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This dataset is collected from the Supplementary Materials part of the paper: Gao, S., Zhou, T., Yi, C., Shi, P., Fang, W., Liu, R., Liang, E., & Julio Camarero, J. (2020). Asymmetric impacts of dryness and wetness on tree growth and forest coverage. Agricultural and Forest Meteorology, 288-289, 107980. doi:10.1016/j.agrformet.2020.107980.

In this paper the researchers took forests in the semi-arid area of the Colorado Plateau in the southwest USA as the research object, comprehensively applied a large amount of tree ring width data, combined with remote sensing forest coverage data, they explored the legacy effect under the influence of the interannual water deficit by designing "natural experiments" at the regional scale, and compared the similarities and differences of the effect of the interannual water status changes on the tree ring width and forest coverage. The study found that the water status in the year when the tree ring was formed can significantly affect the duration and intensity of the legacy effect, and the response of the tree ring width and forest coverage to the interannual water status is different.

This data contains ring-width indices (RWI) of 357 sample sites in 111-hydrological year (i.e., for 1902–2012) and annual water deficit anomaly (Dya) that matched to RWI.

The tree-ring database used in this research was composed of 357 standard chronologies of three major species (Pinus edulis Engelm., Pinus ponderosa Douglas ex C. Lawson and Pseudotsuga menziesii (Mirb.) Franco) in the study region, spanning from 1902 to 2012, resulting in a total of 29,969 site-years. A total of 357 tree-ring width chronologies of three major tree species were obtained from the International Tree-Ring Data Bank (https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/tree-ring). To transform tree-ring width data into ring-width indices (RWI), long-term trends caused by aging and increasing trunk diameter were mostly removed by negative exponential curves using the ARSTAN program (Cook, 1985). After performing standardization, all chronologies were scaled to a standard mean (RWI = 1000) with a comparable variance to reduce the spatial heterogeneity among these tree-ring sites.

In this research, researchers used annual water deficit anomaly (Dya) to explore the impact of water deficit variability on tree radial growth and growth legacies. They matched gridded Dya to RWIs. For tree-ring chronologies within the same grid, they averaged them for each year to reduce bias caused by the rough resolution of climate data.

The data is 1 Excel workbooks, Ring-width indices and annual water deficit anomaly (1902-2012), which contains 3 worksheets as follows:

raw_data

processed_data

variables

The data contains the following fields:

sitename: the name of tree-ring sampled site

Year: the tree-ring formation year

RWI: ring-width indices

latitude: the latitude of tree-ring sampled site

lontitude: the lontitude of tree-ring sampled site

altitude: the altitude of tree-ring sampled site

lon Grid no.: the lontitude grid number of tree-ring sampled site

lat Grid no.: the latitude grid number of tree-ring sampled site

Dya_3: water deficit anomaly of the 3rd year before the tree-ring formation year (i.e. "Year" column)

Dya_2: water deficit anomaly of the 2nd year before the tree-ring formation year (i.e. "Year" column)

Dya_1: water deficit anomaly of the 1st year before the tree-ring formation year (i.e. "Year" column)

Dya_curr: water deficit anomaly of the tree-ring formation year (i.e. "Year" column)

Dya_std: the standard deviation of 111-hydrological year (i.e., for 1902–2012) averaged annual water deficit of the grid