Assessing spatiotemporally varied ecohydrological effects of apple orchards based on regional-scale estimation of tree distribution and ages

Yi Yang, Bingbing Li, Peijun Shi and Zhi Li

Agricultural Water Management, 2023, vol. 287, issue C

Abstract: The forest ecohydrological processes change with time of tree plantation; however, it remains challenging to estimate tree ages at the regional scale. The objective of this study is to estimate the regional-scale apple tree distribution and ages, and further use it as a bridge to quantify the impacts of apple orchards on hydrological processes. Taking the Changwu County on the Loess Plateau of China as example, we first identified the spatial distribution and age structure of apple trees from 1995 to 2020 based on field investigation, remote sensing data and machine learning procedure, and then explored the spatiotemporally varied impacts of apple orchards on water balance through extrapolating the point-scale observation to the whole study area. The area and tree age of apple orchards were satisfactorily identified, with R2 values of 0.94 and 0.68, respectively. The area of apple orchards kept increasing, and its value in 2020 (17117 ha) was 2.5 times of that in 1995 (6842 ha). Young apple trees dominated the apple orchards during 1995–2000, while trees over 15 years old accounted for 72 % of the apple orchards in 2020. With the point-scale relations of tree ages to soil water balance, the regional-scale cumulative soil water consumed by the increasing apple orchards and old trees reached 129 GL by 2020, and the ratio of actual evapotranspiration to precipitation was 109 % when the apple tree was 22 years old. This study proposes a framework to estimate the regional-scale temporal changes in ecohydrological effects of vegetation using tree age as a bridge. The methods in this study can be referred by other studies, and the results can be used for sustainable management of water resources and agricultural production.

Keywords: Tree age; NDVI; Ecohydrological effects; Machine learning; Soil water deficit (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:287:y:2023:i:c:s0378377423003219

DOI: 10.1016/j.agwat.2023.108456

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