29APPLIED A high-quality dataset for monthly pan evaporation in Mexico: 1961-2017

Evaporation, a crucial process in the Earth's water cycle, transforms water from liquid to vapor, transferring it from the Earth's surface and water masses into the atmosphere. The physical process of water evaporation is complex and is influenced by meteorological factors such as solar radiation, temperature, humidity, and wind speed. For this reason, there are enormous differences in the different regions of the planet, depending on their geographic location, latitude, and topography. Mexico's climate varies significantly in the different regions and climatic zones, including deserts, tropical rainforests, and other distinct areas. The original Köppen climate classification system has been adapted in Mexico to effectively characterize regional climate diversity, considering the impact of temperature and precipitation from multiple climates. This adaptation also considers factors such as altitude, relief, terrain exposure, distance to the sea, and vegetation. In Mexico, the climate classification includes seven regions: arid, semi-arid, temperate with dry winters, humid subtropical, humid tropical, humid and dry tropical, and Mediterranean. In this work, a dataset of 280 high-quality monthly pan-evaporation stations has been developed to monitor long-term evaporation trends in Mexico, a significant step in understanding the water cycle in the country. This study focuses on the period 1961-2017, when 5472 climatological stations started operation at different times since 1961. In 2017, only 2927 stations had reported continuously, but significant gaps were detected. As a first step, we removed stations with five consecutive missing years from the available stations; accordingly, 1950 were selected for evaporation. In addition, we applied robust quality control to pick out the missing tolerance in approximately 15% of the data. In addition, we performed relative homogenization on the National Weather Service evaporation data using two modern algorithms to detect inhomogeneities: HOMER and CLIMATOL. Using these two methods, we compared the resulting monthly homogenized series by climatological regions and decadal periods. In addition, to measure the performance of the algorithms as well as the impact on the estimated evaporation tendencies, we compare the differences in the distribution number of inhomogeneities (denoted here as breakpoints or change points in the time series) between the HOMER and CLIMATOL algorithms. A Mann-Kendall test analysis was used at the p-value < 0.05 significance level over each seasonal evaporation time series. To better understand how inhomogeneities affect the spatial patterns of climate change, we analyzed the spatial distribution of annual trends in our six climatic regions, including the separated seasonal dry and wet trends.