Estimating Seasonal Water Losses from Supply-Demand Analysis Using Satellite-Derived Cropping Patterns: A Case Study
DOI:
https://doi.org/10.37680/ssa.v3i2.8700Abstract
The irrigation systems that use canal command require effective, but not expensive, strategies to quantify annual water requirements and water losses. Field-based traditional methods are all costly and cumbersome to generalize. The current research paper demonstrates how satellite imagery and a crop water need model were applied to estimate the extent of water lost or utilized during the season in the 354-acre command of watercourse number 5AR in the UC Chukhi district, Hyderabad, Pakistan. The CROPWAT model was used to estimate reference evapotranspiration (ET0) and crop evapotranspiration (ETc) using 5 years of meteorological data. Landsat eight images processed in ArcGIS 10.1 provided a classified map of major crops and their acreage. Wheat (24.5 percent of the hectares under cultivation) was the primary crop, followed by mango (17.5 percent), banana (14.0 percent), sugarcane (7.1 percent), and others. 29.6 percent of the hectares were under cultivation, while the remaining 70.4 percent were not. Berry volumetric demands were 117.7 acre-ft (wheat), 81.56 acre-ft (mango), 169.4 acre-ft (banana), 77.4 acre-ft (sugarcane), and 66.38 acre-ft (other crops). The surface water and ground water were 430.3 and 403.04 acre-ft, respectively, and the seasonal supply was 833.45 acre-ft. The seasonal losses incurred were estimated at 279.6 acre-ft, equal to 33.5 percent of the amount of water supplied, 15.0 percent of the conveyance losses, and 18.5 percent of the application losses; application losses exceeded conveyance losses by about 3.5 percentage points. The results suggest that combining CROPWAT and Landsat-based crop mapping with straightforward discharge measurements can offer a feasible, cost-effective, and transferable method for quantifying seasonal water losses and contribute to planning and policy-making for irrigation decisions in canal-command contexts of a similar nature.
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Copyright (c) 2026 Mir Moazzam Ali Talpur, Hamza Khalid, Tingting Chang, Mir Ghazzanfar Ali Talpur, Ghulam Hussain Khoso
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