Comparison of Crop Irrigation Water Requirement Estimation Methods under Drought Conditions in a Semi-Arid Region of Türkiye

Accurate estimation of seasonal crop water use is critical for irrigation planning in semi-arid regions. This study benchmarks net irrigation water requirement (NIWR) and crop evapotranspiration (ETc) for a Central Anatolian scheme (Eskişehir, Türkiye) over 2016–2022 using three approaches: the institutional Blaney–Criddle (General Directorate of State Hydraulic Works-DSI) baseline, Food and Agriculture Organization (FAO) CROPWAT 8.0 (Penman–Monteith, monthly climate), and SuET (Penman–Monteith, daily climate with locally tuned parameters). A drought year was additionally constructed from historically dry seasons to assess stress conditions. Across irrigated crops, DSI systematically calculates less NIWR relative to the PM-based tools: mean biases are ~130–170 mm for wheat, ~190–240 mm for potato/onion/sugar beet, ~230–250 mm for maize, ~300 mm for sunflower/melon, and ~550 mm for alfalfa, with RMSE (Root mean square error) values closely tracking these biases. CROPWAT and SuET display strong cross-method coherence—very high year-wise correlations (r ≈ 0.90–0.99) and crop wise agreement that is strong to moderate for most crops—indicating that both capture interannual climate variability consistently when parameterized with harmonized crop calendars and effective rainfall procedures. Under the drought scenario, both ETc and NIWR increase markedly for high-demand crops, underscoring elevated deficit risk if allocations rely on temperature-only norms. Overall, the findings indicate that the traditional DSI Blaney–Criddle values substantially less calculation actual crop water requirements—particularly in dry years— and that Penman–Monteith–based tools such as CROPWAT and SuET should be systematically integrated into regional irrigation planning to improve water-use efficiency and drought resilience. Where legacy DSI tables must be retained, applying crop-specific correction factors derived here can reduce allocation shortfalls and better align deliveries with climatic demand.