Assessment of spatiotemporal variability of reference evapotranspiration and controlling climate factors over decades in China using geospatial techniques
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© 2018 Elsevier B.V. Reference evapotranspiration (ETo) is a key component of the hydrological cycle, and it plays a vital role in agricultural, forest, and environmental management. This study assesses the capability of hot spot geospatial analysis to determine statistically significant spatial clusters of high and low ETo in China over the period of 1970–2014, based on the daily data from 598 weather stations. The global controlling factors affecting ETo across continental China are investigated using global ordinary least square regression (OLS) model. The spatial relationship between ETo and climatic variables is explored using local geographic weighted regression (GWR) model. It was found that for China as a whole, ETo decreased significantly from 1970 to 1993 at a rate of 14.91 mm decade−1, while the trend began increasing by 16.50 mm decade−1 from 1993 to 2014. The hot spot analysis showed that the regional distribution of statistically significant spatial ETo clusters remained relatively steady between years from 1970 to 2014. Hot regions were identified with high values of annual total ETo in North China (NC), South China (SC) and the Turpan Depression of Northwest China (NWC). The cold regions were highly clustered in most parts of Northeast China (NEC) and the borders between NWC, Central China (CC) and Southwest China (SWC). It was also found that statistically significant clusters of hot and cold spots exhibited a migration trend between months. The results of the OLS analysis suggested that over China, the maximum temperature, relative humidity, and wind speed were the controlling meteorological variables affecting ETo. Based on the results of GWR, maximum and minimum temperature were the most influencing climatic variables affecting ET0 over China. GWR was found to be a more powerful method than OLS for modelling ETo in China. Results of this study can be used to help end-users, planners and policy makers to anticipate their decision making, which in turn will improve regional water management in China.
author list (cited authors)
Zhang, L., Traore, S., Cui, Y., Luo, Y., Zhu, G. e., Liu, B. o., ... Singh, V.