Data-Driven Assessment of Basin-Scale Water Information Aggregation: A Comparative Study of Qinghai and Gansu for Smart Water Conservancy

Abstract

High-quality hydrological and engineering data are a prerequisite for smart water conservancy. Basin management agencies routinely collect large, multi-source datasets describing rivers, reservoirs, diversion projects, sluice gates, pumping stations, intake structures, hydrometric sections, and other water-related objects. However, it remains unclear to what extent these operational tables are ready to support intelligent dispatching, risk analysis, and digital governance. This paper presents a data-driven assessment of basin-scale water information aggregation for two representative provinces—Qinghai and Gansu. Standardized aggregation tables are used as the only data source. A Data Completeness Index (DCI) is defined to quantify how well required attributes are populated for each water-related object. Using simple computer-based processing (Python scripts and spreadsheet functions), we compute object counts and completeness indicators and generate visual analytics. Results show that Qinghai has more recorded objects (134) than Gansu (74), especially for water diversion outlets and hydrometric sections. Nevertheless, Gansu exhibits higher overall data completeness (DCI ≈ 0.984) than Qinghai (DCI ≈ 0.883), particularly for engineered structures such as reservoirs, sluice gates, and water diversion projects. Notably, hydropower stations have no valid records in either province, and no water-related objects fall into the "other regions" category except as corrected herein. These findings indicate that Gansu is closer to data readiness for automated regulation, while Qinghai still has gaps in metadata and monitoring attributes. The study demonstrates that even basic computational methods can transform routine aggregation tables into informative indicators and charts, providing practical support for smart water conservancy in large river basins.