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‎toolbox/utils/VIC_Inp_Prep_Github/README.md‎

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**vic_inp_prep.py** --- VIC Input Preparation Python Script\
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writen by Vu Trung Dung (dtvu2205@gmail.com)
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________________________________________________________________________________
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# Overview
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This Python script extracts climate data (e.g., precipitation, maximum and minimum temperature, and wind speed) from NetCDF files and formats them as input for the Variable Infiltration Capacity (VIC) model. The script supports selecting a spatial domain, a range of years, and specific locations for output.
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________________________________________________________________________________
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# Requirements
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Python 3.8+\
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Python packages:\
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os – for file and directory operations\
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pandas – for working with tabular data\
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xarray – for handling NetCDF climate datasets\
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________________________________________________________________________________
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# Directory Structure
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Your project directory should look like:\
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project_dir/\
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├─ prec/ # Precipitation NetCDF files (e.g., chirps-v2.0.2022.days_p05.nc)\
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├─ tmax/ # Maximum temperature NetCDF files (e.g., era5.02m.tmax.1200.22-23.nc)\
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├─ tmin/ # Minimum temperature NetCDF files (e.g., era5.02m.tmin.0000.22-23.nc)\
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├─ wind/ # Wind speed NetCDF files (e.g., era5.10m.wind.0600.22-23.nc)\
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├─ loc_sample.txt # List of locations for VIC input (tab-separated lon/lat)\
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├─ vic_inp/ # Output folder for VIC input files (auto-created by script)\
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├─ vic_input_prep.py # Python script
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________________________________________________________________________________
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# Input Files
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Precipitation:\
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File example: chirps-v2.0.2022.days_p05.nc\
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Variable name: precip
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Maximum Temperature:\
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File example: era5.02m.tmax.1200.22-23.nc\
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Variable name: mx2t
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Minimum Temperature:\
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File example: era5.02m.tmin.0000.22-23.nc\
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Variable name: mn2t
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Wind Speed:\
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File example: era5.10m.wind.0600.22-23.nc\
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Variable name: fg10
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Locations file (loc_sample.txt):\
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Tab-separated file with columns:\
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lon lat\
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90.123 15.456\
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91.234 16.567
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________________________________________________________________________________
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# Configuration
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At the top of the script, adjust the following parameters:
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Spatial domain (latitude and longitude)\
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min_lat, max_lat = 15, 35\
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min_lon, max_lon = 90, 105
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Years to process\
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sta_year = 2022\
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end_year = 2023
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Directories\
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prec_dir = "prec"\
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tmax_dir = "tmax"\
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tmin_dir = "tmin"\
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wind_dir = "wind"\
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outp_dir = "vic_inp"
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________________________________________________________________________________
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# How It Works
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Data Preprocessing:
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- Defines the spatial domain and year range.
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- Reads all NetCDF files for precipitation and slices them to the selected domain.
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- Reads ERA5 data for temperature and wind, inverting latitude if necessary.
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VIC Input Extraction:
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- Loops through each location in loc_sample.txt.
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- Extracts nearest grid values for precip, tmax, tmin, and wind.
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- Converts temperature from Kelvin to Celsius.
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- Writes tab-separated files for VIC input in vic_inp/.
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Export Output: For each location, a file named gf_<lon>_<lat> is created. Files contain 4 columns: prec, tmax, tmin, wind.
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________________________________________________________________________________
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# Notes
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Ensure that NetCDF variable names match your dataset.
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The script automatically creates the output folder if it does not exist.
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Locations that do not exactly match the grid will use the nearest-neighbor selection.

‎toolbox/utils/VIC_Inp_Prep_Github/loc_sample.txt‎

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106.5944 15.6064
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101.2194 17.7939
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103.7194 14.9814

‎toolbox/utils/VIC_Inp_Prep_Github/vic_inp_prep.py‎

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# LIBRARIES ___________________________________________________________________
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import os # For handling file paths
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import pandas as pd # For handling tabular data (VIC input/output)
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import xarray as xr # For working with NetCDF climate data
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# PRE-PROCESSING _____________________________________________________________
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# Define the spatial domain you want to process (latitude and longitude range)
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min_lat, max_lat = 15, 35
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min_lon, max_lon = 90, 105
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# Define the range of years to process
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sta_year = 2022
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end_year = 2023
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# Set directories for input data and output VIC files
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main_dir = os.getcwd()
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prec_dir = os.path.join(main_dir, "prec") # Precipitation data folder
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tmax_dir = os.path.join(main_dir, "tmax") # Maximum temperature folder
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tmin_dir = os.path.join(main_dir, "tmin") # Minimum temperature folder
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wind_dir = os.path.join(main_dir, "wind") # Wind speed folder
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outp_dir = os.path.join(main_dir, "vic_inp") # Output folder for VIC files
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# Ensure the output directory exists
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os.makedirs(outp_dir, exist_ok=True)
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# PROCESSING __________________________________________________________________
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# -------------------- Precipitation --------------------
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print("Processing precipitation data: START")
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prec = [] # List to store each year's data
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years = range(sta_year, end_year+1)
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for year in years:
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print("year: "+str(year))
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file_name = f"chirps-v2.0.{year}.days_p05.nc" # File naming convention
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file_path = os.path.join(prec_dir, file_name)
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# Open NetCDF file with xarray
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data = xr.open_dataset(file_path)
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# Slice the data to the defined spatial domain
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sliced_data = data.sel({"latitude": slice(min_lat, max_lat),
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"longitude": slice(min_lon, max_lon)})
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prec.append(sliced_data)
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# Concatenate all years along the time dimension
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prec = xr.concat(prec, dim="time")
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print("Processing precipitation data: DONE")
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# -------------------- Maximum Temperature --------------------
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print("Processing maximum temperature data: START")
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file_name = "era5.02m.tmax.1200.22-23.nc"
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file_path = os.path.join(tmax_dir, file_name)
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# Open NetCDF file
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data = xr.open_dataset(file_path)
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# ERA5 latitude may be from north to south, invert if needed
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inverted_data = data.isel({"latitude": slice(None, None, -1)})
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# Slice to defined spatial domain
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tmax = inverted_data.sel({"latitude": slice(min_lat, max_lat),
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"longitude": slice(min_lon, max_lon)})
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print("Processing maximum temperature data: DONE")
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# -------------------- Minimum Temperature --------------------
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print("Processing minimum temperature data: START")
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file_name = "era5.02m.tmin.0000.22-23.nc"
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file_path = os.path.join(tmin_dir, file_name)
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data = xr.open_dataset(file_path)
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inverted_data = data.isel({"latitude": slice(None, None, -1)})
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tmin = inverted_data.sel({"latitude": slice(min_lat, max_lat),
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"longitude": slice(min_lon, max_lon)})
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print("Processing minimum temperature data: DONE")
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# -------------------- Wind Speed --------------------
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print("Processing windspeed data: START")
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file_name = "era5.10m.wind.0600.22-23.nc"
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file_path = os.path.join(wind_dir, file_name)
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data = xr.open_dataset(file_path)
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inverted_data = data.isel({"latitude": slice(None, None, -1)})
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wind = inverted_data.sel({"latitude": slice(min_lat, max_lat),
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"longitude": slice(min_lon, max_lon)})
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print("Processing windspeed data: DONE")
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# EXTRACTING AND WRITING OUT DATA FOR VIC _____________________________________
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print("Extracting and writing out data for VIC: START")
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# Load locations (longitude, latitude) where VIC input files are needed
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loc = pd.read_csv("loc_sample.txt", sep="\t", header=None, names=["lon", "lat"])
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# Loop over each location
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for index, row in loc.iterrows():
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lon = row['lon']
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lat = row['lat']
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print("lat: "+str(lat)+", lon: "+str(lon))
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# Extract the nearest grid point data for each variable
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sel_prec = prec["precip"].sel(latitude=lat, longitude=lon, method="nearest")
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sel_tmax = tmax["mx2t"].sel(latitude=lat, longitude=lon, method="nearest")
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sel_tmin = tmin["mn2t"].sel(latitude=lat, longitude=lon, method="nearest")
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sel_wind = wind["fg10"].sel(latitude=lat, longitude=lon, method="nearest")
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# Create a pandas DataFrame with VIC-required variables
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df = pd.DataFrame({
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"prec": sel_prec.values, # Precipitation (mm/day)
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"tmax": sel_tmax.values - 273.15, # Max temperature converted from K to °C
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"tmin": sel_tmin.values - 273.15, # Min temperature converted from K to °C
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"wind": sel_wind.values # Wind speed (m/s)
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})
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# Define output filename and write to file (tab-separated, no header/index)
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filename = f"gf_{lon:.4f}_{lat:.4f}" # Original format, no .txt extension
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file_path = os.path.join(outp_dir, filename)
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df.to_csv(file_path, sep="\t", index=False, header=False, float_format="%.6f")
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print("Extracting and writing out data for VIC: DONE")

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