scholarly journals Availability of data on surface-water quantity and quality for the San Francisco Bay Region, California

10.3133/mf526 ◽  
1974 ◽  
Keyword(s):  
Surface Water ◽  
San Francisco ◽  
San Francisco Bay ◽  
Water Quantity ◽  
Water Quantity And Quality

Response of surface water quantity and quality to agricultural water use intensity in upstream Hutuo River Basin, China

2019 ◽  
Vol 212 ◽  
pp. 378-387
Author(s):  
Shumin Han ◽  
Qiuli Hu ◽  
Yonghui Yang ◽  
Yanmin Yang ◽  
Xinyao Zhou ◽  
...  
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Water Use ◽  
Water Quantity ◽  
Agricultural Water ◽  
Agricultural Water Use ◽  
Water Quantity And Quality

scholarly journals Assessment of surface-water quantity and quality, Eagle River watershed, Colorado, 1947-2007

2011 ◽  
Author(s):  
Cory A. Williams ◽  
Jennifer L. Moore ◽  
Rodney J. Richards
Keyword(s):  
Surface Water ◽  
Water Quantity ◽  
River Watershed ◽  
Water Quantity And Quality

scholarly journals Physics-Informed Data-Driven Models to Predict Surface Runoff Water Quantity and Quality in Agricultural Fields

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 200 ◽  
Author(s):  
Jing Liang ◽  
Wenzhe Li ◽  
Scott Bradford ◽  
Jiří Šimůnek
Keyword(s):  
Surface Water ◽  
Surface Runoff ◽  
Overland Flow ◽  
Data Driven ◽  
Water Quantity ◽  
Soil Conditions ◽  
Runoff Water ◽  
Physically Based ◽  
Physically Based Models ◽  
Water Quantity And Quality

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models (PBMs), which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with PBMs, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. Here we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport. A large number of numerical simulations was then carried out to develop a database containing information about the impact of various relevant factors on surface runoff quantity and quality, such as different weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices. Finally, the resulting database was used to train data-driven models. Several Machine Learning techniques were explored to find input-output functional relations. The results indicate that the Neural Network model with two hidden layers performed the best among selected data-driven models, accurately predicting runoff water quantity and quality over a wide range of parameters.

Surface water quantity and quality assessment in Pinios River, Thessaly, Greece

Desalination ◽  
2010 ◽  
Vol 250 (1) ◽  
pp. 266-273 ◽  
Author(s):  
A. Loukas
Keyword(s):  
Surface Water ◽  
Quality Assessment ◽  
Water Quantity ◽  
Pinios River ◽  
Water Quantity And Quality
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