For the day-by-day evaporation observation data in the Yangtze River Basin from 1951 to 2019, the effects of the gradual shift of observation instruments from 20 cm diameter evaporation pan (D20) to E601 evaporation pan after 1980 are discussed, including inconsistent data series, and missing and anomalous data. This study proposes a governance and improvement method for dual-source evaporation data (GIME). The method can accomplish the homogenization of data from different observation series and solve the problem of inconsistent and missing data, and we applied it in practice on data of the Yangtze River Basin. Firstly, the primary and secondary periods of the data were obtained by wavelet periodicity analysis; secondly, we considered the first cycle of observations to be representative of the sample and calculated the conversion relationship between the primary and secondary periods; thirdly, the conversion coefficient between the dual-source observations was calculated, and the results were corrected for stations outside the main cycle; finally, the daily evaporation dataset of E601 pan was established through data fusion and interpolation technology. The study found that the annual average conversion coefficients of the D20 and E601 pans in the Yangtze River Basin are basically between 0.55 and 0.80, and there are obvious differences in different regions. The conversion coefficient is positively correlated with relative humidity, wind speed, minimum temperature and altitude; and negatively correlated with sunshine duration, average temperature and maximum temperature. Evaporation is high in the upper reaches of the basin and low in the middle and lower reaches; in particular, evaporation is highest in the southwest, which is associated with the drought hazards. In addition, the article presents the spatial distribution of the conversion coefficients of D20 and E601 pans in the Yangtze River Basin. The results can realize the rapid correction of the evaporation data of the local meteorological department, and can be extended to the processing of other types of data in similar areas.

Monitoring and understanding the development of agricultural management requires fine information on multiple agricultural land use classes. According to the main data bulletin of China’s third national land survey released in 2021, the proportion of arable land in China accounts for 16% of the total land area. After excluding areas that are not suitable for development (slope greater than 25 degrees), the remaining area represents arable land affected by human activities, constituting 96.69% of the total arable land area in the country. Artificially irrigated areas are products of the intertwined interactions between the natural environment and human society. However, the original remote sensing image (China’s land use/cover datasets, CLUDs), which is designed to depict the land use and cover patterns in mainland China, categorizes arable land into two main types: paddy fields and dryland. It lacks a subdivision of artificially irrigated areas. We also found a data discrepancy of more than 10% between the statistical data of rice and the paddy field data in CLUDs. To refine land use data, and then improve the simulation accuracy of the hydrological model, this article proposes a dual-source datasets fusion algorithm, learning based on big data, namely the LUCC statistical data fusion (LUSF) algorithm for integrating the remote sensing-based cropland area dataset and statistics dataset. The runoff simulation results show that, in the Yangtze River Basin, using LUSF datasets, the mean absolute percentage error value of monthly simulated runoff decreased by 0.74%, and the root mean square error value decreased by 0.22 million m${}^3$. At the basin scale, the absolute error of the simulated runoff is reduced by an average of 433 million m${}^3$ per year, and 36 million m${}^3$ every month. The LUSF datasets corrected the canopy interception coefficient effectively; the runoff simulation error was reduced by 2.96 billion m${}^3$/a. In the past 40 years, the runoff variation in Dongting Lake and Hanjiang River has been most strongly impacted by changes in the underlying surface. These results reveal that the new data fusion method has some significant improvement over the original method, applicable to the runoff simulation disturbed by strong human activities.