Trend analysis of temperature, rainfall, and reference evapotranspiration for Ludhiana district of Indian Punjab using non-parametric statistical methods.

Climate change, which is one of the main determinants of agricultural production, has started affecting the pattern of crop growth, productivity, and quality of produce from the last few couple of decades in various agro-climatic zones globally. Any change in climatic factors such as temperature, evapotranspiration (ET), and rainfall is bound to have a significant impact on agricultural production. Thus, climate monitoring, trend analysis, and model-based prediction are highly significant to mitigate the climate change impacts on crop growth patterns, production, and quality traits. A study was thus undertaken at Punjab Agricultural University, Ludhiana, India, to (1) estimate reference evapotranspiration (ET_o) using FAO-ET_o calculator; (2) study and detect trend in long-term (1970–2019) recorded temperature (T_min and T_max), rainfall and ET_o using Mann–Kendall's test, Sen's slope test, standard normal homogeneity test (SNHT), and Pettitt's test in XLSTAT software; (3) study correlation of ET_o with T_min, T_max, and rainfall; and (4) develop regression models for estimating ET_o on seasonal and annual basis. All the tests indicated a significant trend in T_min (increasing) and ET_o (decreasing) during all seasons (spring, summer, autumn, and winter), as well as on annual basis at 5% level of significance, whereas no trend was recorded in T_max and rainfall data. The SNHT and Pettitt's test confirmed the existence of a change-point in both ET_o and T_min data for all seasons as well as on annual basis. Both Mann–Kendall's and homogeneity tests indicated no trend or change point in T_max (except a change-point during spring) and rainfall data. The positive correlation of ET_o with T_max, wind speed (v_w), and sunshine hours (SSH) formed an increasing trend in ET_o with increase in these variables and vice-versa. The negative correlation of ET_o with relative humidity (RH_min and RH_max), rainfall, and T_min indicated a decreasing trend in ET_o. The study offers a basis to predict the futuristic climate scenarios in the region for planning crops and manage irrigation to mitigate the climate change impacts on agricultural production. The statistical comparison indicated that the developed models were sufficiently accurate and would be useful in simplified estimation of ET_o on seasonal and annual basis for the study region. [ABSTRACT FROM AUTHOR]