青藏高原水文气象补充观测数据集（2022-2024）

本数据集包含了多个水文气象要素，时间跨度为2022年1月1日至2024年12月31日，最低时间分辨率为逐日，有些数据为逐5分钟、逐小时、逐3小时等。数据内容包括降水、流量、水位、风速风向、空气温湿度和土壤含水量等要素。降水记录降水量，帮助分析降雨模式和水资源管理。流量测量河流或水体的流动速度和体积，关键用于水资源调度和防洪预警。水位监测水体的高度变化，重要用于水库管理和洪水预警。风速风向记录风速和风向，影响蒸发率和降水分布。空气温湿度测量空气的温度和湿度，影响蒸发和降水过程。土壤含水量监测土壤中的水分含量，关键用于农业灌溉和旱情监测。数据来源于奴各沙、昌都、沱沱河、鄂陵湖、布哈河口、札马什克、嘉玉桥、黑山、库鲁克栏干、巴音布鲁克10个水文站，使用水文测验仪器，直接从现场采集导出数据。数据加工方法包括数据清洗、缺失值填补、异常值检测和数据存储。首先，对原始数据进行清洗，去除噪声和无效数据，确保数据的准确性和一致性。其次，使用插值法或其他统计方法填补数据中的缺失值，以保证数据的完整性。然后，通过统计分析和算法检测数据中的异常值，并进行相应的处理，以避免异常数据对分析结果的影响。最后，将处理后的数据存储在数据库中，便于后续的查询和分析。数据质量方面，该数据集能够满足不同研究需求。经过数据清洗和缺失值填补，确保了数据的完整性。通过严格的质量控制和校验，确保了数据的准确性和一致性。数据来源于先进的水文测验仪器，经过科学的处理和存储，具有较高的可靠性。在应用成果及前景方面，通过分析降水、流量和水位数据，可以提高洪水预警的准确性，减少洪灾损失。流量和水位数据有助于优化水库调度和水资源分配，提高水资源利用效率。土壤含水量和空气温湿度数据可以指导农业灌溉，提升农作物产量。长时间跨度的气象数据有助于研究气候变化趋势，提供科学依据。

This dataset contains multiple hydrometeorological elements, with a time span from January 1, 2022 to December 31, 2024. The minimum temporal resolution is daily, while some data are collected at 5-minute, hourly, 3-hourly intervals, etc.

The data elements include precipitation, streamflow, water level, wind speed and direction, air temperature and humidity, and soil moisture content, among others. Precipitation records rainfall amount, which aids in analyzing rainfall patterns and water resources management. Streamflow measures the flow velocity and volume of rivers or water bodies, and is critical for water resources scheduling and flood early warning. Water level monitors the height changes of water bodies, which is important for reservoir management and flood early warning. Wind speed and direction record wind velocity and direction, which affect evaporation rate and precipitation distribution. Air temperature and humidity measure atmospheric temperature and humidity, which influence evaporation and precipitation processes. Soil moisture content monitors the water content in soil, and is critical for agricultural irrigation and drought monitoring.

The data are collected from 10 hydrological stations: Nugesha, Changdu, Tuotuohe, Eling Lake, Buhukou, Zhamashike, Jiayuqiao, Heishan, Kulukelangan, and Bayanbulak. Data are directly collected and exported on-site using hydrological measurement instruments.

Data processing methods include data cleaning, missing value imputation, outlier detection, and data storage. First, clean the raw data to remove noise and invalid data, ensuring data accuracy and consistency. Second, use interpolation or other statistical methods to fill in missing values to guarantee data integrity. Third, detect outliers in the data through statistical analysis and algorithms, and perform corresponding processing to avoid the impact of abnormal data on analysis results. Finally, store the processed data in a database to facilitate subsequent query and analysis.

In terms of data quality, this dataset can meet various research needs. Data integrity is ensured through data cleaning and missing value imputation. Data accuracy and consistency are guaranteed through strict quality control and verification. The data are sourced from advanced hydrological measurement instruments, processed and stored scientifically, thus featuring high reliability.

In terms of application achievements and prospects, analyzing precipitation, streamflow and water level data can improve the accuracy of flood early warning and reduce flood disaster losses. Streamflow and water level data help optimize reservoir scheduling and water resources allocation, enhancing water use efficiency. Soil moisture content and air temperature and humidity data can guide agricultural irrigation and increase crop yields. Long-time-span meteorological data are conducive to studying climate change trends and providing scientific basis.

搜集汇总

背景与挑战

背景概述

该数据集是2022年至2024年青藏高原地区的水文气象补充观测数据，包含降水、流量、水位、风速风向、空气温湿度和土壤含水量等多种要素，时间分辨率从逐5分钟到逐日，空间分辨率为10km至100km，数据来源于10个水文站。数据集经过严格的质量控制处理，适用于洪水预警、水资源优化、农业灌溉和气候变化研究等应用。