In this study, the performance of the modified soil moisture analytical relationship (MSMAR) in concurrent estimation of deep soil moisture (DSM), evapotranspiration (ET), and deep percolation (DP) derived from surface soil moisture (SSM) variations on a sprinkler-irrigated Triticale farm in northeast Iran was investigated. Soil moisture content across the growing season was monitored using time-domain reflectometry (TDR) sensors at seven depths down to 3 m, deployed at five locations on the farm. For model evaluation, HYDRUS-1D served as the benchmark, utilizing initial soil hydraulic parameters derived from RETC and ROSETTA software based on soil texture measurements. As a resource-efficient model, MSMAR underwent two calibration schemes employing MATLAB’s genetic algorithm. The first scheme aimed to minimize the MSMAR’s DSM errors with the TDR measurements, resulting in MSMAR’s consistent DP and ET estimates comparable to those of HYDRUS-1D. Notably, the performance of the MSMAR’s DSM estimates is equal or superior than those of HYDRUS-1D depending on the soil simulation depth. The second calibration scheme aimed to minimize the errors between the MSMAR’s outputs with those of HYDRUS-1D (i.e., SM, ET and DP) demonstrating MSMAR adaptability relying on minimal information about soil texture, climate, and surface soil moisture variations. Detailed analysis via percentage root mean square error and ????2 values across depths highlighted MSMAR’s superior performance within the 50–100 cm soil depth. HYDRUS-1D’s consideration of root water uptake led to sharp declines in DSM and DP at the Triticale root depth (100 cm), contrasting MSMAR’s gradual decline continuing to 200 cm. As a promising tool, MSMAR can be implemented in diverse environmental applications, notably in resource-scarce regions.

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