Evapotranspiration simulation from a sparsely vegetated agricultural field in a semi-arid agro-ecosystem using Penman-Monteith models

Increased competition for water resources for agriculture calls for improved water productivities. Improved water productivities must start with accurate estimation of crop evapotranspiration (ET). Accurate estimation of ET has been a challenge for sparsely vegetated crops especially under varying soil water conditions. In this paper, we aim to improve estimation of ET using the Penman-Monteith (PM) method by (1) improved canopy resistance estimation by better representing soil water (2) improved surface resistance estimation by implementing a segmented approach of surface resistance calibration, and (3) investigating the performance of PM models over sparse vegetation by comparing four different solutions; the standard-PM model (PM-STD), the PM-Coupled (PMCO) single-layer interactive model, the PM Shuttleworth-Wallace (PM-SW) dual-source interactive model and the PM Two-source Patch (PM-TSP) non-interactive model. The model results are evaluated against ET measurements from in-field Bowen ratio-energy balance observations. The results show that, under water-limiting conditions, correctly representing the soil moisture during the different crop development stages enhanced a PM model's ability to accurately estimate ET. The approach raised the Nash-Sutcliffe Efficiency (NSE) of a PM-STD from 0.72 to 0.75. Applying a segmented surface resistance further improved the PM-STD to an NSE of 0.77. When these improvements were implemented on the other PM models, the PM-SW and PM-CO performed superiorly with NSE of 0.83 and RMSE of 0.07 mm hr(-1). The PM-TSP followed with an NSE of 0.79 and RMSE of 0.09 mm hr(-1) while the PM-STD model trailed with an NSE of 0.77 and RMSE of 0.08 mm hr(-1). The performance of all models highlighted the need to separate canopy and soil resistance terms during surface resistance calibration and the value of implementing a segmented approach for resistance calibration. High performance of PM-SW especially demonstrated the need to implement surface energy partitioning and surface aerodynamics interaction for canopy and soil surfaces in PM models

Automated summary

This study aimed to improve ET estimation by enhancing canopy and surface resistance estimation in the Penman-Monteith (PM) method. Results showed that accurately representing soil moisture at different crop development stages enhanced the ability of PM models to estimate ET correctly, especially under water-limiting conditions. The implementation of segmented surface resistance calibration further improved the models' performance, with PM-SW and PM-CO models performing superiorly, highlighting the importance of separating canopy and soil resistance terms during surface resistance calibration.