Data from: Groundwater and remotely sensed phenology reveal vulnerability of riparian trees to drought
Data files
Oct 16, 2025 version files 106.81 MB
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CHA-4_all.csv
5.29 MB
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cha4_ET.csv
80.53 KB
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Mohammadi_et_al_2025.Rmd
57.86 KB
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ndvi_HANTS.csv
605.36 KB
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phenology_dates.csv
4.48 KB
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phenology_synthesis.csv
85.64 MB
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README.md
10.18 KB
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SC-2_all.csv
3.87 MB
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SC-4_all.csv
5.18 MB
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SC-5_all.csv
3.91 MB
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sc2_ET.csv
59.87 KB
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sc4_ET.csv
68.16 KB
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sc5_ET.csv
81.05 KB
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testing.csv
486.94 KB
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training.csv
1.46 MB
Abstract
The increasing frequency and magnitude of climatic extremes are altering water availability in dryland ecosystems globally. However, riparian vulnerability to hydroclimate whiplash remains poorly understood. Here, we examined how riparian willow, cottonwood, and valley oak trees respond to groundwater fluctuations and drought through their water use patterns and phenology. To this end, we combined time-series analysis of in situ, high-frequency groundwater monitoring with high-resolution PlanetScope satellite imagery of a drought-prone and relatively pristine watershed in California (Chalone Creek, Pinnacles National Park). We found that flow regime dictates the potential for trees to access groundwater, while the identity of tree species determines the timing and magnitude of their use. Machine-learning models revealed that at intermittent sites, groundwater depth predominantly controlled vegetation greenness, represented by Normalized Difference Vegetation Index (NDVI). In contrast, variation in photoperiod length dominated at the perennial site where water was more reliably available. During the severe 2020-2022 drought, all species experienced reduced greenness, but phenological responses differed by flow regime. While the start of season was delayed across all sites, trees at intermittent reaches exhibited a substantially earlier end of season during drought, resulting in growing seasons shortened by as much as 37 days. These phenological shifts vastly exceed those documented across aridity classifications in global datasets from satellite observations, ground-based monitoring networks, and experimental precipitation manipulations. Although riparian trees in drylands have been shaped by exposure to drought over evolutionary timescales, our findings show that trees in these intermittent systems may be operating close to critical groundwater thresholds, rendering them particularly vulnerable to increasingly long and severe droughts.
Description
This repository contains the data and R code necessary to reproduce the analyses and figures presented in the manuscript, "Groundwater and remotely sensed phenology reveal vulnerability of riparian trees to drought." The analysis investigates the relationship between groundwater availability, drought, and the phenological shifts in riparian tree species.
Code: Mohammadi_et_al_2025.Rmd
Code description: R Markdown script to reproduce all analyses and figures
Dataset: SC-5_all.csv
Dataset description: This dataset contains groundwater elevation data for well SC-5 (perennial) used in the wavelet analysis
date: date
elevation_ft: elevation (ft) of water table
elevation_m: elevation (m) of water table
Dataset: CHA-4_all.csv
Dataset description: This dataset contains groundwater elevation data for well CHA-4 (intermittent) used in the wavelet analysis
date: date
elevation_ft: elevation (ft) of water table
elevation_m: elevation (m) of water table
Dataset: SC-4_all.csv
Dataset description: This dataset contains groundwater elevation data for well SC-4 (intermittent) used in the wavelet analysis
date: date
elevation_ft: elevation (ft) of water table
elevation_m: elevation (m) of water table
Dataset: SC-2_all.csv
Dataset description: This dataset contains groundwater elevation data for well SC-2 (intermittent) used in the wavelet analysis (presented in the supplemental)
date: date
elevation_ft: elevation (ft) of water table
elevation_m: elevation (m) of water table
Dataset: sc5_ET.csv
Dataset description: This dataset contains evapotranspiration from groundwater data (derived from the White method) for well SC-5 (perennial)
date: date
year: year
ET_mm_per_day: Evapotranspiration from groundwater (ETgw) derived from the White (1932) method using depth to groundwater data (in mm/d)
season: dry or wet season
mean.sos: date of the beginning of the growing season for the calendar year joined from growing season delineation threshold=0.5 method (average of willow, cottonwood, and valley oak)
mean.eos: date of the end of the growing season for the calendar year joined from growing season delineation threshold=0.5 method (average of willow, cottonwood, and valley oak)
growing_season: non-growing season or growing season based on mean.sos and mean.eos
Dataset: cha4_ET.csv
Dataset description: This dataset contains evapotranspiration from groundwater data (derived from the White method) for well CHA-4 (intermittent)
date: date
year: year
ET_mm_per_day: Evapotranspiration from groundwater (ETgw) derived from the White (1932) method using depth to groundwater data (in mm/d)
season: dry or wet season
mean.sos: date of the beginning of the growing season for the calendar year joined from growing season delineation threshold=0.5 method (average of willow, cottonwood, and valley oak)
mean.eos: date of the end of the growing season for the calendar year joined from growing season delineation threshold=0.5 method (average of willow, cottonwood, and valley oak)
growing_season: non-growing season or growing season based on mean.sos and mean.eos
Dataset: sc4_ET.csv
Dataset description: This dataset contains evapotranspiration from groundwater data (derived from the White method) for well SC-4 (intermittent)
date: date
year: year
ET_mm_per_day: Evapotranspiration from groundwater (ETgw) derived from the White (1932) method using depth to groundwater data (in mm/d)
season: dry or wet season
mean.sos: date of the beginning of the growing season for the calendar year joined from growing season delineation threshold=0.5 method (average of willow, cottonwood, and valley oak)
mean.eos: date of the end of the growing season for the calendar year joined from growing season delineation threshold=0.5 method (average of willow, cottonwood, and valley oak)
growing_season: non-growing season or growing season based on mean.sos and mean.eos
Dataset: sc2_ET.csv
Dataset description: This dataset contains evapotranspiration from groundwater data (derived from the White method) for well SC-2 (intermittent) (presented in the supplemental)
date: date
year: year
ET_mm_per_day: Evapotranspiration from groundwater (ETgw) derived from the White (1932) method using depth to groundwater data (in mm/d)
season: dry or wet season
mean.sos: date of the beginning of the growing season for the calendar year joined from growing season delineation threshold=0.5 method (average of willow, cottonwood, and valley oak)
mean.eos: date of the end of the growing season for the calendar year joined from growing season delineation threshold=0.5 method (average of willow, cottonwood, and valley oak)
growing_season: non-growing season or growing season based on mean.sos and mean.eos
Dataset: ndvi_HANTS.csv
Dataset description: Normalized Difference Vegetation Index (NDVI) time series
site: study site code
species: tree species
Date: date
NDVI_raw: Normalized Difference Vegetation Index (NDVI) values before smoothing
smooth_HANTS: NDVI values after weighted HANTS (Harmonic Analysis of NDVI Time Series) smoothing
Dataset: phenology_dates.csv
Dataset description: Normalized Difference Vegetation Index (NDVI) growing season division
site: study site code
species: tree species
doy.flag: year, _1 indicates that one growing season was defined for that year
doy.TRS5.sos: day of the year of the beginning of the growing season for the calendar year joined from growing season delineation threshold=0.5 method
doy.TRS5.eos: day of the year of the end of the growing season for the calendar year joined from growing season delineation threshold=0.5 method
date.TRS5.sos: date of the beginning of the growing season for the calendar year joined from growing season delineation threshold=0.5 method
date.TRS5.eos: date of the end of the growing season for the calendar year joined from growing season delineation threshold=0.5 method
Dataset: training.csv
Dataset description: Data to train the random forest models, randomly partitioned (75%)
Flow: flow regime of study site
well: study site code
species: tree species
NDVI: Normalized Difference Vegetation Index (NDVI) after weighted HANTS (Harmonic Analysis of NDVI Time Series) smoothing
DTW: Depth to groundwater (m)
PDSI: Palmer Drought Severity Index
Photo: photoperiod (hours)
Precip: daily precipitation (mm)
Precip_1m: accumulated monthly precipitation (mm)
Temp: air temperature (°C)
VPD: vapor pressure deficit (kPa)
Dataset: testing.csv
Dataset description: Data to test the random forest models, randomly partitioned (25%)
Flow: flow regime of study site
well: study site code
species: tree species
NDVI: Normalized Difference Vegetation Index (NDVI) after weighted HANTS (Harmonic Analysis of NDVI Time Series) smoothing
DTW: Depth to groundwater (m)
PDSI: Palmer Drought Severity Index
Photo: photoperiod (hours)
Precip: daily precipitation (mm)
Precip_1m: accumulated monthly precipitation (mm)
Temp: air temperature (°C)
VPD: vapor pressure deficit (kPa)
Dataset: phenology_synthesis.csv
Dataset description: Compiled phenology data from intermittent and perennial vegetation from this study; long-term satellite observations from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI 3g dataset (1982-2015) (Liu et al., 2025); ground-based observational data, including the Pan European Phenology Network (PEP725) (1945-2016), the Russian “Chronicles of Nature” Network (RCNN) (1901-2017), and the China Phenological Observation Network (CPON) (1963-2014), as compiled in Liu et al. (2025); and experimental precipitation manipulations (Lu et al., 2023)
type: study type; "Intermittent hydrology (this study)", "Perennial hydrology (this study)" , "Experimental" (from Lu et al., 2023), "Observational (satellite)" (from Liu et al., 2025), and "Observational (ground-based)" (as compiled in Liu et al., 2025)
season_component: phenophase; either Start of Season, End of Season, or Growing Season Length
change: phenological shift (days)
source: study citation
koppen_class: Köppen climate classification based on lat and lon full code
lon: longitude
lat: latitude
koppen_letter: Köppen climate classification first letter indicating five main climate groups: A (tropical), B (arid), C (temperate), D (continental), and E (polar)
aridity class: Humid, Dry Sub-Humid, Semi-Arid, Arid, or Hyper Arid based on Global Aridity Index and Potential Evapotranspiration Database (Zomer et al., 2022)
Note on missing values: Some cells in the lon, lat, and koppen_letter columns are missing (NA). These correspond to data from Liu et al. (2025) for which location data were not provided in the original publication. These NA values therefore indicate data unavailable, not data omission or processing error.
References
Liu, Y., Zhang, Y., Peñuelas, J., Kannenberg, S. A., Gong, H., Yuan, W., Wu, C., Zhou, S., & Piao, S. (2025). Drought legacies delay spring green-up in northern ecosystems. Nature Climate Change, 15, 444–451. https://doi.org/10.1038/s41558-025-02273-6
Lu, C., Zhang, J., Min, X., Chen, J., Huang, Y., Zhao, H., Yan, T., Liu, X., Wang, H., & Liu, H. (2023). Contrasting responses of early‐ and late‐season plant phenophases to altered precipitation. Oikos, 2023(5), e09829. https://doi.org/10.1111/oik.09829
White, W. N. (1932). A method of estimating ground-water supplies based on discharge by plants and evaporation from soil: Results of investigations in Escalante Valley, Utah. U.S. Geological Survey Water Supply Paper 659-A. https://doi.org/10.3133/wsp659a
Zomer, R. J., Xu, J., & Trabucco, A. (2022). Version 3 of the global aridity index and potential evapotranspiration database. Scientific Data, 9(1), 409. https://doi.org/10.1038/s41597-022-01493-1
