Add script to prepare daily forcing data from CHIRPS and ERA5

This commit adds a Python script that generates daily forcing data for a given location (lat/lon) using Google Earth Engine. The script:
- Retrieves CHIRPS daily precipitation and ERA5-Land hourly variables 
  (temperature, wind components).
- Processes ERA5 to derive daily tmin, tmax, and mean wind speed.
- Handles missing CHIRPS precipitation by substituting with zeros.
- Combines CHIRPS and ERA5 data into daily records.
- Exports results as space-delimited text files (precip, tmax, tmin, wind).

The output is saved as `data_<lat>_<lon>` for direct use in VIC-Res model forcing.

Author: HishamEldardiry

toolbox/utils/prepare_vicres_forcing.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jul 22 10:51:59 2025
+
+@author: Hisham Eldardiry
+"""
+
+import ee
+import pandas as pd
+from datetime import datetime, timedelta
+
+# Authenticate and initialize Earth Engine
+ee.Initialize()
+
+
+# --- CONFIGURATION ---
+lat, lon = 11.9189, 107.9069
+start_date = '2022-01-01'
+end_date = '2022-12-31'
+
+point = ee.Geometry.Point([lon, lat])
+
+# --- Generate daily list of dates ---
+start_dt = datetime.strptime(start_date, "%Y-%m-%d")
+end_dt = datetime.strptime(end_date, "%Y-%m-%d")
+date_list = [(start_dt + timedelta(days=i)).strftime("%Y-%m-%d") 
+             for i in range((end_dt - start_dt).days + 1)]
+
+
+# --- CHIRPS Daily Precipitation ---
+chirps = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY") \
+    .filterDate(start_date, end_date) \
+    .filterBounds(point)
+
+# --- ERA5-Land Hourly ---
+era5 = ee.ImageCollection("ECMWF/ERA5_LAND/HOURLY") \
+    .filterDate(start_date, end_date) \
+    .filterBounds(point) \
+    .select(['temperature_2m', 'u_component_of_wind_10m', 'v_component_of_wind_10m'])
+
+# %% Add derived bands: temperature in C, wind speed
+def process_hourly(img):
+    temp_c = img.select('temperature_2m').subtract(273.15).rename('temp_C')
+    u = img.select('u_component_of_wind_10m')
+    v = img.select('v_component_of_wind_10m')
+    wind = u.pow(2).add(v.pow(2)).sqrt().rename('wind_speed')
+    return img.addBands([temp_c, wind])
+
+era5 = era5.map(process_hourly)
+
+
+
+
+# %% Function to create daily combined image
+def daily_reduce(date_str):
+    date = ee.Date(date_str)
+    next_date = date.advance(1, 'day')
+
+    # Handle missing CHIRPS
+    chirps_filtered = chirps.filterDate(date, next_date)
+    chirps_img = ee.Image(0).rename('precipitation')  # default image
+    chirps_img = ee.Image(ee.Algorithms.If(
+        chirps_filtered.size().gt(0),
+        chirps_filtered.mean().select('precipitation'),
+        chirps_img
+    ))
+
+    # ERA5: tmin, tmax, wind
+    daily_imgs = era5.filterDate(date, next_date)
+    tmin = daily_imgs.select('temp_C').min()
+    tmax = daily_imgs.select('temp_C').max()
+    wind = daily_imgs.select('wind_speed').mean()
+
+    combined = chirps_img.addBands([tmax.rename('tmax'), tmin.rename('tmin'), wind.rename('wind')])
+    return combined.set('date', date.format('YYYY-MM-dd'))
+
+# Create image collection for daily combined records
+daily_images = ee.ImageCollection([daily_reduce(d) for d in date_list])
+
+# %% Extract data as list of features
+def extract_record(img):
+    values = img.reduceRegion(
+        reducer=ee.Reducer.first(),
+        geometry=point,
+        scale=5000
+    )
+    return ee.Feature(None, values).set('date', img.get('date'))
+
+feature_collection = ee.FeatureCollection(daily_images.map(extract_record))
+features = feature_collection.getInfo()['features']
+
+
+# %% Convert to DataFrame
+df = pd.DataFrame([
+    {
+        'date': f['properties']['date'],
+        'precip': f['properties'].get('precipitation'),
+        'tmax': f['properties'].get('tmax'),
+        'tmin': f['properties'].get('tmin'),
+        'wind': f['properties'].get('wind')
+    }
+    for f in features
+])
+
+# %% Save Output
+df[['precip', 'tmax', 'tmin', 'wind']].to_csv(
+    f"data_{lat:.4f}_{lon:.4f}", sep=' ', index=False, header=False
+)
+
+print(f"Saved forcing file for lat {lat}, lon {lon}")