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1,831 to 1,840 of 1,841 Results

LSTM_E1_7_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: 16ee2fe91b1129b2a79cc76f21d53d62bb566fd33b7d6d8ef8559748e8aadf04 Monthly water table depth anomalies generated from the proposed LSTM networks of E1.7 (precipitation anomaly, evapotranspiration anomaly and soil moisture anomaly)
LSTM_E2_1_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: b7ec5b5accae616765f768c0becd8e2ad6fab4eb1daaaaee9d0af7ee64cdc357 Monthly water table depth anomalies generated from the proposed LSTM networks of E2.1 (precipitation anomaly, soil moisture anomaly and scaled yearly averaged snow water equivalent)
LSTM_E2_2_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: 005b78e53e7a65064e34348b66ff975a8880336d85f8bb383aaa9d8728c7e4d7 Monthly water table depth anomalies generated from the proposed LSTM networks of E2.2 (precipitation anomaly and soil moisture anomaly at the selected pixels and adjacent pixels)
LSTM_E2_3_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: d090fd16b6a96db6e91c6f418d3ac8fa4e793d0c1145269fce502124c7b80b25 Monthly water table depth anomalies generated from the proposed LSTM networks of E2.3 (precipitation anomaly and soil moisture anomaly at the selected pixels close to rivers and river stage anomaly at the adjacent pixels)
TSMP_G2A_monthly_evapotranspiration_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: a1260d6aed328110afbf60df25cd63a48ede47973954ce3eaea09c136a81a5b8 Monthly evapotranspiration anomalies calculated from the TSMP-G2A data set
TSMP_G2A_monthly_soil_moisture_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Network Common Data Form - 167.9 MB - SHA-256: 6290fd173e798af8c3aa343c1f44445997042ffbe93dd27c78c3e05dc06eb436 Monthly soil moisture anomalies calculated from the TSMP-G2A data set
TSMP_G2A_yearly_averaged_snow_water_equivalent_FZJ-IBG3_TSMP_0.1_degree_v1.2003_2010.txt Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Plain Text - 2.5 MB - SHA-256: e85a674dacaaafd8431b78dac8ad82237a1c3eebe89cc736cce963a777389cac Yearly averaged snow water equivalent calculated from the TSMP-G2A data set
Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe May 31, 2021 Ma, Yueling; Montzka, Carsten; Bayat, Bagher; Kollet, Stefan, 2021, "Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe", https://doi.org/10.26165/JUELICH-DATA/WPRA1F, Jülich DATA, V1 This study utilized spatiotemporally continuous precipitation anomaly (pr_a) and water table depth anomaly (wtd_a) from integrated hydrologic simulation results (i.e., the TSMP-G2A data set) over Europe in combination with Long Short-Term Memory (LSTM) networks to capture the tim...
LSTM_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc May 31, 2021 - Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe Network Common Data Form - 335.9 MB - SHA-256: a0ac27eb52a9fbffc8620acdb5ba8eebe44ad11daa0001aeaf59a78b4aa8744f Monthly groundwater table depth anomalies generated from the proposed LSTM networks
TSMP_G2A_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc May 31, 2021 - Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe Network Common Data Form - 167.9 MB - SHA-256: cb307f74a193b7dc06443d50c61357fd50625c8bd8610094ba485309e4c4445f Monthly groundwater table depth anomalies calculated from the TSMP-G2A data set

LSTM_E1_7_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: 16ee2fe91b1129b2a79cc76f21d53d62bb566fd33b7d6d8ef8559748e8aadf04

Monthly water table depth anomalies generated from the proposed LSTM networks of E1.7 (precipitation anomaly, evapotranspiration anomaly and soil moisture anomaly)

LSTM_E2_1_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: b7ec5b5accae616765f768c0becd8e2ad6fab4eb1daaaaee9d0af7ee64cdc357

Monthly water table depth anomalies generated from the proposed LSTM networks of E2.1 (precipitation anomaly, soil moisture anomaly and scaled yearly averaged snow water equivalent)

LSTM_E2_2_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: 005b78e53e7a65064e34348b66ff975a8880336d85f8bb383aaa9d8728c7e4d7

Monthly water table depth anomalies generated from the proposed LSTM networks of E2.2 (precipitation anomaly and soil moisture anomaly at the selected pixels and adjacent pixels)

LSTM_E2_3_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: d090fd16b6a96db6e91c6f418d3ac8fa4e793d0c1145269fce502124c7b80b25

Monthly water table depth anomalies generated from the proposed LSTM networks of E2.3 (precipitation anomaly and soil moisture anomaly at the selected pixels close to rivers and river stage anomaly at the adjacent pixels)

TSMP_G2A_monthly_evapotranspiration_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: a1260d6aed328110afbf60df25cd63a48ede47973954ce3eaea09c136a81a5b8

Monthly evapotranspiration anomalies calculated from the TSMP-G2A data set

TSMP_G2A_monthly_soil_moisture_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Network Common Data Form - 167.9 MB - SHA-256: 6290fd173e798af8c3aa343c1f44445997042ffbe93dd27c78c3e05dc06eb436

Monthly soil moisture anomalies calculated from the TSMP-G2A data set

TSMP_G2A_yearly_averaged_snow_water_equivalent_FZJ-IBG3_TSMP_0.1_degree_v1.2003_2010.txt

Dec 13, 2021 - An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Plain Text - 2.5 MB - SHA-256: e85a674dacaaafd8431b78dac8ad82237a1c3eebe89cc736cce963a777389cac

Yearly averaged snow water equivalent calculated from the TSMP-G2A data set

Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe

May 31, 2021

Ma, Yueling; Montzka, Carsten; Bayat, Bagher; Kollet, Stefan, 2021, "Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe", https://doi.org/10.26165/JUELICH-DATA/WPRA1F, Jülich DATA, V1

This study utilized spatiotemporally continuous precipitation anomaly (pr_a) and water table depth anomaly (wtd_a) from integrated hydrologic simulation results (i.e., the TSMP-G2A data set) over Europe in combination with Long Short-Term Memory (LSTM) networks to capture the tim...

LSTM_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

May 31, 2021 - Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe

Network Common Data Form - 335.9 MB - SHA-256: a0ac27eb52a9fbffc8620acdb5ba8eebe44ad11daa0001aeaf59a78b4aa8744f

Monthly groundwater table depth anomalies generated from the proposed LSTM networks

TSMP_G2A_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc

May 31, 2021 - Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe

Network Common Data Form - 167.9 MB - SHA-256: cb307f74a193b7dc06443d50c61357fd50625c8bd8610094ba485309e4c4445f

Monthly groundwater table depth anomalies calculated from the TSMP-G2A data set

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