Reservoir and River Water Level Monitoring for Irrigation

Track surface water availability for farm irrigation planning. Klarety processes Landsat 8 and Sentinel-2 multispectral bands to detect reservoir extent, estimate volume changes, and compute recharge and drawdown rates. Weekly reports help water managers allocate irrigation budgets before shortfalls develop.

Plan irrigation

Water availability intelligence for irrigation planning

Klarety AI

Reservoir status reports from one prompt

Klarety agents detect surface water extent from Landsat and Sentinel-2 and return volume change and recharge rate estimates.

MNDWI - Landsat 8

MNDWI surface water extent and drawdown rates

Agents apply Modified NDWI to multispectral bands, delineate water polygons, and compute volume from DEM-derived bathymetry.

GIS Output

Water extent layers for irrigation GIS and GEE

Export reservoir extent polygons and drawdown time-series for irrigation management GIS and Google Earth Engine integration.

MNDWI reservoir extent and volume change estimation

Klarety agents compute MNDWI from Landsat 8 Band 3 and Band 6 to delineate reservoir water surface extent monthly, then estimate volume change using DEM-derived area-capacity curves. Drawdown and recharge rates are computed from consecutive monthly extent deltas. Output is a monthly volume time-series CSV and reservoir extent shapefile.

Klarety AIMonitor reservoir levels in your watershed

analysis/reservoir_water_level.pyAgent code

python

import eeimport numpy as npimport pandas as pd
ee.Initialize()
# Shasta Reservoir, Californiareservoir_aoi = ee.Geometry.Rectangle([-122.5, 40.6, -121.9, 41.0])
def get_water_extent_km2(start, end):    """MNDWI-based water surface area."""    ls8 = (ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')           .filterBounds(reservoir_aoi)           .filterDate(start, end)           .filter(ee.Filter.lt('CLOUD_COVER', 15))           .median())
    # MNDWI = (Green - SWIR1) / (Green + SWIR1)    # Landsat 8 L2: SR_B3=green, SR_B6=SWIR1    green = ls8.select('SR_B3').multiply(0.0000275).add(-0.2)    swir1 = ls8.select('SR_B6').multiply(0.0000275).add(-0.2)    mndwi = green.subtract(swir1).divide(green.add(swir1)).rename('mndwi')
    water_mask = mndwi.gt(0.0)    area = water_mask.multiply(ee.Image.pixelArea()).reduceRegion(        ee.Reducer.sum(), reservoir_aoi, 30, maxPixels=1e9    ).get('mndwi').getInfo()    return round((area or 0) / 1e6, 2)  # km2
# Monthly monitoringmonths = [    ('2025-08-01','2025-08-31','Aug 2025'),    ('2025-09-01','2025-09-30','Sep 2025'),    ('2025-10-01','2025-10-31','Oct 2025'),    ('2025-11-01','2025-11-30','Nov 2025'),    ('2025-12-01','2025-12-31','Dec 2025'),    ('2026-01-01','2026-01-31','Jan 2026'),    ('2026-02-01','2026-02-28','Feb 2026'),    ('2026-03-01','2026-03-31','Mar 2026'),]
# Shasta area-capacity: ~4.55 km3 at full 315 km2 surfacefull_area_km2 = 315.0full_volume_km3 = 4.55
results = []for s, e, label in months:    area = get_water_extent_km2(s, e)    vol  = area / full_area_km2 * full_volume_km3    fill = area / full_area_km2 * 100    results.append({'month': label, 'area_km2': area, 'volume_km3': round(vol,3), 'fill_pct': round(fill,1)})
df = pd.DataFrame(results)print(df.to_string(index=False))

output/reservoir_level_report.mdOutput report