Amending greenroof soil with biochar to affect runoff water quantity and quality

2011 ◽  
Vol 159 (8-9) ◽  
pp. 2111-2118 ◽  
Author(s):  
Deborah A. Beck ◽  
Gwynn R. Johnson ◽  
Graig A. Spolek
Keyword(s):  
Water Quantity ◽  
Runoff Water ◽  
Water Quantity And Quality

scholarly journals Runoff water quantity and quality data from native tallgrass prairie and crop–livestock systems in Oklahoma between 1977 and 1999

2020 ◽  
Vol 49 (4) ◽  
pp. 1062-1072
Author(s):  
Amanda M. Nelson ◽  
Daniel N. Moriasi ◽  
Ann‐Marie Fortuna ◽  
Jean L. Steiner ◽  
Patrick J. Starks ◽  
...  
Keyword(s):  
Tallgrass Prairie ◽  
Quality Data ◽  
Water Quantity ◽  
Runoff Water ◽  
Livestock Systems ◽  
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.

Response of Winter Manure Application on Surface Runoff Water Quantity and Quality from Small Watersheds in South Dakota

2017 ◽  
Vol 228 (10) ◽  
Author(s):  
Shikha Singh ◽  
Nathan Brandenburg ◽  
Laurent Ahiablame ◽  
Arturo Gonzalez ◽  
Jeppe Kjaersgaard ◽  
...  
Keyword(s):  
South Dakota ◽  
Surface Runoff ◽  
Water Quantity ◽  
Runoff Water ◽  
Manure Application ◽  
Small Watersheds ◽  
Water Quantity And Quality

RUNOFF WATER QUANTITY AND QUALITY FROM GREEN ROOF SYSTEMS

2004 ◽  
pp. 369-376 ◽  
Author(s):  
M.A. Monterusso ◽  
D.B. Rowe ◽  
C.L. Rugh ◽  
D.K. Russell
Keyword(s):  
Green Roof ◽  
Water Quantity ◽  
Runoff Water ◽  
Water Quantity And Quality

Simulated wetland conservation-restoration effects on water quantity and quality at watershed scale

2010 ◽  
Vol 91 (7) ◽  
pp. 1511-1525 ◽  
Author(s):  
Xixi Wang ◽  
Shiyou Shang ◽  
Zhongyi Qu ◽  
Tingxi Liu ◽  
Assefa M. Melesse ◽  
...  
Keyword(s):  
Wetland Conservation ◽  
Water Quantity ◽  
Watershed Scale ◽  
Water Quantity And Quality
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