diff --git a/.gitignore b/.gitignore index 81faee9..eed03cd 100644 --- a/.gitignore +++ b/.gitignore @@ -82,4 +82,6 @@ dist/ # Pickle stuff # ################ -model1_state.pkl \ No newline at end of file +model1_state.pkl + +build/ diff --git a/README.md b/README.md index 5f0d05f..380c68e 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,9 @@ This Long Short-Term Memory (LSTM) network was developed for use in the [Next Ge This module is dependent on a trained deep learning model. The forward pass of this LSTM model [`nextgen_cuda_lstm.py`](./lstm/nextgen_cuda_lstm.py) is heavily based on NeuralHydrology's [`CudaLSTM`](https://neuralhydrology.readthedocs.io/en/latest/usage/models.html#cudalstm). Other model classes can be applied but [`bmi_lstm.py`](./lstm/bmi_lstm.py) would need to load it in. More information about the python package NeuralHydrology can be found [here](https://neuralhydrology.readthedocs.io/en/latest/). ## Sample Data -All data required for a test run of this model is available in the [`data/`](./data) directory. This includes: + +### NLDAS sample data +Sample data required for a test run of this model is available in the [`data/`](./data) directory. This includes: * Forcing data: `usgs-streamflow-nldas_hourly.nc` * Observation values: also included in `usgs-streamflow-nldas_hourly.nc` * Static attributes: see an example configuration file for a list of these attributes in [`./bmi_config_files`](./bmi_config_files/01022500_hourly_all_attributes_forcings.yml) @@ -28,6 +30,9 @@ for four USGS gauges: Note that the data found in this repository are simply examples. The LSTM model can be run on any watershed, provided the necessary static attributes and dynamic forcings. The full list of attributes differs depending on the trained LSTM model chosen. Example files (`*.yml`) with the required attributes are located in the [`./bmi_config_files`](./bmi_config_files)directory. The attributes required for these configuration files can be found in the [`camels_attributes_v2.0/`](./data/camels_attributes_v2.0) data directory for catchments in the CAMELS dataset or estimated from [Addor, N., A.J. Newman, N. Mizukami, and M.P. Clark. 2017. The CAMELS data set: catchment attributes and meteorology for large-sample studies. Hydrol. Earth Syst. Sci. 21: 5293-5313. https://doi.org/10.5194/hess-21-5293-2017](https://doi.org/10.5194/hess-21-5293-2017). +### AORC Sample Data +To run a sample with AORC, you can clone this repository that has data from several camples basins: [https://github.com/NWC-CUAHSI-Summer-Institute/CAMELS_data_sample](https://github.com/NWC-CUAHSI-Summer-Institute/CAMELS_data_sample). You'll need to change the paths in the sample AORC notebook. + ## Configurations The LSTM model requires a configuration file for specification of forcings, weights, scalers, run options (like warmup period), runtime period, static basin parameters and model time step. This configuration file needs to be generated for any specific application of the LSTM model. diff --git a/bmi_config_files/01013500_nh_AORC_hourly_ensemble.yml b/bmi_config_files/01013500_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..9b4a60a --- /dev/null +++ b/bmi_config_files/01013500_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 2252.7 +basin_id: 1013500 +basin_name: Fish River near Fort Kent, Maine +elev_mean: 250.31 +initial_state: zero +lat: 47.23739 +lon: -68.58264 +slope_mean: 21.64152 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/01022500_hourly_slope_mean_precip_temp.yml b/bmi_config_files/01022500_hourly_slope_mean_precip_temp.yml deleted file mode 100644 index 94c331a..0000000 --- a/bmi_config_files/01022500_hourly_slope_mean_precip_temp.yml +++ /dev/null @@ -1,11 +0,0 @@ -time_step: '1 hour' -initial_state: 'zero' -basin_name: 'Narraguagus River at Cherryfield, Maine' -basin_id: '01022500' -area_sqkm: 620.38 -lat: 44.60797 -lon: -67.93524 -train_cfg_file: ../trained_neuralhydrology_models/hourly_slope_mean_precip_temp/config.yml -verbose: 0 -elev_mean: 92.68 -slope_mean: 17.79072 diff --git a/bmi_config_files/01022500_nh_AORC_hourly_25yr_1210_112435.yml b/bmi_config_files/01022500_nh_NLDAS_hourly.yml similarity index 81% rename from bmi_config_files/01022500_nh_AORC_hourly_25yr_1210_112435.yml rename to bmi_config_files/01022500_nh_NLDAS_hourly.yml index 72f1525..d1e13b2 100644 --- a/bmi_config_files/01022500_nh_AORC_hourly_25yr_1210_112435.yml +++ b/bmi_config_files/01022500_nh_NLDAS_hourly.yml @@ -5,7 +5,7 @@ basin_id: '01022500' area_sqkm: 620.38 lat: 44.60797 lon: -67.93524 -train_cfg_file: ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435/config.yml +train_cfg_file: ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml verbose: 0 elev_mean: 92.68 slope_mean: 17.79072 diff --git a/bmi_config_files/01333000_nh_AORC_hourly_25yr_1210_112435.yml b/bmi_config_files/01333000_nh_AORC_hourly_25yr_1210_112435.yml deleted file mode 100644 index 76c9e21..0000000 --- a/bmi_config_files/01333000_nh_AORC_hourly_25yr_1210_112435.yml +++ /dev/null @@ -1,11 +0,0 @@ -time_step: '1 hour' -initial_state: 'zero' -basin_name: 'GREEN RIVER AT WILLIAMSTOWN, MA' -basin_id: '01333000' -area_sqkm: 112 -lat: 42.70897 -lon: -73.19677 -train_cfg_file: ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435/config.yml -verbose: 0 -elev_mean: 485.91 -slope_mean: 74.78725 diff --git a/bmi_config_files/01333000_nh_AORC_hourly_ensemble.yml b/bmi_config_files/01333000_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..d928a5f --- /dev/null +++ b/bmi_config_files/01333000_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 112.16 +basin_id: 1333000 +basin_name: GREEN RIVER AT WILLIAMSTOWN, MA +elev_mean: 485.91 +initial_state: zero +lat: 42.70897 +lon: -73.19677 +slope_mean: 74.78725 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/01547700_nh_NLDAS_hourly.yml b/bmi_config_files/01547700_nh_NLDAS_hourly.yml new file mode 100644 index 0000000..2c232aa --- /dev/null +++ b/bmi_config_files/01547700_nh_NLDAS_hourly.yml @@ -0,0 +1,11 @@ +area_sqkm: 142.18 +basin_id: 12010000 +basin_name: NASELLE RIVER NEAR NASELLE, WA +elev_mean: 145.18 +initial_state: zero +lat: 46.37399 +lon: -123.74348 +slope_mean: 47.2961 +time_step: 1 hour +train_cfg_file: ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/02046000_nh_AORC_hourly_ensemble.yml b/bmi_config_files/02046000_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..c71855b --- /dev/null +++ b/bmi_config_files/02046000_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 288.52 +basin_id: 2046000 +basin_name: STONY CREEK NEAR DINWIDDIE, VA +elev_mean: 86.64 +initial_state: zero +lat: 37.06709 +lon: -77.60249 +slope_mean: 6.04481 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/02064000_nh_NLDAS_hourly.yml b/bmi_config_files/02064000_nh_NLDAS_hourly.yml new file mode 100644 index 0000000..2c232aa --- /dev/null +++ b/bmi_config_files/02064000_nh_NLDAS_hourly.yml @@ -0,0 +1,11 @@ +area_sqkm: 142.18 +basin_id: 12010000 +basin_name: NASELLE RIVER NEAR NASELLE, WA +elev_mean: 145.18 +initial_state: zero +lat: 46.37399 +lon: -123.74348 +slope_mean: 47.2961 +time_step: 1 hour +train_cfg_file: ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/03010655_nh_AORC_hourly_ensemble.yml b/bmi_config_files/03010655_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..21eece8 --- /dev/null +++ b/bmi_config_files/03010655_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 254.44 +basin_id: 3010655 +basin_name: Oswayo Creek at Shinglehouse, PA +elev_mean: 635.06 +initial_state: zero +lat: 41.96173 +lon: -78.19807 +slope_mean: 32.95227 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/03015500_nh_NLDAS_hourly.yml b/bmi_config_files/03015500_nh_NLDAS_hourly.yml new file mode 100644 index 0000000..2c232aa --- /dev/null +++ b/bmi_config_files/03015500_nh_NLDAS_hourly.yml @@ -0,0 +1,11 @@ +area_sqkm: 142.18 +basin_id: 12010000 +basin_name: NASELLE RIVER NEAR NASELLE, WA +elev_mean: 145.18 +initial_state: zero +lat: 46.37399 +lon: -123.74348 +slope_mean: 47.2961 +time_step: 1 hour +train_cfg_file: ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/03439000_nh_AORC_hourly_ensemble.yml b/bmi_config_files/03439000_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..5797688 --- /dev/null +++ b/bmi_config_files/03439000_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 178.67 +basin_id: 3439000 +basin_name: FRENCH BROAD RIVER AT ROSMAN, NC +elev_mean: 863.18 +initial_state: zero +lat: 35.14333 +lon: -82.82472 +slope_mean: 63.23118 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/04015330_nh_AORC_hourly_ensemble.yml b/bmi_config_files/04015330_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..fd94880 --- /dev/null +++ b/bmi_config_files/04015330_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 224.35 +basin_id: 4015330 +basin_name: KNIFE RIVER NEAR TWO HARBORS, MN +elev_mean: 337.69 +initial_state: zero +lat: 46.94688 +lon: -91.7924 +slope_mean: 19.16862 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/05057200_nh_AORC_hourly_ensemble.yml b/bmi_config_files/05057200_nh_AORC_hourly_ensemble.yml index bef1018..6f4e846 100644 --- a/bmi_config_files/05057200_nh_AORC_hourly_ensemble.yml +++ b/bmi_config_files/05057200_nh_AORC_hourly_ensemble.yml @@ -1,11 +1,13 @@ -time_step: '1 hour' -initial_state: 'zero' -basin_name: 'BALDHILL CREEK NR DAZEY, ND' -basin_id: '05057200' -area_sqkm: 1900 +area_sqkm: 1897.33 +basin_id: 5057200 +basin_name: BALDHILL CREEK NR DAZEY, ND +elev_mean: 447.5 +initial_state: zero lat: 47.22916 lon: -98.12482 -train_cfg_file: +slope_mean: 2.33897 +time_step: 1 hour +train_cfg_file: - ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml - ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml - ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml @@ -13,5 +15,3 @@ train_cfg_file: - ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml - ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml verbose: 0 -elev_mean: 447.5 -slope_mean: 2.33897 diff --git a/bmi_config_files/05291000_nh_AORC_hourly_ensemble.yml b/bmi_config_files/05291000_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..b27e662 --- /dev/null +++ b/bmi_config_files/05291000_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 1046.78 +basin_id: 5291000 +basin_name: WHETSTONE RIVER NEAR BIG STONE CITY, SD +elev_mean: 432.13 +initial_state: zero +lat: 45.29163 +lon: -96.48756 +slope_mean: 8.90129 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/06221400_nh_AORC_hourly_25yr_1210_112435.yml b/bmi_config_files/06221400_nh_AORC_hourly_25yr_1210_112435.yml deleted file mode 100644 index 427b4b3..0000000 --- a/bmi_config_files/06221400_nh_AORC_hourly_25yr_1210_112435.yml +++ /dev/null @@ -1,11 +0,0 @@ -time_step: '1 hour' -initial_state: 'zero' -basin_name: 'DINWOODY CREEK ABOVE LAKES, NEAR BURRIS, WYO' -basin_id: '06221400' -area_sqkm: 230 -lat: 43.34551 -lon: -109.41014 -train_cfg_file: ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435/config.yml -verbose: 0 -elev_mean: 3336.8 -slope_mean: 127.1643 diff --git a/bmi_config_files/06221400_nh_AORC_hourly_ensemble.yml b/bmi_config_files/06221400_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..fa7da5f --- /dev/null +++ b/bmi_config_files/06221400_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 227.88 +basin_id: 6221400 +basin_name: DINWOODY CREEK ABOVE LAKES, NEAR BURRIS, WYO. +elev_mean: 3336.8 +initial_state: zero +lat: 43.34551 +lon: -109.41014 +slope_mean: 127.16435 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/07057500_nh_AORC_hourly_ensemble.yml b/bmi_config_files/07057500_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..9c779eb --- /dev/null +++ b/bmi_config_files/07057500_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 1456.44 +basin_id: 7057500 +basin_name: North Fork River near Tecumseh, MO +elev_mean: 324.68 +initial_state: zero +lat: 36.62303 +lon: -92.24813 +slope_mean: 11.24276 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/07291000_nh_AORC_hourly_ensemble.yml b/bmi_config_files/07291000_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..bd49ef2 --- /dev/null +++ b/bmi_config_files/07291000_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 479.3 +basin_id: 7291000 +basin_name: HOMOCHITTO RIVER AT EDDICETON, MS +elev_mean: 129.13 +initial_state: zero +lat: 31.50306 +lon: -90.7775 +slope_mean: 7.60783 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/08023080_nh_AORC_hourly_ensemble.yml b/bmi_config_files/08023080_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..df5a504 --- /dev/null +++ b/bmi_config_files/08023080_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 187.61 +basin_id: 8023080 +basin_name: Bayou Grand Cane near Stanley, LA +elev_mean: 86.85 +initial_state: zero +lat: 31.97933 +lon: -93.93408 +slope_mean: 4.78985 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/08267500_nh_AORC_hourly_ensemble.yml b/bmi_config_files/08267500_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..391fa1e --- /dev/null +++ b/bmi_config_files/08267500_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 96.26 +basin_id: 8267500 +basin_name: RIO HONDO NEAR VALDEZ, NM +elev_mean: 3006.6 +initial_state: zero +lat: 36.54169 +lon: -105.5564 +slope_mean: 149.08403 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/09035900_nh_AORC_hourly_ensemble.yml b/bmi_config_files/09035900_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..f9dade5 --- /dev/null +++ b/bmi_config_files/09035900_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 72.84 +basin_id: 9035900 +basin_name: SOUTH FORK OF WILLIAMS FORK NEAR LEAL, CO. +elev_mean: 3240.75 +initial_state: zero +lat: 39.79582 +lon: -106.03057 +slope_mean: 123.91195 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/09386900_nh_AORC_hourly_ensemble.yml b/bmi_config_files/09386900_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..f1ab986 --- /dev/null +++ b/bmi_config_files/09386900_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 184.94 +basin_id: 9386900 +basin_name: RIO NUTRIA NEAR RAMAH, NM +elev_mean: 2341.65 +initial_state: zero +lat: 35.28253 +lon: -108.55341 +slope_mean: 37.39246 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/10234500_nh_AORC_hourly_25yr_1210_112435.yml b/bmi_config_files/10234500_nh_AORC_hourly_25yr_1210_112435.yml deleted file mode 100644 index 8634866..0000000 --- a/bmi_config_files/10234500_nh_AORC_hourly_25yr_1210_112435.yml +++ /dev/null @@ -1,11 +0,0 @@ -time_step: '1 hour' -initial_state: 'zero' -basin_name: 'BEAVER RIVER NEAR BEAVER, UT' -basin_id: '10234500' -area_sqkm: 237 -lat: 38.28053 -lon: -112.56827 -train_cfg_file: ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435/config.yml -verbose: 0 -elev_mean: 2498.74 -slope_mean: 95.20992 diff --git a/bmi_config_files/10234500_nh_AORC_hourly_ensemble.yml b/bmi_config_files/10234500_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..520f13a --- /dev/null +++ b/bmi_config_files/10234500_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 236.42 +basin_id: 10234500 +basin_name: BEAVER RIVER NEAR BEAVER, UT +elev_mean: 2498.74 +initial_state: zero +lat: 38.28053 +lon: -112.56827 +slope_mean: 95.20992 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/10259000_nh_AORC_hourly_ensemble.yml b/bmi_config_files/10259000_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..714d15e --- /dev/null +++ b/bmi_config_files/10259000_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 22.46 +basin_id: 10259000 +basin_name: ANDREAS C NR PALM SPRINGS CA +elev_mean: 1233.96 +initial_state: zero +lat: 33.76002 +lon: -116.55002 +slope_mean: 169.65242 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/12010000_nh_AORC_hourly_ensemble.yml b/bmi_config_files/12010000_nh_AORC_hourly_ensemble.yml new file mode 100644 index 0000000..34635da --- /dev/null +++ b/bmi_config_files/12010000_nh_AORC_hourly_ensemble.yml @@ -0,0 +1,17 @@ +area_sqkm: 142.18 +basin_id: 12010000 +basin_name: NASELLE RIVER NEAR NASELLE, WA +elev_mean: 145.18 +initial_state: zero +lat: 46.37399 +lon: -123.74348 +slope_mean: 47.2961 +time_step: 1 hour +train_cfg_file: +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_7/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_8/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435_9/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed101_0701_143442/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_seq999_seed103_2701_171540/config.yml +- ../trained_neuralhydrology_models/nh_AORC_hourly_slope_elev_precip_temp_seq999_seed101_2801_191806/config.yml +verbose: 0 diff --git a/bmi_config_files/14216500_nh_AORC_hourly_25yr_1210_112435.yml b/bmi_config_files/14216500_nh_AORC_hourly_25yr_1210_112435.yml deleted file mode 100644 index ca81692..0000000 --- a/bmi_config_files/14216500_nh_AORC_hourly_25yr_1210_112435.yml +++ /dev/null @@ -1,11 +0,0 @@ -time_step: '1 hour' -initial_state: 'zero' -basin_name: 'MUDDY CREEK BELOW CLEAR CREEK NEAR COUGAR, WA' -basin_id: '14216500' -area_sqkm: 349.52 -lat: 46.07567 -lon: -121.9987 -train_cfg_file: ../trained_neuralhydrology_models/nh_AORC_hourly_25yr_1210_112435/config.yml -verbose: 0 -elev_mean: 824.9 -slope_mean: 115.75916 \ No newline at end of file diff --git a/bmi_config_files/fake_hourly_slope_mean_precip_temp.yml b/bmi_config_files/fake_hourly_slope_mean_precip_temp.yml deleted file mode 100644 index ea6b83c..0000000 --- a/bmi_config_files/fake_hourly_slope_mean_precip_temp.yml +++ /dev/null @@ -1,20 +0,0 @@ -# This is the configuration file for the trained LSTM. -# This is required to run the model, but should not be modified. -train_cfg_file: ./trained_neuralhydrology_models/hourly_slope_mean_precip_temp/config.yml - -# Meta data about how to run the model -time_step: '1 hour' -initial_state: 'zero' -basin_name: 'FAKE BASIN' -basin_id: 'fake_id' - -# This is required for determining what is printed out -verbose: 0 - -# STATIC Attributes -## Required to run the LSTM -area_sqkm: 100.0 -lat: 35.0 -lon: -100.0 -elev_mean: 100.0 -slope_mean: 25.0 diff --git a/bmi_config_files/make_camels_config_files.py b/bmi_config_files/make_camels_config_files.py index 30871f9..7fa2f56 100644 --- a/bmi_config_files/make_camels_config_files.py +++ b/bmi_config_files/make_camels_config_files.py @@ -1,18 +1,71 @@ +import os +import yaml import pandas as pd -import numpy as np -# Loop through camels basins - # extract the camels attributes - # Write a configuration file +def do_the_config_generation(template_path, output_config_path): + # Load CAMELS basin attributes + basin_attributes = {} + for attribute_type in ['clim', 'geol', 'hydro', 'name', 'soil', 'topo', 'vege']: + attr_path = f"../data/camels_attributes_v2.0/camels_{attribute_type}.txt" + basin_attributes[attribute_type] = pd.read_csv(attr_path, sep=";").set_index("gauge_id") -with open("../data/camels_basin_list_516.txt", "r") as f: - basin_list = pd.read_csv(f, header=None) + # Load template YAML + with open(template_path, "r") as f: + config_template = yaml.safe_load(f) -basin_attributes = {} + # Extract required attributes for the basin + try: + name_info = basin_attributes["name"].loc[basin_id_int] + topo_info = basin_attributes["topo"].loc[basin_id_int] -for attribute_type in ['clim', 'geol', 'hydro', 'name', 'soil', 'topo', 'vege']: - with open("../data/camels_attributes_v2.0/camels_{}.txt".format(attribute_type), "r") as f: - basin_attributes[attribute_type] = pd.read_csv(f, sep=";") - basin_attributes[attribute_type] = basin_attributes[attribute_type].set_index("gauge_id") - print(basin_attributes[attribute_type].loc[1022500, :]) + # Update YAML structure with extracted values + config_template["basin_id"] = basin_id_int + config_template["basin_name"] = name_info["gauge_name"] + config_template["area_sqkm"] = float(topo_info["area_gages2"]) + config_template["lat"] = float(topo_info["gauge_lat"]) + config_template["lon"] = float(topo_info["gauge_lon"]) + config_template["elev_mean"] = float(topo_info["elev_mean"]) + config_template["slope_mean"] = float(topo_info["slope_mean"]) + # Save the modified YAML file + with open(output_config_path, "w") as yaml_file: + yaml.dump(config_template, yaml_file, default_flow_style=False) + + print(f"Generated {output_config_path}") + + except KeyError as e: + print(f"Error: Missing attribute {e} for basin {basin_id_int}") + +basin_ids = [ + "01013500", + "01333000", + "02046000", + "03010655", + "03439000", + "04015330", + "05057200", + "05291000", + "06221400", + "07057500", + "07291000", + "08023080", + "08267500", + "09035900", + "09386900", + "10234500", + "10259000", + "12010000" +] +# for the sample AORC basins from https://github.com/NWC-CUAHSI-Summer-Institute/CAMELS_data_sample +for basin_id_str in basin_ids: + basin_id_int = int(basin_id_str) # Ensure it's a string for lookup + template_path = "05057200_nh_AORC_hourly_ensemble.yml" # Path to the template YAML + output_config_path = f"{basin_id_str}_nh_AORC_hourly_ensemble.yml" # Output file name + do_the_config_generation(template_path, output_config_path) + +# For the Sample NLDAS hourly data in NeuralHydrology: https://github.com/neuralhydrology/neuralhydrology/tree/master/test/test_data/camels_us/hourly +basin_ids = ["03015500", "01547700", "02064000"] +for basin_id_str in basin_ids: + template_path = "01022500_nh_NLDAS_hourly.yml" # Path to the template YAML + output_config_path = f"{basin_id_str}_nh_NLDAS_hourly.yml" # Output file name + do_the_config_generation(template_path, output_config_path) \ No newline at end of file diff --git a/notebooks/run_lstm_with_bmi_aorc.ipynb b/notebooks/run_lstm_with_bmi_aorc.ipynb index 06e21a3..b511f3c 100644 --- a/notebooks/run_lstm_with_bmi_aorc.ipynb +++ b/notebooks/run_lstm_with_bmi_aorc.ipynb @@ -29,6 +29,7 @@ "import torch\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", "from pathlib import Path\n", "from netCDF4 import Dataset\n", "from lstm import bmi_lstm # Load module bmi_lstm (bmi_lstm.py) from lstm package.\n", @@ -64,13 +65,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### This sample dataset comes from NeuralHydrology: \n", - "These are just samples. These models can run with any forcing data. This is just a sample that is used for examples within NeuralHydrology.\n", - "https://github.com/neuralhydrology/neuralhydrology/tree/master/test/test_data/camels_us/hourly\n", - "* 02064000 Falling River nr Naruna, VA\n", - "* 01547700 Marsh Creek at Blanchard, PA\n", - "* 03015500 Brokenstraw Creek at Youngsville, PA\n", - "* 01022500 Narraguagus River at Cherryfield, Maine" + "### This sample dataset comes from [NWC-CUAHSI-Summer-Institute CAMELS_data_sample](https://github.com/NWC-CUAHSI-Summer-Institute/CAMELS_data_sample): \n", + "Sample data for running neural hydrology examples, and others. This is just to be able to EASILY learn to use the CAMELS data with whatever modeling platform you would like. This is not intended for any purpose other than learning to use the full data.\n", + "\n", + "**THIS REPOSITORY MUST BE CLONED ONTO YOUR LOCAL MACHINE**" ] }, { @@ -79,105 +77,44 @@ "metadata": {}, "outputs": [], "source": [ - "basin_id = \"05057200\"" + "basin_id = \"05291000\" # chose from basins available in this data sample: https://github.com/NWC-CUAHSI-Summer-Institute/CAMELS_data_sample/blob/main/sample_basins.txt\n", + "# 01013500, 01333000, 02046000, 04015330, 03010655, 03439000, 05291000, 07291000, 05057200, 06221400, 07057500, 08023080, 08267500, 09035900, 09386900, 10234500, 12010000, 10259000" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " usgs_obs model_pred\n", - "date \n", - "1994-10-01 05:00:00 0.004039 None\n", - "1994-10-01 06:00:00 0.004039 None\n", - "1994-10-01 07:00:00 0.004039 None\n", - "1994-10-01 08:00:00 0.004039 None\n", - "1994-10-01 09:00:00 0.004039 None\n" - ] - } - ], + "outputs": [], "source": [ - "# Load the USGS data\n", + "# Load the USGS data \n", + "# REPLACE THIS PATH WITH YOUR LOCAL FILE PATH:\n", "file_path = f\"/Users/jmframe/CAMELS_data_sample/hourly/usgs-streamflow/{basin_id}-usgs-hourly.csv\"\n", "df_runoff = pd.read_csv(file_path)\n", "df_runoff = df_runoff.set_index(\"date\")\n", "df_runoff.index = pd.to_datetime(df_runoff.index)\n", "df_runoff = df_runoff[[\"QObs(mm/h)\"]].rename(columns={\"QObs(mm/h)\": \"usgs_obs\"})\n", - "df_runoff[\"model_pred\"] = None\n", - "print(df_runoff.head())" + "df_runoff[\"model_pred\"] = None" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " APCP_surface DLWRF_surface DSWRF_surface PRES_surface \\\n", - "time \n", - "1994-10-01 05:00:00 0.0 324.8 0.0 96300.0 \n", - "1994-10-01 06:00:00 0.0 293.0 0.0 96300.0 \n", - "1994-10-01 07:00:00 0.0 291.9 0.0 96340.0 \n", - "1994-10-01 08:00:00 0.0 291.0 0.0 96370.0 \n", - "1994-10-01 09:00:00 0.0 274.7 0.0 96390.0 \n", - "\n", - " SPFH_2maboveground TMP_2maboveground \\\n", - "time \n", - "1994-10-01 05:00:00 0.0049 277.7 \n", - "1994-10-01 06:00:00 0.0049 277.7 \n", - "1994-10-01 07:00:00 0.0047 277.0 \n", - "1994-10-01 08:00:00 0.0046 276.4 \n", - "1994-10-01 09:00:00 0.0044 275.8 \n", - "\n", - " UGRD_10maboveground VGRD_10maboveground \n", - "time \n", - "1994-10-01 05:00:00 -2.2 -3.0 \n", - "1994-10-01 06:00:00 -2.1 -2.0 \n", - "1994-10-01 07:00:00 -1.9 -1.7 \n", - "1994-10-01 08:00:00 -1.7 -1.5 \n", - "1994-10-01 09:00:00 -1.5 -1.2 \n" - ] - } - ], + "outputs": [], "source": [ + "# REPLACE THIS PATH WITH YOUR LOCAL FILE PATH:\n", "forcing_file_path = f\"/Users/jmframe/CAMELS_data_sample/hourly/aorc_hourly/{basin_id}_1980_to_2024_agg_rounded.csv\"\n", "df_forcing = pd.read_csv(forcing_file_path)\n", "df_forcing = df_forcing.set_index(\"time\")\n", "df_forcing.index = pd.to_datetime(df_forcing.index)\n", - "df_forcing = df_forcing[df_runoff.index[0]:df_runoff.index[-1]]\n", - "print(df_forcing.head())" + "df_forcing = df_forcing[df_runoff.index[0]:df_runoff.index[-1]]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [ - "# Load in model comparison data\n", - "comparison_file_path = f\"/Users/jmframe/data/nh_runs/nh_AORC_hourly_25yr_1210_112435/test/model_epoch009/{basin_id}_test_results.csv\"\n", - "df_nh = pd.read_csv(comparison_file_path)\n", - "df_nh = df_nh.set_index(\"date\")\n", - "df_nh.index = pd.to_datetime(df_nh.index)\n", - "\n", - "# Ensure the time alignment with `df_runoff`\n", - "df_nh = df_nh.reindex(df_runoff.index)\n", - "if \"QObs(mm/h)_sim\" in df_nh.columns:\n", - " df_runoff[\"nh\"] = df_nh[\"QObs(mm/h)_sim\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -206,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -215,234 +152,6 @@ " df_runoff[f\"ensemble_{i_ens+1}\"] = None # Initialize ensemble columns with None" ] }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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166604 rows × 9 columns

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" - ], - "text/plain": [ - " usgs_obs model_pred nh ensemble_1 ensemble_2 ensemble_3 \\\n", - "date \n", - "1994-10-01 05:00:00 0.004039 None NaN None None None \n", - "1994-10-01 06:00:00 0.004039 None NaN None None None \n", - "1994-10-01 07:00:00 0.004039 None NaN None None None \n", - "1994-10-01 08:00:00 0.004039 None NaN None None None \n", - "1994-10-01 09:00:00 0.004039 None NaN None None None \n", - "... ... ... .. ... ... ... \n", - "2013-10-02 20:00:00 0.000288 None NaN None None None \n", - "2013-10-02 21:00:00 0.000284 None NaN None None None \n", - "2013-10-02 22:00:00 0.000284 None NaN None None None \n", - "2013-10-02 23:00:00 0.000288 None NaN None None None \n", - "2013-10-03 00:00:00 0.000288 None NaN None None None \n", - "\n", - " ensemble_4 ensemble_5 ensemble_6 \n", - "date \n", - "1994-10-01 05:00:00 None None None \n", - "1994-10-01 06:00:00 None None None \n", - "1994-10-01 07:00:00 None None None \n", - "1994-10-01 08:00:00 None None None \n", - "1994-10-01 09:00:00 None None None \n", - "... ... ... ... \n", - "2013-10-02 20:00:00 None None None \n", - "2013-10-02 21:00:00 None None None \n", - "2013-10-02 22:00:00 None None None \n", - "2013-10-02 23:00:00 None None None \n", - "2013-10-03 00:00:00 None None None \n", - "\n", - "[166604 rows x 9 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_runoff" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -452,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "tags": [] }, @@ -462,7 +171,7 @@ "output_type": "stream", "text": [ "Working, please wait...\n", - "NSE: 0.23\n" + "NSE: 0.35\n" ] } ], @@ -533,14 +242,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] }, "metadata": {}, @@ -549,14 +258,13 @@ ], "source": [ "plot_start = \"1995\"\n", - "plot_end = \"1995\"\n", - "\n", - "# Plot the runoff predictions and observations\n", - "plt.figure(figsize=(6, 4))\n", + "plot_end = \"1996\"\n", + "# Create the figure and primary y-axis\n", + "fig, ax1 = plt.subplots(figsize=(6, 4))\n", "\n", - "# Plot ensemble members in grey\n", + "# Plot ensemble members in grey on the primary y-axis (Runoff)\n", "for i_ens in range(model_instance.N_ENS):\n", - " plt.plot(\n", + " ax1.plot(\n", " df_runoff.loc[plot_start:plot_end].index, \n", " df_runoff.loc[plot_start:plot_end][f\"ensemble_{i_ens+1}\"], \n", " color=\"grey\", \n", @@ -565,15 +273,33 @@ " label=\"_nolegend_\" if i_ens != 0 else \"Ensemble Members\" # Only add legend for the first line\n", " )\n", "\n", - "# Plot mean prediction and observation\n", - "plt.plot(df_runoff.loc[plot_start:plot_end].index, df_runoff.loc[plot_start:plot_end][\"model_pred\"], label=\"Model Mean Prediction\", color=\"blue\")\n", - "plt.plot(df_runoff.loc[plot_start:plot_end].index, df_runoff.loc[plot_start:plot_end][\"usgs_obs\"], label=\"USGS Observation\", color=\"orange\")\n", + "# Plot mean prediction and observation on primary y-axis\n", + "ax1.plot(df_runoff.loc[plot_start:plot_end].index, df_runoff.loc[plot_start:plot_end][\"model_pred\"], \n", + " label=\"Model Mean Prediction\", color=\"blue\")\n", + "ax1.plot(df_runoff.loc[plot_start:plot_end].index, df_runoff.loc[plot_start:plot_end][\"usgs_obs\"], \n", + " label=\"USGS Observation\", color=\"orange\")\n", + "\n", + "# Set labels and title\n", + "ax1.set_ylabel(\"Runoff (mm/hr)\")\n", + "ax1.set_title(f\"Runoff & Precipitation Comparison: Model vs. Observation {basin_id}\")\n", + "\n", + "# Create secondary y-axis for precipitation\n", + "ax2 = ax1.twinx()\n", + "ax2.plot(df_forcing.loc[plot_start:plot_end].index, df_forcing.loc[plot_start:plot_end][\"APCP_surface\"], \n", + " label=\"Precipitation\", color=\"cyan\", linestyle=\"dashed\", linewidth=0.15)\n", + "\n", + "ax2.set_ylabel(\"Precipitation (mm/hr)\")\n", + "\n", + "# Format x-axis to show dates properly\n", + "ax1.xaxis.set_major_locator(mdates.AutoDateLocator()) # Auto-adjust based on the date range\n", + "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m')) # Format as YYYY-MM-DD\n", "\n", - "# Add labels and legend\n", - "plt.ylabel(\"Runoff (mm/hr)\")\n", - "plt.title(f\"Runoff Comparison: Model vs. Observation {basin_id}\")\n", - "plt.xticks(rotation=45)\n", - "plt.legend()\n", + "# Add legends\n", + "ax1.legend(loc=\"upper left\")\n", + "ax2.legend(loc=\"upper right\")\n", + "plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45)\n", + "plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45)\n", + "# Adjust layout and show plot\n", "plt.tight_layout()\n", "plt.show()" ] diff --git a/notebooks/run_lstm_with_bmi_nldas.ipynb b/notebooks/run_lstm_with_bmi_nldas.ipynb new file mode 100644 index 0000000..f3b8123 --- /dev/null +++ b/notebooks/run_lstm_with_bmi_nldas.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic Model Interface (BMI) for streamflow prediction using Long Short-Term Memory (LSTM) networks\n", + "This Long Short-Term Memory (LSTM) network was developed for use in the [Next Generation National Water Model (Nextgen)](https://github.com/NOAA-OWP/ngen). Nextgen runs models with [Basic Model Interface (BMI)](https://bmi.readthedocs.io/en/latest/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### These libraries should all be available in the environment.yml through the command\n", + "`conda activate bmi_lstm`\n", + "#### Make sure that the library is installed\n", + "`pip install lstm`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "from netCDF4 import Dataset\n", + "from lstm import bmi_lstm # Load module bmi_lstm (bmi_lstm.py) from lstm package.\n", + "import pickle\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set path to the project/repo folder for the LSTM model\n", + "\n", + "When the LSTM Python model is used within NextGen, this folder will be in the \"ngen\" folder at: ngen/extern/lstm_py, and the LSTM Python package will be at: ngen/extern/{repo_name}/{package_name}. Note that paths to required datasets will be relative to this project folder.\n", + "\n", + "You will need to set your full local path `lstm_dir` to directory where you cloned the repo\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os, os.path\n", + "lstm_dir = os.path.expanduser('../lstm/')\n", + "os.chdir( lstm_dir )\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### This sample dataset comes from NeuralHydrology: \n", + "These are just samples. These models can run with any forcing data. This is just a sample that is used for examples within NeuralHydrology.\n", + "https://github.com/neuralhydrology/neuralhydrology/tree/master/test/test_data/camels_us/hourly\n", + "* 02064000 Falling River nr Naruna, VA\n", + "* 01547700 Marsh Creek at Blanchard, PA\n", + "* 03015500 Brokenstraw Creek at Youngsville, PA\n", + "* 01022500 Narraguagus River at Cherryfield, Maine" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "sample_data = Dataset('../data/usgs-streamflow-nldas_hourly.nc', 'r')\n", + "sample_basins = {sample_data['basin'][x]:x for x in range(len(list(sample_data['basin'])))}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "basin_id = \"01022500\" # Chose from: [\"01022500\", \"03015500\", \"01547700\", \"02064000\"] " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Will not print anything except errors because verbosity set to 0\n", + "self.verbose 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jmframe/miniconda3/envs/bmi_lstm/lib/python3.10/site-packages/lstm/bmi_lstm.py:268: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " trained_state_dict = torch.load(trained_model_file, map_location=torch.device('cpu'))\n" + ] + } + ], + "source": [ + "# Create an instance of the LSTM model with BMI\n", + "model_instance = bmi_lstm.bmi_LSTM()\n", + "\n", + "# Initialize the model with a configuration file\n", + "model_instance.initialize(bmi_cfg_file=Path(f'../bmi_config_files/{basin_id}_nh_NLDAS_hourly.yml'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test the model BMI implimentation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forcing data info:\n", + " n_precip = 26304\n", + " n_temp = 26304\n", + " precip_data.dtype = float32\n", + " temp_data.dtype = float32\n", + " precip: min, max = 0.0 , 18.51529\n", + " temp: min, max = 245.45935 , 305.75153\n", + "\n", + "Working, please wait...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSE: 0.58\n" + ] + } + ], + "source": [ + "# Get the index of the basin\n", + "basin_index = np.where(sample_data[\"basin\"][:] == basin_id)[0][0] # Extracts the correct index\n", + "\n", + "# Get sample data time series for precip and temp\n", + "precip_data = sample_data['total_precipitation'][basin_index, :].data # Index by both dimensions\n", + "temp_data = sample_data['temperature'][basin_index, :].data + 273.15 # Index by both dimensions\n", + "n_precip = precip_data.size\n", + "\n", + "print('Forcing data info:')\n", + "print(' n_precip =', n_precip)\n", + "print(' n_temp =', temp_data.size)\n", + "print(' precip_data.dtype =', precip_data.dtype)\n", + "print(' temp_data.dtype =', temp_data.dtype)\n", + "print(' precip: min, max =', precip_data.min(), ',', precip_data.max() )\n", + "print(' temp: min, max =', temp_data.min(), ',', temp_data.max() )\n", + "print()\n", + "\n", + "# Store output values in an array, so we can plot it afterwards (faster)\n", + "runoff_output_ = np.zeros( n_precip )\n", + "\n", + "k = 0\n", + "VERBOSE = False\n", + "print('Working, please wait...')\n", + "for k in range( n_precip ):\n", + " precip = precip_data[k]\n", + " temp = temp_data[k]\n", + " if (VERBOSE):\n", + " print('k, precip, temp =', k, ',', precip, ',', temp)\n", + "\n", + " # Set the model forcings to those in the sample data\n", + "\n", + " model_instance.set_value('atmosphere_water__liquid_equivalent_precipitation_rate', precip)\n", + " model_instance.set_value('land_surface_air__temperature',temp)\n", + "\n", + " # Updating the model calculates the runoff from the inputs and the model state at this time step\n", + " model_instance.update()\n", + "\n", + " # Add the output to a list so we can plot\n", + " dest_array = np.zeros(1)\n", + "\n", + " model_instance.get_value('land_surface_water__runoff_depth', dest_array)\n", + " runoff_ = dest_array[0]\n", + " # print('val =', runoff_limited)\n", + " \n", + " #------------------------------------------------\n", + " # Make output unit consistant with CAMELS mm/hr\n", + " #------------------------------------------------\n", + " runoff_ *= 1000 # (correct factor is 1000)\n", + " runoff_output_[ k ] = runoff_\n", + "\n", + " \n", + "# Define the start and end for plotting\n", + "start_plot = 0\n", + "end_plot = 7000\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "\n", + "# Extract observed discharge (qobs) for the correct basin and date range\n", + "obs_qobs = sample_data['qobs_CAMELS_mm_per_hour'][basin_index, start_plot:end_plot].data\n", + "\n", + "# Plot observed data\n", + "plt.plot(obs_qobs, label='Observed QObs', color='k')\n", + "\n", + "# Plot model predictions\n", + "plt.plot(runoff_output_[start_plot:end_plot], label='LSTM BMI')\n", + "\n", + "# Labels and legend\n", + "plt.ylabel('Streamflow (mm/hr)')\n", + "plt.xlabel('Hours')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Calculate a metric\n", + "obs = np.array(sample_data['qobs_CAMELS_mm_per_hour'][basin_index])\n", + "sim = runoff_output_\n", + "### sim = np.array(runoff_output_list_limited)\n", + "denominator = ((obs - obs.mean())**2).sum()\n", + "numerator = ((sim - obs)**2).sum()\n", + "value = 1 - numerator / denominator\n", + "print(\"NSE: {:.2f}\".format(1 - numerator / denominator))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bmi_lstm", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}