Current integrated modeling frameworks for simulating nutrient sources and dynamics are inadequate for large regional watersheds dominated by groundwater‐surface water interactions due to their simplistic representations of groundwater. In this study, we develop a coupled model that integrates comprehensive surface water, 3‐D groundwater, soil erosion, and nutrient processes. The model is intended to enhance the understanding of nutrient dynamics and sources in the Pearl River Basin (PRB). The model exhibits satisfactory performance in simulating streamflow and sediment transport patterns, capturing essential seasonal variations in water quality indicators. Hydrological budget assessments from 2002 to 2020 in the PRB reveal that 54% of precipitation drains into the South China Sea as surface water, while groundwater discharge as baseflow accounts for 18% of the streamflow. The nutrient budget for the PRB indicates that non‐point sources are the dominant contributors to both nitrogen (N) and phosphorus (P), ranging between 64% and 90%. Improved sewage collection and treatment have reduced point source nutrient contributions over the evaluation period. Groundwater remains a significant and consistent source of N, contributing between 11% and 19%. Natural disturbances and fertilization have led to an upward trend in river N inputs, while afforestation and sewage reduction efforts have resulted in a downward trend in river P inputs. Increased fertilization emerges as a central concern for the PRB, suggesting cost‐effective mitigation of fertilizer usage a pragmatic solution. The coupled simulation model developed in this study offers a novel systems approach for basin‐wide nutrient analysis and pollution control strategies, considering both natural and human‐induced disturbances.

Estimation of submarine groundwater discharge (SGD) has traditionally relied on isotopic mass balance calculation, which is a form of regional box modeling. Here we propose an entirely new approach for SGD estimation by coupling a general marine hydrodynamic simulation model with a global optimization algorithm. The newly developed approach has the distinct advantage of being able to map the spatial distribution of SGD. The results of this study show that the estimated SGD exhibits a strong spatial heterogeneity. Furthermore, this study underscores the important effect of isotope sampling time on SGD estimation. Thus, the fluctuation of seawater level and a lag in isotope activity to water level changes should be considered to assure the representativeness of measured isotope activities. Overall, the simulation‐optimization framework adds a versatile and powerful new tool for more detailed SGD estimation.

Rapid urbanization has exacerbated urban-stormwater challenges, highlighting the critical need for coordinated surface-water and groundwater management through rainfall recharge. However, current sponge city construction methods often overlook the crucial role of underground aquifers in regulating the water cycle and mostly rely on simplified engineering approaches. To address these limitations, this study proposes a spatial optimization framework for urban-scale sponge systems that integrates a hydrodynamic model (FVCOM), geographic information systems (GIS), and Monte Carlo simulations. This framework establishes a comprehensive evaluation system that synergistically integrates surface water inundation depth, geological lithology, and groundwater depth to quantitatively assess sponge city suitability. The FVCOM was employed to simulate surface water inundation processes under extreme rainfall scenarios, while GIS facilitated spatial analysis and data integration. The Monte Carlo simulation was utilized to optimize the spatial layout by objectively determining factor weights and evaluate result uncertainty. Using Shenzhen City in China as a case study, this research combined the “matrix-corridor-patch” theory from landscape ecology to optimize the spatial structure of the sponge system. Furthermore, differentiated planning and management strategies were proposed based on regional characteristics and uncertainty analysis. The research findings provide a replicable and verifiable methodology for developing sponge city systems in high-density urban areas. The core value of this methodology lies in its creation of a scientific decision-making tool for direct application in urban planning. This tool can significantly enhance a city’s climate resilience and facilitate the coordinated, optimal management of water resources amid environmental changes.