该数据集为黄河上游、中游和下游全流域的河流网络数据集。
当汇流量达到一定值的时候,就会产生地表水流,所有汇流量大于阈值的栅格就是潜在的水流路径,由这些水流路径构成的网络,就是河网。
本数据基于大于15000的汇流累积提取,并采用Strahler法对河网进行分级
采集时间 | 2020/01/01 - 2020/03/01 |
---|---|
采集地点 | 黄河流域 |
数据量 | 441.0 MiB |
数据格式 | TIF |
数据空间分辨率(/米) | 30 |
坐标系 | WGS84 |
投影 | WGS_1984_Word_Merctor |
自主生产,原始黄河流域地形坡度数据集空间分辨率为30m
Hydrology水文工具软件提取,地表径流漫流模型
本数据集经过DEM改正,平面精度和高程精度均小于一个像元
# | 标题 | 文件大小 |
---|---|---|
1 | StreamL_Stre15000.tfw | 94 Bytes |
2 | StreamL_Stre15000.tif | 164.7 MiB |
3 | StreamL_Stre15000.tif.aux.xml | 2.3 KiB |
4 | StreamL_Stre15000.tif.ovr | 55.9 MiB |
5 | StreamL_Stre15000.tif.vat.cpg | 5 Bytes |
6 | StreamL_Stre15000.tif.vat.dbf | 874.3 KiB |
7 | StreamO_Strahler15000.tfw | 94 Bytes |
8 | StreamO_Strahler15000.tif | 164.2 MiB |
9 | StreamO_Strahler15000.tif.aux.xml | 1.4 KiB |
10 | StreamO_Strahler15000.tif.ovr | 55.4 MiB |
# | 时间 | 姓名 | 用途 |
---|---|---|---|
1 | 2025/02/25 04:26 | 刘*旭 |
论文题目:北洛河流域水生态功能分区
数据在研究中的作用:作为水生态功能分区的环境要素指标
论文类型:毕业论文
导师姓名:田雨露
|
2 | 2025/02/28 06:10 | 安*举 |
论文题目:基于PLUS-InVEST耦合的黄河上中游流域土地利用和生境质量演变特征划定生态功能区
数据在研究中的作用:帮助佐证分析结果
论文类型:研究型论文
导师姓名:李王成
|
3 | 2025/02/10 02:24 | 严*伟 |
几大流域的矢量数据学习分析,认识黄河流域的边界条件和水系网络
|
4 | 2025/01/22 12:51 | 陈* |
水文研究,水文模拟,洪涝灾害风险模拟预测
|
5 | 2025/01/10 23:26 | 张* |
论文题目:黄河上游多尺度智能耦合模型及风险分析应用
数据在研究中的作用:基础数据
论文类型:硕士论文
导师姓名:苑希民
|
6 | 2025/01/10 21:49 | 尤*骅 |
Paper title:Spatial Scale Effects of Runoff and Its Driving Factors: A Case Study of the Yellow River Basin, China
Paper abstract:Effective water resource management requires a comprehensive understanding of how runoff processes operate at different scales and how they interconnect across these scales. However, the existence of scale effects limits the ability to directly establish a spatial scaling rule that translates runoff research outcomes from fine to broad scales, and vice versa. Previous studies have mainly focused on the scale effects of micro-scale research areas, with less attention given to the scale effects between large-scale basins. Furthermore, traditional point-based, short-term, or infrequent measurements are insufficient to accurately capture the nonlinear characteristics of large-scale runoff processes. To address these issues, interpretable machine learning methods were used to analyze the runoff processes of multiple nested watersheds in the Yellow River Basin (YRB). This study assessed the spatial scale effects of runoff in large-scale basins and the driving factors of scale effects. Results indicate that the runoff coefficient (RC) of most watersheds in the YRB exhibits a multiple scaling effect, and the spatial structure of RC varies within different scale ranges. There is a significant nonlinear relationship between the RC and watershed area. The variation of RC with scale (scale effect) is not merely a single trend of change, but rather more complex and variable. Additionally, scale effects exhibit scale thresholds. The RC of the YRB decreases and then increases from upstream to downstream, with the RC in the middle reaches generally being smaller. Surface conditions are the main driving factors for the scale effects of large-scale watersheds, while the climate-related variables are the main driving factors for small and medium-sized watersheds. The rainfall distribution significantly impacts the scale effects in small and medium-sized watersheds. The impact of surface conditions on scale effects will gradually become apparent as the watershed scale increases.
Paper type:期刊论文
Tutor:尤烽骅
|
7 | 2025/01/04 17:08 | 茹*军 |
黄河重点水域渔业资源与环境专项研究需进行流域划分分析
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8 | 2025/01/01 19:21 | 肖*阳 |
绘制黄河流域的范围图,裁剪栅格数据,做黄河流域的分析等等
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9 | 2024/12/25 19:14 | 赵**轩 |
地貌学与第四纪环境课程教学作业,需要使用黄土高原相关数据
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10 | 2024/12/24 20:36 | 孙*楠 |
地球科学与技术学院团队,将数据用于黄河流域的科研性质项目的数字产品中
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