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| Type: | Resource | |
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| Created: | May 12, 2026 at 7:17 p.m. (UTC) | |
| Last updated: | May 16, 2026 at 8:46 p.m. (UTC) (Metadata update) | |
| Published date: | May 16, 2026 at 8:46 p.m. (UTC) | |
| DOI: | 10.4211/hs.9067ac2d076d486ea2c9cb78898155d9 | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Published |
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Abstract
Groundwater pumping in agricultural aquifers reflects both hydroclimatic conditions and anthropogenic controls, but their relative importance can change over time. We analyzed annual pumping in the High Plains Aquifer Hydrologic Observatory Area (HPAHOA) from 1960 to 2020 using Deep Temporal Clustering, Bayesian changepoint detection, regime-aware XGBoost, and SHAP attribution. Approximately 13,000 cell-level pumping trajectories separated into three behavioral clusters, with asynchronous changepoints in 1974, 2000, and 2005. Models trained only on pre-changepoint data transferred poorly to post-changepoint periods, while models including post-changepoint information showed the greatest improvement in Cluster 3, indicating regime-dependent predictor–response relationships. SHAP attribution showed that dominant predictors differed across clusters and between full-record and changepoint-year conditions: anthropogenic predictors were more influential in Clusters 1 and 2, whereas precipitation was more influential in Cluster 3. These results show that pumping nonstationarity across the HPAHOA is spatially heterogeneous, asynchronous, and regime-dependent.
Subject Keywords
Coverage
Spatial
Temporal
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Content
readme.txt
This repository contains the code and data used to generate the figures in the paper. ================================================================ Dataset Description ================================================================ 1. dtc_latent_features -------------------------- The latent feature representations learned by the DTC model. These are the low-dimensional embeddings produced by the temporal autoencoder component of DTC, which compress each input time series into a compact feature vector. The clustering step is performed in this latent space rather than on the raw time series. 2. dtc_cluster_assignments ------------------------------ The cluster labels assigned to each sample by the DTC model. Each entry corresponds to the cluster index that the DTC model assigned to the sample at the same position in the input dataset. 3. cluster_mean_annual_pumping_2020 --------------------------------------- The mean annual groundwater pumping rate for the year 2020, computed per cluster. For each cluster, all samples assigned to that cluster are aggregated and their annual pumping rates are averaged. 4. Data_c0, Data_c1, Data_c2 ---------------------------------------- Preprocessed climate and anthropogenic feature data for each cluster, separated by cluster ID: - Data_c0 -- samples assigned to cluster 1 - Data_c1 -- samples assigned to cluster 2 - Data_c2 -- samples assigned to cluster 3 Each file contains the processed input features (climate variables and anthropogenic drivers) for the members of that cluster.
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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