑地形的重要性。然后,探索了NPP差异的时空特征,并通过一系列模拟实验确定了主要贡献变量。结果表明,与基于地形的气候数据集相比,整个武陵山区的常规气候数据集平均低估了NPP 12.5%。与冬季相比,夏季气候数据的地形影响对NPP估计的影响更大。在空间上,普通NPP与基于地形的NPP之间的差异在200 m以下和200 m以上可忽略不计,其差异先增大后稳定减小。 NPP之间的差异随着坡度的增加而不断增加;北部和西北部比南部和东南部更容易受到NPP的影响。武陵山区造成NPP差异的主要气候变量是温度,其次是全球太阳辐射。本案例研究中开发的研究方法也可以应用于其他研究领域和其他生态系统模型。 (C)2015 Elsevier B.V.保留所有权利。

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