• 关键词：营养盐  LOADSET  通量  采样频率  极端气候

• 基金项目：国家自然科学基金（No.41701631）；云南省科技计划项目重点研发计划（No.2018BC002）；云南省应用基础研究计划（No.2017FD065）

• 程国微
• 1. 云南大学国际河流与生态安全研究院, 昆明 650091;2. 云南大学生态与环境学院, 云南省高原山地生态与退化环境修复重点实验室, 昆明 650091

• 杜展鹏
• 云南大学生态与环境学院, 云南省高原山地生态与退化环境修复重点实验室, 昆明 650091

• 严长安
• 昆明市环境系统模拟与风险评估重点实验室, 昆明 650032

• 高伟
• 云南大学生态与环境学院, 云南省高原山地生态与退化环境修复重点实验室, 昆明 650091

• 摘要：河流是流域氮磷营养盐的主要输出途径之一，准确掌握其通量变化和驱动因素对流域营养盐管理具有重要意义.本研究以滇池主要入湖河流宝象河为例，基于周水质观测数据和逐日水量数据，构建了河流氮磷通量LOADSET模型.估算了宝象河不同时间尺度（日、季、年）TN和TP的通量，评估了4种低频水质采样和极端气候指数对河流氮磷通量计算的影响.结果表明：①2018年宝象河的TN和TP年通量分别为270.49 t和11.19 t，存在显著的年内差异，夏季是通量最高的季节，分别占TN和TP年通量的40.78%和41.96%.②基于LOADEST模型的低频水质采样的氮磷估算结果与高频采样差异较小，宝象河TN、TP通量估算受采样频率影响较小.③宝象河的TN和TP通量变化受连续5日最大降水量、平均最低气温、平均最高气温、最低气温、最低气温极大值、最高气温极小值和平均温差7种极端气候指数的显著影响.

• Abstract：Rivers are one of the main export pathways for nitrogen and phosphorus in watersheds, and it is of great importance to accurately identify their flux changes and driving factors with regard to watershed nutrient management. In this study, a LOADSET model was developed for estimating riverine nutrient and phosphorus fluxes based on weekly water quality data and daily water quantity data from the Baoxiang River, which is the second largest river draining to the Dianchi Lake. The LOADSET model was used to estimate nitrogen and phosphorus fluxes in Baoxiang River at various time scales (e.g. daily, seasonal, and annual), and the impacts of four low-frequency sampling schemes and extreme weather indexes on riverine nutrient and phosphorus flux estimations were quantified. The results demonstrated that: ①In 2018, the annual fluxes of TN and TP of the Baoxiang River were 270.49 t and 11.19 t, respectively. There were significant variations of fluxes throughout the year, and the coefficients of variation were 51.11% and 54.59%. The highest fluxes occurred during summer, accounting for 40.78% and 41.96% of the annual fluxes for TN and TP, respectively. ② Relatively small differences in LOADEST model estimations of TN and TP fluxes were observed between the low- and high-frequency sampling schemes in the Baoxiang River, leading to the conclusion that the TN and TP flux estimations in the river were less affected by the sampling frequency.③The TN and TP fluxes in the Baoxiang River were significantly affected by 7 extreme climate indexes, including the five-day running maximum precipitation, average minimum temperature, average maximum temperature, minimum temperature, maximum of the minimum temperatures, minimum of the maximum temperature, and the average diurnal temperature ranges.

• Key words：nutrients  LOADSET  flux  sampling frequency  extreme climate

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