scholarly journals Surface-Water Quantity and Quality of the Upper Milwaukee River, Cedar Creek, and Root River Basins, Wisconsin, 2004

2006 ◽  
Author(s):  
David W. Hall
Keyword(s):  
Surface Water ◽  
River Basins ◽  
Water Quantity ◽  
Cedar Creek ◽  
Root River ◽  
Water Quantity And Quality

scholarly journals Evaluation and Monitoring of Water Quantity and Quality of the Buriganga River in Bangladesh using Multi-temporal Landsat Images

2020 ◽  
Vol 8 (1) ◽  
pp. 61-69
Author(s):  
Romana Afroz ◽  
Md Bodruddoza Mia ◽  
Md Saiful Islam
Keyword(s):  
Surface Water ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Water Distribution ◽  
Water Bodies ◽  
Water Quantity ◽  
Buriganga River ◽  
Water Quantity And Quality

Buriganga River, the study area, is one of the most polluted and decreasing expeditiously its area in Bangladesh due to rapid urbanization, effluents of industries and factories surrounding the river, sewage disposal from Dhaka City and some anthropogenic activities. The objective of this study is to evaluate and monitor the water quantity and quality of the river using satellite remote sensing techniques. Unsupervised and indices based classification were used to derive and monitor landuse-landcover (LULC), surface water distribution (SWD), land surface temperature(LST) and total suspended material (TSM) using four sets of Landsat TM/ETM+/OLI/TIRS images of the study area from 1989 to 2015. The indices are Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). LULC classification results showed that the water bodies and vegetation decreased and consequently urban as well bared area increased from 1989 to 2015. Results of indices (NDVI and NDWI) analysis are similar to that of unsupervised LULC outputs, that is, the water bodies decreased with increasing urban structures of the study area. The surface water distribution monitoring results from the suitable change detection GIS model indicate that the water bodies have decreased about 31.07% and accretion rate increased rapidly from 1989 to 2015 along the river bank due to urbanization and accretion activity is more prominent in north, northeast, northwest, south, southeast and eastern part. The study also shows that the rate of TSM is sporadically increasing during the study period i.e., the maximum and minimum value of TSM was 56215.53 and 1956 mg/l in 1989 and 14188714.35 mg/l and 333942 mg/l in 2015 respectively; this indicates that the water is harmful for aquatic life. Both the analyzed satellite image outcome and in situ observations reveal that land surface temperature is also increased in some part of the study area. The study results could be used to make policy for upgrading the water quality and to maintain the extent and water quantity for agriculture, navigation and fisheries sectors of the Buriganga River. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 8(1), 2019, P 61-69

Environmental Water Stress in Transboundary River Basins

2017 ◽  
Author(s):  
Tales Carvalho-Resende
Keyword(s):  
Water Stress ◽  
Surface Water ◽  
Environmental Water ◽  
River Basins ◽  
Water Quantity ◽  
Natural Flow Regime ◽  
Transboundary River ◽  
Transboundary River Basins ◽  
Natural Flow ◽  
Water Uses

The Environmental Water Stress in Transboundary River Basins indicator focuses on the water quantity aspect and considers hydrological alterations from monthly dynamics of the natural flow regime due to anthropogenic water uses and dam operations. For more information, visit: http://twap-rivers.org/ Basin Stress Surface water Transboundary

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.

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