Water resources utilization, social economy development, and ecological environment protection are key factors in regional sustainable development. Scientific evaluation of regional coordinated development status and diagnosis of regional uncoordinated development constraints will improve the management level of decision-makers. At present, most developing countries have the problem of unbalanced regional development caused by the one-sided pursuit of a certain system. Taking 14 prefecture-level cities in Hunan Province as cases, this paper analyzed the spatial and temporal distribution characteristics of the carrying capacity level of the water resources system, the development level of the social economy system and the protection level of the ecological environment system in each evaluation unit based on entropy weight method and order parameter analysis. Based on the theory of coordinated development, a calculation model of a coordinated development degree was constructed, and the corresponding evaluation criteria were formulated. The spatial and temporal distribution characteristics of a coordinated development degree in each research unit were analyzed and evaluated. The results showed that the average coordinated development degree of Hunan Province from 2004 to 2016 evolved from “Light disorder recession” to “Nearly disorder recession”, then to “Reluctance coordinated development”. Restricted by different systems, the coordinated development degree in each research unit presented spatial and temporal differences. According to different development stages and the characteristics of different regions, corresponding development strategies can be formulated to provide the guidance for coordinated the development of regions.

Water resources supply is closely related to food production and economy development. The importance of such a complex system is increasingly recognized in academia and policy. An integrated System Dynamics Model for the water-stressed Loudi city in Hunan Province, China, assessing water balance, and agricultural yield and revenue to 2050, is presented. Considering the uncertainty of future inflows, the formulation of water supply policies, and changes in cropping regimes, this study simulated multiple scenarios to better understand the impacts on water, food, and economic security and their interactions to highlight Loudi's realization of potential pathways to a more sustainable future. Current water resource over-exploitation can be mitigated while still allowing for agricultural development. While most simulations hinted at continued over-exploitation, some suggested that improvements can be achieved by altering parameters such as per-capita domestic water demand, per-capita industrial water demand and the cropping regime. Relevant policies should be considered in parallel to introduce redundancy into the policy framework. From initial results, it is hypothesized that by producing excess crops, a virtual water trading market can be developed, thereby further improving water balance, yield and revenue. This study used system dynamics as a powerful tool to explore complex, feedback-driven water-food systems, providing a viable framework for water resource management approaches and policies at the regional scale.

Satellite precipitation products serve as valuable global data sources for hydrological modeling, yet their applicability across different hydrological models remains insufficiently explored. The distributed physics-informed deep learning model (DPDL), as a representative of emerging differentiable, physics-based hydrological models, requires a systematic evaluation of the suitability of multi-source precipitation products within its modeling framework. This study focuses on the Xiangjiang River Basin in southern China, where both a DPDL model and a Soil and Water Assessment Tool (SWAT) model were constructed. In addition, two model training strategies were designed: S1 (fixed parameters) and S2 (product-specific recalibration). Multiple precipitation products were used to drive both hydrological models, and their streamflow simulation performance was evaluated under different training schemes to analyze the compatibility between precipitation products and hydrological modeling frameworks. The results show that: (1) In the Xiangjiang River Basin of southern China, GSMaP demonstrated the best overall performance with a Critical Success Index of 0.70 and a correlation coefficient (Corr) of 0.79; IMERG-F showed acceptable accuracy with a Corr of 0.75 but had a relatively high false alarm rate (FAR) of 0.32; while CMORPH exhibited the most significant systematic underestimation with a relative bias (RBIAS) of −8.48%. (2) The DPDL model more effectively captured watershed hydrological dynamics, achieving a validation period correlation coefficient of 0.82 and a Nash–Sutcliffe efficiency (NSE) of 0.79, outperforming the SWAT model. However, the DPDL model showed a higher RBIAS of +16.69% during the validation period, along with greater overestimation fluctuations during dry periods, revealing inherent limitations of differentiable hydrological models when training samples are limited. (3) The S2 strategy (product-specific recalibration) improved the streamflow simulation accuracy for most precipitation products, with the maximum increase in the NSE coefficient reaching 15.8%. (4) The hydrological utility of satellite products is jointly determined by model architecture and training strategy. For the DPDL model, IMERG-F demonstrated the best overall robustness, while GSMaP achieved the highest accuracy under the S2 strategy. This study aims to provide theoretical support for optimizing differentiable hydrological modeling and to offer new perspectives for evaluating the hydrological utility of satellite precipitation products.