In order to investigate the spatial-temporal evolution pattern and potential driving mechanism of lakes on a long time-scale, based on the K?ppen climate classification, we classify Chinese climate as 4 climate zones, 6 climatic types and select 34 lakes which have reliable dating, and its lake records have certain continuity since the Last Glacial Maximum. At the same time, NCEP/NCAR 0.5°×0.5° 1900-2015 grid data are used to verify our traditional monsoon region which is defined based on water vapor transportation field. Meanwhile, this study uses a series of models, i.e., the NCAR CCSM 3, a lake energy-balance and a lake water-balance model, to examine the lake-level evolution process and potential driving mechanism in monsoonal Asia and arid central Asia since the Last Glacial Maximum. Our results indicate that the evolution of lakes in China is mainly affected by millennial-scale atmospheric circulation, and lake-level changes in all climate zones have no obvious regularity. In the monsoon region, there are two kinds of evolvement rules, a relatively high lake-level in the early and mid-Holocene and a relatively high lake-level in the Last Glacial Maximum and early Holocene. Meanwhile, in the arid region of East Asia controlled by westerlies, there are also two kinds of evolvement rules. One is that the lake-level in mid- and late Holocene is relatively high, and the other is that the lake-level is relatively high in mid-Holocene and the Last Glacial Maximum. This study provides a large amount of new evidence, which reflects the past climate change and mechanism of lake evolvement, as well as a new perspective to comprehensively understand lake-level changes since the Last Glacial Maximum.

Evapotranspiration (ET) is one of the core processes of water cycle in ecosystem and ET modeling is a hotspot and frontier in the field of the global climate changes. It is therefore important to provide spatiotemporal information of ET across diverse ecosystems in order to predict the response of ecosystem carbon and water cycles to changes in global climate and land use. The SWH model incorporates the Ball-Berry stomatal conductance model and a light use efficiency-based gross primary productivity (GPP) model into the Shuttleworth-Wallace model, which can simulate both ET and GPP. The newly developed SWH model presents a satisfactory prediction ability of simulating ET in a forest and a grassland ecosystem, respectively. However, the SWH model still lacks comprehensive evaluation and uncertainty analysis at regional scale. In this study, we (1) tested the model's performances on estimating ET and GPP at seasonal and annual time scales; (2) quantified the uncertainties of the model parameters and driving variables, including Normalized Difference Vegetation Index, NDVI and meteorological data; (3) quantified the sensitivity of model outputs to the parameters and driving variables; (4) quantified and separated the uncertainties of ET simulation from the parameters and driving variables. Results showed that the SWH model performed well for ET simulation at regional scale as indicated by high coefficient of determination (R² = 0.75) of linear regression of modeled against measured ET. Among the key parameters in the SWH model, two parameters related to estimating canopy stomatal conductance (g₀ and a₁) make great contribution to the model uncertainty. Among the forcing variables, NDVI is most critical in estimating GPP, which contributes much to uncertainty in ET simulation. In comparison, the climatic forcing variables contributes less to uncertainty in ET simulation owing to the high accuracy of the climate data (such as radiation and air temperature) or model’s low sensitivities to some variables (such as precipitation).

Based on monthly streamflow data from 372 stations covering the period 1960-2000 and the monthly streamflow data from 41 stations covering the period 2001-2014 across China, human- and climate-induced impacts on hydrological processes were quantified for 10 river basins in China based on development of Budyko-based coupled water-energy balance model. Penman-Monteith potential evapotranspiration model was used to analyze evapotranspiration processes. Besides, elasticity coefficient was also quantified for the impacts of meteorological variables on streamflow changes. The results indicated that: (1) Compared to southern China, streamflow changes are more sensitive to climate changes and human activities in northern China. Generally, relative humidity changes have positive impacts on streamflow changes. However, the maximum temperature, minimum temperature, solar radiation, wind speed and LUCC changes tend to go against streamflow changes. The elasticity coefficients of streamflow changes for meteorological variables are: precipitation > LUCC > relative humidity > solar radiation > maximum temperature > wind speed > minimum temperature; (2) Climate changes during 1980-2000 generally help to increase annual streamflow, and the increase of streamflow by precipitation changes is most evident, and the increase of streamflow depth reaches 12.1 mm. However, impacts of meteorological variables on streamflow changes are shifting from one river basin to another, e.g. the maximum temperature and relative humidity help to increase streamflow in northern China but decrease streamflow magnitude in southern China; (3) In general, human activities tend to decrease streamflow. Changes of streamflow in the Yangtze, Songhua, Northwest, and Southeast river basins are 78.7%, 76.9%, 65.7%, and 84.2%, respectively, which can be attributed to climate changes. However, human activities play a dominant role in modifications of streamflow changes, such as Pearl River basin, Huaihe River basin, Haihe River basin, Yellow River basin, Liaohe River basin and southwest river basins, with fractional contribution being 59.4%, 77.3%, 66.2%, 69.7%, 75.3%, and 70.4%, respectively. Generally, the fraction of human activities and climate changes to streamflow changes in the river basins across China can reach 71.0% and 29.0% respectively in river basins, where climate changes play a dominant role in streamflow changes. The results of this study can be helpful to human mitigation to climate changes in terms of water resources management.