In the context of climate change, the problem of water scarcity is becoming increasingly serious, and improving the efficiency of water resources use is an important measure to alleviate this problem. The evaluation of water resources utilization efficiency has become the basis of water resource management. Data envelopment analysis (DEA) and analytic hierarchy process (AHP) are widely used in the evaluation of water resources utilization efficiency. However, one of these methods is mostly used for evaluation, which cannot reflect the influence of both objective and subjective factors. Therefore, in this study, we propose a water resources utilization efficiency index (WEI) to evaluate the water resources utilization efficiency of each region in the Tumen River Basin (TRB), combining both DEA and AHP methods. Firstly, the DEA-CCR model was used to quantify domestic, agricultural and industrial water use efficiency in the TRB. The DEA-BCC model was used to analyze the main influences on water use efficiency in each sector. Secondly, the WEI was constructed by assigning weights using the AHP model based on the importance of each water use sector. The results show that the WEI values for most areas within the TRB trended upwards between 2014 and 2019. In particular, domestic water use efficiency ranged from 0.294 to 0.775, while agricultural and industrial water use efficiency ranged from 0.039 to 0.054 and 0.031 to 0.375, respectively. Technical efficiency is the main factor influencing water use efficiency in TRB. This study could provide a basis for water resource management and mitigation of water scarcity in the context of climate change.

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.

Climate change and human activities are two important factors affecting surface runoff. In water resource management and planning, it is generally important to separate the contribution of these factors when assessing runoff changes. The Changbai Mountain area is rich in water resources and is an important hydropower energy base for Northeast China. This study used Sen’s slope estimator to explore trends in runoff precipitation and evapotranspiration from 1960 to 2016, and the results showed a downward trend in runoff and an upward trend in precipitation and evaporation in most areas. The mutation point of the annual time series for the observed runoff was estimated, and the time series was divided into the base period (1960–1975) and impact period (1976–2016). Based on the Budyko framework, we performed attribution analysis of the runoff changes, and analyzed the difference between the mountainous region and the whole basin. We determined that the impacts of climate change and human activities, on average, accounted for decreases in the runoff by 60.15% and 39.85%, respectively, for the Second Songhua River Basin; 73.74% and 26.26%, respectively, for the Tumen River Basin; 84.76% and 15.24%, respectively, for the Yalu River Basin; human activities were the main causes of runoff changes in the Changbai Mountain area; climate change was the main cause of runoff changes in mountainous regions. The results of this study show that the reasons for the change in runoff in mountainous regions and the whole basin in the same area are different, which has some illuminating significance for water resources management of different elevation areas.