The obvious decline in stream flow to the Biliu River reservoir over the period 1990-2005 has raised increasing concerns. Climate change and human activities, which mainly include land use changes, hydraulic constructions and artificial water consumption, are considered to be the most likely reasons for the decline in stream flow. This study centres on a detailed analysis of the runoff response to changes in human activities. Using a distributed hydrological model, (Soil and Water Assessment Tool), we simulated runoffs under different human activity and climate scenarios to understand how each scenario impacts stream flow. The results show that artificial water consumption correlates with the precipitation (wet, normal and dry) of the year in question and is responsible for most of the decrease in runoff during each period and for each different wetness year. A Fuzzy Inference Model is also used to find the relationship between the precipitation and artificial water consumption for different years, as well as to make inferences regarding the future average impact on runoff. Land use changes in the past have increased the runoff by only a small amount, while another middle reservoir (Yunshi) has been responsible for a decrease in runoff since operation began in 2001. We generalized the characteristics of the human activities to predict future runoff using climate change scenarios. The future annual flow will increase by approximately 10% from 2011 to 2030 under normal human activities and future climate change scenarios, as indicated by climate scenarios with a particularly wet year in the next 20 years. This study could serve as a framework to analyse and predict the potential impacts of changes both in the climate and human activities on runoff, which can be used to inform the decision making on the river basin planning and management.

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Abstract. Near-surface air temperature (Ta) is an important physical parameter that reflects climate change. Many methods are used to obtain the daily maximum (Tmax⁡), minimum (Tmin⁡), and average (Tavg) temperature, but are affected by multiple factors. To obtain daily Ta data (Tmax⁡, Tmin⁡, and Tavg) with high spatio-temporal resolution in China, we fully analyzed the advantages and disadvantages of various existing data. Different Ta reconstruction models were constructed for different weather conditions, and the data accuracy was improved by building correction equations for different regions. Finally, a dataset of daily temperature (Tmax⁡, Tmin⁡, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1∘. For Tmax⁡, validation using in situ data shows that the root mean square error (RMSE) ranges from 0.86 to 1.78∘, the mean absolute error (MAE) varies from 0.63 to 1.40∘, and the Pearson coefficient (R2) ranges from 0.96 to 0.99. For Tmin⁡, the RMSE ranges from 0.78 to 2.09∘, the MAE varies from 0.58 to 1.61∘, and the R2 ranges from 0.95 to 0.99. For Tavg, the RMSE ranges from 0.35 to 1.00∘, the MAE varies from 0.27 to 0.68 ∘, and the R2 ranges from 0.99 to 1.00. Furthermore, various evaluation indicators were used to analyze the temporal and spatial variation trends of Ta, and the Tavg increase was more than 0.03 ∘C yr−1, which is consistent with the general global warming trend. In summary, this dataset has high spatial resolution and high accuracy, which compensates for the temperature values (Tmax⁡, Tmin⁡, and Tavg) previously missing at high spatial resolution and provides key parameters for the study of climate change, especially high-temperature drought and low-temperature chilling damage. The dataset is publicly available at https://doi.org/10.5281/zenodo.5502275 (Fang et al., 2021a).