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Title: Data accompanying "Drought Characterization with GPS: Insights into Groundwater and Reservoir Storage in California" [Young et al., (2024)]

The data provided here accompany the publication "Drought Characterization with GPS: Insights into Groundwater and Reservoir Storage in California" [Young et al., (2024)] which is currently under review with Water Resources Research. (as of 28 May 2024)


Please refer to the manuscript and its supplemental materials for full details. (A link will be appended following publication)

File formatting information is listed below, followed by a sub-section of the text describing the Geodetic Drought Index Calculation.



The longitude, latitude, and label for grid points are provided in the file "loading_grid_lon_lat".




Time series for each Geodetic Drought Index (GDI) time scale are provided within "GDI_time_series.zip".

The included time scales are for 00- (daily), 1-, 3-, 6-, 12- 18- 24-, 36-, and 48-month GDI solutions.

Files are formatted following...

Title: "grid point label L****"_"time scale"_month

File Format: ["decimal date" "GDI value"]




Gridded, epoch-by-epoch, solutions for each time scale are provided within "GDI_grids.zip".

Files are formatted following...

Title: GDI_"decimal date"_"time scale"_month

File Format: ["longitude" "latitude" "GDI value" "grid point label L****"]


2.2 GEODETIC DROUGHT INDEX CALCULATION

We develop the GDI following Vicente-Serrano et al. (2010) and Tang et al. (2023), such that the GDI mimics the derivation of the SPEI, and utilize the log-logistic distribution (further details below). While we apply hydrologic load estimates derived from GPS displacements as the input for this GDI (Figure 1a-d), we note that alternate geodetic drought indices could be derived using other types of geodetic observations, such as InSAR, gravity, strain, or a combination thereof. Therefore, the GDI is a generalizable drought index framework.

A key benefit of the SPEI is that it is a multi-scale index, allowing the identification of droughts which occur across different time scales. For example, flash droughts (Otkin et al., 2018), which may develop over the period of a few weeks, and persistent droughts (>18 months), may not be observed or fully quantified in a uni-scale drought index framework. However, by adopting a multi-scale approach these signals can be better identified (Vicente-Serrano et al., 2010). Similarly, in the case of this GPS-based GDI, hydrologic drought signals are expected to develop at time scales that are both characteristic to the drought, as well as the source of the load variation (i.e., groundwater versus surface water and their respective drainage basin/aquifer characteristics). Thus, to test a range of time scales, the TWS time series are summarized with a retrospective rolling average window of D (daily with no averaging), 1, 3, 6, 12, 18, 24, 36, and 48-months width (where one month equals 30.44 days).

From these time-scale averaged time series, representative compilation window load distributions are identified for each epoch. The compilation window distributions include all dates that range ±15 days from the epoch in question per year. This allows a characterization of the estimated loads for each day relative to all past/future loads near that day, in order to bolster the sample size and provide more robust parametric estimates [similar to Ford et al., (2016)]; this is a key difference between our GDI derivation and that presented by Tang et al. (2023). Figure 1d illustrates the representative distribution for 01 December of each year at the grid cell co-located with GPS station P349 for the daily TWS solution. Here all epochs between between 16 November and 16 December of each year (red dots), are compiled to form the distribution presented in Figure 1e.

This approach allows inter-annual variability in the phase and amplitude of the signal to be retained (which is largely driven by variation in the hydrologic cycle), while removing the primary annual and semi-annual signals. Solutions converge for compilation windows >±5 days, and show a minor increase in scatter of the GDI time series for windows of ±3-4 days (below which instability becomes more prevalent). To ensure robust characterization of drought characteristics, we opt for an extended ±15-day compilation window. While Tang et al. (2023) found the log-logistic distribution to be unstable and opted for a normal distribution, we find that, by using the extended compiled distribution, the solutions are stable with negligible differences compared to the use of a normal distribution. Thus, to remain aligned with the SPEI solution, we retain the three-parameter log-logistic distribution to characterize the anomalies. Probability weighted moments for the log-logistic distribution are calculated following Singh et al., (1993) and Vicente-Serrano et al., (2010). The individual moments are calculated following Equation 3.

These are then used to calculate the L-moments for shape (), scale (), and location () of the three-parameter log-logistic distribution (Equations 4 – 6).

The probability density function (PDF) and the cumulative distribution function (CDF) are then calculated following Equations 7 and 8, respectively.

The inverse Gaussian function is used to transform the CDF from estimates of the parametric sample quantiles to standard normal index values that represent the magnitude of the standardized anomaly. Here, positive/negative values represent greater/lower than normal hydrologic storage. Thus, an index value of -1 indicates that the estimated load is approximately one standard deviation dryer than the expected average load on that epoch.

*Equations can be found in the main text.




 
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Award ID(s):
2021637
NSF-PAR ID:
10515149
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
figshare
Date Published:
Subject(s) / Keyword(s):
Geodesy Surface water hydrology Groundwater hydrology
Format(s):
Medium: X Size: 2770810580 Bytes
Size(s):
2770810580 Bytes
Sponsoring Org:
National Science Foundation
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    References

    [1] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-11-04 as indexed by the Global Biodiversity Informatics Facility (GBIF) with provenance hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36 hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c.
    [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .
    [3] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132
    [4] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-10-08. https://www.gbif.org/occurrence/3323647301 . hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c

    This work is funded in part by grant NSF OAC 1839201 and NSF DBI 2102006 from the National Science Foundation. 
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    <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> <http://purl.org/pav/previousVersion> <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> .
    <hash://sha256/e46cd4b0d7fdb51ea789fa3c5f7b73591aca62d2d8f913346d71aa6cf0745c9f> <http://purl.org/pav/previousVersion> <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> .
    <hash://sha256/9215d543418a80510e78d35a0cfd7939cc59f0143d81893ac455034b5e96150a> <http://purl.org/pav/previousVersion> <hash://sha256/e46cd4b0d7fdb51ea789fa3c5f7b73591aca62d2d8f913346d71aa6cf0745c9f> .
    <hash://sha256/1448656cc9f339b4911243d7c12f3ba5366b54fff3513640306682c50f13223d> <http://purl.org/pav/previousVersion> <hash://sha256/9215d543418a80510e78d35a0cfd7939cc59f0143d81893ac455034b5e96150a> .
    <hash://sha256/7ee6b16b7a5e9b364776427d740332d8552adf5041d48018eeb3c0e13ccebf27> <http://purl.org/pav/previousVersion> <hash://sha256/1448656cc9f339b4911243d7c12f3ba5366b54fff3513640306682c50f13223d> .
    <hash://sha256/34ccd7cf7f4a1ea35ac6ae26a458bb603b2f6ee8ad36e1a58aa0261105d630b1> <http://purl.org/pav/previousVersion> <hash://sha256/7ee6b16b7a5e9b364776427d740332d8552adf5041d48018eeb3c0e13ccebf27> .

    To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.

    $ java -jar preston.jar verify
    hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca    file:/home/preston/preston-bhl/data/e0/c1/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca    OK    CONTENT_PRESENT_VALID_HASH    49458087    hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca
    hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99    file:/home/preston/preston-bhl/data/1a/57/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99    OK    CONTENT_PRESENT_VALID_HASH    25745    hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99
    hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c    file:/home/preston/preston-bhl/data/85/ef/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c    OK    CONTENT_PRESENT_VALID_HASH    519892    hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c
    hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743    file:/home/preston/preston-bhl/data/25/1e/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743    OK    CONTENT_PRESENT_VALID_HASH    787414    hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743

    Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".

    Files in this data publication:

    --- start of file descriptions ---

    -- description of archive and its contents (this file) --
    README

    -- executable java jar containing preston[2] v0.1.15. --
    preston.jar

    -- preston archives containing BHL data files, associated provenance logs and a provenance index --
    preston-[00-ff].tar.gz

    -- individual provenance index files --
    2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a
    2b1104cb7749e818c9afca78391b2d0099bbb0a32f2b348860a335cd2f8f6800
    4081bc59dff58d63f6a86c623cb770f01e9a355a42495b205bcb538cd526190f
    47a2816f8b5600b24487093adcddfea12434cc4f270f3ab09d9215fbdd546cd2
    6f99a1388823fca745c9e22ac21e2da909a219aa1ace55170fa9248c0276903c
    7ae46d7cd9b5a0f5889ba38bac53c82e591b0bdf8b605f5e48c0dce8fb7b717f
    82903464889fea7c53f53daedf4e41fa31092f82619edeb3415eb2b473f74af3
    9e8c86243df39dd4fe82a3f814710eccf73aa9291d050415408e346fa2b09e70
    a8308fbf4530e287927c471d881ce0fc852f16543d46e1ee26f1caba48815f3a
    bcec6df2ea7f74e9a6e2830d0072e6b2fbe65323d9ddb022dd6e1349c23996e2
    cfe47c25ec0210ac73c06b407beb20d9c58355cb15bae427fdc7541870ca2e4e
    f73fc9e70bce8f21f0c96b8ef0903749d8f223f71343ab5a8910968f99c9b8b6

    --- end of file descriptions ---


    References

    [1] Biodiversity Heritage Library (BHL, https://biodiversitylibrary.org) accessed from 2019-05-19 to 2020-05-09 with provenance hash://sha256/34ccd7cf7f4a1ea35ac6ae26a458bb603b2f6ee8ad36e1a58aa0261105d630b1.
    [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .


    This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.

     
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  5. Abstract

    Meteorological drought indices like the Standardized Precipitation Evaporation Index (SPEI) are frequently used to diagnose “ecological drought,” despite the fact that they were not explicitly designed for this purpose. More recently developed indices like the Evaporative Stress Index (ESI), which is based on the degree of coupling between actual to potential evapotranspiration, may better capture dynamic plant response to moisture limitations. However, the skill of these indices at describing plant water stress is rarely evaluated at sub‐seasonal timescales over which drought evolves. Moreover, it remains unclear how variability in phenological timing impacts and complicates early drought detection. Here, we compared the ability of ESI and SPEI to reflect the dynamics of ecological drought in forests and grasslands, based on anomalies of Gross Primary Productivity (GPP), surface conductance (Gs, a proxy for stomatal conductance), soil moisture, and vapor pressure deficit. ESI performed better than SPEI in capturing the dynamics of GPP andGs, but still missed early ecological drought signals due to biases linked to earlier onset of spring leaf development. Thus, we developed a modified variant of the ESI () that accounts for the complicating effects of phenological shifts in leaf area index (LAI). Thedetected drought onset up to 7–10 weeks earlier than SPEI and ESI. Additionally, drought onset dates determined fromare close to (±2 weeks) the dates determined from LAI‐corrected anomalies ofGs, and GPP, as well as the onset dates of soil water deficit and atmospheric aridity.

     
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