An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis (ICPSR doi:10.26165/JUELICH-DATA/AMQ6NI)

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

doi:10.26165/JUELICH-DATA/AMQ6NI

Jülich DATA

2021-12-13

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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

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

doi:10.26165/JUELICH-DATA/AMQ6NI

Ma, Yueling (Forschungszentrum Jülich)

Montzka, Carsten (Forschungszentrum Jülich)

Bayat, Bagher (Forschungszentrum Jülich)

Kollet, Stefan (Forschungszentrum Jülich)

Jülich DATA

Ma, Yueling

Ma, Yueling

2021-12-13

Earth and Environmental Sciences, Groundwater, Long Short-Term Memory (LSTM) networks, Anomalies, Europe

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 experiments. The input variables in the experiments E1 and E2 are: E1.1: pr_a; E1.2: evapotranspiration anomaly (ET_a); E1.3: soil moisture anomaly (θ_a); E1.4: pr_a and ET_a; E1.5: pr_a and θ_a; E1.6: ET_a and θ_a; E1.7: pr_a, ET_a and θ_a; E2.1: pr_a, θ_a and scaled yearly averaged snow water equivalent (SWE_scaled); E2.2: pr_a, θ_a at the selected pixels and adjacent pixels; and E2.3: pr_a, θ_a at the selected pixels close to rivers and river stage anomaly (rs_a) at the adjacent pixels. The data files provide the TSMP-G2A ET_a, the TSMP-G2A θ_a, and the LSTM wtd_a data (obtained from E1.2 to E2.3) for the period 1996-2016, with a spatial resolution of 0.11 degrees. The TSMP-G2A yearly averaged snow water equivalent (SWE) data are also provided.

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https://doi.org/10.3389/frwa.2021.723548

Ma, Y., Montzka, C., Bayat, B. and Kollet, S.: An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis, Front. Water, 3, doi:10.3389/frwa.2021.723548, 2021.

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LSTM_E1_2_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

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

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LSTM_E1_3_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

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

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LSTM_E1_4_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

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

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LSTM_E1_5_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

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

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LSTM_E1_6_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

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Monthly water table depth anomalies generated from the proposed LSTM networks of E1.6 (evapotranspiration anomaly and soil moisture anomaly)

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LSTM_E1_7_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

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

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LSTM_E2_1_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

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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)

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LSTM_E2_2_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

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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)

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LSTM_E2_3_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.nc

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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)

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TSMP_G2A_monthly_evapotranspiration_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc

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Monthly evapotranspiration anomalies calculated from the TSMP-G2A data set

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TSMP_G2A_monthly_soil_moisture_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.nc

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

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TSMP_G2A_yearly_averaged_snow_water_equivalent_FZJ-IBG3_TSMP_0.1_degree_v1.2003_2010.txt

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

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