tgcn-soil-moisture

Code for our AAAI 2023 paper:

01_Gen_Clustering_Data

run Data_input_hru.ipynb and store numpy arrays of numeric hru features at location data_path Reads: Region 02 data read from .hru output files and saved into numpy arrays. Parameters: Clustering_feature_names : Change the features used for clustering. Saves: monthly average over 38 years of the simulation (months, hrus, features) for each HRU. The output file is subbasin level 'subbasinid.num_hrus'

02_Elbow_test

run 01_Gen_Clustering_Data and have numpy arrays of hru clustering features data stored at location data_path Parameters:

• iterations : number of subsampling iterations
• sampled hrus : number of hrus to subsample from total ~9k hrus Output: Number of clusters $k$ for the kmeans clustering

03_Clustering

Prerequisites:

• Run 01_Gen_Clustering_Data and have numpy arrays of hru clustering features data stored at location data_path
• Run 02_Elbow_test to identify the number of clusters

Parameters:

• normalization : number of hrus to subsample from total ~9k hrus
• max_iter : number of max iterations for convergence of the clustering algorithm Output: saves hrus and t

04_Gen_SMest_Data:

Generates data for SM estimation code

05_SingleStep_TGCN

07_MultiStep_TCN

p.s.

/path/ : refers to local path of data - replace with your local path