Quantifying the combined effects of climatic, crop and soil factors on surface resistance in a maize field

• The response of surface resistance to climatic, crop and soil factors was investigated.
• A surface resistance model was construed based on the response pattern.
• The surface resistance model was compared to the widely used Jarvis and KP models.
• The surface resistance model combined with PM estimated sparse vegetation ET accurately.

SummaryLand surface evapotranspiration (ET) is the central process in hydrological cycle. The accuracy in simulating ET is affected by the calculation of underlying surface resistance. However, the surface resistance is difficult to be measured and greatly affected by climatic, crop and soil factors. How to quantify the combined effects of these factors on surface resistance is still a challenge for hydrologists. Our study attempted to construct and validate a semi-empirical surface resistance model based on the analysis of the response pattern of surface resistance to climatic resistance, leaf area index (LAI) and soil moisture. The surface resistance was derived by the re-arranged Penman–Monteith (PM) equation and the measured maize ET using eddy covariance in 2007. Results indicate that the ratio of surface resistance to climatic resistance showed a logarithmic relationship with LAI, and an exponential function as soil moisture when LAI was below 2. But the ratio was nearly constant and not sensitive to variation in LAI and soil moisture when LAI exceeded 2. Based on the relationships, a surface resistance model was further constructed and compared to the widely used Katerji–Perrier and Jarvis models over the sparse maize and grape canopy. Our resistance model combined with the PM equation improved the accuracy in estimating daily maize ET by 11% in 2007 and 4% in 2008, and vineyard ET by 7% against the Katerji–Perrier model combined with PM method, while by 32% in 2007 and 104% in 2008, and vineyard ET by 5% against the Jarvis model combined with PM method. Thus our model significantly improved the performance in simulating sparse vegetation ET and can be used to estimate daily surface resistance under the partial canopy condition.