Using monthly streamflow data from the 1960-2000 period and annual 16 streamflow data from the 2001-2014 period, and also meteorological data from the 17 1960-2014 period from 815 meteorological stations across China, the Budyko-based 18 hydrothermal balance model was used to quantitatively evaluate the fractional 19 contributions of climate change and human activities to streamflow changes in ten 20 river basins across China. Particular importance was attached to human activities, 21 such as population density and Gross Domestic Product (GDP), and also water 22 reservoirs in terms of their relationship with streamflow changes. Results indicated 23 that: (1) streamflow changes of river basins in northern China were more sensitive to 24 3 climate change than those of river basins in southern China. Based on the degree of 25 sensitivity, the influencing factors to which streamflow changes are sensitive included: 26 precipitation > human activities > relative humidity > solar radiation > maximum 27 temperature > wind speed > minimum temperature. Hence, it can be argued that 28 hydrological systems in northern China are more fragile and more sensitive to 29 changing environment than those in southern China and hence water resources 30 management in northern China is more challenging; (2) during 1980-2000, climate 31 change tended to increase streamflow changes across China and have a dominant role 32 in streamflow variation. However, climate change tends to decrease streamflow in 33 river basins of northern China. Generally, human activities cause a decrease of 34 streamflow across China; (3) In recent years such as a period of 2001-2014, human 35 activities tend to have increasing or enhancing impacts on instream flow changes, and 36 fractional contributions of climate change and human activities to streamflow changes 37 are, respectively, 53.5% and 46.5%. Increasing human-induced impacts on 38 streamflow changes have the potential to add more uncertainty in the management of 39 water resources at different spatial and temporal scales.40 41

Abstract:Variations in streamflows of five tributaries of the Poyang Lake basin, China, because of the influence of human activities and climate change were evaluated using the Australia Water Balance Model and multivariate regression. Results indicated that multiple regression models were appropriate with precipitation, potential evapotranspiration of the current month, and precipitation of the last month as explanatory variables. The NASH coefficient for the Australia Water Balance Model was larger than 0.842, indicating satisfactory simulation of streamflow of the Poyang Lake basin. Comparison indicated that the sensitivity method could not exclude the benchmark-period human influence, and the human influence on streamflow changes was overestimated. Generally, contributions of human activities and climate change to streamflow changes were 73.2% and 26.8% respectively. However, human-induced and climate-induced influences on streamflow were different in different river basins. Specifically, climate change was found to be the major driving factor for the increase of streamflow within the Rao, Xin, and Gan River basins; however, human activity was the principal driving factor for the increase of streamflow of the Xiu River basin and also for the decrease of streamflow of the Fu River basin. Meanwhile, impacts of human activities and climate change on streamflow variations were distinctly different at different temporal scales. At the annual time scale, the increase of streamflow was largely because of climate change and human activities during the 1970s-1990s and the decrease of streamflow during the 2000s. At the seasonal scale, climate change was the main factor behind the increase of streamflow in the spring and summer season. Human activities increase the streamflow in autumn and winter, but decrease the streamflow in spring. At the monthly scale, different influences of climate change and human activities were detected. Climate change was the main factor behind the decrease of streamflow during May to June and human activities behind the decrease of streamflow during February to May. Results of this study can provide a theoretical basis for basin-scale water resources management under the influence of climate change and human activities.

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Abstract. The partitioning of precipitation into runoff (R) and evapotranspiration (E), governed by the controlling parameter in the Budyko framework (i.e., n parameter in the Choudhury and Yang equation), is critical to assessing the water balance at global scale. It is widely acknowledged that the spatial variation in this controlling parameter is affected by landscape characteristics, but characterizing its temporal variation remains yet to be done. Considering effective precipitation (Pe), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration, E0), we proposed a climate seasonality and asynchrony index (SAI) in terms of both phase and amplitude mismatch between P and E0. Considering streamflow changes in 26 large river basins as a case study, SAI was found to the key factor explaining 51 % of the annual variance of parameter n. Furthermore, the vegetation dynamics (M) remarkably impacted the temporal variation in n, explaining 67 % of the variance. With SAI and M, a semi-empirical formula for parameter n was developed at the annual scale to describe annual runoff (R) and evapotranspiration (E). The impacts of climate variability (Pe, E0 and SAI) and M on R and E changes were then quantified. Results showed that R and E changes were controlled mainly by the Pe variations in most river basins over the globe, while SAI acted as the controlling factor modifying R and E changes in the East Asian subtropical monsoon zone. SAI, M and E0 have larger impacts on E than on R, whereas Pe has larger impacts on R.

Decreased streamflow of the Yellow River basin has become the subject of considerable concern in recent years due to the critical importance of the water resources of the Yellow River basin for northern China. This study investigates the changing properties and underlying causes for the decreased streamflow by applying streamflow data for the period 1960 to 2014 to both the Budyko framework and the hydrological modelling techniques. The results indicate that (1) streamflow decreased 21% during the period 1980-2000, and decreased 19% during the period 2000-2014 when compared to 1960-1979; (2) higher precipitation and relative humidity boost streamflow, while maximum/minimum air temperature, solar radiation, wind speed, and the underlying parameter, n, all have the potential to adversely affect them; (3) decreased streamflow is also linked to increased cropland, grass, reservoir, urban land, and water areas and other human activities associated with GDP and population; (4) human activity is the main reason for the decrease of streamflow in the Yellow River basin, with the mean fractional contribution of 73.4% during 1980-2000 and 82.5% during 2001-2014. It is clear that the continuing growth of human-induced impacts on streamflow likely to add considerable uncertainty to the management of increasingly scarce water resources. Overall, these results provide strong evidence to suggest that human activity is the key factor behind the decreased streamflow in the Yellow River basin.

Precipitation-temperature relations are regulated by the Clausius-Clapeyron (C-C) equation (Wang et al., 2017;Westra et al., 2014), whereby the water vapor holding capacity of the atmosphere increases by 6-7% for each degree of warming. This scaling relationship theoretically explains increasing precipitation intensity in a warming climate (

Soil moisture (SM) is a key hydrological component regulating the net ecosystem energy exchange at the land‐atmosphere boundary layer over the continents via heat fluxes and relevant feedback on precipitation. Due to its ecological and meteor‐hydrological implications, SM change is of great significance in Eurasia that has the highest population density and fragile ecological environment. Using monthly data from the Global Land Data Assimilation System, this study investigated SM changes over Eurasia during the warm season (May–September). It was found that recent 63 years witnessed widespread decreasing SM across Eurasia during the warm season. Regions with a drying SM tendency kept expanding till the 1990s. Specifically, the largest decreasing magnitude of SM with the aridity index ranging 0.5–0.6 and 1.0–1.1 was found along the semi‐arid and dry‐humid transition regions, respectively. In addition, more significant drying SM was observed in Sahel, northern Asia, northeastern Asia, and western Europe. Weakening West African monsoon (WAM)/East Asia summer monsoon did not benefit the propagation of water vapor flux to the Sahel regions/northeastern and northern Asia, and hence decreased SM in these regions. Besides, results by the maximum covariance analysis highlighted the roles of warming climate in SM variations over Eurasia during the warm season. Global climate models also indicate decreased SM due to global warming and projects continuously decreasing SM in the warm season over the 21st century under Representative Concentration Pathway (RCP)2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios. Decreasing SM across the Eurasia and related ecological and environmental implications should cause international concern.

A large population of global soil moisture data sets generated by a variety of models is compared with the latest satellite‐based Essential Climate Variable (ECV) soil moisture product in a common framework. The model‐based surface soil moisture data sets include Global Land Data Assimilation System (GLDAS), reanalysis products, Coupled Model Intercomparison Project Phase 5 Global Climate Models (GCMs), and Inter‐Sectoral Impact Model Intercomparison Project (including observation‐driven outputs ISI‐MIP_OBS and GCM‐driven outputs ISI‐MIP_GCM). We evaluate the model‐based surface soil moisture against ECV with focuses on spatial patterns, temporal correlations, long‐term trends, and relationships with precipitation and Normalized Difference Vegetation Index. The results indicate that all data sets reach a good agreement on the spatial patterns of surface soil moisture, which are also consistent with that of precipitation. However, data sets produced by different techniques have considerable discrepancies in the absolute values of surface soil moisture. Specifically, GCMs tend to underestimate the absolute values of surface soil moisture relative to ECV. In comparisons that remove the influence of absolute values (e.g., unbiased root‐mean‐square error), all model‐based data sets show comparable performances against ECV. GLDAS, reanalysis, and ISI‐MIP_OBS data sets show significant positive temporal correlations with ECV. Model‐based data sets and ECV consistently indicate widespread drying trends during 1980–2005, but the regional trends vary in different data sets. Compared to ECV, GLDAS and reanalysis data sets exhibit more intensive drying trends, while Coupled Model Intercomparison Project Phase 5 and ISI‐MIP_GCM tend to underestimate the drying. In most of the regions, the wetting/drying trends are consistent with the increases/decreases in precipitation and Normalized Difference Vegetation Index.