Streamflow modelling is one of the most important elements for the management of water resources and flood control in the context of future climate change. With the advancement of numerical weather prediction and modern detection technologies, more and more high-resolution hydro-meteorological data can be obtained, while traditional physical hydrological models cannot make full use of them. In this study, a hybrid deep learning approach is proposed for the simulation of daily streamflow in two mountainous catchments of the Daqing River Basin, northern China. Two-dimensional high-resolution (1 km) output data from a WRF model were used as the model input, a convolutional neural network (CNN) model was used to extract the physical and meteorological characteristics of the catchment at a certain time, and the long short-term memory (LSTM) model was applied to simulate the streamflow using the time-series data extracted by the CNN model. To reduce model input noise and avoid overfitting, the Gamma test method was adopted and the correlations between the input variables were checked to select the optimal combination of input variables. The performance of the CNN-LSTM models was acceptable without using the Gamma test (i.e., with all WRF input variables included), with NSE and RMSE values of 0.9298 and 9.0047 m³/s, respectively, in the Fuping catchment, and 0.8330 and 1.1806 m³/s, respectively, in the Zijingguan catchment. However, it was found that the performance of the model could be significantly improved by the use of the Gamma test. Using the best combination of input variables selected by the Gamma test, the NSE of the Fuping catchment increased to 0.9618 and the RMSE decreased to 6.6366 m³/s, and the NSE of the Zijingguan catchment increased to 0.9515 and the RMSE decreased to 0.6366 m³/s. These results demonstrate the feasibility of the CNN-LSTM approach for flood streamflow simulation using WRF-downscaled high-resolution data. By using this approach to assess the potential impacts of climate change on streamflow with the abundant high-resolution meteorological data generated by different climate scenarios, water managers can develop more effective strategies for managing water resources and reducing the risks associated with droughts and floods.

The influence of climate change and human activities on hydrological elements has increased along with increasing dependence on water resources. Therefore, quantitative attribution of hydrological elements has received wide attention. In this study, the double mass curve (DMC) is used to assess the abrupt change point of the hydrological data series, based on which the periods with/without large-scale human activities causing runoff attenuation are separated. The land use transition matrix is then employed to analyze the land use types at different historical stages, and the sensitivities of the runoff attenuation to different land use/cover change (LUCC) categories are quantified. A soil and water assessment tool (SWAT) model that considers the underlying surface is constructed with six designed scenarios of different climate and LUCC conditions. Taking three typical mountainous basins in North China as the study area, the quantitative contributions of climate change and human activities to the water resources are identified. The results of the study have brought enlightenment to the water resource sustainable utilization and management in North China, and the methodologies can be transferred to runoff attribution analysis in water shortage areas.