DOI: 10.5061/dryad.cjsxksnj7
title: Data from: Groundwater and remotely sensed phenology reveal vulnerability of riparian trees to drought

description: 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: # Data from: Groundwater and remotely sensed phenology reveal
 vulnerability of riparian trees to drought ## 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 ](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](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](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](https://doi.org/10.1038/s41597-022-01493-1)