Stream metabolism estimates and surface water chemistry in Glenbrook Creek (NV) and Blackwood Creek (CA) in the Lake Tahoe Basin, 2021-2024

The goal of this project was to develop a process-based understanding of how in-stream productivity and nitrogen dynamics respond to hydroclimatic volatility. We used a combined approach of high-frequency sensor deployment and maintenance, ecosystem metabolism modeling, and routine monitoring of water chemistry and other parameters. The data we collected as part of this project demonstrate how variable ecosystem productivity is in time and space in within and among mountain streams. Although maintenance of the sensor arrays during the exceptionally wet winter of 2023 was challenging, we were able to estimate a time series of stream metabolism within the upper and lower reaches of two streams with different basin characteristics and through hydroclimatic conditions (2021 to 2024). Throughout this project we: 1. We generated four years of daily estimates of ecosystem metabolism (gross primary productivity, ecosystem respiration, and net ecosystem productivity) from upper and lower reach stations on both the east and west shores of the Lake Tahoe Basin. 2. We measured ammonium (NH4+) and nitrate (NO3-) concentrations in surface water and sediment samples from both Glenbrook and Blackwood creeks. 3. We modeled nitrogen supply and demand dynamics at each reach location. See this git code repository for project analysis: https://github.com/kellyloria/Mountain-stream-biogeochem-and-productivity.

本项目旨在基于过程层面解析河道生产力与氮素动态对水文气候波动的响应机制。我们采用高频传感器布设与维护、生态系统代谢建模，以及水化学及其他参数常规监测相结合的综合研究方法。

本项目采集的数据表明，山地溪流内部及不同溪流间的生态系统生产力在时空尺度上存在显著变异。尽管2023年异常湿润的冬季期间，传感器阵列的维护工作颇具挑战，但我们仍成功估算了两条具有不同流域特征的溪流上下游河段的溪流代谢时间序列，观测时段覆盖2021至2024年，涵盖各类水文气候条件。

本项目实施期间完成了以下工作：
1. 针对太浩湖盆地（Lake Tahoe Basin）东西两岸的上下游河段监测站点，生成了四年的生态系统代谢日估算数据，涵盖总初级生产力（gross primary productivity）、生态系统呼吸（ecosystem respiration）与净生态系统生产力（net ecosystem productivity）三项指标。
2. 对格伦布鲁克溪（Glenbrook Creek）与布莱克伍德溪（Blackwood Creek）的地表水及沉积物样本中的铵态氮（ammonium, NH₄⁺）与硝态氮（nitrate, NO₃⁻）浓度进行了测定。
3. 针对每个河段的氮素供给与需求动态构建了相应模型。

本项目的分析代码可通过以下Git代码仓库获取：https://github.com/kellyloria/Mountain-stream-biogeochem-and-productivity.