The Ugii Nuur Lake is not only one of the small hydrologically closed lakes located in the Orkhon River Basin in Central Mongolia but also the most vulnerable area for global climate change. Therefore, this study aims to investigate the impacts of recent global climate change on the water surface area. The data we analyzed were various measured hydro-meteorological variables of the lake basin and the lake surface area, which was estimated from Landsat series satellite data from 1986 to 2018. The methods we used were Mann-Kendall (MK), Innovative trend analysis method (ITAM), Sen’s slope estimator test, correlation, and regression analysis. The variation of lake water surface area has a strong positive correlation with the change of the lake water level (r = 0.95). The Mann-Kendall trend analysis has indicated that under a significant decrease in total annual precipitation ( $\mathrm{Z}$ = −0.902) and inflow river discharge ( $\mathrm{Z}$ = −5.392) and a considerable increase in total annual evaporation ( $\mathrm{Z}$ = 4.385) and annual average air temperature ( $\mathrm{Z}$ = 4.595), the surface area of the Ugii Nuur Lake has decreased sharply ( $\mathrm{Z}$ = −6.021). The total annual evaporation (r = −0.64) and inflow river discharge (r = 0.67) were the essential hydro-meteorological factors affecting the surface area of the Ugii Nuur Lake. The lake surface area decreased by 13.5% in 2018 compared with 1986. In the near future, it is vital to conduct scientific studies considering the volume of lake water, groundwater, and the anthropogenic impact.

This study evaluated the Advanced Microwave Scanning Radiometer 2 (AMSR2) L2 soil moisture product (ver. 3) using in situ hydrological observational data, acquired over 7 years (2012–2018), from a 50 × 50 km flat area of the Mongolian Plateau covered with bare soil, pasture and shrubs. Although AMSR2 slightly underestimated soil moisture content at 3-cm depth, satisfactory timing was observed in both the response patterns and the in situ soil moisture data, and the differences between these factors were not large. In terms of the relationship between AMSR2 soil moisture from descending orbits and in situ measured soil moisture at 3-cm depth, the values of the RMSE (m³/m³) and the bias (m³/m³) varied from 0.028 to 0.063 and from 0.011 to − 0.001 m³/m³, respectively. The values of the RMSE and bias depended on rainfall condition. The mean value of the RMSE for the 7-year period was 0.042 m³/m³, i.e., lower than the target accuracy 0.050 m³/m³. The validation results for descending orbits were found slightly better than for ascending orbits. Comparison of the Soil Moisture and Ocean Salinity (SMOS) soil moisture product with the AMSR2 L2 soil moisture product showed that AMSR2 could observe surface soil moisture with nearly same accuracy and stability. However, the bias of the AMSR2 soil moisture measurement was slightly negative and poorer than that of SMOS with deeper soil moisture measurement. It means that AMSR2 cannot effectively measure soil moisture at 3-cm depth. In situ soil temperature at 3-cm depth and surface vegetation (normalized difference vegetation index) did not influence the underestimation of AMSR2 soil moisture measurements. These results suggest that a possible cause of the underestimation of AMSR2 soil moisture measurements is the difference between the depth of the AMSR2 observations and in situ soil moisture measurements. Overall, this study proved the AMSR2 L2 soil moisture product has been useful for monitoring daily surface soil moisture over large grassland areas and it clearly demonstrated the high-performance capability of AMSR2 since 2012.

Extreme high flows can have negative economic, social, and ecological effects and are expected to become more severe in many regions due to climate change. Knowledge of maximum flow regimes and estimation of extreme rainfall is important, especially in ungauged dry regions, for planning and infrastructure development. In this study, we propose a regional method for estimating extreme flow regimes and modeled extreme rainfall using the extreme value theory, with examples from the Gobi region of Mongolia. The first step is to apply the Generalized Extreme Value (GEV) theory for the maximum rainfall data using 44-year observational data covering the period 1978–2022. Then, estimated rainfall with a 100-year return period is used for the empirical equation of the maximum flood calculation. As a result, most stations’ maximum rainfall follows a Fréchet distribution and 100-year return period rainfall values that range between 27.8–130.6 mm. The local reference value in the 100-year return period rainfall is defined as 90 mm for the whole Gobi region. Our results show that extremely high rainfall in the Gobi region has changed from −7% to 16%, leading to higher flood events. These findings further provide evidence for the maximum rainfall for flood calculation, climate change impact assessment, water resource planning, and management studies.