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This paper aims to investigate the spatial and temporal variation characteristics of the potential evapotranspiration (ET0) and its response to climate change using the collected data from the daily weather observation of 23 meteorological stations in the Beijing-Tianjin-Hebei region for 57 years from 1960-2016. The specific parameters of dataset included the daily average air temperature, the maximum and minimum air temperature, relative humidity, wind speed and sunshine hours. We chose the Penman-Monteith formula to calculate the daily ET0 at each site, and then analyzed the spatial and temporal variation characteristics of the calculated ET0. A sensitivity coefficient was used to quantitatively study the sensitivity of ET0 to various meteorological elements and its spatial and temporal distribution. The contribution rate was set to evaluate the influence of meteorological elements changes on the variation of ET0 during the calculation. The results show that: 1) The spatial distribution of ET0 shows a decreasing trend from south to north across the Beijing-Tianjin-Hebei region, except a high ET0 in the central region, such as Tanggu, Huangye and Baoding stations. There was a linear decline rate of -0.92mm /a. e in the whole distribution of ET0. The spatial distribution of ET0 MK (Penman-Monteith) decreased from the northwest to southeast. In temporal characteristics, spring has the most obvious decrease, followed by autumn, summer, and winter. 2) The sensitivity coefficient of ET0 to the average air temperature, sunshine hours, relative humidity and wind speed are 0.26, 0.28, 0.31 and -044, respectively, indicating the most sensitive to the changes of relative humidity in the Beijing-Tianjin-Hebei region, followed by the wind speed and sunshine hours. The sensitivity coefficient of ET0 to meteorological elements presents in a fluctuating trend from1960-2016, the sensitivity coefficient of ET0 to the wind speed and relative humidity in a downward trend, while the sensitivity coefficient of ET0 to average temperature and sunshine hours in an upward trend. The spatial distribution of the sensitivity coefficient from northwest to southeast: the wind speed and average temperature gradually increased, while the sunshine hours and relative humidity gradually decreased. The spatial distribution characteristics of the sensitivity coefficient of ET0 to relative humidity and average temperature were the opposite situation, where the high sensitivity zone of average temperature belongs to the low sensitive zone of relative humidity. The opposite phenomenon also occurred in the spatial distribution characteristics of the sensitivity coefficient of ET0 to sunshine hours and wind speed sensitivity, i.e., the high sensitivity zone of sunshine hours belongs to the low sensitive zone of wind speed. 3) The contribution rate of meteorological elements to ET0 depends on the sensitivity coefficient and the relative change of meteorological elements. ET0 has the most sensitivity to the changes of relative humidity, but the change rate of the relative humidity was small. ET0 has the lower sensitive to the changes of wind speed, but the change rate of the wind speed was large, resulting the most contribution to ET0 in the whole region was the wind speed. However, the stations number of the wind speed as the dominant factor decreased with the measure time, whereas the stations number of the average temperature as the dominant factor increased with the measure time, especially in the period of 2010—2014 to 2010s, 12 stations with the average temperature as the dominant factor. The stations number of the sunshine hours as the dominant factor also increased with the measured time. It infers that the average temperature and sunshine hours have much significant influences on ET0 in recent years. From 2013 to 2016, the correlation coefficients of the winter average air temperature, sunshine hours, relative humidity, wind speed and PM2.5 concentration were -0.44, -0.53, 0.42 and -0.35, respectively, while the correlation coefficients of ET0 and PM2.5 concentration were -0.37. Therefore, it is possible that the haze, especially winter haze, has a certain impact on meteorological elements, and thereby on ET0 in the Beijing-Tianjin-Hebei region. [ABSTRACT FROM AUTHOR]

Drought has become a problem that is universally faced by global terrestrial ecosystems. Northeast China is dominated by a temperate monsoon climate and located in an area sensitive to global climate changes, and one of the main impacts of climate changes in Northeast China is manifested as drought in growing seasons. The drought area has a gradual increase trend, and drought has become the main agro-meteorological disaster in this region which is also the main maize planting area in China. Drought normalization seriously restricts the stable development of maize production in Northeast China. So in this paper, we took spring maize in Liaoning Province as an example and made clear the drought variation characteristics in different growth stages by using the data of MODIS, FY-3A, relative soil moisture and growth stages and the methods of RS, GIS and statistical analysis. Then we analyzed the correlation between multi-time scale remote sensing indexes and relative soil moisture in different soil depth, and established the remote sensing monitoring index set and models of relative soil moisture for each growth stage and soil depth. The accuracy of the monitoring models was verified and the application research of monitoring the drought of spring maize in Liaoning Province was studied. The results showed that: From 1993 to 2012, drought occurred in each growth stage of spring maize in Liaoning Province and the highly frequent drought occurred in the period from 1999 to 2002. The drought grade in each year was mainly light drought, and the drought was the most serious at milk stage, followed by heading stage. There was no significant correlation between the monitoring indexes and relative soil moisture at sowing and emergence stage, but there was a significant or extremely significant correlation between them at three-leaf and maturity stage. The monitoring index LSWI (land surface water index) had a strong ability to monitor relative soil moisture, followed by WI (water index). Multi-index coordination could improve the monitoring ability of relative soil moisture by remote sensing means, and the monitoring ability at 10-day scale was generally higher than that at 5-day scale and 20-day scale. Based on the monitoring models of soil relative humidity, the drought condition of partial growth stages and soil depth from 2001 to 2004 was monitored. The overall monitoring accuracy was 73.32%, in which the monitoring accuracy of jointing and three-leaf stage was more than 85%. The dynamic monitoring of drought grades in different growth stages in 2000 was realized. The monitoring results were consistent with the agro-meteorological observation records in terms of the growth stages and space. This study would be very significant to improve the capacity of disaster prevention and mitigation by monitoring spring maize accurately and synchronously on a large range. [ABSTRACT FROM AUTHOR]