scholarly journals Quantifying input uncertainty in the calibration of water quality models: reshuffling errors via the secant method

2020 ◽  
Author(s):  
Xia Wu ◽  
Lucy Marshall ◽  
Ashish Sharma
Keyword(s):  
Water Quality ◽  
Synthetic Data ◽  
Water Quality Modeling ◽  
Secant Method ◽  
Sampling Errors ◽  
Quality Model ◽  
Input Uncertainty ◽  
Structural Error ◽  
Estimated Error ◽  
Input Error

Abstract. Uncertainty in inputs can significantly impair parameter estimation in water quality modeling, necessitating accurate quantification of input errors. However, decomposing input error from model residual error is still challenging. This study develops a new algorithm, referred to as Bayesian error analysis with reshuffling (BEAR), to address this problem. The basic approach requires sampling errors from a pre-estimated error distribution and then reshuffling them with their inferred ranks via the secant method. This approach is demonstrated in the case of total suspended solids (TSS) simulation via a conceptual water quality model. Based on case studies using synthetic data, the BEAR method successfully isolates the input error and parameter error. The results of a real case study demonstrate that even with the presence of model structural error and output data error, the BEAR method can approximate the true input and bring a better model fit through an effective input modification. However, its effectiveness is limited by the assumption that the input uncertainty should be dominant and that the prior information of the input error model can be estimated. The application of the BEAR method in TSS simulation is effective for understanding a range of water quality conditions and the further developed algorithm can be extended to other water quality predictions.

Application of Expert Systems Technology in Water Quality Modeling

1989 ◽  
Vol 21 (8-9) ◽  
pp. 1045-1056 ◽  
Author(s):  
Thomas O. Barnwell ◽  
Linfield C. Brown ◽  
Wiktor Marek
Keyword(s):  
Water Quality ◽  
Expert Systems ◽  
Knowledge Engineering ◽  
Stream Water ◽  
Environmental Modeling ◽  
Water Quality Modeling ◽  
Stream Water Quality ◽  
Quality Model ◽  
Knowledge Based ◽  
Available Technology

Computerized modeling is becoming an integral part of decision making in water pollution control. Expert systems is an innovative methodology that can assist in building, using, and interpreting the output of these models. This paper reviews the use and evaluates the potential of expert systems technology in environmental modeling and describes elements of an expert advisor for the stream water quality model QUAL2E. Some general conclusions are presented about the tools available to develop this system, the level of available technology in knowledge-based engineering, and the value of approaching problems from a knowledge engineering perspective.

scholarly journals Water Quality Modeling of Bhima River by using HEC-RAS Software

2020 ◽  
Vol 9 (3) ◽  
pp. 2886-2889
Keyword(s):  
Water Quality ◽  
River Water ◽  
Hydrodynamic Model ◽  
Algal Blooms ◽  
Agricultural Runoff ◽  
Water Quality Modeling ◽  
Water Quality Model ◽  
River Water Quality ◽  
Water Levels ◽  
Quality Model

The River has got religious importance in India. The Bhima River is beginning from Bhimashankar hill and it flows through some parts of Maharashtra and Karnataka state. The assessment of water quality for the development of the places near the bank of River is important. These is controlled by various manmade activities. The quality of river water resources is facing problems because of the continuous agricultural runoff, development and urbanization. Due to mixing of nutrients causes algal blooms, which results eutrophication. The modeling of water quality can be deliberated as useful tool for assessing river water. Bhima River is demarcated as a major and important water body located in Pandharpur, dist. Solapur, Maharashtra. As Pandharpur is having historical background and known as one of the famous Holly places in Maharashtra, this place is facing huge population fluctuation due to migrated pilgrims and rapid growth of urbanization. These two things detrimentally affect River water quality. The main objective of current study was to develop a hydrodynamic model combined with river water quality model for the Bhima River to measure and recognize the processes harmful for the River. For Bhima River a hydrodynamic model was constructed using the HEC-RAS 4.1 software combined with a river water quality model to estimate the amount, distribution and sources of algae, nitrate and temperature. The river model has standardized with the help of previous water levels near the Pandharpur region. It has standardized and calibrated for the assessed parameters by competing them with the present data. The result showed a relationship between DO and temperature range. DO level in Pandharpur and Gopalpur were observed to be fluctuating with respective temperature and during Vari season. However, wastewater discharge from Nalha in sample station 3 i.e. Goplapur shows slit changes in DO and due to this there is necessity to learn other parameters also.

Comparison of field measurements to predicted reaeration coefficients, k2, in the application of a water quality model, QUAL2E, to a tropical river

2002 ◽  
Vol 46 (9) ◽  
pp. 47-54 ◽  
Author(s):  
M. Mohamed ◽  
J.D. Stednick ◽  
F.M. Smith
Keyword(s):  
Water Quality ◽  
Standard Error ◽  
Field Measurements ◽  
Water Quality Modeling ◽  
Low Flow ◽  
Quality Model ◽  
Predictive Equations ◽  
Discharge Data ◽  
Study Results ◽  
Reaeration Coefficient

Some of the many tools used for watershed management are mathematical and computer models for wasteload allocations. QUAL2E is one of the most popular water quality models used for such purposes. The question arises as to whether the model is applicable in a different climate such as that in the tropics. In this study, QUAL2E was used to model Sg. Selangor River in Malaysia using the predictive equations for reaeration coefficient (k2) within the model and the measured reaeration coefficients for the river. The study results indicated that use of the reaeration coefficient (k2) measured at Sg. Selangor River did give the lowest standard error (SE) for the simulation of water quality during the 7Q10 low-flow period which is considered as the worst scene scenario in water quality modeling. But during calibration and validation using actual low-flow discharge data, the measured reaeration coefficients did not give the lowest standard error (SE). In conclusion, the results indicated that QUAL2E is applicable in tropical rivers when used with the modeled river parameters (i.e. hydraulic parameters, meteorological conditions etc.). Measured reaeration coefficients produced good results and several predictive equations also produced comparatively good results.

On the effect of scaling conceptual model complexity on stochastic response for water quality modeling

2011 ◽  
Vol 63 (2) ◽  
pp. 360-366
Author(s):  
G. T. Parker
Keyword(s):  
Water Quality ◽  
Conceptual Model ◽  
Water Quality Modeling ◽  
Response Surfaces ◽  
Model Complexity ◽  
Quality Model ◽  
Real World Data ◽  
Quality Models ◽  
Modelling Framework ◽  
Water Quality Models

This paper extends previous work comparing the response of water quality models under uncertainty. A new model, the River Water Quality Model no. 1 (RWQM1), is compared to the previous work of two commonly used water quality models. Additionally, the effect of conceptual model scaling within a single modelling framework, as allowed by RWQM1, is explored under uncertainty. Model predictions are examined using against real-world data for the Potomac River with a Generalized Likelihood Uncertainty Estimation used to assess model response surfaces to uncertainty. Generally, it was found that there are tangible model characteristics that are closely tied to model complexity and thresholds for these characteristics were discussed. The novel work has yielded an illustrative example but also a conceptually scaleable water quality modelling tool, alongside defined metrics to assess when scaling is required under uncertainty. The resulting framework holds substantial, unique, promise for a new generation of modelling tools that are capable of addressing classically intractable problems.

scholarly journals Improving In-Stream Nutrient Routines in Water Quality Models Using Stable Isotope Tracers: A Review and Synthesis

2018 ◽  
Vol 61 (1) ◽  
pp. 139-157 ◽  
Author(s):  
Alexandria Jensen ◽  
William Ford ◽  
James Fox ◽  
Admin Husic
Keyword(s):  
Water Quality ◽  
Stable Isotopes ◽  
Stable Isotope ◽  
Water Quality Modeling ◽  
Water Quality Model ◽  
Model Structure ◽  
Quality Model ◽  
Quality Models ◽  
Stable Isotope Signatures ◽  
Water Quality Models

Abstract. Water quality models serve as an economically feasible alternative to quantify fluxes of nutrient pollution and to simulate effective mitigation strategies; however, their applicability is often questioned due to broad uncertainties in model structure and parameterization, leading to uncertain outputs. We argue that reduction of uncertainty is partially achieved by integrating stable isotope data streams within the water quality model architecture. This article outlines the use of stable isotopes as a response variable within water quality models to improve the model boundary conditions associated with nutrient source provenance, constrain model parameterization, and elucidate shortcomings in the model structure. To assist researchers in future modeling efforts, we provide an overview of stable isotope theory; review isotopic signatures and applications for relevant carbon, nitrogen, and phosphorus pools; identify biotic and abiotic processes that impact isotope transfer between pools; review existing models that have incorporated stable isotope signatures; and highlight recommendations based on synthesis of existing knowledge. Broadly, we find existing applications that use isotopes have high efficacy for reducing water quality model uncertainty. We make recommendations toward the future use of sediment stable isotope signatures, given their integrative capacity and practical analytical process. We also detail a method to incorporate stable isotopes into multi-objective modeling frameworks. Finally, we encourage watershed modelers to work closely with isotope geochemists to ensure proper integration of stable isotopes into in-stream nutrient fate and transport routines in water quality models. Keywords: Isotopes, Nutrients, Uncertainty analysis, Water quality modeling, Watershed.

Water quality modeling to evaluate BMPs in rice paddies

2006 ◽  
Vol 53 (2) ◽  
pp. 253-261 ◽  
Author(s):  
J.H. Jeon ◽  
C.G. Yoon ◽  
H.S. Hwang ◽  
K.W. Jung
Keyword(s):  
Water Quality ◽  
Best Management Practices ◽  
Nutrient Concentration ◽  
Management Practices ◽  
Nutrient Loading ◽  
Water Quality Modeling ◽  
Rice Paddies ◽  
Quality Model ◽  
Dirac Delta ◽  
Surface Drainage

A water quality model applicable to rice paddies was developed using field data from 1999–2002. Use of the Dirac delta function efficiently explained the nutrient-concentration characteristics of ponded water. The model results agreed reasonably well with the observed data. The ponded-water quality was influenced primarily by fertilization; nutrient concentration was especially high during early cultivation periods. Reducing surface drainage during the fertilization period may substantially reduce nonpoint source loading from paddies. Increased weir heights and shallow irrigation methods were evaluated by the model as practical methods for reducing nutrient loading from paddies. These methods were effective in reducing surface drainage and are suggested as “best management practices” (BMPs) if applied based on site-specific paddy conditions.

scholarly journals Development of Optimal Management Strategies for the Interception System Using River Water Quality Modeling

2018 ◽  
Vol 175 ◽  
pp. 03024
Author(s):  
Chen-Yao Ma ◽  
Yi-Chu Huang ◽  
Chih-Ming Kao
Keyword(s):  
Water Quality ◽  
Management Strategies ◽  
Oxygen Demand ◽  
Ammonia Nitrogen ◽  
Water Quality Modeling ◽  
Water Bodies ◽  
Quality Analysis ◽  
Quality Model ◽  
Quality Modeling ◽  
The Impact

This study adopted the water quality model [Water Quality Analysis Simulation Program (WASP)] to simulate and evaluate the impacts of the opening and closure of an interception system at the tributary of Love River on mainstream water quality. The gates were opened respectively for 4, 12, and 24 hours to assess the impact on biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N) in the water bodies of Love River. The WASP model was used to evaluate the self-purification capacity of the river. According to the results of the model estimation, it takes 5 days for NH3-N and BOD in the water bodies of Love River to return to normal and for the water to restore its original water quality after the closure of the Baozhu Ditch gate. Results of this study can be used as a reference for Love River watershed management, and the WASP modeling can be applied for decision makers to develop appropriate management strategies of the interception system.

Biology and Management of Inland Striped Bass and Hybrid Striped Bass

2013 ◽  
Keyword(s):  
Water Quality ◽  
South Carolina ◽  
Striped Bass ◽  
Water Quality Modeling ◽  
Injection System ◽  
Hybrid Striped Bass ◽  
Quality Model ◽  
Oxygen Injection ◽  
White Bass ◽  
Hydropower Operation

<em>Abstract</em>.—Striped bass <em>Morone saxatilis</em> habitat in water bodies is affected by many factors such as hydrological and meteorological conditions, eutrophication, reservoir operations, dam outlet levels, lake characteristics, and watershed characteristics. The CE-QUAL-W2 water quality model is a tool that can integrate the effects of all these factors on striped bass habitat. Once a baseline model is calibrated, it can be used to diagnose constraints to striped bass habitat, identify potential enhancement measures, and evaluate ways to alleviate the impacts of conflicting water uses. Importantly, the model integrates the best available information within the best available scientific method of evaluating water quality or habitat issues. Centering the discussion around an agreed-upon scientific tool helps to ensure that the subjective concerns expressed by stakeholders are objectively evaluated. In the three case studies explored in this paper, a change of hydropower operation was agreed to for Lake Murray, South Carolina that would help maintain summer habitat for striped bass; simulations indicated that hydropower operations were not a major factor affecting striped bass habitat in Clay-tor Lake, Virginia; and an efficient oxygen injection system was designed for J. Strom Thurmond Reservoir, South Carolina and Georgia to mitigate for habitat loss associated with a change in hydropower operation. Water quality modeling is an important tool for objectively evaluating the maintenance or enhancement of striped bass and hybrid striped bass (white bass <em>M. chrysops</em> × striped bass) habitat in reservoirs.

scholarly journals Prediction of the longitudinal dispersion coefficient for small watercourses

2017 ◽  
Vol 39 (3) ◽  
pp. 291 ◽  
Author(s):  
Vanessa Vaz de Oliveira ◽  
Marcos Vinícius Mateus ◽  
Julio Cesar De Souza Inácio Gonçalves ◽  
Alex Garcez Utsumi ◽  
Marcius Fantozzi Giorgetti
Keyword(s):  
Water Quality ◽  
Dispersion Coefficient ◽  
Water Quality Modeling ◽  
Longitudinal Dispersion ◽  
Quality Model ◽  
Longitudinal Dispersion Coefficient ◽  
Empirical Prediction ◽  
Stimulus Response ◽  
Natural Streams ◽  
Semi Empirical

 Longitudinal dispersion coefficient (DL) is considered an essential physical parameter to water quality modeling in rivers. Therefore, the estimation of this parameter with high accuracy guarantees the reliability of the results of a water quality model. In this study, the observed values of longitudinal dispersion coefficient are determined for natural streams (with discharge less than 2.84 m3s-1), based on sets of measured data from stimulus-response tests using sodium chloride as a tracer. Additionally, a semi-empirical equation for prediction of DL is derived using dimensional analysis and multiple linear regression technique. The performance of the produced equation was compared to five empirical prediction equations of DL selected from literature. It presented correlation coefficient r2 = 0.87, suggesting that this equation is suitable for the estimation of DL in streams. It also presented better results for predicting the DL than the five equations from literature, showing an accuracy of 71%. 

scholarly journals Water quality modeling for determination of suitability of water for shrimp farming in tidal rivers

2018 ◽  
Vol 54 (1) ◽  
pp. 34-46
Author(s):  
Navid Dolatabadi Farahani ◽  
Hamid Taheri Shahraiyni ◽  
Reza Sheikhi
Keyword(s):  
Water Quality ◽  
Water Channels ◽  
Water Quality Modeling ◽  
Shrimp Farming ◽  
Quality Data ◽  
Quality Model ◽  
Water Quality Data ◽  
Shrimp Culture ◽  
Standard Values

Abstract In this study, the water quality of the Bahmanshir River and its water channels where Choebdeh Shrimp Farms (the largest shrimp culture complex in Iran) are located were simulated using MIKE11 software. First, an integrated hydraulic and salinity model of the river and its water channels was developed. Then, Manning and dispersion coefficients of the river were calibrated and validated. The most important parameters in the water quality model were determined by sensitivity analysis and these parameters were calibrated using in situ measured water quality data. The errors of salinity, temperature, nitrate, ammonia and dissolved oxygen (DO) models in the verification step were 7.9, 1.2, 0.34, 0.79 and 12%, respectively. Then, two scenarios were applied to the river and the effects of these scenarios on the water quality of the river and its channels were evaluated. The results demonstrated that the site selection of the shrimp culture complex had been performed well because different scenarios could not affect the water quality in the channels. Finally, the water quality in the channels was compared with the standard values of shrimp survival parameters. All of the parameters in the channels were in the range of standard values except DO, which was slightly under the standard value.

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