Modeled groundwater levels across Central Valley, CA, from March 2015 to August 2020, using GP-DNN regression

This dataset contains groundwater level trends and time series data across a discretized grid of California's Central Valley, modeled with well data using hierarchical Gaussian process and neural network regression methodology. The spatial grid consists of 400 cells, spanning latitudes 34.91 to 40.895 degrees, and 220 cells, spanning longitudes -122.6 to -118.658 degrees. The temporal axis spans March 2015 to Aug 2020, discretized at biweekly intervals, with a total of 132 cells. The spatiotemporal grid details are present in relevant files. The first dataset is contained in the following Python pickle file. 1. 'CV_water_level_trends_Mar2015_Aug2020.pkl': This file contains a nested Python dictionary with following pairs: 1.1. 'longitude': Numpy array of shape 400 x 220 1.2. 'longitude': Numpy array of shape 400 x 220 1.3. 'mean': Python dictionary with mean long-term and seasonal water level trends 1.4. 'P10': Python dictionary with P10 long-term and seasonal water level trends 1.5. 'P90': Python dictionary with P90 long-term and seasonal water level trends Each of the dictionary in 1.3., 1.4. and 1.5. contain the following key and values: 'initial_water_level_ft': Mean/P10/P90 of March 2015 water levels in feet stored as Numpy array of shape 400 x 220 'water_level_decline_rate_ft/biweek': Mean/P10/P90 of March 2015 - Aug 2020 water level decline rates in ft/biweek stored as Numpy array of shape 400 x 220 'water_level_amplitude_ft': Mean/P10/P90 of seasonal water level oscillation amplitude stored as Numpy array of shape 400 x 220 'water_level_phase_deg': Mean/P10/P90 of time to peak seasonal signal in degrees stored as Numpy array of shape 400 x 220 The second dataset is contained in the following Python pickle file. 2. 'CV_water_level_time_series_Mar2015_Aug2020.pkl': This file contains a Python dictionary with following pairs. 2.1. 'longitude': Numpy array of shape 400 x 220 2.2. 'longitude': Numpy array of shape 400 x 220 2.3. 'time_axis': Python list on length 132 containing strings for biweekly periods from March 2015 - August 2020 2.4. 'water_level_well_ft': Processed water level observations in feet from 1744 wells, irregularly sampled across time. The data is stored as Numpy array of shape 400 x 220 x 132, with missing values as nans. 2.5. 'water_level_modeled_mean_ft': Modeled mean water level time series in feet stored as Numpy array of shape 400 x 220 x 132 2.6. 'water_level_modeled_P10_ft': Modeled P10 water level time series in feet stored as Numpy array of shape 400 x 220 x 132 2.6. 'water_level_modeled_P90_ft': Modeled P90 water level time series in feet stored as Numpy array of shape 400 x 220 x 132