As the demand for water management information systems continues to increase, addressing issues such as poor generalizability, low reusability, and difficulties in updating and maintaining water resource planning cloud model service platforms becomes crucial. To achieve goals like business-oriented functionality, high availability, and reliability, this study proposes constructing a cloud model service platform for basin water resource planning based on cloud computing technology and business workflows. This study couples water cycle models with multi-objective optimization models for water resource allocation, using digital topological water networks to achieve dynamic regional water resource allocation. The cloud service platform adopts a business-oriented modeling method based on B/S development architecture. This paper takes the Weihe River Basin as an example to simulate and analyze the evolution of the water cycle pattern and optimize the annual water resources allocation plan. Results show that: (1) the water cycle model of the cloud model service platform can better describe the runoff change process in the verification period; (2) through the cloud platform service model, the water shortage rate of the Weihe River Basin in 2025 is 7.95%. The research findings provide technical references for intelligent water management and refined allocation of water resources in the Weihe River Basin.

Physics‐informed neural networks (PINNs) provide a promising framework for river hydrodynamic simulation and parameter inversion under sparse observations. However, spatial heterogeneity in channel geometry and roughness causes strong variations in flood‐wave propagation and parameter sensitivity, challenging local conservation and joint state‐parameter estimation. This study proposes a Characteristic‐aligned Control‐volume Physics‐Informed Neural Network for Roughness Estimation (CC‐PINN‐RE). It constructs dynamic diamond‐shaped space‐time control volumes from local characteristic speeds of the Saint‐Venant equations, enforces mass and momentum conservation in integral form, and combines adaptive control‐volume management with local‐sensitivity‐weighted roughness regularization. The method is evaluated on three one‐dimensional channel cases of increasing complexity against strong‐form PINN‐RE and fixed‐control‐volume cvPINN‐RE. CC‐PINN‐RE achieves the lowest errors in eight of nine relative metrics for water depth, discharge, and roughness. In Case 3, where channel width, bed slope, and roughness vary spatially, it reduces the corresponding errors by 57.0%, 82.2%, and 57.3% relative to PINN‐RE and reduces the maximum absolute error in peak water depth by 69.7%. Ablation experiments with three random seeds support the contributions of the three proposed components. Observation‐deployment experiments show that increasing the number of monitoring stations is generally more effective than increasing temporal sampling frequency at individual stations. Under 0%–2% observational noise in Case 3, the model remains stable. These results indicate that CC‐PINN‐RE provides a physically consistent framework for integrating characteristic propagation, local conservation, and parameter inversion, offering a useful basis for joint hydrodynamic reconstruction and spatially varying roughness estimation in heterogeneous river channels.

Agricultural land use in arid regions is subject to binding water-resource constraints, yet existing studies of crop rotation and fallowing have focused mainly on cultivated-land restoration and crop production, with limited attention to spatial allocation and compensation under water constraints. This study develops a plot-level framework under the principle of determining land use by water availability. Taking Yanchi County, Ningxia, as a case study, ecological, water-resource, and land-resource constraints are integrated through spatial overlay and suitability assessment, and groundwater-, ecological-, and cultivated-land-protection schemes are developed. Results identify 9309 hm² as potentially feasible for crop rotation and 4568 hm² for fallowing. Under the high scenario with maize–potato rotation, irrigation-water reductions are 6.37, 4.69, and 4.31 million m³, with corresponding net-profit losses of 13.75, 9.53, and 9.60 million yuan, respectively. The groundwater-protection scheme achieves the largest absolute water savings and best matches current groundwater-management needs. Baseline compensation standards are 6357, 1390, and 615 yuan/hm² for maize fallowing, maize–potato rotation, and maize–sorghum rotation, respectively. The findings support differentiated, phased implementation combined with loss protection and water-saving incentives.