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.

This study simulated urban flooding under various land use and drainage system conditions and described the process of historical ground–underground construction and its influence on spatial variations in waterlogging, taking Handan City as an example. The obtained results can provide support for urban water security and sustainable urban water resource management. The land use change, represented by the expansion of sealed surfaces, has a positive impact on the distribution and the volume of flood in Handan City, while the drainage system has the opposite effect. The flooding distribution changes over decades reveal that flooding risk is reduced in most areas by improved drainage conditions but exacerbated in impervious areas and riversides due to increasing impermeable areas, the rapid draining of pipes, and poor outlet conditions. This study demonstrates how the dual changes in land use and drainage pipeline networks affect urban flooding distribution; we suggest considering land use and the extension of drainage pipelines in future construction.

Farm dams may exert various pressures on the flow network depending on the position and scale, which may influence the magnitude, timing, and duration of the flow in the basin. Considering the cumulative effects of farm dams is important for understanding their spatial impacts on the rainfall-runoff process. However, a few studies have been able to reckon the temporal and spatial variation in the flow. In this study, we developed an integrated approach based on remote sensing and hydrologic–hydrodynamic modeling to simulate the rainfall-runoff process in a farm dam-dominated basin. Compared with the classical Xinanjiang model (XAJ), the developed coupled hydrological–hydrodynamic model (coupled-XAJ) shows improved performance in the simulation of the no-linear confluence process in terms of flood flow and peak appearance time. It demonstrates that water retention of multiple farm dams is eminent and that the developed model is effective and feasible in farm dam-dominated basins. Furthermore, the integrated approach enables to control and utilize the rain and flood resources with the safety of arm dams guaranteed. This study provides an innovative method for the scientific management of water resources under the influence of human activities and environmental changes.

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.

Floods are one of the main natural disaster threats to the safety of people’s lives and property. Flood hazards intensify as the global risk of flooding increases. The control of flood disasters on the basin scale has always been an urgent problem to be solved that is firmly associated with the sustainable development of water resources. As important nonengineering measures for flood simulation and flood control, the hydrological and hydraulic models have been widely applied in recent decades. In our study, on the basis of sufficient remote-sensing and hydrological data, a hydrological (Xin’anjiang (XAJ)) and a two-dimensional hydraulic (2D) model were constructed to simulate flood events and provide support for basin flood management. In the Chengcun basin, the two models were applied, and the model parameters were calibrated by the parameter estimation (PEST) automatic calibration algorithm in combination with the measured data of 10 typical flood events from 1990 to 1996. Results show that the two models performed well in the Chengcun basin. The average Nash–Sutcliffe efficiency (NSE), percentage error of peak discharge (PE), and percentage error of flood volume (RE) were 0.79, 16.55%, and 18.27%, respectively, for the XAJ model, and those values were 0.76, 12.83%, and 11.03% for 2D model. These results indicate that the models had high accuracy, and hydrological and hydraulic models both had good application performance in the Chengcun basin. The study can a provide decision-making basis and theoretical support for flood simulation, and the formulation of flood control and disaster mitigation measures in the basin.