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.

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.

Water quality modeling in the watershed-based approach for waste load allocations

1998 ◽  
Vol 38 (10) ◽  
pp. 165-172 ◽  
Author(s):  
Ruochuan Gu ◽  
Mei Dong
Keyword(s):  
Water Quality ◽  
Point Source ◽  
Water Quality Modeling ◽  
Point Sources ◽  
Low Flow ◽  
Quality Analysis ◽  
Management Approach ◽  
Water Quality Control ◽  
Quality Modeling ◽  
Waste Load

The conventional method for waste load allocations (WLA) employs spatial-differentiation, considering individual point sources, and temporal-integration, using a constant flow, typically 7Q10 low flow. This paper presents a watershed-based seasonal management approach, in which non-point source as well as point sources are incorporated, seasonal design flows are used for water quality analysis, and WLA are performend in a watershed scale. The strategy for surface water quality modeling in the watershed-based approach is described. The concept of seasonal discharge management is discussed and suggested for the watershed-based approach. A case study using the method for the Des Moines River, Iowa, USA is conducted. Modeling considerations and procedure are presented. The significance of non-point source pollutant load and its impact on water quality of the river is evaluated by analyzing field data. A water quality model is selected and validated against field measurements. The model is applied to projections of future water quality situations under different watershed management and water quality control scenarios with respect to river flow and pollutant loading rate.

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.

Sensitivity analysis of non-point sources in a water quality model applied to a dammed low-flow-reach river

2008 ◽  
Vol 57 (8) ◽  
pp. 1295-1300
Author(s):  
Nayana G. M. Silva ◽  
Marcos von Sperling
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Initial Conditions ◽  
Quality Parameters ◽  
Water Quality Model ◽  
Point Sources ◽  
Low Flow ◽  
Treated Wastewater ◽  
Quality Model ◽  
River Bed

Downstream of Capim Branco I hydroelectric dam (Minas Gerais state, Brazil), there is the need of keeping a minimum flow of 7 m3/s. This low flow reach (LFR) has a length of 9 km. In order to raise the water level in the low flow reach, the construction of intermediate dikes along the river bed was decided. The LFR has a tributary that receives the discharge of treated wastewater. As part of this study, water quality of the low-flow reach was modelled, in order to gain insight into its possible behaviour under different scenarios (without and with intermediate dikes). QUAL2E equations were implemented in FORTRAN code. The model takes into account point-source pollution and diffuse pollution. Uncertainty analysis was performed, presenting probabilistic results and allowing identification of the more important coefficients in the LFR water-quality model. The simulated results indicate, in general, very good conditions for most of the water quality parameters The variables of more influence found in the sensitivity analysis were the conversion coefficients (without and with dikes), the initial conditions in the reach (without dikes), the non-point incremental contributions (without dikes) and the hydraulic characteristics of the reach (with dikes).

The influence of rainfall time resolution for urban water quality modelling

2010 ◽  
Vol 61 (9) ◽  
pp. 2381-2390 ◽  
Author(s):  
Gabriele Freni ◽  
Giorgio Mannina ◽  
Gaspare Viviani
Keyword(s):  
Water Quality ◽  
Temporal Resolution ◽  
Rainfall Data ◽  
Urban Water ◽  
Water Quality Modelling ◽  
Quality Model ◽  
Discharge Data ◽  
Urban Water Quality ◽  
Quality Aspects ◽  
The Impact

The objective of this paper is the definition of a methodology to evaluate the impact of the temporal resolution of rainfall measurements in urban drainage modelling applications. More specifically the effect of the temporal resolution on urban water quality modelling is detected analysing the uncertainty of the response of rainfall–runoff modelling. Analyses have been carried out using historical rainfall–discharge data collected for the Fossolo catchment (Bologna, Italy). According to the methodology, the historical rainfall data are taken as a reference, and resampled data have been obtained through a rescaling procedure with variable temporal windows. The shape comparison between ‘true’ and rescaled rainfall data has been carried out using a non-dimensional accuracy index. Monte Carlo simulations have been carried out applying a parsimonious urban water quality model, using the recorded data and the resampled events. The results of the simulations were used to derive the cumulative probabilities of quantity and quality model outputs (peak discharges, flow volume, peak concentrations and pollutant mass) conditioned on the observation according to the GLUE (Generalized Likelihood Uncertainty Estimation) methodology. The results showed that when coarser rainfall information is available, the model calibration process is still efficient even if modelling uncertainty progressively increases especially with regards to water quality aspects.

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.

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.

scholarly journals Longitudinal River Monitoring and Modelling Substantiate the Impact of Weirs on Nitrogen Dynamics

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 189
Author(s):  
Geovanni Teran-Velasquez ◽  
Björn Helm ◽  
Peter Krebs
Keyword(s):  
Water Quality ◽  
Nitrogen Dynamics ◽  
Low Flow ◽  
Quality Data ◽  
Quality Model ◽  
Spatiotemporal Resolution ◽  
Water Quality Data ◽  
River Monitoring ◽  
The Impact

The fluvial nitrogen dynamics at locations around weirs are still rarely studied in detail. Eulerian data, often used by conventional river monitoring and modelling approaches, lags the spatial resolution for an unambiguous representation. With the aim to address this knowledge gap, the present study applies a coupled 1D hydrodynamic–water quality model to a 26.9 km stretch of an upland river. Tailored simulations were performed for river sections with water retention and free-flow conditions to quantify the weirs’ influences on nitrogen dynamics. The water quality data were sampled with Eulerian and Lagrangian strategies. Despite the limitations in terms of required spatial discretization and simulation time, refined model calibrations with high spatiotemporal resolution corroborated the high ammonification rates (0.015 d−1) on river sections without weirs and high nitrification rates (0.17 d−1 ammonium to nitrate, 0.78 d−1 nitrate to nitrite) on river sections with weirs. Additionally, using estimations of denitrification based on typical values for riverbed sediment as a reference, we could demonstrate that in our case study, weirs can improve denitrification substantially. The produced backwater lengths can induce a means of additional nitrogen removal of 0.2-ton d−1 (10.9%) during warm and low-flow periods.

scholarly journals The influence of artificial cutoff on a monitoring system and the water quality of the Keelung River

2002 ◽  
Vol 46 (11-12) ◽  
pp. 231-236 ◽  
Author(s):  
S.L. Lo ◽  
J.T. Kuo ◽  
S.M. Wang
Keyword(s):  
Water Quality ◽  
Monitoring Network ◽  
Water Quality Monitoring ◽  
Water Quality Model ◽  
Quality Monitoring ◽  
Low Flow ◽  
Quality Model ◽  
Significance Level ◽  
Water Quality Monitoring Network ◽  
State Water

The purpose of this study was to design a water quality monitoring network for the Keelung River in order to evaluate the effects of artificial cutoff across two bend channels. A steady-state water quality model was used to simulate the BOD and DO curves. The Kriging theory was applied to select the optimal locations for a water quality monitoring network. The sampling frequency was determined by the coefficients of variation of water quality and by considering the significance level and confidence interval. After calibration and verification of the water quality model, the model was applied and the simulation results indicated that the values of DO in the new channel would be higher than those of the old channel reaches. The critical point of the oxygen sag curve would shift to the mouth of river under Q75 low-flow conditions, and the BOD values in the new channel would also slightly increase. The results further indicated that more monitoring stations would be needed in the downstream reaches.

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