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Publication Year: 2021

61 to 70 of 374 Results

LSTM_TL_RD1_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1981_2019.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 623.8 MB - SHA-256: 8f27b8c82d6d73250112146a0223a227c88fd1cd6ac12df05eb3996ddc69400d Reconstructed European monthly water table depth anomaly (wtd_a) data RD1, obtained by LSTM-TL with ERA5 bias corrected precipitation anomalies (pr_a) and ERA5 Land soil moisture anomalies (θ_a) as input
LSTM_TL_RD2_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1980_2019.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 639.7 MB - SHA-256: c60c893c86f972589f784c97b6051c3d4ed8ad09427b1a16d2889ed973adf7c9 Reconstructed European monthly water table depth anomaly (wtd_a) data RD2, obtained by LSTM-TL with ERA5 bias corrected precipitation anomalies (pr_a) and GLEAM soil moisture anomalies (θ_a) as input
LSTM_TL_RD3_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1995_082019.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 394.5 MB - SHA-256: b6a76b48b023f524141fa7cc457adbc1989be928e4ab3149b540a5d328774b07 Reconstructed European monthly water table depth anomaly (wtd_a) data RD3, obtained by LSTM-TL with COSMO-REA6 precipitation anomalies (pr_a) and ERA5 Land soil moisture anomalies (θ_a) as input
LSTM_TL_RD4_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1995_082019.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 394.5 MB - SHA-256: fa4354b03f608f69de350c36685c787cafd35c9dc8122e26b4ca10e3ebc0ebfd Reconstructed European monthly water table depth anomaly (wtd_a) data RD4, obtained by LSTM-TL with COSMO-REA6 precipitation anomalies (pr_a) and GLEAM soil moisture anomalies (θ_a) as input
LSTM_TL_RD5_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1981_052021.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 646.4 MB - SHA-256: 0824e4de3b2c67e1d15c675a2f6ebc1b45ad23e312c07675cef64339c42c242f Reconstructed European monthly water table depth anomaly (wtd_a) data RD5, obtained by LSTM-TL with ERA5 Land precipitation anomalies (pr_a) and ERA5 Land soil moisture anomalies (θ_a) as input
LSTM_TL_RD6_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1981_2020.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 639.7 MB - SHA-256: c582ead1d144681750a6b0435c338cd8eb99f473440f30d8704c41a9f08cfb2c Reconstructed European monthly water table depth anomaly (wtd_a) data RD6, obtained by LSTM-TL with ERA5 Land precipitation anomalies (pr_a) and GLEAM soil moisture anomalies (θ_a) as input
obs_averaged_monthly_precipitation_anomaly_FZJ-IBG3_0.1_degree_v1.1996_2016.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 167.9 MB - SHA-256: d79a0dbb404e140e6777f2c0404d05ac3d1c91d263813741a1af0d1f57bd829a Input averaged monthly precipitation anomalies (pr_a) from observational datasets for the period 1996-2016
obs_averaged_monthly_soil_moisture_anomaly_FZJ-IBG3_0.1_degree_v1.1996_2016.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 167.9 MB - SHA-256: 3473241cdc8d38c631f496de78817381eafe258d35f2c73aa8b1570fdce97765 Input averaged monthly soil moisture anomalies (θ_a) from observational datasets for the period 1996-2016
An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Dec 13, 2021 - Campus Collection Ma, Yueling; Montzka, Carsten; Bayat, Bagher; Kollet, Stefan, 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", https://doi.org/10.26165/JUELICH-DATA/AMQ6NI, Jülich DATA, V1 This study introduced a number of hydrometeorological variables in addition to precipitation anomaly (pr_a) in the construction of Long Short-Term Memory (LSTM) networks to arrive at improved water table depth anomaly (wtd_a) at individual pixels over Europe in various experiment...
LSTM_E1_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: 9ff63866d5dd8fda2554928f14414f952e705a4febed42a6b24f8c263f37e20d Monthly water table depth anomalies generated from the proposed LSTM networks of E1.2 (evapotranspiration anomaly)

LSTM_TL_RD1_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1981_2019.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 623.8 MB - SHA-256: 8f27b8c82d6d73250112146a0223a227c88fd1cd6ac12df05eb3996ddc69400d

Reconstructed European monthly water table depth anomaly (wtd_a) data RD1, obtained by LSTM-TL with ERA5 bias corrected precipitation anomalies (pr_a) and ERA5 Land soil moisture anomalies (θ_a) as input

LSTM_TL_RD2_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1980_2019.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 639.7 MB - SHA-256: c60c893c86f972589f784c97b6051c3d4ed8ad09427b1a16d2889ed973adf7c9

Reconstructed European monthly water table depth anomaly (wtd_a) data RD2, obtained by LSTM-TL with ERA5 bias corrected precipitation anomalies (pr_a) and GLEAM soil moisture anomalies (θ_a) as input

LSTM_TL_RD3_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1995_082019.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 394.5 MB - SHA-256: b6a76b48b023f524141fa7cc457adbc1989be928e4ab3149b540a5d328774b07

Reconstructed European monthly water table depth anomaly (wtd_a) data RD3, obtained by LSTM-TL with COSMO-REA6 precipitation anomalies (pr_a) and ERA5 Land soil moisture anomalies (θ_a) as input

LSTM_TL_RD4_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1995_082019.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 394.5 MB - SHA-256: fa4354b03f608f69de350c36685c787cafd35c9dc8122e26b4ca10e3ebc0ebfd

Reconstructed European monthly water table depth anomaly (wtd_a) data RD4, obtained by LSTM-TL with COSMO-REA6 precipitation anomalies (pr_a) and GLEAM soil moisture anomalies (θ_a) as input

LSTM_TL_RD5_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1981_052021.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 646.4 MB - SHA-256: 0824e4de3b2c67e1d15c675a2f6ebc1b45ad23e312c07675cef64339c42c242f

Reconstructed European monthly water table depth anomaly (wtd_a) data RD5, obtained by LSTM-TL with ERA5 Land precipitation anomalies (pr_a) and ERA5 Land soil moisture anomalies (θ_a) as input

LSTM_TL_RD6_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_TL_0.1_degree_v1.1981_2020.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 639.7 MB - SHA-256: c582ead1d144681750a6b0435c338cd8eb99f473440f30d8704c41a9f08cfb2c

Reconstructed European monthly water table depth anomaly (wtd_a) data RD6, obtained by LSTM-TL with ERA5 Land precipitation anomalies (pr_a) and GLEAM soil moisture anomalies (θ_a) as input

obs_averaged_monthly_precipitation_anomaly_FZJ-IBG3_0.1_degree_v1.1996_2016.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 167.9 MB - SHA-256: d79a0dbb404e140e6777f2c0404d05ac3d1c91d263813741a1af0d1f57bd829a

Input averaged monthly precipitation anomalies (pr_a) from observational datasets for the period 1996-2016

obs_averaged_monthly_soil_moisture_anomaly_FZJ-IBG3_0.1_degree_v1.1996_2016.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 167.9 MB - SHA-256: 3473241cdc8d38c631f496de78817381eafe258d35f2c73aa8b1570fdce97765

Input averaged monthly soil moisture anomalies (θ_a) from observational datasets for the period 1996-2016

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

Dec 13, 2021 - Campus Collection

Ma, Yueling; Montzka, Carsten; Bayat, Bagher; Kollet, Stefan, 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", https://doi.org/10.26165/JUELICH-DATA/AMQ6NI, Jülich DATA, V1

This study introduced a number of hydrometeorological variables in addition to precipitation anomaly (pr_a) in the construction of Long Short-Term Memory (LSTM) networks to arrive at improved water table depth anomaly (wtd_a) at individual pixels over Europe in various experiment...

LSTM_E1_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: 9ff63866d5dd8fda2554928f14414f952e705a4febed42a6b24f8c263f37e20d

Monthly water table depth anomalies generated from the proposed LSTM networks of E1.2 (evapotranspiration anomaly)

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