Campus Collection

A "catch all" like collection of datasets for research data not fitting elsewhere.

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

1,901 to 1,910 of 1,922 Results

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 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_E1_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: b555722ae86359273a47c0859629ae89915fd4c1b19e4cb5c082d1fcfe63535c Monthly water table depth anomalies generated from the proposed LSTM networks of E1.3 (soil moisture anomaly)
LSTM_E1_4_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: 1ced5300bd9667c97f4df42dd4e90a9cb3d169c0d5e250f62b060e6b64652506 Monthly water table depth anomalies generated from the proposed LSTM networks of E1.4 (precipitation anomaly and evapotranspiration anomaly)
LSTM_E1_5_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: a79d79d6bef3880a62d80027442b6633943627c73f67b47149778081bde4b442 Monthly water table depth anomalies generated from the proposed LSTM networks of E1.5 (precipitation anomaly and soil moisture anomaly)

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

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_E1_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: b555722ae86359273a47c0859629ae89915fd4c1b19e4cb5c082d1fcfe63535c

Monthly water table depth anomalies generated from the proposed LSTM networks of E1.3 (soil moisture anomaly)

LSTM_E1_4_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: 1ced5300bd9667c97f4df42dd4e90a9cb3d169c0d5e250f62b060e6b64652506

Monthly water table depth anomalies generated from the proposed LSTM networks of E1.4 (precipitation anomaly and evapotranspiration anomaly)

LSTM_E1_5_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: a79d79d6bef3880a62d80027442b6633943627c73f67b47149778081bde4b442

Monthly water table depth anomalies generated from the proposed LSTM networks of E1.5 (precipitation anomaly and soil moisture anomaly)

Add Data

Share Dataverse

Link Dataverse

Reset Modifications