Estimation of the Interaction Between Groundwater and Surface Water Based on Flow Routing Using an Improved Nonlinear Muskingum-Cunge Method

2021 ◽  
Author(s):  
Chengpeng Lu ◽  
Keyan Ji ◽  
Wanjie Wang ◽  
Yong Zhang ◽  
Tema Koketso Ealotswe ◽  
...  
Keyword(s):  
Surface Water ◽  
Flow Routing ◽  
Water Based

Semi-automated extraction of surface water based on ZhuHai-1 hyperspectral satellite images

2021 ◽  
Vol 12 (8) ◽  
pp. 750-756
Author(s):  
Xiaowang Zhang ◽  
Jingchao Zhang ◽  
Wuyang Chen ◽  
Wei Liu ◽  
Zunju Zhang ◽  
...  
Keyword(s):  
Surface Water ◽  
Satellite Images ◽  
Automated Extraction ◽  
Water Based

scholarly journals Temporal variability of exchange between groundwater and surface water based on high-frequency direct measurements of seepage at the sediment-water interface

2013 ◽  
Vol 49 (5) ◽  
pp. 2975-2986 ◽  
Author(s):  
Donald O. Rosenberry ◽  
Richard W. Sheibley ◽  
Stephen E. Cox ◽  
Frederic W. Simonds ◽  
David L. Naftz
Keyword(s):  
Surface Water ◽  
Temporal Variability ◽  
High Frequency ◽  
Water Interface ◽  
Direct Measurements ◽  
Water Based

Mechanisms of arsenic removal from simulated surface water based on As(III) retention on thiol chelating resins

2021 ◽  
pp. 100532
Author(s):  
Graziele Duarte ◽  
Mônica C. Teixeira ◽  
Sunday J. Olusegun ◽  
Virginia S.T. Ciminelli
Keyword(s):  
Surface Water ◽  
Arsenic Removal ◽  
Chelating Resins ◽  
Water Based ◽  
Simulated Surface

Response of phytoplankton community to different water types in the western Arctic Ocean surface water based on pigment analysis in summer 2008

2017 ◽  
Vol 36 (8) ◽  
pp. 109-121 ◽  
Author(s):  
Haiyan Jin ◽  
Yanpei Zhuang ◽  
Hongliang Li ◽  
Jianfang Chen ◽  
Shengquan Gao ◽  
...  
Keyword(s):  
Surface Water ◽  
Arctic Ocean ◽  
Phytoplankton Community ◽  
Ocean Surface ◽  
Pigment Analysis ◽  
Western Arctic Ocean ◽  
Water Types ◽  
Water Based ◽  
Western Arctic

scholarly journals Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

2021 ◽  
Vol 13 (12) ◽  
pp. 2368
Author(s):  
Lawrence V. Stanislawski ◽  
Ethan J. Shavers ◽  
Shaowen Wang ◽  
Zhe Jiang ◽  
E. Lynn Usery ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Surface Water ◽  
High Performance ◽  
Land Development ◽  
Training Data ◽  
Topographic Map ◽  
Feature Maps ◽  
Flow Routing ◽  
Novel Approach

Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area.

Identifying sources of particle organic carbon in surface water based on stable isotope tracing in the Jinshui River

2014 ◽  
Vol 34 (19) ◽  
Author(s):  
谭慧娟 TAN Huijuan ◽  
夏晓玲 XIA Xiaoling ◽  
吴川 WU Chuan ◽  
张全发 ZHANG Quanfa
Keyword(s):  
Surface Water ◽  
Stable Isotope ◽  
Organic Carbon ◽  
Isotope Tracing ◽  
Water Based
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