enyfeo/spt_compute

Runs streamflow forecasts using ECMWF predicted runoff and RAPID (Forked from: https://github.com/CI-WATER/erfp_data_process_ubuntu_aws).

spt_compute

(Previously spt_ecmwf_autorapid_process)

Computational framework to ingest ECMWF ensemble runoff forcasts or other Land Surface Model input; generate input for and run the RAPID (rapid-hub.org) program using HTCondor or Python's Multiprocessing; and upload to CKAN in order to be used by the Streamflow Prediction Tool (SPT). There is also an experimental option to use the AutoRoute program for flood inundation mapping.

How it works:

Snow, Alan D., Scott D. Christensen, Nathan R. Swain, E. James Nelson, Daniel P. Ames, Norman L. Jones, Deng Ding, Nawajish S. Noman, Cedric H. David, Florian Pappenberger, and Ervin Zsoter, 2016. A High-Resolution National-Scale Hydrologic Forecast System from a Global Ensemble Land Surface Model. Journal of the American Water Resources Association (JAWRA) 1-15, DOI: 10.1111/1752-1688.12434

Snow, Alan Dee, "A New Global Forecasting Model to Produce High-Resolution Stream Forecasts" (2015). All Theses and Dissertations. Paper 5272. http://scholarsarchive.byu.edu/etd/5272

Installation

Step 1: Install RAPID and RAPIDpy

See: https://github.com/erdc-cm/RAPIDpy

Step 2: Install HTCondor (if not using Amazon Web Services and StarCluster or not using Multiprocessing mode)

On Ubuntu

apt-get install -y libvirt0 libdate-manip-perl vim
wget http://ciwckan.chpc.utah.edu/dataset/be272798-f2a7-4b27-9dc8-4a131f0bb3f0/resource/86aa16c9-0575-44f7-a143-a050cd72f4c8/download/condor8.2.8312769ubuntu14.04amd64.deb
dpkg -i condor8.2.8312769ubuntu14.04amd64.deb

On RedHat/CentOS 7

See: https://research.cs.wisc.edu/htcondor/yum/

After Installation:

#if master node uncomment CONDOR_HOST and comment out CONDOR_HOST and DAEMON_LIST lines
#echo CONDOR_HOST = \$\(IP_ADDRESS\) >> /etc/condor/condor_config.local
echo CONDOR_HOST = 10.8.123.71 >> /etc/condor/condor_config.local
echo DAEMON_LIST = MASTER, SCHEDD, STARTD >> /etc/condor/condor_config.local
echo ALLOW_ADMINISTRATOR = \$\(CONDOR_HOST\), 10.8.123.* >> /etc/condor/condor_config.local
echo ALLOW_OWNER = \$\(FULL_HOSTNAME\), \$\(ALLOW_ADMINISTRATOR\), \$\(CONDOR_HOST\), 10.8.123.* >> /etc/condor/condor_config.local
echo ALLOW_READ = \$\(FULL_HOSTNAME\), \$\(CONDOR_HOST\), 10.8.123.* >> /etc/condor/condor_config.local
echo ALLOW_WRITE = \$\(FULL_HOSTNAME\), \$\(CONDOR_HOST\), 10.8.123.* >> /etc/condor/condor_config.local
echo START = True >> /etc/condor/condor_config.local
echo SUSPEND = False >> /etc/condor/condor_config.local
echo CONTINUE = True >> /etc/condor/condor_config.local
echo PREEMPT = False >> /etc/condor/condor_config.local
echo KILL = False >> /etc/condor/condor_config.local
echo WANT_SUSPEND = False >> /etc/condor/condor_config.local
echo WANT_VACATE = False >> /etc/condor/condor_config.local

NOTE: if you forgot to change lines for master node, change CONDOR_HOST = $(IP_ADDRESS) and restart condor as ROOT

If Ubuntu:

# . /etc/init.d/condor stop
# . /etc/init.d/condor start

If RedHat:

# systemctl stop condor
# systemctl start condor

Step 3: Install Prerequisite Packages

On Ubuntu:

$ apt-get install libssl-dev libffi-dev
$ sudo su
$ pip install requests_toolbelt tethys_dataset_services condorpy
$ exit

On RedHat/CentOS 7:

$ yum install libffi-devel openssl-devel
$ sudo su
$ pip install requests_toolbelt tethys_dataset_services condorpy
$ exit

If you are on RHEL 7 and having troubles, add the epel repo:

$ wget https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
$ sudo rpm -Uvh epel-release-7*.rpm

If you are on CentOS 7 and having troubles, add the epel repo:

$ sudo yum install epel-release

Then install packages listed above.

Step 4: (Optional) Install AutoRoute and AutoRoutePy

If you want to try out the forecasted AutoRoute flood inundation (BETA), you will need to complete this section.

Follow the instructions here: https://github.com/erdc-cm/AutoRoutePy

Step 5: Install Submodule Dependencies

See: https://github.com/erdc-cm/spt_dataset_manager

Step 6: Download and install the source code

$ cd /path/to/your/scripts/
$ git clone https://github.com/erdc-cm/spt_ecmwf_autorapid_process.git
$ cd spt_ecmwf_autorapid_process
$ python setup.py install

Step 7: Create folders for RAPID input and for downloading ECMWF

$ cd /your/working/directory
$ mkdir -p rapid-io/input rapid-io/output ecmwf logs subprocess_logs era_interim_watershed mp_execute

Step 8: Change the locations in the files

Create a file run_ecmwf_rapid.py and change these variables for your instance. See below for different configurations.

# -*- coding: utf-8 -*-
from spt_compute import run_ecmwf_forecast_process
#------------------------------------------------------------------------------
#main process
#------------------------------------------------------------------------------
if __name__ == "__main__":
    run_ecmwf_forecast_process(
        rapid_executable_location='/home/alan/scripts/rapid/src/rapid',
        rapid_io_files_location='/home/alan/rapid-io',
        ecmwf_forecast_location ="/home/alan/ecmwf",
        era_interim_data_location="/home/alan/era_interim_watershed",
        subprocess_log_directory='/home/alan/subprocess_logs',
        main_log_directory='/home/alan/logs',
        data_store_url='http://your-ckan/api/3/action',
        data_store_api_key='your-ckan-api-key',
        data_store_owner_org="your-organization",
        app_instance_id='your-streamflow_prediction_tool-app-id',
        #sync_rapid_input_with_ckan=False,
        download_ecmwf=True,
        ftp_host="ftp.ecmwf.int",
        ftp_login="",
        ftp_passwd="",
        ftp_directory="",
        upload_output_to_ckan=True,
        initialize_flows=True,
        create_warning_points=True,
        delete_output_when_done=True,
        mp_mode='htcondor',
        #mp_execute_directory='',
    )

run_ecmwf_rapid_process Function Variables

Variable	Data Type	Description	Default
rapid_executable_location	String	Path to RAPID executable.
rapid_io_files_location	String	Path to RAPID input/output directory.
ecmwf_forecast_location	String	Path to ECMWF forecasts.
main_log_directory	String	Path to store HTCondor/multiprocess logs.
data_store_url	String	(Optional) CKAN API url (e.g. http://your-ckan/api/3/action)	""
data_store_api_key	String	(Optional) CKAN API Key (e.g. abcd-1234-defr-3345)	""
data_store_owner_org	String	(Optional) CKAN owner organization (e.g. erdc).	""
app_instance_id	String	(Optional) Streamflow Prediction tool instance ID.	""
sync_rapid_input_with_ckan	Boolean	(Optional) If set to true, this will download ECMWF-RAPID input cooresponding to your instance of the Streamflow Prediction Tool.	False
download_ecmwf	Boolean	(Optional) If set to true, this will download the most recent ECMWF forecasts for today before runnning the process.	True
date_string	String	(Optional) This string will be used to modify the date of the forecasts downloaded and/or the forecasts ran. It is in the format yyyymmdd (e.g. 20160808).	None
ftp_host	String	(Optional) ECMWF ftp site path (e.g. ftp.ecmwf.int).	""
ftp_login	String	(Optional) ECMWF ftp login name.	""
ftp_passwd	String	(Optional) ECMWF ftp password.	""
ftp_directory	String	(Optional) ECMWF ftp directory.	""
delete_past_ecmwf_forecasts	Boolean	(Optional) If True, it deletes all past forecasts before the next download.	True
upload_output_to_ckan	Boolean	(Optional) If true, this will upload the output to CKAN for the Streamflow Prediction Tool to download.	False
delete_output_when_done	String	(Optional) If true, all output will be deleted when the process completes. It is used when using operationally with upload_output_to_ckan set to true.	False
initialize_flows	String	(Optional) If true, this will initialize flows from all avaialble methods (e.g. Past forecasts, historical data, streamgage data).	False
warning_flows_threshold	Float	(Optional) Minimum value for return period in m3/s to generate warning.	10
era_interim_data_location	String	(Optional) Path to ERA Interim based historical streamflow, return period data, and seasonal average data.	""
create_warning_points	Boolean	(Optional) Generate waring points for Streamflow Prediction Tool. This requires return period data to be located in the era_interim_data_location.	False
autoroute_executable_location	String	(Optional/Beta) Path to AutoRoute executable.	""
autoroute_io_files_location	String	(Optional/Beta) Path to AutoRoute input/output directory.	""
geoserver_url	String	(Optional/Beta) Url to API endpoint ending in geoserver/rest.	""
geoserver_username	String	(Optional/Beta) Username for geoserver.	""
geoserver_password	String	(Optional/Beta) Password for geoserver.	""
mp_mode	String	(Optional) This defines how the process is run (HTCondor or Python's Multiprocessing). Valid options are htcondor and multiprocess.	htcondor
mp_execute_directory	String	(Optional/Required if using multiprocess mode) Directory used in multiprocessing mode to temporarily store files begin generated.	""

Possible run configurations

There are many different configurations. Here are some examples.

Mode 1: Run ECMWF-RAPID for Streamflow Prediction Tool using HTCondor to run and CKAN to upload

run_ecmwf_forecast_process(
    rapid_executable_location='/home/alan/scripts/rapid/src/rapid',
    rapid_io_files_location='/home/alan/rapid-io',
    ecmwf_forecast_location ="/home/alan/ecmwf",
    era_interim_data_location="/home/alan/era_interim_watershed",
    subprocess_log_directory='/home/alan/subprocess_logs',
    main_log_directory='/home/alan/logs',
    data_store_url='http://your-ckan/api/3/action',
    data_store_api_key='your-ckan-api-key',
    data_store_owner_org="your-organization",
    app_instance_id='your-streamflow_prediction_tool-app-id',
    download_ecmwf=True,
    ftp_host="ftp.ecmwf.int",
    ftp_login="",
    ftp_passwd="",
    ftp_directory="",
    upload_output_to_ckan=True,
    initialize_flows=True,
    create_warning_points=True,
    delete_output_when_done=True,
)

Mode 2: Run ECMWF-RAPID for Streamflow Prediction Tool using HTCondor to run and CKAN to upload & to download model files

run_ecmwf_forecast_process(
    rapid_executable_location='/home/alan/scripts/rapid/src/rapid',
    rapid_io_files_location='/home/alan/rapid-io',
    ecmwf_forecast_location ="/home/alan/ecmwf",
    era_interim_data_location="/home/alan/era_interim_watershed",
    subprocess_log_directory='/home/alan/subprocess_logs',
    main_log_directory='/home/alan/logs',
    data_store_url='http://your-ckan/api/3/action',
    data_store_api_key='your-ckan-api-key',
    data_store_owner_org="your-organization",
    app_instance_id='your-streamflow_prediction_tool-app-id',
    sync_rapid_input_with_ckan=True,
    download_ecmwf=True,
    ftp_host="ftp.ecmwf.int",
    ftp_login="",
    ftp_passwd="",
    ftp_directory="",
    upload_output_to_ckan=True,
    initialize_flows=True,
    create_warning_points=True,
    delete_output_when_done=True,
)

Mode 3: Run ECMWF-RAPID for Streamflow Prediction Tool using Multiprocessing to run and CKAN to upload

run_ecmwf_forecast_process(
    rapid_executable_location='/home/alan/scripts/rapid/src/rapid',
    rapid_io_files_location='/home/alan/rapid-io',
    ecmwf_forecast_location ="/home/alan/ecmwf",
    era_interim_data_location="/home/alan/era_interim_watershed",
    subprocess_log_directory='/home/alan/subprocess_logs',
    main_log_directory='/home/alan/logs',
    data_store_url='http://your-ckan/api/3/action',
    data_store_api_key='your-ckan-api-key',
    data_store_owner_org="your-organization",
    app_instance_id='your-streamflow_prediction_tool-app-id',
    download_ecmwf=True,
    ftp_host="ftp.ecmwf.int",
    ftp_login="",
    ftp_passwd="",
    ftp_directory="",
    upload_output_to_ckan=True,
    initialize_flows=True,
    create_warning_points=True,
    delete_output_when_done=True,
    mp_mode='multiprocess',
    mp_execute_directory='/home/alan/mp_execute',
)

Mode 4: (BETA) Run ECMWF-RAPID for Streamflow Prediction Tool with AutoRoute using Multiprocessing to run

Note that in this example, CKAN was not used. However, you can still add CKAN back in to this example with the parameters shown in the previous examples.

run_ecmwf_forecast_process(
    rapid_executable_location='/home/alan/rapid/src/rapid',
    rapid_io_files_location='/home/alan/rapid-io',
    ecmwf_forecast_location ="/home/alan/ecmwf",
    era_interim_data_location="/home/alan/era_interim_watershed",
    subprocess_log_directory='/home/alan/subprocess_logs', #path to store HTCondor/multiprocess logs
    main_log_directory='/home/alan/logs',
    download_ecmwf=True,
    ftp_host="ftp.ecmwf.int",
    ftp_login="",
    ftp_passwd="",
    ftp_directory="",
    upload_output_to_ckan=True,
    initialize_flows=True,
    create_warning_points=True,
    delete_output_when_done=False,
    autoroute_executable_location='/home/alan/scripts/AutoRoute/src/autoroute',
    autoroute_io_files_location='/home/alan/autoroute-io',
    geoserver_url='http://localhost:8181/geoserver/rest',
    geoserver_username='admin',
    geoserver_password='password',
    mp_mode='multiprocess',
    mp_execute_directory='/home/alan/mp_execute',
)

Step 9: Make sure permissions are correct for these files and any directories the script will use

Example:

$ chmod u+x run_ecmwf_rapid.py

Step 10: Add RAPID files to the rapid-io/input directory

To generate these files see: https://github.com/erdc-cm/RAPIDpy/wiki/GIS-Tools. If you are using the sync_rapid_input_with_ckan option, then you would upload these files through the Streamflow Prediction Tool web interface and this step is unnecessary.

Make sure the directory is in the format [watershed_name]-[subbasin_name] with lowercase letters, numbers, and underscores only. No spaces!

Example:

$ ls /rapid/input
nfie_texas_gulf_region-huc_2_12
$ ls /rapid/input/nfie_texas_gulf_region-huc_2_12
comid_lat_lon_z.csv
k.csv
rapid_connect.csv
riv_bas_id.csv
weight_ecmwf_t1279.csv
weight_ecmwf_tco639.csv
x.csv

Step 11: Create CRON job to run the scripts hourly

To run this automatically, it is necessary to generate cron jobs to run the script. There are many ways to do this and two are presented here.

Method 1: In terminal using crontab command

$ crontab -e

Then add:

@hourly /usr/bin/env python /path/to/run_ecmwf_rapid.py # ECMWF RAPID PROCESS

Method 2: Use create_cron.py to create the CRON jobs:

1. Install crontab Python package.

$ pip install python-crontab

2. Create and run a script to initialize cron job create_cron.py.

from spt_compute.setup import create_cron

create_cron(execute_command='/usr/bin/env python /path/to/run_ecmwf_rapid.py')

Step 12: Create CRON job to release lock on script

If the server is killed in the middle of a process, the lock with persist. To prevent this, add a cron job to release the lock on bootup.

Create Script

Create a script to reset the lock info file. Example path: /path/to/ecmwf_rapid_server_reset.py Then, change the path to the lock info file. To do this, add spt_compute_ecmwf_run_info_lock.txt to your main_log_directory from the run_ecmwf_rapid.py script.

#! /usr/bin/env python

from spt_compute import reset_lock_info_file

if __name__ == "__main__":
    LOCK_INFO_FILE = '/logs/spt_compute_ecmwf_run_info_lock.txt'
    reset_lock_info_file(LOCK_INFO_FILE)

Create Cron Job

$ crontab -e

Then add:

@reboot /usr/bin/env python /path/to/ecmwf_rapid_server_reset.py # RESET ECMWF RAPID PROCESS LOCK

Troubleshooting

If you see this error: ImportError: No module named packages.urllib3.poolmanager

$ pip install pip --upgrade

Restart your terminal

$ pip install requests --upgrade