Single-objective vs. multi-objective autocalibration in modelling total suspended solids and phosphorus in a small agricultural watershed with SWAT

2012 ◽  
Vol 65 (4) ◽  
pp. 643-652 ◽  
Author(s):  
Santatriniaina Denise Rasolomanana ◽  
Paul Lessard ◽  
Peter A. Vanrolleghem
Keyword(s):  
Suspended Solids ◽  
Assessment Tool ◽  
Model Performance ◽  
Total Suspended Solids ◽  
Agricultural Watershed ◽  
Time Step ◽  
Multi Objective ◽  
Shuffled Complex Evolution ◽  
Model Components ◽  
Single Objective

To obtain greater precision in modelling small agricultural watersheds, a shorter simulation time step is beneficial. A daily time step better represents the dynamics of pollutants in the river and provides more realistic simulation results. However, with a daily evaluation performance, good fits are rarely obtained. With the Shuffled Complex Evolution (SCE) method embedded in the Soil and Water Assessment Tool (SWAT), two calibration approaches are available, single-objective or multi-objective optimization. The goal of the present study is to evaluate which approach can improve the daily performance with SWAT, in modelling flow (Q), total suspended solids (TSS) and total phosphorus (TP). The influence of weights assigned to the different variables included in the objective function has also been tested. The results showed that: (i) the model performance depends not only on the choice of calibration approach, but essentially on the influential parameters; (ii) the multi-objective calibration estimating at once all parameters related to all measured variables is the best approach to model Q, TSS and TP; (iii) changing weights does not improve model performance; and (iv) with a single-objective optimization, an excellent water quality modelling performance may hide a loss of performance of predicting flows and unbalanced internal model components.

scholarly journals Robust multi-objective calibration strategies – possibilities for improving flood forecasting

2012 ◽  
Vol 16 (10) ◽  
pp. 3579-3606 ◽  
Author(s):  
T. Krauße ◽  
J. Cullmann ◽  
P. Saile ◽  
G. H. Schmitz
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Model Performance ◽  
Data Depth ◽  
Automatic Calibration ◽  
Multi Objective ◽  
Robust Model ◽  
Robust Parameter ◽  
Robust Parameter Estimation ◽  
Single Objective

Abstract. Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently, automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless of whether the objective is aggregated of several criteria that measure different (possibly opposite) aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. Nonetheless, there is a major disadvantage of automatic calibration procedures that understand the problem of model calibration just as the solution of an optimisation problem: due to the complex-shaped response surface, the estimated solution of the optimisation problem can result in different near-optimum parameter vectors that can lead to a very different performance on the validation data. Bárdossy and Singh (2008) studied this problem for single-objective calibration problems using the example of hydrological models and proposed a geometrical sampling approach called Robust Parameter Estimation (ROPE). This approach applies the concept of data depth in order to overcome the shortcomings of automatic calibration procedures and find a set of robust parameter vectors. Recent studies confirmed the effectivity of this method. However, all ROPE approaches published so far just identify robust model parameter vectors with respect to one single objective. The consideration of multiple objectives is just possible by aggregation. In this paper, we present an approach that combines the principles of multi-objective optimisation and depth-based sampling, entitled Multi-Objective Robust Parameter Estimation (MOROPE). It applies a multi-objective optimisation algorithm in order to identify non-dominated robust model parameter vectors. Subsequently, it samples parameter vectors with high data depth using a further developed sampling algorithm presented in Krauße and Cullmann (2012a). We study the effectivity of the proposed method using synthetical test functions and for the calibration of a distributed hydrologic model with focus on flood events in a small, pre-alpine, and fast responding catchment in Switzerland.

Accuracy of different sensors for the estimation of pollutant concentrations (total suspended solids, total and dissolved chemical oxygen demand) in wastewater and stormwater

2013 ◽  
Vol 68 (2) ◽  
pp. 462-471 ◽  
Author(s):  
Mathieu Lepot ◽  
Jean-Baptiste Aubin ◽  
Jean-Luc Bertrand-Krajewski
Keyword(s):  
Chemical Oxygen Demand ◽  
Suspended Solids ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Total Suspended Solids ◽  
Detection Methods ◽  
Time Step ◽  
Pollutant Concentrations ◽  
Uv Visible ◽  
Wet Weather

Many field investigations have used continuous sensors (turbidimeters and/or ultraviolet (UV)-visible spectrophotometers) to estimate with a short time step pollutant concentrations in sewer systems. Few, if any, publications compare the performance of various sensors for the same set of samples. Different surrogate sensors (turbidity sensors, UV-visible spectrophotometer, pH meter, conductivity meter and microwave sensor) were tested to link concentrations of total suspended solids (TSS), total and dissolved chemical oxygen demand (COD), and sensors' outputs. In the combined sewer at the inlet of a wastewater treatment plant, 94 samples were collected during dry weather, 44 samples were collected during wet weather, and 165 samples were collected under both dry and wet weather conditions. From these samples, triplicate standard laboratory analyses were performed and corresponding sensors outputs were recorded. Two outlier detection methods were developed, based, respectively, on the Mahalanobis and Euclidean distances. Several hundred regression models were tested, and the best ones (according to the root mean square error criterion) are presented in order of decreasing performance. No sensor appears as the best one for all three investigated pollutants.

scholarly journals PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model

2018 ◽  
Vol 11 (6) ◽  
pp. 2429-2453 ◽  
Author(s):  
Edwin H. Sutanudjaja ◽  
Rens van Beek ◽  
Niko Wanders ◽  
Yoshihide Wada ◽  
Joyce H. C. Bosmans ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Model Performance ◽  
Water Withdrawal ◽  
Groundwater Depletion ◽  
Total Water ◽  
Time Step ◽  
Discharge Data ◽  
Source Codes ◽  
Model Components

Abstract. We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D–2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results.

scholarly journals Robust multi-objective calibration strategies – chances for improving flood forecasting

2011 ◽  
Vol 8 (2) ◽  
pp. 3693-3741 ◽  
Author(s):  
T. Krauße ◽  
J. Cullmann ◽  
P. Saile ◽  
G. H. Schmitz
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Particle Swarm ◽  
Model Performance ◽  
Data Depth ◽  
Particle Swarm Optimisation ◽  
Multi Objective ◽  
Optimisation Algorithm ◽  
Robust Parameter ◽  
Single Objective

Abstract. Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless whether the objective is aggregated of several criteria that measure different (possibly opposite) aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. One possible approach to estimate the Pareto set effectively and efficiently is the particle swarm optimisation (PSO). It has already been successfully applied in various other fields and has been reported to show effective and efficient performance. Krauße and Cullmann (2011b) presented a method entitled ROPEPSO which merges the strengths of PSO and data depth measures in order to identify robust parameter vectors for hydrological models. In this paper we present a multi-objective parameter estimation algorithm, entitled the Multi-Objective Robust Particle Swarm Parameter Estimation (MO-ROPE). The algorithm is a further development of the previously mentioned single-objective ROPEPSO approach. It applies a newly developed multi-objective particle swarm optimisation algorithm in order to identify non-dominated robust model parameter vectors. Subsequently it samples robust parameter vectors by the application of data depth metrics. In a preliminary assessment MO-PSO-GA is compared with other multi-objective optimisation algorithms. In the frame of a real world case study MO-ROPE is applied identifying robust parameter vectors of a distributed hydrological model with focus on flood events in a small, pre-alpine, and fast responding catchment in Switzerland. The method is compared with existing robust parameter estimation methods.

scholarly journals PCR-GLOBWB 2: a 5 arc-minute global hydrological and water resources model

2017 ◽  
Author(s):  
Edwin H. Sutanudjaja ◽  
Rens van Beek ◽  
Niko Wanders ◽  
Yoshihide Wada ◽  
Joyce H. C. Bosmans ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Model Performance ◽  
Water Withdrawal ◽  
Groundwater Depletion ◽  
Time Step ◽  
Discharge Data ◽  
Source Codes ◽  
Model Components ◽  
Main Components

Abstract. We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster-Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arc-minute resolution, but a version parameterized at 30 arc-minute resolution is also available. Both versions are available as open source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model. PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1D-2D-hydrodynamic models, respectively. Here, we describe the main components of the model, compare results of the 30 arcminute and the 5 arc-minute versions and evaluate their model performance using GRDC discharge data. Results show that model performance of the 5 arc-minute version is notably better than that of the 30 arc-minute version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arc-minute model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated water withdrawal, by source and sector, matches reasonably well with reported water withdrawal from AQUASTAT.

scholarly journals Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 147
Author(s):  
Mohammadreza Moeini ◽  
Ali Shojaeizadeh ◽  
Mengistu Geza
Keyword(s):  
Machine Learning ◽  
Suspended Solids ◽  
Nearest Neighbor ◽  
Model Performance ◽  
Total Suspended Solids ◽  
Supervised Machine Learning ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Adaptive Boosting

Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R2) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R2 values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable.

Establishing an empirical equation for the relationship between total suspended solids and total phosphorus concentrations in the downstream Red river water

2020 ◽  
Vol 20 (3) ◽  
pp. 325-332
Author(s):  
Le Nhu Da ◽  
Le Thi Phuong Quynh ◽  
Phung Thi Xuan Binh ◽  
Duong Thi Thuy ◽  
Trinh Hoai Thu ◽  
...  
Keyword(s):  
Total Phosphorus ◽  
Suspended Solids ◽  
Empirical Relationship ◽  
Water Environment ◽  
Total Suspended Solids ◽  
River System ◽  
Red River ◽  
Reservoir Impoundment ◽  
Downstream Section ◽  
Reasonable Prediction

Recently, the Asian rivers have faced the strong reduction of riverine total suspended solids (TSS) flux due to numerous dam/reservoir impoundment. The Red river system is a typical example of the Southeast Asian rivers that has been strongly impacted by reservoir impoundment in both China and Vietnam, especially in the recent period. It is known that the reduction in total suspended solids may lead to the decrease of some associated elements, including nutrients (N, P, Si) which may affect coastal ecosystems. In this paper, we establish the empirical relationship between total suspended solids and total phosphorus concentrations in water environment of the Red river in its downstream section from Hanoi city to the Ba Lat estuary based on the sampling campaigns conducted in the dry and wet seasons in 2017, 2018 and 2019. The results show a clear relationship with significant coefficient between total suspended solids and total phosphorus in the downstream Red river. It is expressed by a simple equation y = 0.0226x0.3867 where x and y stand for total suspended solids and total phosphorus concentrations (mg/l) respectively with the r2 value of 0.757. This equation enables a reasonable prediction of total phosphorus concentrations of the downstream Red river when the observed data of total suspended solids concentrations are available. Thus, this work opens up the way for further studies on the calculation of the total phosphorus over longer timescales using daily available total suspended solids values.

Aeration and Water Hyacinths in Waste Stabilization Ponds

1987 ◽  
Vol 19 (12) ◽  
pp. 265-271
Author(s):  
P. R. Thomas ◽  
H. O. Phelps
Keyword(s):  
Biochemical Oxygen Demand ◽  
Suspended Solids ◽  
Oxygen Demand ◽  
Total Suspended Solids ◽  
Substantial Improvement ◽  
Additional Treatment ◽  
Coliform Bacteria ◽  
Effluent Quality ◽  
Stabilization Ponds ◽  
Water Hyacinths

The investigation was based on two facultative stabilization ponds initially designed to operate in parallel, and now receive wastewater in excess of their capacities from a fast expanding housing estate in the Caribbean Island of Trinidad. Because of the deterioration of the effluent quality relative to acceptable standards, an attempt was made to upgrade the ponds using water hyacinths at the early stages. However, from the results, it was clear that the introduction of water hyacinths in the test pond did not lead to any substantial improvement in the effluent because of the high loading on the pond. Therefore the ponds were modified to operate in series with surface aerators installed in the first pond. Initially, the effluent quality was monitored in terms of total suspended solids, volatile suspended solids, biochemical oxygen demand, faecal coliform bacteria, pH and dissolved oxygen with aeration in the first pond and no aquatic plants in the second pond. Although there was a significant improvement in the effluent quality, the values remained above the standards. As a result, water hyacinths were introduced in the second pond and the effluent quality monitored together with aeration in the first pond. The effluent quality improved with total suspended solids and biochemical oxygen demand values both as low as 10 mg/l in certain months, but additional treatment was needed to reduce faecal conforms.

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