Dataset of Winter Water Supply and Resource Input Monitoring for the Lithium-Rich Zabuye Salt Lake, Xizang

The main elements such as lithium (Li) and boron (B) were determined by inductively coupled plasma emission spectroscopy (ICP-OES), and trace elements such as rubidium (Rb) and cesium (Cs) were determined by inductively coupled plasma mass spectrometry (ICP-MS). The dataset is composed of. xlx. tables and contains 40 sets of core data, including flow rate, flow velocity, hydrochemical composition, and classification correction factors.

The table records river names (such as Chabuye Island Spring Water Group, Luoju Zangbu, etc.), average river depth (m), river width (m), average flow velocity (m/s) and average flow rate (m3/s) measured by Doppler current meter, average flow velocity and flow rate measured by float method, and converted annual winter flow rate (107m3/a). In addition, the chemical analysis table provides a detailed list of the mass concentrations (mg/L) of Li, Rb, Cs and other elements at each sampling point.

The accompanying. png file visually displays the three-dimensional flow velocity structure and resource element contribution rate distribution of each river section. To ensure data reliability, strict quality control measures were implemented during the collection process.During the measurement process of Doppler flowmeter, the signal-to-noise ratio (SNR) is controlled above 10dB, the standard deviation threshold is set at 0.01m/s, and the peak threshold is controlled within 10%.

The hydrochemical analysis is validated using the Charge Balance Error Method (CBE) to ensure that the relative deviation of charge balance for all valid samples is less than or equal to 5%. Based on actual measurement data, this dataset proposes for the first time a classification flow correction coefficient for salt lake recharge rivers on the Qinghai Tibet Plateau: 0.62 for Quanji River and 0.97 for mountain rivers, providing a scientific benchmark for calibrating historical buoy method data.

Due to extreme outdoor environments, this dataset currently only includes measured data from November, and lacks a comparison between the summer peak period and the spring snowmelt period. It is recommended to combine regional seasonal hydrological models for comprehensive evaluation when using it.