Initial commit

analyze_glacier_data.py

+import pandas as pd
+import os
+
+# Define the file paths
+gtn_report_path = 'GTN Report.xlsx'
+glacier_data_dir = './glacier_data'
+
+# Load the GTN report
+gtn_data = pd.read_excel(gtn_report_path)
+
+# Prepare a dictionary for region-to-country mapping based on GTN report
+region_to_country = {
+    'alaska': 'US - UNITED STATES',
+    'scandinavia': 'SE - SWEDEN',
+    'new_zealand': 'NZ - NEW ZEALAND',
+    'antarctic_and_subantarctic': 'AQ - ANTARCTICA',
+    'russian_arctic': 'RU - RUSSIAN FEDERATION',
+    'greenland_periphery': 'GL - GREENLAND',
+    'iceland': 'IS - ICELAND',
+    'svalbard': 'NO - NORWAY',
+    'western_canada_us': 'CA - CANADA',
+    'arctic_canada_north': 'CA - CANADA',
+    'arctic_canada_south': 'CA - CANADA',
+    'south_asia_east': 'IN - INDIA',
+    'south_asia_west': 'PK - PAKISTAN',
+    'central_asia': 'KZ - KAZAKHSTAN',
+    'caucasus_middle_east': 'GE - GEORGIA',
+    'central_europe': 'DE - GERMANY',
+    'north_asia': 'CN - CHINA',
+    'low_latitudes': 'BR - BRAZIL',
+    'southern_andes': 'CL - CHILE'
+}
+
+# Initialize an empty list for storing region-country mapping
+region_country_mapping = []
+
+# Loop through each glacier CSV file in the directory
+for file_name in os.listdir(glacier_data_dir):
+    if file_name.endswith('.csv'):
+        region_name = file_name.split('.')[0]
+        # Check if region exists in the predefined mapping
+        if region_name in region_to_country:
+            country = region_to_country[region_name]
+            region_country_mapping.append({'region': region_name, 'country': country})
+
+# Create a DataFrame for region-country mapping
+region_country_df = pd.DataFrame(region_country_mapping)
+
+# Save the mapping to a CSV file
+region_country_mapping_file = './data/region_to_country_mapping.csv'
+region_country_df.to_csv(region_country_mapping_file, index=False)
+
+print(f"Region to Country mapping saved to {region_country_mapping_file}")
+
+# Now you can load this CSV into your project for future use

app.py

+import dash
+from dash import dcc, html, Input, Output
+def trigger_event(event):
+    pass
+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+import os
+import zipfile
+import numpy as np
+
+
+
+
+# Load and prepare data
+zip_path = "Glacier.zip"
+gtn_report_path = "GTN Report.xlsx"
+extract_dir = "glacier_data"
+
+# Extract glacier zip contents
+with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+    zip_ref.extractall(extract_dir)
+
+# Load regional glacier CSV files and assign approximate coordinates for visualization
+region_coordinates = {
+    '1_alaska': (64.2008, -149.4937),
+    '2_western_canada_us': (52.9399, -106.4509),
+    '3_arctic_canada_north': (75.0000, -100.0000),
+    '4_arctic_canada_south': (65.0000, -100.0000),
+    '5_greenland_periphery': (72.0000, -40.0000),
+    '6_iceland': (64.9631, -19.0208),
+    '7_svalbard': (78.0000, 16.0000),
+    '8_scandinavia': (60.0000, 15.0000),
+    '9_russian_arctic': (70.0000, 100.0000),
+    '10_north_asia': (60.0000, 90.0000),
+    '11_central_europe': (47.0000, 10.0000),
+    '12_caucasus_middle_east': (42.0000, 45.0000),
+    '13_central_asia': (43.0000, 75.0000),
+    '14_south_asia_west': (35.0000, 70.0000),
+    '15_south_asia_east': (27.0000, 85.0000),
+    '16_low_latitudes': (0.0000, -60.0000),
+    '17_southern_andes': (-40.0000, -70.0000),
+    '18_new_zealand': (-41.2865, 174.7762),
+    '19_antarctic_and_subantarctic': (-75.0000, 0.0000)
+}
+
+# Load glacier data into a combined DataFrame
+glacier_data = []
+region_files = [file for file in os.listdir(extract_dir) if file.endswith('.csv') and file != '0_global.csv']
+for file in region_files:
+    region_name = file.replace('.csv', '')
+    df_region = pd.read_csv(os.path.join(extract_dir, file))
+    df_region['region'] = region_name
+    df_region['latitude'], df_region['longitude'] = region_coordinates.get(region_name, (0, 0))
+    glacier_data.append(df_region)
+
+df_glacier_regions = pd.concat(glacier_data, ignore_index=True)
+
+# Simulate river discharge based on glacier melt trends
+def simulate_discharge(glacier_mass_loss):
+    max_discharge = 500  # Max discharge in cubic meters per second
+    normalized_loss = (glacier_mass_loss - glacier_mass_loss.min()) / (glacier_mass_loss.max() - glacier_mass_loss.min())
+    simulated_discharge = max_discharge * (1 - normalized_loss) + np.random.normal(0, 10, size=len(glacier_mass_loss))
+    return np.clip(simulated_discharge, 50, max_discharge)
+
+# Enhance DataFrame with simulated discharge
+df_glacier_regions['simulated_discharge'] = simulate_discharge(df_glacier_regions['combined_gt'])
+
+# Initialize Dash app
+app = dash.Dash(__name__)
+app.title = "Glacier Melt Impact on Water Resources - Interactive Visualization"
+
+# Layout with 3D globe map and a side chart for selected region details
+app.layout = html.Div([
+    html.H1("🌍 Glacier Melt and Water Resources Visualization",
+            style={'textAlign': 'center', 'fontSize': '28px', 'marginBottom': '20px', 'color': '#2C3E50'}),
+
+    html.Div([
+        dcc.Graph(id='world-map-visualization', style={'height': '75vh', 'width': '65%', 'display': 'inline-block'}),
+        dcc.Graph(id='region-detail-chart', style={'height': '75vh', 'width': '33%', 'display': 'inline-block', 'paddingLeft': '1%'})
+    ], style={'display': 'flex', 'justifyContent': 'center'}),
+
+    html.Div([
+        html.Label("⏩ Animation Speed:", style={'fontWeight': 'bold', 'fontSize': '16px'}),
+        dcc.Slider(
+            id='animation-speed',
+            min=50,
+            max=1000,
+            step=50,
+            value=300,
+            marks={50: 'Fast', 500: 'Medium', 1000: 'Slow'},
+            tooltip={"placement": "bottom", "always_visible": True}
+        )
+    ], style={'width': '60%', 'margin': 'auto', 'paddingTop': '20px'})
+])
+
+# Callback to update the 3D world map for smoother panning and zooming
+@app.callback(
+    Output('world-map-visualization', 'figure'),
+    [Input('animation-speed', 'value')]
+)
+def update_world_map(animation_speed):
+    min_size, max_size = 4, 15
+    normalized_sizes = (
+        (df_glacier_regions['glacier_area'] - df_glacier_regions['glacier_area'].min()) /
+        (df_glacier_regions['glacier_area'].max() - df_glacier_regions['glacier_area'].min())
+    )
+    bubble_sizes = normalized_sizes * (max_size - min_size) + min_size
+
+    fig = go.Figure()
+    fig.add_trace(go.Scattergeo(
+        lat=df_glacier_regions['latitude'],
+        lon=df_glacier_regions['longitude'],
+        mode='markers',
+        marker=dict(
+            size=bubble_sizes,
+            color=df_glacier_regions['simulated_discharge'],
+            colorscale='Viridis',
+            cmin=df_glacier_regions['simulated_discharge'].min(),
+            cmax=df_glacier_regions['simulated_discharge'].max(),
+            showscale=True,
+            colorbar=dict(title="Discharge (m³/s)", thickness=15, len=0.5),
+            line=dict(width=0.4, color='DarkSlateGrey')
+        ),
+        text=df_glacier_regions.apply(lambda row: (
+            f"Region: {row['region']}<br>Year: {row['end_dates']}<br>Glacier Area: {row['glacier_area']:.2f} km²<Br>Discharge: {row['simulated_discharge']:.2f} m³/s"
+        ), axis=1),
+        hoverinfo='text',
+        customdata=df_glacier_regions[['region']]
+    ))
+
+    fig.update_geos(
+        projection_type="orthographic",
+        showland=True,
+        landcolor="rgb(230, 230, 230)",
+        showocean=True,
+        oceancolor="rgb(180, 210, 255)",
+        showcountries=True,
+        countrycolor="rgb(150, 150, 150)",
+        center=dict(lat=20, lon=0),
+        resolution=50,
+    )
+
+    fig.update_layout(
+        title="🌍 3D Globe - Glacier Melt and Water Resources",
+        margin=dict(l=0, r=0, t=40, b=0),
+        dragmode='pan',
+        geo=dict(
+            projection_rotation=dict(lon=0, lat=0),
+            projection_scale=0.9,
+            lataxis=dict(range=[-90, 90]),
+            lonaxis=dict(range=[-180, 180]),
+        ),
+        font=dict(family="Arial", size=13, color="#2C3E50"),
+    )
+
+    return fig
+
+# Callback to update the region detail chart when a bubble is clicked
+@app.callback(
+    Output('region-detail-chart', 'figure'),
+    [Input('world-map-visualization', 'clickData')]
+)
+def update_region_chart(clickData):
+    if not clickData:
+        return go.Figure(layout=dict(title="Select a region to see details", font=dict(size=14)))
+
+    selected_region = clickData['points'][0]['customdata'][0]
+    region_data = df_glacier_regions[df_glacier_regions['region'] == selected_region].sort_values(by='end_dates')
+
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=region_data['end_dates'],
+        y=region_data['combined_gt'],
+        mode='lines+markers',
+        name='Glacier Mass Change (Gt)',
+        line=dict(color='blue')
+    ))
+    fig.add_trace(go.Scatter(
+        x=region_data['end_dates'],
+        y=region_data['simulated_discharge'],
+        mode='lines+markers',
+        name='Simulated River Discharge (m³/s)',
+        line=dict(color='green')
+    ))
+
+    fig.update_layout(
+        title=f"Glacier Mass vs Water Discharge - {selected_region.replace('_', ' ').title()}",
+        xaxis_title="Year",
+        yaxis_title="Value",
+        legend=dict(x=0.02, y=0.98),
+        margin=dict(l=30, r=20, t=40, b=40),
+        font=dict(family="Arial", size=12)
+    )
+
+    return fig
+
+if __name__ == '__main__':
+    app.run_server(debug=True, port=8060)
+

css/styles.css

+:root {
+  --background-color: #f4f4f9;
+  --text-color: #34495e;
+  --button-bg: #34495e;
+  --button-text: #fff;
+  --box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+  --border-radius: 12px;
+}
+
+.dark-mode {
+  --background-color: #2c3e50;
+  --text-color: #ecf0f1;
+  --button-bg: #e74c3c;
+  --button-text: #fff;
+  --box-shadow: 0 4px 8px rgba(0, 0, 0, 0.4);
+}
+
+body {
+  font-family: Arial, sans-serif;
+  background-color: var(--background-color);
+  color: var(--text-color);
+  margin: 0;
+  padding: 10px;
+  height: 100vh;
+  display: flex;
+  flex-direction: column;
+}
+
+h1 {
+  font-size: 30px;
+  font-weight: bold;
+  margin-bottom: 20px;
+  text-align: center;
+}
+
+#controls {
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  gap: 15px;
+  padding: 10px;
+  margin-bottom: 15px;
+  background-color: var(--background-color);
+  border-radius: var(--border-radius);
+  box-shadow: var(--box-shadow);
+}
+
+.year-control {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+}
+
+#year-input {
+  width: 70px;
+  padding: 5px;
+  border-radius: 6px;
+  text-align: center;
+  border: 1px solid #ccc;
+}
+
+#year-slider {
+  width: 200px;
+}
+
+button, select, input[type="number"], input[type="range"] {
+  padding: 6px 12px;
+  border-radius: 8px;
+  border: 1px solid #ccc;
+  cursor: pointer;
+  transition: all 0.2s ease;
+}
+
+button:hover, select:hover, input:hover {
+  box-shadow: 0 0 8px rgba(0, 0, 0, 0.3);
+}
+
+#container {
+  display: flex;
+  justify-content: space-between;
+  gap: 20px;
+  flex-grow: 1;
+}
+
+#map-container {
+  flex: 1;
+  max-width: 70%;
+  height: 80vh;
+  border-radius: var(--border-radius);
+  box-shadow: var(--box-shadow);
+}
+
+#map {
+  width: 100%;
+  height: 100%;
+  border-radius: var(--border-radius);
+}
+
+#chart-container {
+  flex: 1;
+  max-width: 28%;
+  display: flex;
+  flex-direction: column;
+  gap: 20px;
+  height: 80vh;
+}
+
+#region-chart-1, #region-chart-2 {
+  width: 100%;
+  height: 220px;
+  border-radius: var(--border-radius);
+  box-shadow: var(--box-shadow);
+}
+
+/* Legend */
+#legend {
+  position: absolute;
+  bottom: 15px;
+  right: 10px;
+  background-color: rgba(255, 255, 255, 0.8);
+  padding: 15px;
+  border-radius: 8px;
+  box-shadow: var(--box-shadow);
+  font-weight: bold;
+  width: 220px;
+}
+
+#legend .color-gradient {
+  width: 100%;
+  height: 20px;
+  background: linear-gradient(to right, #003366, #33cc33, #ff3300);
+  border-radius: 8px;
+  margin-bottom: 10px;
+}
+
+#legend .legend-item {
+  display: flex;
+  justify-content: space-between;
+  font-size: 12px;
+}

data/region_to_country_mapping.csv

+

glacier_data/0_global.csv

+start_dates,end_dates,glacier_area,region,combined_gt,combined_gt_errors,combined_mwe,combined_mwe_errors
+2000.0,2001.0,704082.6956499998,global,-78.04414729402814,111.56095088251794,-0.1111786793375206,0.1583621138501352
+2001.0,2002.0,701805.4987500003,global,-215.16931097001844,95.3001011576375,-0.3075164866023069,0.1350498121659405
+2002.0,2003.0,699528.3018500003,global,-128.46636738674923,91.14634450070768,-0.1841997326262217,0.1289299604899674
+2003.0,2004.0,697251.1049500001,global,-192.6724697716301,72.79887688516011,-0.2771630264606625,0.1028951725208123
+2004.0,2005.0,694973.9080500001,global,-229.2369109878561,73.022158218317,-0.3308421919121849,0.1028436503023272
+2005.0,2006.0,692696.71115,global,-315.0371756633185,74.19065798809451,-0.4561666396999292,0.1044578602427483
+2006.0,2007.0,690419.51425,global,-279.8898739792978,72.77317030930298,-0.4066108567264724,0.1025138185768302
+2007.0,2008.0,688142.31735,global,-323.16570882967466,71.78509732133082,-0.4710335566158922,0.1010360421851532
+2008.0,2009.0,685865.12045,global,-178.6324817323364,71.86184704387401,-0.2612321087124307,0.1011209402057232
+2009.0,2010.0,683587.9235500002,global,-231.2381243724868,71.29354455724742,-0.3392890923913098,0.1001020384598227
+2010.0,2011.0,681310.7266500002,global,-231.1966727048064,72.2582027545506,-0.3403621001158775,0.101352981542154
+2011.0,2012.0,679033.5297499998,global,-365.9472468839626,68.11267549573648,-0.540545313270877,0.0950926143458494
+2012.0,2013.0,676756.3328500001,global,-188.0208512243996,67.35045471659744,-0.2786624991943321,0.0941254547446842
+2013.0,2014.0,674479.1359500001,global,-304.73798136525033,66.47051377096227,-0.4531718327704068,0.0925856747213776
+2014.0,2015.0,672201.9390499997,global,-149.889416279315,65.94044282966604,-0.2236536803279121,0.0920570437100916
+2015.0,2016.0,669924.74215,global,-284.97423508815865,66.77259130583126,-0.4266624477275931,0.0932270670335857
+2016.0,2017.0,667647.5452499996,global,-340.6076946742656,68.35959222730513,-0.5116960211749099,0.0952916518024702
+2017.0,2018.0,665370.3483500001,global,-217.68675972042345,66.39070274109359,-0.3281507593734814,0.0924077513562398
+2018.0,2019.0,663093.1514499999,global,-243.81282612823085,66.54077158950466,-0.3687965484035694,0.0924649491234518
+2019.0,2020.0,660815.9545499999,global,-412.9645781490086,69.99144862252744,-0.6268117422048606,0.0969733224466875
+2020.0,2021.0,658538.75765,global,-405.0394156368366,76.20680182507151,-0.6169085501621713,0.1065416966938755
+2021.0,2022.0,656261.5607500002,global,-217.7163545547422,81.6846283807594,-0.3327506622695744,0.1151000186974428
+2022.0,2023.0,653984.3638500002,global,-460.2989143829609,100.8285184125692,-0.7059556646949124,0.138700930079604
+2023.0,2024.0,651707.1669499998,global,-548.025110248126,120.16099017378784,-0.8434372990227316,0.1662574332317422