REMOTE SENSING-BASED APPROACH TO EVALUATE ACTUAL SURFACE WATER USE FOR IRRIGATION IN BILATE WATERSHED, RIFT VALLEY LAKES BASIN, ETHIOPIA

Abstract

The Bilate watershed, situated within Ethiopia's Rift Valley lakes basin, confronts escalating water scarcity driven by rapid irrigation expansion aimed at bolstering food production. This expansion has significantly altered river flows and compromised ecosystem integrity, particularly during irrigation seasons. Accurate estimation of irrigation water abstraction is paramount for sustainable water resource management, enhanced agricultural productivity, environmental preservation, and conflict mitigation. This study addresses critical data gaps by leveraging high-resolution remote sensing (RS) imagery to quantify irrigation water use, while acknowledging the inherent challenges in validating RS-derived products across diverse climatic and landscape conditions. This research focused on evaluating the accuracy of land use/land cover (LULC) classification, irrigated area mapping, and actual evapotranspiration (AET) estimation, all crucial components of irrigation water use assessment. Landsat 8's blend of spectral capabilities, spatial and temporal resolution, extensive historical archive, free access, and reliable imagery make it an outstanding option for LULC classification and monitoring. Yielding nine dominant classes and demonstrating seasonal variations in accuracy, with overall accuracies ranging from 80% to 90% and Kappa coefficients from 0.8 to 0.9. Irrigated areas were mapped using Sentinel-1, uses SAR technology operate in all weather conditions. It provides high-resolution imagery, allowing detailed and frequent monitoring of irrigated areas. Its dual polarization (VV and VH) enhances the accuracy of irrigation mapping. Advanced classification algorithms, Random Forest, were applied, resulting in accuracies of 88% for the Bilate watershed and 87% for the Gumara watershed, with Kappa coefficients of 0.74 and 0.73. The two watersheds were selected due to their similar emergence of small-scale irrigation and contrasting rainfall and land cover characteristics. These maps revealed significant discrepancies with global irrigation datasets, underscoring the necessity for localized validation. In this study, a thorough evaluation of five satellite-derived AET products—including national (FAO WaPOR), continental (modisSSEBop), and global (Climate Engine, modisSSEBop, and Synthesis) datasets—was conducted. The comparison against ground-based reference evapotranspiration showed weak correlations (< 0.35) and significant differences in magnitude. Notably, FAO WaPOR displayed the most consistent agreement with the other products. A spatiotemporal analysis of irrigation water withdrawal and availability from 2017 to 2022 was conducted utilizing Google Earth Engine (GEE), HEC-HMS, and FAO-CROPWAT. The high-resolution imagery from Sentinel-2 within GEE allows for efficient monitoring of actual irrigated areas. With frequent revisits and multispectral data, along with its free availability, it is ideal for accurately mapping and tracking changes in small-scale irrigation systems. FAO-CROPWAT was used to determine net and gross irrigation water requirements (IWR), considering climatic data, crop parameters, and soil properties. The HEC-HMS model, calibrated with hydro-meteorological data from the Ethiopian Meteorology Institute and Ministry of Water and Energy, simulated streamflow with high accuracy (Nash-Sutcliffe Efficiency ranging from 0.91 to 0.96). This study provides valuable insights for optimizing irrigation water management in the Bilate watershed. The observed seasonal variability in LULC classification accuracy highlights the importance of selecting appropriate temporal data for analysis. The identified spatial patterns of IWR across sub-watersheds underscore the need for targeted water management strategies and the promotion of water conservation practices, such as rainwater harvesting. By providing an accurate assessment of irrigation water withdrawal through a robust RS-based approach, this research aims to enhance water use efficiency, minimize water losses, and support sustainable water resource management initiatives in the region.