In this study, we assessed the performance of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general climate models (GCMs) for simulating the observed temperature over the Lower Mekong Basin (LMB) in 1961–2004. An improved score-based method was used to rank the performance of the GCMs over the LMB. Two methods of multi-model ensemble (MME), sub-ensemble from the top 25% ranked GCMs and full ensemble from the entire GCMs, were calculated using arithmetic mean (AM) method and downscaled using the Delta method to project future temperature change during two future time periods, the near future (2006–2049) and the far future (2050–2093), under representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5 scenarios) over the LMB. The improved score-based method combining multiple criteria showed a robust assessment of the GCMs performance over the LMB, which can provide good information for projecting future temperature change. The results showed a significant increase in temperature over the LMB under the two ensembles. However, there were differences in the magnitudes of the future temperature increase between the two ensemble methods, with a higher mean annual temperature increase from full ensemble and sub-ensemble at 1.26 °C (1.09 °C), 1.90 °C (1.70 °C), and 2.97 °C (2.78 °C) during 2050–2093 under the RCP2.6, RCP4.5, and RCP8.5 scenarios compared to the values at 0.93 °C (0.87 °C), 0.99 °C (0.95 °C), and 1.09 °C (1.06 °C) during 2006–2049, respectively, relative to the reference time period of 1961–2004. In the future (2006–2093), the temperature is likely to increase at 0.04 °C, 0.16 °C, and 0.37 °C decade-1 under the RCP2.6, RCP4.5, and RCP8.5 scenarios by the sub-ensemble, while a higher temperature increase at 0.05 °C, 0.17 °C, and 0.39 °C was found by the full ensemble over the LMB, relative to the reference time period of 1961–2004. On the whole, the higher warming mainly occurred in the northern and central areas over the LMB, while the lower warming mainly occurred in the southeast and the southwest, especially under the RCP4.5 and RCP8.5 scenarios, with the warming increased with increasing RCP for both ensembles. Moreover, in order to reduce the uncertainty of temperature projection in further studies in the LMB, multiple methods of GCMs ensemble should be considered and compared.

This study assessed the performances of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) in reproducing observed precipitation over the Lower Mekong Basin (LMB). Observations from gauge-based data of the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) precipitation data were obtained from 1975 to 2004. An improved score-based method was used to rank the performance of the GCMs in reproducing the observed precipitation over the LMB. The results revealed that most GCMs effectively reproduced precipitation patterns for the mean annual cycle, but they generally overestimated the observed precipitation. The GCMs showed good ability in reproducing the time series characteristics of precipitation for the annual period compared to those for the wet and dry seasons. Meanwhile, the GCMs obviously reproduced the spatial characteristics of precipitation for the dry season better than those for annual time and the wet season. More than 50% of the GCMs failed to reproduce the positive trend of the observed precipitation for the wet season and the dry season (approximately 52.9% and 64.7%, respectively), and approximately 44.1% of the GCMs failed to reproduce positive trend for annual time over the LMB. Furthermore, it was also revealed that there existed different robust criteria for assessing the GCMs’ performances at a seasonal scale, and using multiple criteria was superior to a single criterion in assessing the GCMs’ performances. Overall, the better-performed GCMs were obtained, which can provide useful information for future precipitation projection and policy-making over the LMB.

The climate of the Eurasian inland basins (EIB) is characterized by limited precipitation and high potential evapotranspiration, making water storage in the EIB vulnerable to global warming and human activities. There is increasing evidence regarding varying trends in water storage across different regions; however, a consistent conclusion on the main attributes of these trends is lacking. Based on the hydrological budget in a closed inland basin, the main attributes of changes in actual evapotranspiration (AET) and terrestrial water storage (TWS) were identified for the EIBs and each closed basin. In the EIB and most of its closed basins (13/16), the TWS and AET showed significantly decreasing and non-significant increasing trends, respectively. The primary cause underlying the significantly decreasing TWS in the EIB was the increase in AET. Approximately 70% of the increase in AET was attributed to increased irrigation diversions from groundwater and glacial melt runoff. At the basin scale, similar to the EIB, changes in AET were the predominant factor driving changes in TWS in most basins, with the exception of the Balkhash Lake basin (BLB), Iran inland river basin (IIRB), Qaidam basin (QB) and Turgay River basin (TuRB). In these basins, changes in precipitation largely contributed to the TWS changes. The AET consumption of other water resources was the main factor contributing to AET changes in seven of the 16 basins, including the Aral Sea,

CryoSat-2 altimetry has become a valuable tool for monitoring the water level of lakes. In this study, a concentrated probability density function (PDF) method was proposed for preprocessing CryoSat-2 Geophysical Data Record (GDR) data. CryoSat-2 altimetry water levels were preprocessed and evaluated by in situ gauge data from 12 lakes in China. Results showed that the accuracy of the raw GDR data was limited due to outliers in most of the along-track segments. The outliers were generally significantly lower than the in situ values by several meters, and some by more than 30 m. Outlier detection, therefore, improves upon the accuracy of CryoSat-2 measurements. The concentrated PDF method was able to greatly improve the accuracy of CryoSat-2 measurements. The preprocessed CryoSat-2 measurements were able to observe lake levels with a high accuracy at nine of the twelve lakes, with an absolute mean difference of 0.09 m, an absolute standard deviation difference of 0.04 m, a mean root mean square error of 0.27 m, and a mean correlation coefficient of 0.84. Overall, the accuracy of CryoSat-2-derived lake levels was validated in China. In addition, the accuracy of Database for Hydrological Time Series of Inland Waters (DAHITI) and HYDROWEB water level products was also validated by in situ gauge data.

Extreme cold and meteorological drought in the Mongolian Plateau (MP) were investigated during 1969–2017. Several drought indices were evaluated by analyzing recorded historical drought data in the Chinese region of the MP. The evaluated drought indices were then applied to detect drought characteristics in the entire MP. The trends of extreme cold indices showed that the climate of the MP has warmed during the past 49 years; however, the frequency of cold day/night has increased in the Mongolian region. The climate of Mongolia has also become colder in the spring season. The comprehensive meteorological drought index (CMDI) and the standardized precipitation index with a six-month scale (SPI6) exhibited better performances, showing high consistency between the spatial patterns of the two indices. However, drought represented by the SPI6 was enhanced greater than that expressed by the CMDI. Drought in the MP has been enhanced during the past 49 years, particularly in the Ordos and Alashan plateaus and the Xiliao River basin in China. Moreover, drought has been enhanced from August to October, particularly in the Mongolian region. However, spring drought has shown a weakening trend, which has been beneficial for agriculture and husbandry sectors in some regions of the MP.

Abstract. Based on the assessment from 230 flux site observations, intra-day and daytime ground heat flux (G) accounted for 19.2 % and 28.8 % of the corresponding net radiation, respectively. This indicates that G plays an important role in remote-sensing (RS) energy-balance-based evapotranspiration (ET) models. The G empirical estimation methods have been evaluated at many individual sites, while there have been relatively few multi-site evaluation studies. The accuracy of the five empirical G simulation methods in the surface-energy-balance-based RS–ET models was evaluated using half-hourly observations. The linear coefficient (LC) method and the two methods embedded with the normalized difference vegetation index (NDVI) were able to accurately simulate a half-hourly G series at most sites. The mean and median Nash–Sutcliffe efficiency (NSE) values of all sites were generally higher than 0.50 in each half-hour period. The accuracy of each method varied significantly at different sites and at half-hour intervals. The highest accuracy was exhibited during 06:00–07:00 LST (all times hereafter are LST), followed by the period of 17:00–18:00. There were 92 % (211/230) sites with an NSE of the LC method greater than 0.50 at 06:30. It showed a slightly higher accuracy during nighttime periods than during daytime periods. The lowest accuracy was observed during the period of 10:00–15:30. The sites with an NSE exceeding 0.50 only accounted for 51 % (118/230) and 43 % (100/230) at 10:30 and 13:30, respectively. The accuracy of the model was generally higher in Northern Hemisphere sites than in Southern Hemisphere sites. In general, the highest and lowest accuracies were observed at the high- and low-latitude sites, respectively. The performance of the LC method and the methods embedded with NDVI were generally satisfactory at the Eurasian and North American sites, with the NSE values of most sites exceeding 0.70. Conversely, it exhibited relatively poor performance at the African, South American, and Oceanian sites, especially the African sites. Both the temporal and spatial distributions of the accuracy of the G simulation were positively correlated with the correlation between G and the net radiation. Although the G simulation methods accurately simulated the G series at most sites and time periods, their performance was poor at some sites and time periods. The application of RS ET datasets covering these sites requires caution. Further improvement of G simulations at these sites and time periods is recommended for the RS ET modelers. In addition, variable parameters are recommended in empirical methods of G simulation to improve accuracy. Instead of the  Rn, finding another variable that has a physical connection and strong correlation with G might be a more efficient solution for the improvement, since the weak correlation between G and Rn is the main reason for the poor performance at these regions.

Evapotranspiration (ET) is essential for connecting ecosystems and directly affects the water consumption of forests, grasslands, and farmlands. Eight global remote sensing-based ET (RS_ET) datasets generated using satellite imagery and ground-based observations were comprehensively assessed using monthly ET time series simulated by the water balance (WB) method at the catchment scale in the Hengduan Mountain (HDM) region, including the Nu River, Lancang River, and Jinsha River basins. The complementary relationship (CR) model, which derives ET from meteorological data, was also evaluated against WB-based ET (WB_ET). In addition, WB_ET, RS_ET, and CR-based ET (CR_ET) data were used to investigate ET spatial and temporal variations at the catchment, grid, and site scale, respectively. Most RS_ET datasets accurately simulated monthly ET with an average index of agreement ranging from 0.71–0.91. The Operational Simplified Surface Energy Balance dataset outperformed other RS_ET datasets, with Nash–Sutcliffe efficiency coefficient (NSE) and Kling–Gupta efficiency values of 0.80 and 0.90, respectively. RS_ET datasets generally performed better in northern semiarid areas than in humid southern areas. The monthly ET simulation by the CR model was consistent with that of the WB_ET in the HDM region, with mean values of correlation coefficient (cc) and NSE at each site of 0.89 and 0.68, respectively. The model showed better performance in simulating monthly ET in the Lancang River Basin than in the Nu River and Lancang River basins, with mean cc and NSE of 0.92 and 0.83, respectively. Generally, annual ET trends were consistent at the catchment, grid, and site scale, as estimated by the WB method, RS_ET datasets, and CR model. It showed a significant decreasing trend in the northern semiarid region of the HDM while exhibiting an increasing trend in the humid southern region.

Based on observed precipitation and runoff data, monthly actual evapotranspiration (ET a ) was calculated by the hydrological budget balance method in the three parallel river basins. The performance of three developed complementary relationship methods, the nonlinear advection-aridity (non-AA) method, generalized complementary relationship method (B2015), and sigmoid generalized complementary function (H2018), on simulating ET a were evaluated. The evaluation results showed that three methods were able to accurately simulate monthly ET a series. The Nash-Sutcliffe e ciency coe cient between the monthly ET a simulated by the non-AA, B2015, and H2018 methods and the water-balance-derived ET a were 0.74, 0.78, and 0.79, respectively. The correlation coe cient were 0.84, 0.89, and 0.90, respectively. And the root mean square errors were 10.76 mm mon − 1 , 10.01 mm mon − 1 , and 9.78 mm mon − 1 , respectively. The ET a increased spatially from upstream region to downstream region at catchment scale. Annual ET a simulated by the non-AA, B2015 and H2018 models showed signi cant increasing trends during 1956-2018 in the basins, with the increasing magnitudes of 1.53 mm/a, 1.66 mm/a and 1.47 mm/a, respectively. Research on the in uence between meteorological factors and ET a showed that there was a positive correlation between ET a and precipitation, temperature, wind and sunshine hours, with the average correlation coe cient of 0.40 0.64 0.63 and 0.72, respectively. The value between ET a and relative humidity was − 0.38. The ET a in the basins was highly sensitive to temperature, wind speed and sunshine hours, with the average sensitivity coe cient of 0.26, 0.21 and 0.27, respectively. And moderately sensitive to relative humidity, with a sensitivity of -0.18.