Water_Scape.ipynb

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   "source": [
    "####Dependencies\n",
    "import ee\n",
    "import geemap\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize,LogNorm\n",
    "import os\n",
    "import rasterio\n",
    "from shapely.geometry import box\n",
    "import matplotlib.patches as patches"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b3d6fa7",
   "metadata": {},
   "source": [
    "## Inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbf2cccb",
   "metadata": {},
   "source": [
    "#### Precipitation"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Input and Output File Paths\n",
    "file_path = \"./Input_Data/Meteo/Otta_Daily_Precip_per_cell.csv\"\n",
    "output_file = \"./Input_Data/Meteo/annual_precip_per_cell.csv\"\n",
    "total_precip_file = \"./Input_Data/Meteo/total_annual_precip.csv\"\n",
    "\n",
    "# Grid configuration\n",
    "xmin, ymin, xmax, ymax = 95000, 6837000, 213000, 6917000\n",
    "cell_size = 1000  # 1 km x 1 km\n",
    "cell_area = 1000000  # Area of each cell in square meters\n",
    "n_cells_x = int((xmax - xmin) / cell_size)  # Number of cells along x-axis\n",
    "n_total_cells = n_cells_x * n_cells_y  # Total number of cells (1 to 9440)\n",
    "\n",
    "# Load the dataset\n",
    "rainfall_data = pd.read_csv(file_path)\n",
    "\n",
    "# Extract rainfall columns\n",
    "rainfall_columns = [col for col in rainfall_data.columns if col.startswith(\"X\")]\n",
    "rainfall_data = rainfall_data[[\"x\", \"y\"] + rainfall_columns]\n",
    "\n",
    "# Convert the rainfall columns to datetime for filtering\n",
    "rainfall_data.columns = [\"x\", \"y\"] + [\n",
    "    pd.to_datetime(col[1:], format=\"%Y.%m.%d.%H.%M.%S\") for col in rainfall_columns\n",
    "]\n",
    "\n",
    "# Filter data for years 2000 to 2020\n",
    "start_date = pd.Timestamp(\"2000-01-01\")\n",
    "end_date = pd.Timestamp(\"2020-12-31\")\n",
    "rainfall_data = rainfall_data[\n",
    "    [\"x\", \"y\"]\n",
    "    + [col for col in rainfall_data.columns if isinstance(col, pd.Timestamp) and start_date <= col <= end_date]\n",
    "]\n",
    "\n",
    "# Calculate Cell_IDs based on grid\n",
    "rainfall_data[\"Cell_ID\"] = (\n",
    "    ((ymax - rainfall_data[\"y\"]) // cell_size) * n_cells_x\n",
    "    + ((rainfall_data[\"x\"] - xmin) // cell_size)\n",
    "    + 1\n",
    ").astype(int)\n",
    "\n",
    "# Convert rainfall data to m³ by multiplying by cell area\n",
    "rainfall_data.iloc[:, 2:-1] *= cell_area / 1000.0  # Convert mm to m³ (1 mm * cell_area = volume in m³)\n",
    "\n",
    "# Calculate annual precipitation per cell\n",
    "annual_rainfall_per_cell = (\n",
    "    rainfall_data.iloc[:, 2:-1]\n",
    "    .groupby(rainfall_data.columns[2:-1], axis=1)\n",
    "    .sum()\n",
    "    .groupby(lambda col: col.year, axis=1)\n",
    "    .sum()\n",
    ")\n",
    "annual_rainfall_per_cell[\"Cell_ID\"] = rainfall_data[\"Cell_ID\"]\n",
    "\n",
    "# Ensure all Cell_IDs (1 to 9440) are included, fill missing values with 0\n",
    "complete_cell_ids = pd.DataFrame({\"Cell_ID\": range(1, n_total_cells + 1)})\n",
    "annual_rainfall_per_cell = pd.merge(\n",
    "    complete_cell_ids, annual_rainfall_per_cell, on=\"Cell_ID\", how=\"left\"\n",
    ").fillna(0)\n",
    "\n",
    "# Calculate the mean annual precipitation (ignoring the Cell_ID column)\n",
    "annual_rainfall_per_cell[\"Mean_Annual\"] = annual_rainfall_per_cell.iloc[:, 1:-1].mean(axis=1)\n",
    "\n",
    "# Save the results to a CSV, including the \"Mean_Annual\" column\n",
    "annual_rainfall_per_cell.to_csv(output_file, index=False)\n",
    "\n",
    "# Calculate total annual precipitation across all cells (ignoring \"Mean_Annual\")\n",
    "total_annual_precip = annual_rainfall_per_cell.iloc[:, 1:-1].drop(columns=\"Mean_Annual\").sum(axis=0).reset_index()\n",
    "total_annual_precip.columns = [\"Year\", \"Total_Annual_Precipitation (m³)\"]\n",
    "\n",
    "# Save total annual precipitation to a separate CSV\n",
    "total_annual_precip.to_csv(total_precip_file, index=False)\n",
    "\n",
    "print(f\"Annual rainfall per cell with Mean_Annual saved to {output_file}.\")\n",
    "print(f\"Total annual precipitation saved to {total_precip_file}.\")\n",
    "\n"
   ]
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   "cell_type": "markdown",
   "id": "b8d01377",
   "metadata": {},
   "source": [
    "### Outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f605aaa",
   "metadata": {},
   "source": [
    "#### ET from Remote Sensing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "41d06595",
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       "<p>To authorize access needed by Earth Engine, open the following\n",
       "        URL in a web browser and follow the instructions:</p>\n",
       "        <p><a href=https://code.earthengine.google.com/client-auth?scopes=https%3A//www.googleapis.com/auth/earthengine%20https%3A//www.googleapis.com/auth/cloud-platform%20https%3A//www.googleapis.com/auth/devstorage.full_control&request_id=Ng-NL1IvgaG_jFEkzvs8tm1cyCDRKZ-HMfbej7r4xDU&tc=01FLrvF2tPDXLqlm2QuciWjeG9M_77lQ4s1ndVTvvIc&cc=Wl4kGqVkj1Nf5xroNzCcynsn3QLfyBbKx0mRQ4r09oo>https://code.earthengine.google.com/client-auth?scopes=https%3A//www.googleapis.com/auth/earthengine%20https%3A//www.googleapis.com/auth/cloud-platform%20https%3A//www.googleapis.com/auth/devstorage.full_control&request_id=Ng-NL1IvgaG_jFEkzvs8tm1cyCDRKZ-HMfbej7r4xDU&tc=01FLrvF2tPDXLqlm2QuciWjeG9M_77lQ4s1ndVTvvIc&cc=Wl4kGqVkj1Nf5xroNzCcynsn3QLfyBbKx0mRQ4r09oo</a></p>\n",
       "        <p>The authorization workflow will generate a code, which you should paste in the box below.</p>\n",
       "        "
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     "text": [
      "Enter verification code: 4/1AeanS0ZPGD78pOI5wNeqSkkEc74mtPdqWsfSlhOTG4FCuXr-uQmoaIH4nk8\n",
      "\n",
      "Successfully saved authorization token.\n"
     ]
    }
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   "source": [
    "ee.Authenticate() #### Authenticate Access to Google Earth Engine"
   ]
  },
  {
   "cell_type": "code",
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   "id": "bce34062",
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   "source": [
    "####Initialize Earth Engine API\n",
    "ee.Initialize()\n",
    "\n",
    "# Load the MODIS dataset\n",
    "modis = ee.ImageCollection(\"MODIS/061/MOD16A2GF\")\n",
    "\n",
    "# Define the list of years\n",
    "years = list(range(2000, 2021))  # 2000 to 2020 inclusive\n",
    "\n",
    "# Define pixel area (for 1 km x 1 km pixels)\n",
    "pixel_area = 1000000  # m²\n",
    "\n",
    "# Load your shapefile as a FeatureCollection \n",
    "shapefile = ee.FeatureCollection(\"projects/cm-zv-2024/assets/Otta_mask_4326\")\n",
    "\n",
    "# Function to calculate cumulative annual ET in cubic meters\n",
    "def calculate_annual_et(year):\n",
    "    start_date = f\"{year}-01-01\"\n",
    "    end_date = f\"{year}-12-31\"\n",
    "\n",
    "    # Filter MODIS ET and QC bands\n",
    "    modis_et = modis.filterDate(start_date, end_date)\n",
    "    et = modis_et.select(\"ET\")\n",
    "    qc = modis_et.select(\"ET_QC\")\n",
    "\n",
    "    # Apply quality control: Keep only high-quality pixels\n",
    "    def apply_quality_mask(image):\n",
    "        qc_image = qc.filter(ee.Filter.eq('system:index', image.get('system:index'))).first()\n",
    "        return image.updateMask(qc_image.eq(0))  # Retain only high-quality pixels\n",
    "\n",
    "    high_quality_et = et.map(apply_quality_mask)\n",
    "\n",
    "    # Sum the 8-day ET values for the year, convert to mm, and then to cubic meters\n",
    "    annual_et = (\n",
    "        high_quality_et.sum()\n",
    "        .multiply(0.1)  # Convert kg/m² to mm\n",
    "        .multiply(pixel_area / 1000)  # Convert mm to cubic meters\n",
    "        .clip(shapefile)  # Apply the mask\n",
    "        .unmask(0)  # Set areas outside the mask to 0\n",
    "    )\n",
    "\n",
    "    return annual_et.set(\"year\", year)  # Add year property\n",
    "\n",
    "\n",
    "# Export each annual ET image to the local folder\n",
    "for year in years:\n",
    "    year_image = calculate_annual_et(year)\n",
    "\n",
    "    output_file = os.path.join(output_folder, f\"ET_{year}.tif\")\n",
    "\n",
    "    # Export to local GeoTIFF using geemap\n",
    "    geemap.ee_export_image(\n",
    "        image=year_image,\n",
    "        filename=output_file,\n",
    "        scale=1000,  # 1km resolution\n",
    "        crs=\"EPSG:25833\",\n",
    "        region=shapefile.geometry().bounds().getInfo()[\"coordinates\"],  # Export region\n",
    "        max_pixels=1e13,  # Increase for large areas\n",
    "    )\n",
    "\n",
    "    print(f\"Exported ET_{year}.tif to {output_file}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51100d6",
   "metadata": {},
   "source": [
    "#### ET_Rasters to CSV"
   ]
  },
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   "execution_count": 44,
   "id": "fced6308",
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     "text": [
      "ET rasters successfully saved to ./Input_Data/Water/Total_Annual_ET.csv.\n"
     ]
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   ],
   "source": [
    "# Input and Output Paths\n",
    "\n",
    "input_folder = \"./Input_Data/Water/ET_Rasters\"\n",
    "output_csv = \"./Input_Data/Water/Total_Annual_ET.csv\"\n",
    "##Set Extent as per Grid and Cell size\n",
    "extent = (95000, 6837000, 213000, 6917000)  # xmin, ymin, xmax, ymax\n",
    "cell_size = 1000  # 1000m x 1000m cells\n",
    "n_cells_x = int((extent[2] - extent[0]) / cell_size)  # Number of cells along x-axis\n",
    "n_cells_y = int((extent[3] - extent[1]) / cell_size)  # Number of cells along y-axis\n",
    "n_total_cells = n_cells_x * n_cells_y  # Total cells\n",
    "crs = \"EPSG:25833\"\n",
    "\n",
    "# Function to extract year from file name\n",
    "def extract_year(filename):\n",
    "    # Assumes the format ET_<year>.tif\n",
    "    return int(filename.split(\"_\")[1].split(\".\")[0])\n",
    "\n",
    "# Initialize a dictionary to store data\n",
    "data = {\"Cell_ID\": np.arange(1, n_total_cells + 1)}\n",
    "\n",
    "# Read each raster file\n",
    "for file in os.listdir(input_folder):\n",
    "    if file.endswith(\".tif\"):\n",
    "        file_path = os.path.join(input_folder, file)\n",
    "        year = extract_year(file)\n",
    "        \n",
    "        # Open the raster\n",
    "        with rasterio.open(file_path) as src:\n",
    "            # Check CRS\n",
    "            if src.crs.to_string() != crs:\n",
    "                raise ValueError(f\"CRS of {file} does not match {crs}.\")\n",
    "            \n",
    "            # Read data and handle missing values\n",
    "            raster_data = src.read(1)\n",
    "            raster_data[np.isnan(raster_data)] = 0  # Replace NaN with 0\n",
    "            \n",
    "            # Pad missing columns and row with zeros\n",
    "            padded_data = np.zeros((n_cells_y, n_cells_x), dtype=raster_data.dtype)\n",
    "            padded_data[:raster_data.shape[0], :raster_data.shape[1]] = raster_data  # Fill existing data\n",
    "            \n",
    "            # Flatten the padded raster to 1D array\n",
    "            raster_data_flat = padded_data.flatten()\n",
    "            \n",
    "            if len(raster_data_flat) != n_total_cells:\n",
    "                raise ValueError(f\"Raster {file} padded array length mismatch.\")\n",
    "            \n",
    "            # Store the data for the year\n",
    "            data[year] = raster_data_flat\n",
    "\n",
    "# Convert data dictionary to DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Save to CSV\n",
    "df.to_csv(output_csv, index=False)\n",
    "\n",
    "print(f\"Total_Annual_ET successfully saved to {output_csv}.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10a6d5cd",
   "metadata": {},
   "source": [
    "#### Outflow"
   ]
  },
  {
   "cell_type": "code",
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   "id": "17ca58d3",
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       "\n",
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       "\n",
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       "                }\n",
       "\n",
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     "output_type": "stream",
     "text": [
      "Annual discharge saved to ./Input_Data/Water/Annual_Discharge.csv\n"
     ]
    }
   ],
   "source": [
    "# Load daily discharge data\n",
    "daily_discharge_file = './Input_Data/Water/Lalm Daily (1990-2020).csv'  # Replace with your file path\n",
    "output_annual_discharge_file = './Input_Data/Water/Annual_Discharge.csv'\n",
    "\n",
    "# Read the data\n",
    "discharge_data = pd.read_csv(daily_discharge_file, parse_dates=['Date'])\n",
    "discharge_data['Year'] = discharge_data['Date'].dt.year\n",
    "\n",
    "\n",
    "\n",
    "# Add a 'Year' column\n",
    "discharge_data['Year'] = discharge_data['Date'].dt.year\n",
    "\n",
    "# Convert daily discharge to daily volume (m³)\n",
    "discharge_data['Daily_Volume'] = discharge_data['Discharge (m³/s)'] * 86400\n",
    "\n",
    "# Calculate total annual discharge\n",
    "annual_discharge = discharge_data.groupby('Year')['Daily_Volume'].sum().reset_index()\n",
    "annual_discharge.rename(columns={'Daily_Volume': 'Total_Annual_Discharge (m³)'}, inplace=True)\n",
    "\n",
    "# Save the result to a CSV\n",
    "annual_discharge.to_csv(output_annual_discharge_file, index=False)\n",
    "\n",
    "print(f\"Annual discharge saved to {output_annual_discharge_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8ba6a8",
   "metadata": {},
   "source": [
    "## Consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "490b2f63",
   "metadata": {},
   "outputs": [
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     "text": [
      "Water consumption CSV files generated:\n",
      "- Residential: ./Outputs/Water/residential_consumption.csv\n",
      "- Business: ./Outputs/Water/comercial_consumption.csv\n",
      "Mean consumption per cell saved to: ./Outputs/Water/mean_dom_consumption_per_cell.csv\n"
     ]
    }
   ],
   "source": [
    "#### Domestic Consumption\n",
    "# Paths to input files\n",
    "population_csv = \"./Outputs/Pop/Tot_Pop.csv\"  # Population data\n",
    "residences_raster = \"./Input_Data/Pop/Otta_Dwell_2024.tif\"\n",
    "business_raster = \"./Input_Data/Pop/Otta_Estab_2024.tif\"\n",
    "\n",
    "# Output files\n",
    "output_residential = \"./Outputs/Water/residential_consumption.csv\"\n",
    "output_business = \"./Outputs/Water/comercial_consumption.csv\"\n",
    "#output_total = \"./Outputs/Water/total_consumption.csv\"\n",
    "mean_con_per_cell = \"./Outputs/Water/mean_dom_consumption_per_cell.csv\"\n",
    "\n",
    "# Water consumption parameters (base year 2024, m3/year)\n",
    "W_res_base = 200 / 1000 * 365  # Per-capita residential water consumption (convert to m3/year)\n",
    "W_bus_base = 500 / 1000 * 365  # Water per business property (convert to m3/year)\n",
    "\n",
    "# Load population data\n",
    "population_data = pd.read_csv(population_csv, index_col=\"Cell_ID\")\n",
    "population_data.columns = population_data.columns.astype(int)  # Ensure year columns are integers\n",
    "\n",
    "# Extract years from the population data\n",
    "years = population_data.columns\n",
    "\n",
    "# Read raster data as numpy arrays and align with population_data indices\n",
    "with rasterio.open(residences_raster) as res_src, \\\n",
    "     rasterio.open(business_raster) as bus_src:\n",
    "    \n",
    "    # Read raster data as 2D arrays\n",
    "    residences = res_src.read(1)\n",
    "    businesses = bus_src.read(1)\n",
    "    \n",
    "    # Flatten rasters to 1D arrays for processing\n",
    "    residences_flat = residences.flatten()\n",
    "    businesses_flat = businesses.flatten()\n",
    "\n",
    "    # Map raster indices to population Cell_ID\n",
    "    cell_ids = np.arange(len(residences_flat))  # Assume a direct mapping of raster cells to Cell_IDs\n",
    "\n",
    "# Initialize DataFrames for each type of consumption\n",
    "residential_consumption = pd.DataFrame(index=population_data.index, columns=years)\n",
    "business_consumption = pd.DataFrame(index=population_data.index, columns=years)\n",
    "total_consumption = pd.DataFrame(index=population_data.index, columns=years)\n",
    "\n",
    "# Base year population for scaling\n",
    "P_base = population_data[2024]\n",
    "\n",
    "# Loop through each year and calculate consumption\n",
    "for year in years:\n",
    "    scaling_factor = population_data[year] / P_base  # Scaling factor for property consumption\n",
    "\n",
    "    # Calculate consumption for each type\n",
    "    residential_consumption[year] = population_data[year] * W_res_base\n",
    "    business_consumption[year] = businesses_flat * W_bus_base * scaling_factor.values\n",
    "\n",
    "    # Calculate total consumption\n",
    "    total_consumption[year] = (\n",
    "        residential_consumption[year] +\n",
    "        business_consumption[year]\n",
    "    )\n",
    "\n",
    "#### Mean Consumption per cell from 2000 to 2020\n",
    "columns_to_average = [year for year in range(2000, 2021)]  # Use integers for column names\n",
    "# Calculate the mean consumption for each cell from 2000 to 2020\n",
    "total_consumption['Mean_Consumption'] = total_consumption[columns_to_average].mean(axis=1)\n",
    "# Select only the Cell_ID and Mean_Consumption columns\n",
    "mean_con = total_consumption[['Mean_Consumption']]\n",
    "mean_con.insert(0, \"Cell_ID\", mean_con.index)\n",
    "\n",
    "# Save outputs to CSV\n",
    "residential_consumption.to_csv(output_residential)\n",
    "business_consumption.to_csv(output_business)\n",
    "#total_consumption.to_csv(output_total)\n",
    "mean_con.to_csv(mean_con_per_cell, index=False)\n",
    "\n",
    "print(\"Water consumption CSV files generated:\")\n",
    "print(f\"- Residential: {output_residential}\")\n",
    "print(f\"- Business: {output_business}\")\n",
    "#print(f\"- Total: {output_total}\")\n",
    "print(f\"Mean consumption per cell saved to: {mean_con_per_cell}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e27bca26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#########Optional for ET0 calculations ##########\n",
    "## We use Samani and Hargreeves due to relatively low data requirements but Pennman Monthieth is Recommended\n",
    "\n",
    "#####function for extraterrestrial radiation####\n",
    "\n",
    "\n",
    "#from pyeto import extraterrestrial_radiation\n",
    "#from datetime import datetime\n",
    "#latlong = ./Input_Data/Meteo/Latlong.csv\n",
    "#dates = pd.date_range(start='2000-01-01', end='2020-12-31', freq='D')  \n",
    "\n",
    "# Function to calculate day of the year\n",
    "#def day_of_year(date):\n",
    "#    return date.timetuple().tm_yday\n",
    "# Function to calculate extraterrestrial radiation for daily values using pyeto\n",
    "#def calculate_daily_radiation_pyeto(dates, latitudes):\n",
    "    \n",
    "    #Calculate daily extraterrestrial radiation for a list of dates and latitudes using the pyeto library.\n",
    "    #Parameters:\n",
    "    #- dates (list or pandas.Series): List of dates in datetime format.\n",
    "    #- latitudes (list or pandas.Series): List of latitudes corresponding to locations.\n",
    "\n",
    "    #Returns:\n",
    "    #- DataFrame with latitudes and daily extraterrestrial radiation for each date.\n",
    "   \n",
    "#    results = []\n",
    "#    for date in dates:\n",
    "#        day = day_of_year(date)\n",
    "#        for lat in latitudes:\n",
    "#            ra = extraterrestrial_radiation(day_of_year=day, latitude=lat)\n",
    "#            results.append({'Date': date, 'Latitude': lat, 'Radiation': ra})\n",
    "\n",
    "#    return pd.DataFrame(results)\n",
    "\n",
    "#ra= calculate_daily_radiation_pyeto(dates, latlong['lat'])\n",
    "\n",
    "# Calculate Reference Evapotranspiration (ET0) Using Samani and Hargreaves Equation\n",
    "#def calculate_et0(t_min, t_max, ra):\n",
    "    \n",
    "    #Calculate ET0 using the Samani and Hargreaves equation.\n",
    "    #ET0 = 0.0023 * Ra * (T_avg + 17.8) * (T_max - T_min)^0.5\n",
    "    \n",
    "#    t_avg = (t_max + t_min) / 2\n",
    "#    return 0.0023 * ra * (t_avg + 17.8) * np.sqrt(t_max - t_min)\n",
    "\n",
    "#ET0 = calculate_et0(climate_data['T_min'], climate_data['T_max'], climate_data['Ra'])\n",
    "#ET0.to_csv(./Input_Data/Meteo/DailyPETOtta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c6a313ad",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "C:\\Users\\zvirk20\\AppData\\Local\\Temp\\ipykernel_25788\\1015710625.py:23: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  Tmax = pd.read_csv(Max_temp_path, parse_dates=['Date'])\n",
      "C:\\Users\\zvirk20\\AppData\\Local\\Temp\\ipykernel_25788\\1015710625.py:24: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  Tmin = pd.read_csv(Min_temp_path, parse_dates=['Date'])\n",
      "C:\\Users\\zvirk20\\AppData\\Local\\Temp\\ipykernel_25788\\1015710625.py:25: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  rainfall_data = pd.read_csv(Precip_Path, parse_dates=['Date'])\n",
      "C:\\Users\\zvirk20\\AppData\\Local\\Temp\\ipykernel_25788\\1015710625.py:26: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  ET0_data = pd.read_csv(ET0_path, parse_dates=['Date'])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Crop  Year   Total_CWR_m3\n",
      "0   Potato  2000    1614.309185\n",
      "1   Potato  2001     605.970620\n",
      "2   Potato  2002    2152.714410\n",
      "3   Potato  2003    3114.971091\n",
      "4   Potato  2004    1795.445550\n",
      "..     ...   ...            ...\n",
      "58   Grass  2016  612988.169930\n",
      "59   Grass  2017  482288.853850\n",
      "60   Grass  2018  706499.781449\n",
      "61   Grass  2019  495997.262187\n",
      "62   Grass  2020  509950.699495\n",
      "\n",
      "[63 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "#### Agricultural Water Consumption\n",
    "# Define Crop Data\n",
    "crop_data = pd.DataFrame({\n",
    "    'Crop': ['Potato', 'Barley', 'Grass'],\n",
    "    'Planting_date': ['04-15', '05-15', '01-01'],  # Grass is perennial\n",
    "    'Harvest_date': ['09-21', '09-11', '12-31'],\n",
    "    'Area_ha': [92, 112, 5869],  # Based on County Governer's Department on Agriculture\n",
    "    'Kc_initial': [0.4, 0.3, 0.7],  # FAO Kc - Crop Coefficients - Paper 56\n",
    "    'Kc_mid': [1.15, 1.15, 1.2],   # FAO Kc - Crop Coefficients - Paper 56\n",
    "    'Kc_late': [0.75, 0.5, 1.0]    # FAO Kc - Crop Coefficients - Paper 56\n",
    "})\n",
    "\n",
    "# Load Climate and Rainfall Data\n",
    "# Input Paths\n",
    "Max_temp_path = './Input_Data/Meteo/Otta_DailyMaxTemp.csv'\n",
    "Min_temp_path = './Input_Data/Meteo/Otta_DailyMinTemp.csv'\n",
    "Precip_Path = './Input_Data/Meteo/Otta_Daily_Precip.csv'\n",
    "ET0_path = './Input_Data/Meteo/DailyPETOtta.csv'\n",
    "# Output Paths\n",
    "crop_output = './Outputs/Water/annual_crop_water_requirement.csv'\n",
    "\n",
    "# Load data\n",
    "Tmax = pd.read_csv(Max_temp_path, parse_dates=['Date'])\n",
    "Tmin = pd.read_csv(Min_temp_path, parse_dates=['Date'])\n",
    "rainfall_data = pd.read_csv(Precip_Path, parse_dates=['Date'])\n",
    "ET0_data = pd.read_csv(ET0_path, parse_dates=['Date'])\n",
    "\n",
    "# Merge all climate data\n",
    "climate_data = pd.merge(Tmax[['Date', 'Tmax']], Tmin[['Date', 'Tmin']], on='Date', how='inner')\n",
    "climate_data = pd.merge(climate_data, ET0_data[['Date', 'PET']], on='Date', how='inner')\n",
    "climate_data = pd.merge(climate_data, rainfall_data[['Date', 'P']], on='Date', how='inner')\n",
    "\n",
    "# Calculate Effective Rainfall Using SCS Curve Number Method\n",
    "def calculate_effective_rainfall(rainfall, curve_number):\n",
    "    \"\"\"\n",
    "    Calculate effective rainfall using the SCS Curve Number method.\n",
    "    \"\"\"\n",
    "    S = (25400 / curve_number) - 254\n",
    "    runoff = (rainfall - 0.2 * S)**2 / (rainfall + 0.8 * S)\n",
    "    runoff[rainfall <= 0.2 * S] = 0  # No runoff if rainfall is too low\n",
    "    effective_rainfall = rainfall - runoff\n",
    "    effective_rainfall[effective_rainfall < 0] = 0  # Avoid negative values\n",
    "    return effective_rainfall\n",
    "\n",
    "# Curve number for glacial till soil which is the predominant soil type in Otta\n",
    "curve_number = 75\n",
    "\n",
    "# Add effective rainfall to climate data\n",
    "climate_data['Effective_Rainfall'] = calculate_effective_rainfall(climate_data['P'], curve_number)\n",
    "\n",
    "# Calculate Crop Water Requirement\n",
    "def calculate_cwr(crop_data, climate_data):\n",
    "    results = []\n",
    "    for _, crop in crop_data.iterrows():\n",
    "        planting_month, planting_day = map(int, crop['Planting_date'].split('-'))\n",
    "        harvest_month, harvest_day = map(int, crop['Harvest_date'].split('-'))\n",
    "\n",
    "        for year in range(2000, 2021):\n",
    "            planting_date = pd.Timestamp(year, planting_month, planting_day)\n",
    "            harvest_date = pd.Timestamp(year, harvest_month, harvest_day)\n",
    "\n",
    "            # Filter climate data for crop period\n",
    "            crop_period = climate_data[(climate_data['Date'] >= planting_date) & \n",
    "                                       (climate_data['Date'] <= harvest_date)].copy()\n",
    "\n",
    "            # Assign crop coefficient (Kc) based on growth stages\n",
    "            crop_period.loc[:, 'Kc'] = np.where(\n",
    "                (crop_period['Date'] - planting_date).dt.days < 30, crop['Kc_initial'],  # Initial\n",
    "                np.where(\n",
    "                    (harvest_date - crop_period['Date']).dt.days < 30, crop['Kc_late'],  # Late\n",
    "                    crop['Kc_mid']  # Mid\n",
    "                )\n",
    "            )\n",
    "\n",
    "            # Calculate ETc and Net CWR\n",
    "            crop_period.loc[:, 'ETc'] = crop_period['PET'] * crop_period['Kc']\n",
    "            crop_period.loc[:, 'Net_CWR'] = crop_period['ETc'] - crop_period['Effective_Rainfall']\n",
    "            crop_period.loc[:, 'Net_CWR'] = crop_period['Net_CWR'].clip(lower=0)  # Avoid negative values\n",
    "\n",
    "            # Total CWR in m3\n",
    "            total_cwr = crop_period['Net_CWR'].sum() * crop['Area_ha'] * 10  # Scale to m3\n",
    "            results.append({'Crop': crop['Crop'], 'Year': year, 'Total_CWR_m3': total_cwr})\n",
    "\n",
    "    return pd.DataFrame(results)\n",
    "\n",
    "# Calculate CWR\n",
    "cwr_results = calculate_cwr(crop_data, climate_data)\n",
    "\n",
    "# Output Results\n",
    "print(cwr_results)\n",
    "cwr_results.to_csv(crop_output, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d78e938",
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "C:\\Users\\zvirk20\\AppData\\Local\\Temp\\ipykernel_25508\\3904459748.py:19: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  Tmax = pd.read_csv(Max_temp_path, parse_dates=['Date'])\n",
      "C:\\Users\\zvirk20\\AppData\\Local\\Temp\\ipykernel_25508\\3904459748.py:20: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  Tmin = pd.read_csv(Min_temp_path, parse_dates=['Date'])\n",
      "C:\\Users\\zvirk20\\AppData\\Local\\Temp\\ipykernel_25508\\3904459748.py:21: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  rainfall_data = pd.read_csv(Precip_Path, parse_dates=['Date'])\n",
      "C:\\Users\\zvirk20\\AppData\\Local\\Temp\\ipykernel_25508\\3904459748.py:22: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  ET0_data = pd.read_csv(ET0_path, parse_dates=['Date'])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Specie  Year  Total_Consumption_m3\n",
      "0   Spruce  2000          3.139037e+08\n",
      "1   Spruce  2001          2.651391e+08\n",
      "2   Spruce  2002          3.332063e+08\n",
      "3   Spruce  2003          4.898960e+08\n",
      "4   Spruce  2004          4.029616e+08\n",
      "..     ...   ...                   ...\n",
      "58   Birch  2016          1.207007e+09\n",
      "59   Birch  2017          1.087610e+09\n",
      "60   Birch  2018          1.550343e+09\n",
      "61   Birch  2019          9.940550e+08\n",
      "62   Birch  2020          1.127183e+09\n",
      "\n",
      "[63 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "##Forest Water Consumption\n",
    "# Define Forest Data\n",
    "forest_data = pd.DataFrame({\n",
    "    'Specie': ['Spruce', 'Pine', 'Birch'],\n",
    "    'Area_km2': [79.695616, 265.40032, 289.188864],  # Forest area in km² based on SR16 year 2022\n",
    "    'Kc': [0.5, 0.5, 0.6]  # Adjusted Kc values for Spruce, Pine, and Birch (Source: G Sun et al https://www.srs.fs.usda.gov/pubs/ja/2016/ja_2016_amatya_008.pdf)\n",
    "})\n",
    "\n",
    "# Load Climate and Rainfall Data\n",
    "# Input Paths\n",
    "Max_temp_path = './Input_Data/Meteo/Otta_DailyMaxTemp.csv'\n",
    "Min_temp_path = './Input_Data/Meteo/Otta_DailyMinTemp.csv'\n",
    "Precip_Path = './Input_Data/Meteo/Otta_Daily_Precip.csv'\n",
    "ET0_path = './Input_Data/Meteo/DailyPETOtta.csv'\n",
    "# Output Path\n",
    "forest_output = './Outputs/Water/annual_forest_water_consumption.csv'\n",
    "\n",
    "# Load data\n",
    "Tmax = pd.read_csv(Max_temp_path, parse_dates=['Date'])\n",
    "Tmin = pd.read_csv(Min_temp_path, parse_dates=['Date'])\n",
    "rainfall_data = pd.read_csv(Precip_Path, parse_dates=['Date'])\n",
    "ET0_data = pd.read_csv(ET0_path, parse_dates=['Date'])\n",
    "\n",
    "# Merge all climate data\n",
    "climate_data = pd.merge(Tmax[['Date', 'Tmax']], Tmin[['Date', 'Tmin']], on='Date', how='inner')\n",
    "climate_data = pd.merge(climate_data, ET0_data[['Date', 'PET']], on='Date', how='inner')\n",
    "climate_data = pd.merge(climate_data, rainfall_data[['Date', 'P']], on='Date', how='inner')\n",
    "\n",
    "# Calculate Effective Rainfall Using SCS Curve Number Method\n",
    "def calculate_effective_rainfall(rainfall, curve_number):\n",
    "    \"\"\"\n",
    "    Calculate effective rainfall using the SCS Curve Number method.\n",
    "    \"\"\"\n",
    "    S = (25400 / curve_number) - 254\n",
    "    runoff = (rainfall - 0.2 * S)**2 / (rainfall + 0.8 * S)\n",
    "    runoff[rainfall <= 0.2 * S] = 0  # No runoff if rainfall is too low\n",
    "    effective_rainfall = rainfall - runoff\n",
    "    effective_rainfall[effective_rainfall < 0] = 0  # Avoid negative values\n",
    "    return effective_rainfall\n",
    "\n",
    "# Define curve number for glacial till soil\n",
    "curve_number = 75\n",
    "\n",
    "# Add effective rainfall to climate data\n",
    "climate_data['Effective_Rainfall'] = calculate_effective_rainfall(climate_data['P'], curve_number)\n",
    "\n",
    "# Calculate Forest Water Consumption\n",
    "def calculate_forest_water_consumption(forest_data, climate_data):\n",
    "    results = []\n",
    "    for _, forest in forest_data.iterrows():\n",
    "        for year in range(2000, 2021):\n",
    "            # Filter climate data for the year\n",
    "            year_data = climate_data[climate_data['Date'].dt.year == year].copy()\n",
    "\n",
    "            # Calculate ETc and Net Forest Water Consumption\n",
    "            year_data['ETc'] = year_data['PET'] * forest['Kc']\n",
    "            year_data['Net_Consumption'] = year_data['ETc'] - year_data['Effective_Rainfall']\n",
    "            year_data['Net_Consumption'] = year_data['Net_Consumption'].clip(lower=0)  # Avoid negative values\n",
    "\n",
    "            # Total water consumption in m³\n",
    "            total_consumption = year_data['Net_Consumption'].sum() * forest['Area_km2'] * 1e6  # Scale km² to m²\n",
    "            results.append({'Specie': forest['Specie'], 'Year': year, 'Total_Consumption_m3': total_consumption})\n",
    "\n",
    "    return pd.DataFrame(results)\n",
    "\n",
    "# Calculate Forest Water Consumption\n",
    "forest_results = calculate_forest_water_consumption(forest_data, climate_data)\n",
    "\n",
    "# Output Results\n",
    "print(forest_results)\n",
    "forest_results.to_csv(forest_output, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ba349eec",
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   ],
   "source": [
    "###Distribute Agriculture and Forest consumption to Agriculture and Forest Areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5108b010",
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     "text": [
      "Forest water consumption saved to: ./Outputs/Water/forest_water_con_per_cell.csv\n",
      "Agriculture water consumption saved to: ./Outputs/Water/ag_water_con_per_cell.csv\n"
     ]
    }
   ],
   "source": [
    "# Input paths\n",
    "forest_raster_path = './Input_Data/maps/for_area/for_ar.tif'\n",
    "agriculture_raster_path = './Input_Data/maps/agri_area/agri_ar.tif'\n",
    "forest_consumption_file = './Outputs/Water/annual_forest_water_consumption.csv'\n",
    "agriculture_consumption_file = './Outputs/Water/annual_crop_water_requirement.csv'\n",
    "\n",
    "# Output paths\n",
    "forest_consumption_csv = './Outputs/Water/forest_water_con_per_cell.csv'\n",
    "agriculture_consumption_csv = './Outputs/Water/ag_water_con_per_cell.csv'\n",
    "\n",
    "# Grid configuration\n",
    "xmin, ymin, xmax, ymax = 95000, 6837000, 213000, 6917000\n",
    "cell_size = 1000  # 1 km x 1 km\n",
    "n_cells_x = int((xmax - xmin) / cell_size)\n",
    "n_cells_y = int((ymax - ymin) / cell_size)\n",
    "n_total_cells = n_cells_x * n_cells_y\n",
    "cell_ids = np.arange(1, n_total_cells + 1)\n",
    "\n",
    "# Function to extract valid raster values and corresponding cell IDs\n",
    "def extract_raster_values(raster_path, no_data_value=255):\n",
    "    with rasterio.open(raster_path) as src:\n",
    "        data = src.read(1)  # Read first band\n",
    "        data[data == no_data_value] = 0  # Replace no-data values (255) with 0\n",
    "        valid_indices = np.where(data > 0)  # Indices where values are > 0\n",
    "        values = data[valid_indices].flatten()\n",
    "        cell_ids = np.ravel_multi_index(valid_indices, data.shape) + 1  # Convert to 1D cell IDs\n",
    "        return cell_ids, values\n",
    "\n",
    "# Extract forest and agriculture raster values\n",
    "forest_cell_ids, forest_values = extract_raster_values(forest_raster_path)\n",
    "agriculture_cell_ids, agriculture_values = extract_raster_values(agriculture_raster_path)\n",
    "\n",
    "# Load consumption data\n",
    "forest_consumption = pd.read_csv(forest_consumption_file)\n",
    "agriculture_consumption = pd.read_csv(agriculture_consumption_file)\n",
    "\n",
    "# Calculate annual total consumption\n",
    "forest_annual_consumption = forest_consumption.groupby('Year')['Total_Consumption_m3'].sum().to_dict()\n",
    "agriculture_annual_consumption = agriculture_consumption.groupby('Year')['Total_CWR_m3'].sum().to_dict()\n",
    "\n",
    "# Function to distribute annual consumption to valid cells\n",
    "def distribute_consumption(cell_ids, annual_consumption):\n",
    "    unique_cells = np.unique(cell_ids)\n",
    "    distributed = pd.DataFrame({'Cell_ID': unique_cells})\n",
    "    for year, total_consumption in annual_consumption.items():\n",
    "        distributed[f'{year}'] = total_consumption / len(unique_cells)\n",
    "    distributed['Mean_Consumption'] = distributed.iloc[:, 1:].mean(axis=1)\n",
    "    return distributed\n",
    "\n",
    "# Distribute forest and agriculture consumption\n",
    "forest_distributed = distribute_consumption(forest_cell_ids, forest_annual_consumption)\n",
    "agriculture_distributed = distribute_consumption(agriculture_cell_ids, agriculture_annual_consumption)\n",
    "\n",
    "# Save to CSV\n",
    "forest_distributed.to_csv(forest_consumption_csv, index=False)\n",
    "agriculture_distributed.to_csv(agriculture_consumption_csv, index=False)\n",
    "\n",
    "print(f\"Forest water consumption saved to: {forest_consumption_csv}\")\n",
    "print(f\"Agriculture water consumption saved to: {agriculture_consumption_csv}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7df14f6",
   "metadata": {},
   "source": [
    "### Total Consumption "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d72f168",
   "metadata": {},
   "source": [
    "#### Total Annual Consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2412e519",
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   "outputs": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total consumption per year saved to ./Outputs/Water/total_annual_consumption.csv\n"
     ]
    }
   ],
   "source": [
    "####Total Annual Consumption (Calculated from per cell consumptions)\n",
    "# File paths\n",
    "for_con_csv = './Outputs/Water/forest_water_con_per_cell.csv'\n",
    "ag_con_csv = './Outputs/Water/ag_water_con_per_cell.csv'\n",
    "res_con_csv = './Outputs/Water/residential_consumption.csv'\n",
    "com_con_csv = './Outputs/Water/comercial_consumption.csv'\n",
    "### Output Path\n",
    "Total_con_csv = './Outputs/Water/total_annual_consumption.csv'\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Load the data\n",
    "for_con = pd.read_csv(for_con_csv)\n",
    "ag_con = pd.read_csv(ag_con_csv)\n",
    "res_con = pd.read_csv(res_con_csv)\n",
    "com_con = pd.read_csv(com_con_csv)\n",
    "\n",
    "# Sum all rows for each year in each file\n",
    "sum1 = for_con.iloc[:, 1:22].sum(axis=0)  # Skip 'Cell_ID' column\n",
    "sum2 = ag_con.iloc[:, 1:22].sum(axis=0)\n",
    "sum3 = res_con.iloc[:, 5:].sum(axis=0)\n",
    "sum4 = com_con.iloc[:, 5:].sum(axis=0)\n",
    "# Combine the sums and create a DataFrame with 'Year' and 'Total_Consumption'\n",
    "total_consumption = sum1 + sum2 + sum3 +sum4\n",
    "result = pd.DataFrame({'Year': total_consumption.index, 'Total_Annual_Consumption': total_consumption.values})\n",
    "\n",
    "# Save the result to a CSV\n",
    "result.to_csv(Total_con_csv , index=False)\n",
    "\n",
    "print(f\"Total consumption per year saved to {Total_con_csv }\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1b0ea4",
   "metadata": {},
   "source": [
    "#### Total Consumption Per Cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "82da1b1a",
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merged consumption data saved to ./Outputs/Water/total_consumption_per_cell.csv\n"
     ]
    }
   ],
   "source": [
    "#### Total Consumption per cell\n",
    "### Input paths\n",
    "for_con_csv = './Outputs/Water/forest_water_con_per_cell.csv'\n",
    "ag_con_csv = './Outputs/Water/ag_water_con_per_cell.csv'\n",
    "dom_con_csv = './Outputs/Water/mean_dom_consumption_per_cell.csv'\n",
    "### Output Path\n",
    "Total_con_csv = './Outputs/Water/total_consumption_per_cell.csv'\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Load the data\n",
    "for_con = pd.read_csv(for_con_csv)\n",
    "ag_con = pd.read_csv(ag_con_csv)\n",
    "dom_con = pd.read_csv(dom_con_csv)\n",
    "\n",
    "# Initialize an empty DataFrame for the full range of Cell_IDs (1-9440)\n",
    "cell_ids = pd.DataFrame({'Cell_ID': range(1, 9441)})\n",
    "\n",
    "# Merge the data with the full Cell_ID range and fill missing values with 0\n",
    "merged_con = cell_ids.merge(for_con[['Cell_ID', 'Mean_Consumption']], on='Cell_ID', how='left') \\\n",
    "    .merge(ag_con[['Cell_ID', 'Mean_Consumption']], on='Cell_ID', how='left', suffixes=('_Forest', '_Agriculture')) \\\n",
    "    .merge(dom_con[['Cell_ID', 'Mean_Consumption']], on='Cell_ID', how='left', suffixes=('', '_Domestic'))\n",
    "\n",
    "merged_con.fillna(0, inplace=True)\n",
    "\n",
    "# Calculate the total mean consumption\n",
    "merged_con['Total_Mean_Consumption'] = merged_con[['Mean_Consumption_Forest', 'Mean_Consumption_Agriculture', 'Mean_Consumption']].sum(axis=1)\n",
    "\n",
    "# Keep only the required columns\n",
    "final_con = merged_con[['Cell_ID', 'Total_Mean_Consumption']]\n",
    "\n",
    "# Save the result to a CSV\n",
    "final_con.to_csv(Total_con_csv, index=False)\n",
    "print(f\"Merged consumption data saved to {Total_con_csv}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23bea161",
   "metadata": {},
   "source": [
    "### Storage "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1291bd4",
   "metadata": {},
   "source": [
    "#### Lakes and Glaciers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a00a4994",
   "metadata": {},
   "outputs": [
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    {
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     "output_type": "stream",
     "text": [
      "Total volumes saved to ./Outputs/Water/Lake_volume_by_cell.csv.\n",
      "Total volumes saved to ./Outputs/Water/Glacier_volume_by_cell.csv.\n"
     ]
    }
   ],
   "source": [
    "#### Estimation of water storage from Lakes and Glaciers\n",
    "###Inputs\n",
    "# Process lake and glacier shapefiles\n",
    "lake_shapefile = \"./Input_Data/maps/lakes/Otta_lakes.shp\"  # Replace with your lake shapefile path\n",
    "glacier_shapefile = \"./Input_Data/maps/glaciers/OttaGlaciers.shp\"  # Replace with your glacier shapefile path\n",
    "\n",
    "###Outputs\n",
    "Glacier_Volume_csv= \"./Outputs/Water/Glacier_volume_by_cell.csv\"\n",
    "Lake_Volume_csv= \"./Outputs/Water/Lake_volume_by_cell.csv\"\n",
    "\n",
    "# Constants\n",
    "grid_extent = (95000, 6837000, 213000, 6917000)  # xmin, ymin, xmax, ymax\n",
    "cell_size = 1000  # 1000m x 1000m cells\n",
    "n_cells_x = int((grid_extent[2] - grid_extent[0]) / cell_size)  # Number of cells along x-axis\n",
    "crs = \"EPSG:25833\"  # Coordinate Reference System\n",
    "\n",
    "# Area-Volume scaling parameters\n",
    "lake_c, lake_gamma = 0.1, 1.25  # Scaling constants for lakes\n",
    "glacier_c, glacier_gamma = 0.034, 1.375  # Scaling constants for glaciers\n",
    "\n",
    "# Function to calculate Cell_ID based on coordinates\n",
    "def calculate_cell_id(x, y):\n",
    "    col = int((x - grid_extent[0]) / cell_size)\n",
    "    row = int((grid_extent[3] - y) / cell_size)\n",
    "    return row * n_cells_x + col + 1\n",
    "\n",
    "# Function to calculate volume using the area-volume relationship\n",
    "def calculate_volume(area, c, gamma):\n",
    "    return c * (area ** gamma)\n",
    "\n",
    "# Function to process a shapefile and calculate total volume per Cell_ID\n",
    "def process_shapefile(shapefile_path, c, gamma, volume_label):\n",
    "    # Load shapefile\n",
    "    gdf = gpd.read_file(shapefile_path)\n",
    "    \n",
    "    # Ensure the CRS is correct\n",
    "    if gdf.crs.to_string() != crs:\n",
    "        gdf = gdf.to_crs(crs)\n",
    "    \n",
    "    # Calculate area in m² and volume\n",
    "    gdf[\"Area_m2\"] = gdf.geometry.area\n",
    "    gdf[volume_label] = calculate_volume(gdf[\"Area_m2\"], c, gamma)\n",
    "    \n",
    "    # Assign Cell_IDs based on geometry centroids\n",
    "    gdf[\"Cell_ID\"] = gdf.geometry.centroid.apply(lambda geom: calculate_cell_id(geom.x, geom.y))\n",
    "    \n",
    "    # Aggregate total volume by Cell_ID\n",
    "    volume_per_cell = gdf.groupby(\"Cell_ID\")[volume_label].sum().reset_index()\n",
    "    return volume_per_cell\n",
    "\n",
    "\n",
    "\n",
    "lake_volumes = process_shapefile(lake_shapefile, lake_c, lake_gamma, \"Lake_Volume_m3\")\n",
    "glacier_volumes = process_shapefile(glacier_shapefile, glacier_c, glacier_gamma, \"Glacier_Volume_m3\")\n",
    "\n",
    "\n",
    "lake_volumes.to_csv(Lake_Volume_csv, index=False)\n",
    "print(f\"Lake volumes saved to {Lake_Volume_csv}.\")\n",
    "glacier_volumes.to_csv(Glacier_Volume_csv, index=False)\n",
    "print(f\"Glacier volumes saved to {Glacier_Volume_csv}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2462943f",
   "metadata": {},
   "source": [
    "#### Reservoirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "fd1b034c",
   "metadata": {},
   "outputs": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total annual reservoir volumes saved to: ./Outputs/Water/total_annual_storage.csv\n",
      "Reservoir mean volumes per cell saved to: ./Outputs/Water/Reservoir_Mean_Volumes_per_cell.csv\n"
     ]
    }
   ],
   "source": [
    "##### Reservoir Total Annual and Mean Annual Storage Per Cell\n",
    "# Input file paths\n",
    "reservoir_volumes_file = \"./Input_Data/Water/Reservoir_Volumes.csv\"\n",
    "storage_file = \"./Input_Data/Water/Water_Scape.xlsx\"\n",
    "\n",
    "# Output file paths\n",
    "annual_volume_output = \"./Outputs/Water/total_annual_storage.csv\"\n",
    "mean_volume_output = \"./Outputs/Water/Reservoir_Mean_Volumes_per_cell.csv\"\n",
    "\n",
    "# Step 1: Read Reservoir Volume Data\n",
    "reservoir_volumes = pd.read_csv(reservoir_volumes_file)\n",
    "\n",
    "# Total Annual Volumes (Million m3 to m3)\n",
    "reservoir_columns = ['1', '2', '3', '4', '5']\n",
    "reservoir_volumes['Total_Volume'] = reservoir_volumes[reservoir_columns].sum(axis=1) * 1e6 \n",
    "\n",
    "\n",
    "# Save the total annual reservoir volumes\n",
    "annual_volumes = reservoir_volumes.groupby('Year')['Total_Volume'].mean().reset_index()   \n",
    "annual_volumes['Total_Volume'] += 1817437143 + 391575385.8 #Adding Fixed Glacier and Lake Volumes respectively to every year volume \n",
    "annual_volumes.to_csv(annual_volume_output, index=False)\n",
    "print(f\"Total annual reservoir volumes saved to: {annual_volume_output}\")\n",
    "\n",
    "# Step 2: Compute Mean Volumes for Each Reservoir\n",
    "reservoir_means = reservoir_volumes[reservoir_columns].mean().reset_index()\n",
    "reservoir_means.columns = ['Res_Id', 'Mean_Volume']\n",
    "reservoir_means['Mean_Volume'] *= 1e6  # Convert to m3 (Million to regular)\n",
    "\n",
    "# Step 3: Read Storage Data (Mapping Res_Id to Cell_IDs)\n",
    "storage_data = pd.read_excel(storage_file, sheet_name=\"Storage\")\n",
    "storage_data['Res_Id'] = storage_data['Res_Id'].astype(str)  # Ensure Res_Id is a string\n",
    "reservoir_means['Res_Id'] = reservoir_means['Res_Id'].astype(str)\n",
    "\n",
    "# Merge reservoir means with storage data to get Cell_ID mapping\n",
    "storage_data = storage_data.merge(reservoir_means, on=\"Res_Id\", how=\"left\")\n",
    "\n",
    "# Step 4: Distribute Mean Volumes to Each Cell_ID\n",
    "expanded_result = []\n",
    "for _, row in storage_data.iterrows():\n",
    "    cell_ids = eval(row['Cell_ID'])  # Convert Cell_ID string to list\n",
    "    mean_volume = row['Mean_Volume'] / len(cell_ids)  # Divide equally among cells\n",
    "    for cell_id in cell_ids:\n",
    "        expanded_result.append({'Cell_ID': cell_id, 'Mean_Volume': mean_volume})\n",
    "\n",
    "# Step 5: Convert to DataFrame and Aggregate by Cell_ID\n",
    "final_result_df = pd.DataFrame(expanded_result)\n",
    "final_result_df = final_result_df.groupby('Cell_ID', as_index=False)['Mean_Volume'].sum()\n",
    "\n",
    "# Save Final Mean Volume Output\n",
    "final_result_df.to_csv(mean_volume_output, index=False)\n",
    "print(f\"Reservoir mean volumes per cell saved to: {mean_volume_output}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440789f4",
   "metadata": {},
   "source": [
    "#### Total Storage per cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "01606117",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total storage per cell saved to ./Outputs/Water/Total_Storage_per_cell.csv\n"
     ]
    }
   ],
   "source": [
    "# Input_Paths\n",
    "lake_path = \"./Outputs/Water/Lake_volume_by_cell.csv\"\n",
    "glacier_path = \"./Outputs/Water/Glacier_volume_by_cell.csv\"\n",
    "reservoir_path =\"./Outputs/Water/Reservoir_Mean_Volumes_per_cell.csv\"\n",
    "\n",
    "##Output_Path\n",
    "output_dir=\"./Outputs/Water/Total_Storage_per_cell.csv\"\n",
    "# Step 1: Load lake, glacier, and reservoir volumes\n",
    "lake_volumes = pd.read_csv(f\"{lake_path}\")\n",
    "glacier_volumes = pd.read_csv(f\"{glacier_path}\")\n",
    "reservoir_volumes = pd.read_csv(f\"{reservoir_path}\")\n",
    "\n",
    "# Ensure all have a consistent Cell_ID column\n",
    "lake_volumes = lake_volumes.rename(columns={lake_volumes.columns[0]: \"Cell_ID\", lake_volumes.columns[1]: \"Lake_Volume\"})\n",
    "glacier_volumes = glacier_volumes.rename(columns={glacier_volumes.columns[0]: \"Cell_ID\", glacier_volumes.columns[1]: \"Glacier_Volume\"})\n",
    "reservoir_volumes = reservoir_volumes.rename(columns={reservoir_volumes.columns[0]: \"Cell_ID\", reservoir_volumes.columns[1]: \"Reservoir_Volume\"})\n",
    "\n",
    "# Step 2: Create a grid of all Cell_IDs (1 to 9440)\n",
    "all_cells = pd.DataFrame({'Cell_ID': range(1, 9441)})\n",
    "\n",
    "# Step 3: Merge lake, glacier, and reservoir volumes with the grid\n",
    "all_volumes = all_cells.merge(lake_volumes, on='Cell_ID', how='left').fillna(0)\n",
    "all_volumes = all_volumes.merge(glacier_volumes, on='Cell_ID', how='left').fillna(0)\n",
    "all_volumes = all_volumes.merge(reservoir_volumes, on='Cell_ID', how='left').fillna(0)\n",
    "\n",
    "# Step 4: Calculate total storage per cell\n",
    "all_volumes['Total_Storage'] = all_volumes[['Lake_Volume', 'Glacier_Volume', 'Reservoir_Volume']].sum(axis=1)\n",
    "\n",
    "# Step 5: Save the final result to a CSV file\n",
    "all_volumes.to_csv(f\"{output_dir}\", index=False)\n",
    "\n",
    "print(f\"Total storage per cell saved to {output_dir}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e6ae9a",
   "metadata": {},
   "source": [
    "### Annual Balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "de9fd240",
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annual balance data saved to: ./Outputs/Water/Annual_Water_Balance.csv\n"
     ]
    }
   ],
   "source": [
    "# File paths\n",
    "water_data_file = \"./Input_Data/Water/Water_Scape.xlsx\"\n",
    "total_annual_precipitation_file = \"./Input_Data/Meteo/total_annual_precip.csv\"\n",
    "total_annual_storage_file = \"./Outputs/Water/total_annual_storage.csv\"\n",
    "total_consumption_file = \"./Outputs/Water/total_annual_consumption.csv\"\n",
    "annual_balance_output_file = \"./Outputs/Water/Annual_Water_Balance.csv\"\n",
    "\n",
    "# Read data\n",
    "import_data = pd.read_excel(water_data_file, sheet_name=\"Import\")[['Year', 'IBT(m3/year)']]\n",
    "export_data = pd.read_excel(water_data_file, sheet_name=\"Export\")[['Year', 'Total_Output(m3/year)']]\n",
    "storage_data = pd.read_csv(total_annual_storage_file)\n",
    "precipitation_data = pd.read_csv(total_annual_precipitation_file)\n",
    "consumption_data = pd.read_csv(total_consumption_file)\n",
    "\n",
    "# Filter years from 2000 to 2020\n",
    "years = range(2000, 2021)\n",
    "\n",
    "# Initialize the annual balance DataFrame\n",
    "annual_balance = pd.DataFrame({\"Year\": years})\n",
    "\n",
    "# Process import data\n",
    "import_data_filtered = import_data[import_data[\"Year\"].isin(years)]\n",
    "annual_balance = annual_balance.merge(import_data_filtered, on=\"Year\", how=\"left\")\n",
    "annual_balance.rename(columns={\"IBT(m3/year)\": \"Import\"}, inplace=True)\n",
    "\n",
    "# Process export data\n",
    "export_data_filtered = export_data[export_data[\"Year\"].isin(years)]\n",
    "annual_balance = annual_balance.merge(export_data_filtered, on=\"Year\", how=\"left\")\n",
    "annual_balance.rename(columns={\"Total_Output(m3/year)\": \"Export\"}, inplace=True)\n",
    "\n",
    "# Process consumption data\n",
    "consumption_data_filtered = consumption_data[consumption_data[\"Year\"].isin(years)]\n",
    "annual_balance = annual_balance.merge(consumption_data_filtered, on=\"Year\", how=\"left\")\n",
    "annual_balance.rename(columns={\"Total_Annual_Consumption\": \"Consumption\"}, inplace=True)\n",
    "\n",
    "# Process storage data\n",
    "storage_data_filtered = storage_data[storage_data[\"Year\"].isin(years)]\n",
    "annual_balance = annual_balance.merge(storage_data_filtered, on=\"Year\", how=\"left\")\n",
    "annual_balance.rename(columns={\"Total_Volume\": \"Storage\"}, inplace=True)\n",
    "\n",
    "# Process precipitation data\n",
    "precipitation_data_filtered = precipitation_data[precipitation_data[\"Year\"].isin(years)]\n",
    "annual_balance = annual_balance.merge(precipitation_data_filtered, on=\"Year\", how=\"left\")\n",
    "annual_balance.rename(columns={\"Total_Annual\": \"Precipitation\"}, inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "# Save to CSV\n",
    "annual_balance.to_csv(annual_balance_output_file, index=False)\n",
    "\n",
    "print(f\"Annual balance data saved to: {annual_balance_output_file}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7443291",
   "metadata": {},
   "source": [
    "### Results Plots"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e59a6110",
   "metadata": {},
   "source": [
    "# Input file paths\n",
    "grid_consumption_file = \"./Outputs/Water/total_consumption_per_cell.csv\"\n",
    "grid_production_file = \"./Input_Data/Meteo/Annual_precip_per_cell.csv\"\n",
    "grid_storage_file = \"./Outputs/Water/Total_Storage_per_cell.csv\"\n",
    "boundary_shapefile = \"./Input_Data/maps/mask/Otta_mask.shp\"\n",
    "annual_balance_file = \"./Outputs/Water/Annual_Water_Balance.csv\"\n",
    "\n",
    "# CRS and extent\n",
    "crs = \"EPSG:25833\"\n",
    "extent = [95000, 6837000, 213000, 6917000]\n",
    "cell_size = 1000\n",
    "\n",
    "# Load grid data\n",
    "grid_consumption = pd.read_csv(grid_consumption_file, usecols=[\"Cell_ID\", \"Total_Mean_Consumption\"])\n",
    "grid_production = pd.read_csv(grid_production_file, usecols=[\"Cell_ID\", \"Mean_Annual\"])\n",
    "grid_storage = pd.read_csv(grid_storage_file, usecols=[\"Cell_ID\", \"Total_Storage\"])\n",
    "\n",
    "# Load boundary shapefile\n",
    "boundary = gpd.read_file(boundary_shapefile).to_crs(crs)\n",
    "\n",
    "# Load annual balance data\n",
    "annual_balance = pd.read_csv(annual_balance_file)\n",
    "\n",
    "# Create the grid geometry\n",
    "n_cols = int((extent[2] - extent[0]) / cell_size)\n",
    "n_rows = int((extent[3] - extent[1]) / cell_size)\n",
    "grid_x, grid_y = np.meshgrid(\n",
    "    np.linspace(extent[0] + cell_size / 2, extent[2] - cell_size / 2, n_cols),\n",
    "    np.linspace(extent[3] - cell_size / 2, extent[1] + cell_size / 2, n_rows),\n",
    ")\n",
    "grid_cells = pd.DataFrame({\n",
    "    \"Cell_ID\": range(1, n_cols * n_rows + 1),\n",
    "    \"X\": grid_x.flatten(),\n",
    "    \"Y\": grid_y.flatten(),\n",
    "})\n",
    "\n",
    "# Merge grid data with Cell_ID\n",
    "consumption_grid = grid_cells.merge(grid_consumption, on=\"Cell_ID\", how=\"left\")\n",
    "production_grid = grid_cells.merge(grid_production, on=\"Cell_ID\", how=\"left\")\n",
    "storage_grid = grid_cells.merge(grid_storage, on=\"Cell_ID\", how=\"left\")\n",
    "\n",
    "# Ensure no NaN values in merged data\n",
    "consumption_grid[\"Mean_Consumption\"] = consumption_grid[\"Total_Mean_Consumption\"].fillna(0)\n",
    "production_grid[\"Total_Production\"] = production_grid[\"Mean_Annual\"].fillna(0)\n",
    "storage_grid[\"Total_Storage\"] = storage_grid[\"Total_Storage\"].fillna(0)\n",
    "\n",
    "# Plot settings\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(16, 12))\n",
    "titles = [\"Inputs (m3)\", \"Consumption (m3)\", \"Storage (m3)\", \"Annual Balance\"]\n",
    "data_grids = [\n",
    "    (production_grid, \"Mean_Annual\"),\n",
    "    (consumption_grid, \"Total_Mean_Consumption\"),\n",
    "    (storage_grid, \"Total_Storage\")\n",
    "]\n",
    "# Define column names and their corresponding colors\n",
    "column_color_map = {\n",
    "    'Consumption': '#FFFFCC',  # Light Yellow\n",
    "    'Import': '#89cbbf',             # Light Teal\n",
    "    'Export': '#BFE3E3',             # Aqua Blue\n",
    "    'Precipitation': '#1D91C0',   # Medium Blue\n",
    "    'Storage': '#253494'       # Dark Blue\n",
    "}\n",
    "\n",
    "# Reorder the colors to match datafram columns\n",
    "stacked_colors = [column_color_map[col] for col in annual_balance.columns if col != 'Year']\n",
    "\n",
    "# Panel labels\n",
    "panel_labels = ['(a)', '(b)', '(c)', '(d)']\n",
    "# Plot production, consumption, and storage maps\n",
    "for i, (ax, (data_grid, col)) in enumerate(zip(axes.flat[:3], data_grids)):\n",
    "    # Handle case where data might be uniform or zero\n",
    "    if data_grid[col].max() > 0:\n",
    "        norm = Normalize(vmin=data_grid[col].min(), vmax=data_grid[col].max())\n",
    "    else:\n",
    "        norm = Normalize(vmin=0, vmax=1)  # Default range for zero or uniform data\n",
    "\n",
    "    # Plot grid data\n",
    "    sc = ax.scatter(\n",
    "        data_grid[\"X\"], data_grid[\"Y\"],\n",
    "        c=data_grid[col],\n",
    "        cmap='YlGnBu',\n",
    "        s=25,\n",
    "        marker='s',\n",
    "        norm=norm\n",
    "    )\n",
    "    ax.set_title(titles[i])\n",
    "    ax.set_xlim(extent[0], extent[2])\n",
    "    ax.set_ylim(extent[1], extent[3])\n",
    "    ax.set_xlabel(\"Easting (m)\")\n",
    "    ax.set_ylabel(\"Northing (m)\")\n",
    "    ax.text(\n",
    "        -0.05, 1.275, panel_labels[i],\n",
    "        transform=ax.transAxes, fontsize=12, fontweight='bold',\n",
    "        va='top', ha='left', color='black'\n",
    "    )\n",
    "\n",
    "    # Overlay boundary\n",
    "    boundary.plot(ax=ax, edgecolor=\"black\", facecolor=\"none\")\n",
    "    fig.colorbar(sc, ax=ax, orientation=\"vertical\", label=\"Water (m3)\")\n",
    "\n",
    "# Plot annual balance (stacked bar plot)\n",
    "ax = axes[1, 1]\n",
    "annual_balance.set_index(\"Year\").plot(\n",
    "    kind=\"bar\",\n",
    "    stacked=True,\n",
    "    ax=ax,\n",
    "    title=\"Annual Water Balance\",\n",
    "    ylabel=\"Water (m3)\",\n",
    "    xlabel=\"Year\",\n",
    "    color=stacked_colors\n",
    ")\n",
    "#ax.set_yscale('log')\n",
    "#ax.set_ylim(0, 1e7)  # Set limits to ensure small values are visible\n",
    "\n",
    "# Add panel label\n",
    "ax.text(\n",
    "    -0.05, 1.10, panel_labels[3],\n",
    "    transform=ax.transAxes, fontsize=16, fontweight='bold',\n",
    "    va='top', ha='left', color='black'\n",
    ")\n",
    "ax.legend(loc='upper right',title=\"Water Components\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./Results/Water_plot.jpg\", dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a2bc4a56",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Define consistent column-color mapping\n",
    "column_color_map = {\n",
    "    'Consumption': '#FFFFCC',   # Light Yellow\n",
    "    'Import': '#89cbbf',        # Light Teal\n",
    "    'Export': '#BFE3E3',        # Aqua Blue\n",
    "    'Precipitation': '#1D91C0', # Medium Blue\n",
    "    'Storage': '#253494'        # Dark Blue\n",
    "}\n",
    "\n",
    "# Reorder colors for the bar plot\n",
    "stacked_colors = [column_color_map[col] for col in annual_balance.columns if col != 'Year']\n",
    "\n",
    "# Plot settings\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(16, 12))\n",
    "titles = [\"Inputs (m3)\", \"Consumption (m3)\", \"Storage (m3)\", \"Annual Balance\"]\n",
    "data_grids = [\n",
    "    (production_grid, \"Mean_Annual\"),\n",
    "    (consumption_grid, \"Total_Mean_Consumption\"),\n",
    "    (storage_grid, \"Total_Storage\")\n",
    "]\n",
    "panel_labels = ['(a)', '(b)', '(c)', '(d)']\n",
    "\n",
    "# Plot production, consumption, and storage maps\n",
    "for i, (ax, (data_grid, col)) in enumerate(zip(axes.flat[:3], data_grids)):\n",
    "    # Adjust color scaling with LogNorm for better visibility\n",
    "    if data_grid[col].max() > 0:\n",
    "        norm = LogNorm(vmin=max(data_grid[col].min(), 1), vmax=data_grid[col].quantile(0.99))\n",
    "    else:\n",
    "        norm = Normalize(vmin=0, vmax=1)\n",
    "\n",
    "    # Plot grid data\n",
    "    sc = ax.scatter(\n",
    "        data_grid[\"X\"], data_grid[\"Y\"],\n",
    "        c=data_grid[col],\n",
    "        cmap='YlGnBu',  # Consistent color map for maps\n",
    "        s=20,\n",
    "        marker='s',\n",
    "        norm=norm\n",
    "    )\n",
    "    ax.set_title(titles[i])\n",
    "    ax.set_xlim(extent[0], extent[2])\n",
    "    ax.set_ylim(extent[1], extent[3])\n",
    "    ax.set_xlabel(\"Easting (m)\")\n",
    "    ax.set_ylabel(\"Northing (m)\")\n",
    "    ax.text(\n",
    "        -0.05, 1.275, panel_labels[i],\n",
    "        transform=ax.transAxes, fontsize=12, fontweight='bold',\n",
    "        va='top', ha='left', color='black'\n",
    "    )\n",
    "\n",
    "    # Overlay boundary\n",
    "    boundary.plot(ax=ax, edgecolor=\"black\", facecolor=\"none\")\n",
    "    fig.colorbar(sc, ax=ax, orientation=\"vertical\", label=\"Water (m3)\")\n",
    "\n",
    "# Plot annual balance (stacked bar plot)\n",
    "ax = axes[1, 1]\n",
    "annual_balance.set_index(\"Year\").plot(\n",
    "    kind=\"bar\",\n",
    "    stacked=True,\n",
    "    ax=ax,\n",
    "    title=\"Annual Water Balance\",\n",
    "    ylabel=\"Water (m3)\",\n",
    "    xlabel=\"Year\",\n",
    "    color=stacked_colors  # Apply consistent colors\n",
    ")\n",
    "\n",
    "# Add panel label and legend\n",
    "ax.text(\n",
    "    -0.05, 1.10, panel_labels[3],\n",
    "    transform=ax.transAxes, fontsize=16, fontweight='bold',\n",
    "    va='top', ha='left', color='black'\n",
    ")\n",
    "ax.legend(loc='upper right', title=\"Water Components\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./Results/Water_plot.jpg\", dpi=300, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3ce2778c",
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1600x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Final Plot\n",
    "# Input file paths\n",
    "grid_consumption_file = \"./Outputs/Water/total_consumption_per_cell.csv\"\n",
    "grid_production_file = \"./Input_Data/Meteo/Annual_precip_per_cell.csv\"\n",
    "grid_storage_file = \"./Outputs/Water/Total_Storage_per_cell.csv\"\n",
    "boundary_shapefile = \"./Input_Data/maps/mask/Otta_mask.shp\"\n",
    "annual_balance_file = \"./Outputs/Water/Annual_Water_Balance.csv\"\n",
    "\n",
    "# Define consistent column-color mapping\n",
    "column_color_map = {\n",
    "    'Consumption': '#FFFFCC',   # Light Yellow\n",
    "    'Import': '#89cbbf',        # Light Teal\n",
    "    'Export': '#BFE3E3',        # Aqua Blue\n",
    "    'Precipitation': '#1D91C0', # Medium Blue\n",
    "    'Storage': '#253494'        # Dark Blue\n",
    "}\n",
    "\n",
    "# Reorder colors for the bar plot\n",
    "stacked_colors = [column_color_map[col] for col in annual_balance.columns if col != 'Year']\n",
    "\n",
    "# Plot settings\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(16, 12))\n",
    "titles = [\"Inputs (m3)\", \"Consumption (m3)\", \"Storage (m3)\", \"Annual Balance\"]\n",
    "data_grids = [\n",
    "    (production_grid, \"Mean_Annual\"),\n",
    "    (consumption_grid, \"Total_Mean_Consumption\"),\n",
    "    (storage_grid, \"Total_Storage\")\n",
    "]\n",
    "panel_labels = ['(a)', '(b)', '(c)', '(d)']\n",
    "\n",
    "\n",
    "# Plot production, consumption, and storage maps with grid overlay\n",
    "for i, (ax, (data_grid, col)) in enumerate(zip(axes.flat[:3], data_grids)):\n",
    "    # Adjust color scaling with LogNorm for better visibility\n",
    "    if data_grid[col].max() > 0:\n",
    "        norm = LogNorm(vmin=max(data_grid[col].min(), 1), vmax=data_grid[col].quantile(0.99))\n",
    "    else:\n",
    "        norm = Normalize(vmin=0, vmax=1)\n",
    "    # Add grid overlay\n",
    "    add_grid_overlay(ax, data_grid, cell_size)\n",
    "    # Plot grid data\n",
    "    sc = ax.scatter(\n",
    "        data_grid[\"X\"], data_grid[\"Y\"],\n",
    "        c=data_grid[col],\n",
    "        cmap='YlGnBu',  # Consistent color map for maps\n",
    "        s=10,\n",
    "        marker='s',\n",
    "        norm=norm\n",
    "    )\n",
    "    # Plot formatting\n",
    "    ax.set_title(titles[i])\n",
    "    ax.set_xlim(extent[0], extent[2])\n",
    "    ax.set_ylim(extent[1], extent[3])\n",
    "    ax.set_xlabel(\"Easting (m)\")\n",
    "    ax.set_ylabel(\"Northing (m)\")\n",
    "    ax.text(\n",
    "        -0.05, 1.275, panel_labels[i],\n",
    "        transform=ax.transAxes, fontsize=16, fontweight='bold',\n",
    "        va='top', ha='left', color='black'\n",
    "    )\n",
    "\n",
    "    # Overlay boundary\n",
    "    boundary.plot(ax=ax, edgecolor=\"black\", facecolor=\"none\")\n",
    "    fig.colorbar(sc, ax=ax, orientation=\"vertical\", label=\"Water (m3)\")\n",
    "    \n",
    "\n",
    "# Grid settings (extent and size)\n",
    "cell_size = 1000  # Grid cell size\n",
    "grid_color = 'gray'  # Grid line color\n",
    "grid_linewidth = 0.10  # Grid line thickness\n",
    "\n",
    "# Function to add grid overlay\n",
    "def add_grid_overlay(ax, grid_cells, cell_size):\n",
    "    for _, row in grid_cells.iterrows():\n",
    "        rect = patches.Rectangle(\n",
    "            (row[\"X\"] - cell_size / 2, row[\"Y\"] - cell_size / 2),  # Bottom-left corner\n",
    "            cell_size, cell_size,  # Width and height\n",
    "            linewidth=grid_linewidth, edgecolor=grid_color, facecolor='none'\n",
    "        )\n",
    "        ax.add_patch(rect)\n",
    "    \n",
    "    \n",
    "# Plot annual balance (stacked bar plot)\n",
    "ax = axes[1, 1]\n",
    "annual_balance.set_index(\"Year\").plot(\n",
    "    kind=\"bar\",\n",
    "    stacked=True,\n",
    "    ax=ax,\n",
    "    title=\"Annual Water Balance\",\n",
    "    ylabel=\"Water (Water (m$^3$))\",\n",
    "    xlabel=\"Year\",\n",
    "    color=stacked_colors  # Apply consistent colors\n",
    ")\n",
    "\n",
    "# Add panel label and legend\n",
    "ax.text(\n",
    "    -0.05, 1.10, panel_labels[3],\n",
    "    transform=ax.transAxes, fontsize=16, fontweight='bold',\n",
    "    va='top', ha='left', color='black'\n",
    ")\n",
    "ax.legend(loc='upper right', title=\"Water Components\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./Results/Water_plot.jpg\", dpi=300, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
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   "file_extension": ".py",
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