Hydrological simulation plays a very important role in understanding the hydrological processes and is of great significance to flood forecasting and optimal allocation of water resources in the watershed. The development of deep learning techniques has brought new opportunities and methods for long-term hydrological simulation research at the watershed scale. Different from traditional hydrological models, the application of deep learning techniques in the hydrological field has greatly promoted the development trend of runoff prediction and provides a new paradigm for hydrological simulation. In this study, a CNN–LSTM model based on the convolutional neural network (CNN) and long short-term memory (LSTM) network, and a CNN–GRU model based on CNN and gated recurrent unit (GRN) are constructed to study the watershed hydrological processes. To compare the performance of deep learning techniques and the hydrological model, we also constructed the distributed hydrological model: Soil and Water Assessment Tool (SWAT) model based on remote sensing data. These models were applied to the Xixian Basin, and the promising results had been achieved, which verified the rationality of the method, with the majority of percent bias error (PBE) values ranging between 3.17 and 13.48, Nash–Sutcliffe efficiency (NSE) values ranging between 0.63 and 0.91, and Kling–Gupta efficiency (KGE) values ranging between 0.70 and 0.90 on a monthly scale. The results demonstrated their strong ability to learn complex hydrological processes. The results also indicated that the proposed deep learning models could provide the certain decision support for the water environment management at the watershed scale, which was of great significance to improve the hydrological disaster prediction ability and was conducive to the sustainable development of water resources.

Inter-basin water transfer projects are widely used in water-stressed areas. North China is facing severe imbalance between water demand and water supply. The South-to-North Water Diversion (SNWD) Project was built to transfer water from the Yangtze River Basin to the Hai River Basin. The Fuyang River Basin in the southern part of the Hai River Basin, passing through the Handan city, was chosen as the study area. To identify the effects of the SNWD Project on the water-receiving area, this paper used the decision support system AQUATOOL to simulate the water-dispatching scheme while using the water from the SNWD Project for domestic need in different level years. The results indicate that the SNWD Project provided 128.32 × 10⁶ m³ of water in a wet year, 109.88 × 10⁶ m³ in a normal year and 135.14 × 10⁶ m³ in a dry year to this area. The added quantity of recycled water is 56.75 × 10⁶ m³, 50.59 × 10⁶ m³ and 57.52 × 10⁶ m³, respectively. The water shortage in normal years was covered by the SNWD Project and the water shortage in dry years was reduced by 62.4%. Local environment was improved because of the SNWD Project, i.e., the SNWD water replaced and reduced the use of groundwater and increased the inflow to the Fuyang River and the Yongnian Wetland by increasing the recycled water. This research has demonstrated the SNWD Project has started to play a key role in securing water use and improving the environment in the water-receiving area since its completion in 2014.

As an ecological consequence of intensified anthropogenic activities, more frequent extreme rainfalls have resulted in significant increases in water levels and discharge in southwestern China. This phenomenon presents a significant challenge in flood risk and ecological management. Land use is one of the major factors significantly affecting the flooding process, and it is inextricably tied to the ecological risk of floods. Hence, flood risk estimates based on land use are essential for flood control and land use planning. In this study, a coupled hydrologic–hydraulic model was developed to analyze the relationship between flood ecological risk and land use in order to provide new insights into current flood risk management practices. Ten real flood events (of different magnitudes) in the Zhaojue river basin (650 km²) were chosen to evaluate the credibility and performance of the coupled model’s application. Promising results were obtained, with sufficient reliability for flood risk assessment purposes. The results of our flood risk analysis also indicated that the model effectively reproduced overland flow and competently accounted for flood evolution. This work is significant in the understanding of the mechanism of the flood process and its relationship with land use, and it can be used in decision support for the prevention and mitigation of flood disasters and for land use planning.

The study of runoff under the influence of human activities is a research hot spot in the field of water science. Land-use change is one of the main forms of human activities and it is also the major driver of changes to the runoff process. As for the relationship between land use and the runoff process, runoff yield theories pointed out that the runoff yield capacity is spatially heterogeneous. The present work hypothesizes that the distribution of the runoff yield can be divided by land use, which is, areas with the same land-use type are similar in runoff yield, while areas of different land uses are significantly different. To prove it, we proposed a land-use-based framework for runoff yield calculations based on a conceptual rainfall–runoff model, the Xin’anjiang (XAJ) model. Based on the framework, the modified land-use-based Xin’anjiang (L-XAJ) model was constructed by replacing the yielding area (f/F) in the water storage capacity curve of the XAJ model with the area ratio of different land-use types (L/F; L is the area of specific land-use types, F is the whole basin area). The L-XAJ model was then applied to the typical cultivated–urban binary land-use-type basin (Taipingchi basin) to evaluate its performance. Results showed great success of the L-XAJ model, which demonstrated the area ratio of different land-use types can represent the corresponding yielding area in the XAJ model. The L-XAJ model enhanced the physical meaning of the runoff generation in the XAJ model and was expected to be used in the sustainable development of basin water resources.

The spatial distribution of water storage capacity has always been the critical content of the study of saturation-excess runoff. Xin’anjiang model uses the water storage capacity curve (WSCC) to characterize the distribution of water storage capacity for runoff yield calculation. However, the mathematical and physical foundations of WSCC are unclear, which is impossible to simulate runoff generation with complex basins accurately. To fill this gap, we considered the dominant role of basin physical characteristics in water storage capacity and developed a new integrated approach to solve the spatial distribution of water storage capacity (L-WSCC) to account for the spatiotemporal dynamics of their impact on runoff generation. The main contribution of L-WSCC was to confer WSCC more physical meaning and the spatial distribution of water storage capacity was explicitly represented more accurately, so as to better express the runoff generation and provide a new approach for runoff yield calculation in non-data basin. L-WSCC was applied to Misai basin in China and promising results had been achieved, which verified the rationality of the method (the mean Nash–Sutcliffe efficiency (NSE):0.86 and 0.82 in daily and hourly scale, respectively). Compared with WSCC, the performance of L-WSCC was improved (mean NSE: 0.82 > 0.78, mean absolute value of flood peak error (PE): 12.74% < 21.66%). Moreover, the results of local sensitivity analyses demonstrated that land-use and land cover was the major driving factor of runoff yield (the change of mean absolute error (ΔMAE): 131.38%). This work was significant for understanding the mechanisms of runoff generation, which can be used for hydrological environmental management and land-use planning.