Water Resources Planning – A Research Program

1987 ◽  
Vol 19 (9) ◽  
pp. 119-124
Author(s):  
M. A. Santos ◽  
J. R. Costa
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
River Water ◽  
Drainage Basin ◽  
National Laboratory ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Quality Modeling ◽  
Resources Planning

A research project on “Methodologies for Water Resources Policy Analysis” is under current development at the National Laboratory for Civil Engineering, Portugal. Its main objectives are to develop and test techniques, computational tools and procedures which may help in the design of water resources plans, in the comparison and evaluation of alternative strategies and in real time drainage basin management and operation. In order to achieve these objectives the technical activity of the project has tackled such water resources problems as the assessment of water availability and demands, the characterization of river water quality and wastewater, water pollution control and river water quality modeling. Also, effective technology transfer from technicians to local, regional and national managers and decision-makers has been tried. In this paper, the main project activities are summarized, some of the achievements are pointed out and its most significant results are presented.

scholarly journals Evaluation of Uncertainty Propagation Predictions in River Water Quality Modeling

2021 ◽  
Author(s):  
André Fonseca ◽  
Cidália Botelho ◽  
Rui Boaventura ◽  
Vitor Vilar
Keyword(s):  
Water Quality ◽  
Monte Carlo ◽  
River Water ◽  
Fecal Coliform ◽  
Statistical Evaluation ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Evaluation Framework ◽  
Sensitivity Analyses ◽  
Quality Modeling

Abstract The uncertainty on model predictions to evaluate river water quality is often high to delineate appropriate conclusions. This study presents the statistical evaluation of the water quality modeling system Hydrologic Simulation Program FORTRAN as a tool to improve monitoring planning and mitigate uncertainty in water quality predictions. It also presents findings in determining HSPF model’s sensitivity analysis concerning water quality predictions. The computer model was applied to Ave River watershed, Portugal. The hydrology was calibrated at two stations from January 1990 to December 1994 and validated from January 1995 to December 1999. A two-step statistical evaluation framework is presented based on the most common hydrology criteria for model calibration and validation and, a Monte Carlo methodology uncertainty evaluation approach coupled with multi parametric sensitivity analyses to assess model uncertainty and parameter sensitivity. Fourteen HSPF water quality parameters probability distributions are used as input factors for the Monte Carlo simulation. The simulation results for in stream fecal coliform concentrations was found to be most sensitive to parameters that represent first order decay rate and surface runoff that removes 90 percent of fecal coliform from pervious land surface rather than accumulation and maximum storage rates. Regarding oxygen governing process (DO, BOD, NO3, PO4), benthal oxygen demand and nitrification/denitrification rates were the most sensitive parameters.

River water quality modeling

2021 ◽  
pp. 157-204
Author(s):  
Clark C.K. Liu ◽  
Pengzhi Lin ◽  
Hong Xiao
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Quality Modeling

Intensive river survey in river water quality modeling

2021 ◽  
pp. 205-229
Author(s):  
Clark C.K. Liu ◽  
Pengzhi Lin ◽  
Hong Xiao
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Quality Modeling

scholarly journals Machine learning techniques in river water quality modelling: a research travelogue

2020 ◽  
Author(s):  
Sakshi Khullar ◽  
Nanhey Singh
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Resources ◽  
River Water ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Aquatic Life ◽  
Learning Techniques

Abstract Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers has been adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a matter of great concern as it is deteriorating the water quality, making it unfit for any type of use. Contaminated water resources can cause serious effects on humans as well as aquatic life. Hence, water quality monitoring of reservoirs is essential. Recently, water quality modeling using AI techniques has generated a lot of interest and it can be very beneficial in ecological and water resources management. This paper presents the state-of-the-art application of machine learning techniques in forecasting river water quality. It highlights the different key techniques, advantages, disadvantages, and applications with respect to monitoring the river water quality. The review also intends to find the existing challenges and opportunities for future research.

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