Welcome to HidroCL-OOP

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What is HidroCL-OOP?

HidroCL-OOP is a Python library for downloading, pre-processing, and extracting hydrometeorological variables from satellite and climate reanalysis products, in order to build and maintain geospatial databases at the catchment scale for Chile.

The library integrates four main modules:

Module	Description
`hidrocl.variables`	Management and querying of hydrometeorological variable databases
`hidrocl.download`	Download of satellite products and climate reanalysis data
`hidrocl.preprocess`	Pre-processing of raw data before extraction
`hidrocl.products`	Zonal statistics extraction per catchment

Developed by the Universidad de Valparaíso and Universidad de La Serena, with funding from ANID through FONDEF IDeA I+D ID21i10093.

Supported products

Category	Product	Class
Vegetation	MOD13Q1, VNP13Q1	`Mod13q1`, `Vnp13q1`, `Mod13q1agr`, `Vnp13q1agr`
Snow	MOD10A1F, VNP10A1F, MOD10A2	`Mod10a1f`, `Vnp10a1f`, `Mod10a2`
Evapotranspiration	MOD16A2	`Mod16a2`
Land cover	MCD12Q1	`Mod12q1`
Leaf area index	MCD15A2H, VNP15A2H	`Mcd15a2h`, `Vnp15a2h`
Precipitation (observed)	GPM IMERG, IMERG GIS, PDIR-NOW	`Gpm_3imrghhl`, `ImergGIS`, `Pdirnow`
ERA5 reanalysis	ERA5 hourly	`Era5`, `Era5ppmax`, `Era5pplen`, `Era5_pressure`, `Era5_rh`
ERA5-Land reanalysis	ERA5-Land hourly	`Era5_land`
GFS forecast	GFS 0.25°	`Gfs`
GLDAS reanalysis	GLDAS Noah	`Gldas_noah`

Installation

pip install git+https://github.com/MeteorologiaUV/HidroCL-OOP/

Requirements: Python ≥ 3.10

Configuration

Set project path

import hidrocl
hidrocl.set_project_path('/path/to/project')

Project directory structure

project/
├── base/
│   └── boundaries/
│       ├── Agr_ModisSinu.shp
│       ├── HidroCL_boundaries.shp
│       ├── HidroCL_boundaries_sinu.shp
│       ├── HidroCL_boundaries_utm.shp
│       ├── HidroCL_north.shp
│       └── HidroCL_south.shp
├── databases/
│   ├── forecasted/
│   └── observed/
├── logs/
├── pcdatabases/
│   ├── forecasted/
│   └── observed/
└── products/
    └── observed/

Each shapefile must include a gauge_id column identifying the catchments.

Credentials

CDSAPI (ERA5/Copernicus) — ~/.cdsapirc:

url: https://cds.climate.copernicus.eu/api/v2
key: XXXXXXXXXXX

Earthdata (MODIS, VIIRS, GPM) — ~/.netrc:

machine urs.earthdata.nasa.gov
    login XXXXXXXXXXX
    password XXXXXXXXXXX

Environment variables — workflow/server/.env:

PROJECT_PATH=/path/to/project
DOWNLOAD_SCRIPT_PATH=/path/to/workflow/server
DOWNLOAD_LOG_PATH=/path/to/logs

Quick start

import hidrocl
import hidrocl.paths as hcl

# Enable automatic database creation
hidrocl.variables.create = True

# Create variables
ndvi = hidrocl.HidroCLVariable("ndvi",
                               hcl.veg_o_modis_ndvi_mean_b_d16_p0d,
                               hcl.veg_o_modis_ndvi_mean_pc)
evi  = hidrocl.HidroCLVariable("evi",
                               hcl.veg_o_modis_evi_mean_b_d16_p0d,
                               hcl.veg_o_modis_evi_mean_pc)
nbr  = hidrocl.HidroCLVariable("nbr",
                               hcl.veg_o_int_nbr_mean_b_d16_p0d,
                               hcl.veg_o_int_nbr_mean_pc)

# Extract MODIS vegetation indices
mod13 = hidrocl.Mod13q1(ndvi, evi, nbr,
                        product_path=hcl.mod13q1_path,
                        vector_path=hcl.hidrocl_sinusoidal,
                        ndvi_log=hcl.log_veg_o_modis_ndvi_mean,
                        evi_log=hcl.log_veg_o_modis_evi_mean,
                        nbr_log=hcl.log_veg_o_int_nbr_mean)

mod13.run_maintainer()
mod13.run_extraction()

Run the operational workflow

cd workflow/server/
python run_all.py   # all satellite + reanalysis products
python run_gfs.py   # GFS forecast (handles retries automatically)