Code and data to "Statistical learning and topkriging improve spatio-temporal low-flow estimation"
Code and data to "Statistical learning and topkriging improve spatio-temporal low-flow estimation"
This data and software supports the manuscript "Statistical learning and topkriging improve spatio-temporal low-flow estimation" (https:://doi.org/10.1029/2024WR038329). The dataset consists of: all produced predictions of the models (data/predictions.RDS and data/predictions_csv/*) observational data (data/observations.csv) additional catchment data (data/catchment_data.csv) used for presenting the figures state boundaries of Austria as a shape file (data/boundaries.*) partial predictions of a model-based boosting approach (data/partial_predictions.csv) Example output of number of EOF, due to long computational time (data/number_eofs.RDS) IDs of near natural catchments (data/ids_low_flow.csv) Additionally, the code is provided to: Compute the number of EOFs (functions/number_eofs.R) Produce all the figures and tables in the paper (scripts/plotting_results.R)
statistical learning, drought, topkriging, space-time models, low-flow
statistical learning, drought, topkriging, space-time models, low-flow
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