five

UNM WR 573 Fall 2021 - Las Huertas data|水体研究数据集|水资源管理数据集

收藏
DataONE2022-04-15 更新2024-06-08 收录
水体研究
水资源管理
下载链接:
https://search.dataone.org/view/sha256:a044efbb757c9505eb438c6ce29677d89b75916c82b626337d719caf6851645f
下载链接
链接失效反馈
资源简介:
The hydrology, chemistry, and biology of a stream are strongly interconnected, and must all be considered when assessing the overall state of a water body. In this investigation, we seek to answer the following Research Question: What are the differences in water quality and quantity between a rural headwater stream and an urban main-stem river? For our investigation, we measured, analyzed, and compared water quality and quantity characteristics in a rural headwater stream (Las Huertas Creek, abbreviated as LH) and an urban main-stem river (The Rio Grande, abbreviated as RG) located near and in Albuquerque, New Mexico. At each of our two locations, we measured water quality and quantity at a downstream site (abbreviated as D), a midstream site (abbreviated as M), and an upstream site (abbreviated as U) for a total of six sites in our study. We defined these areas as the location abbreviation followed by the site abbreviation; for example, the Las Huertas Downstream site was defined as LH_D while the Rio Grande Upstream site was defined as RG_U. To answer our research question, we measured hydrologic, chemical, and biological parameters at each of our six sites. For hydrology, we measured discharge and soil hydraulic conductivity; for chemistry, we measured temperature, specific conductivity, conductivity, total dissolved solids, salinity, dissolved oxygen, pH, turbidity, alkalinity, anions, and cations; for biology, we measured chlorophyll a, benthic macroinvertebrates, organic matter, and riparian vegetation. Below is a description of our study locations and our parameter methods followed by parameter results and a discussion.
创建时间:
2022-04-15
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作