A balanced calibration of water quantity and quality by multi-objective optimization for integrated water system model

Zhang, Yongyong; Shao, Quanxi; Taylor, John A.

doi:10.1016/j.jhydrol.2016.05.001

Cited by 23 publications

(8 citation statements)

References 74 publications

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“…Third, nitrate transport processes are mostly driven by hydrological processes. While in model calibration, nitrate observations can help to constrain hydrological processes (e.g., indicating runoff partitioning which cannot be reflected in discharge observations (Zhang et al, )). Thus a weight‐aggregated multi‐variable function was constructed as follows:

{O F}_{m u l t i - v} = min ({}|, w_{q} \cdot {O F}_{m u l t i - s}^{q} + w_{n} \cdot {O F}_{m u l t i - s}^{n}),

where

w_{q} = 0.9

and

w_{n} = 0.1

denote weights for discharge and

N - {N O}_{3}^{-}

concentration objectives, respectively.…”

Section: Methodsmentioning

confidence: 99%

A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale

Yang

Jomaa

Zink

et al. 2018

Water Resources Research

104

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Hydrological water quality models have gained wide acceptance from environmental scientists and water managers to address deterioration of surface water quality. Higher spatiotemporal accuracy of such models is increasingly required for better understanding the functional heterogeneity of catchments and improving management decisions at different governance levels. However, balancing spatial representation and model complexity remains challenging. We present a new flexibly designed, fully distributed nitrate transport and removal model (mHM‐Nitrate) at catchment scale. The model was developed mainly based on the mesoscale Hydrological Model (mHM) and the Hydrological Predictions for the Environment (HYPE) model. The mHM‐Nitrate model was tested in the Selke catchment (Central Germany), which is characterized by heterogeneous physiographic and land‐use conditions, using adequate observed hydrological and nitrate data at three nested gauging stations. Long term (1997–2015) daily simulations showed that the model well reproduced the seasonal dynamics of biweekly nitrate observations in forested, agricultural and urban areas. High‐frequency measurements (2010‐2015) were additionally used to validate model performance of simulating short‐term changes in stream‐water concentrations that reflect changes in runoff partitioning and event‐based dilution effects. Uncertainty analysis confirmed the model's robustness. Moreover, model calculations showed that mean terrestrial nitrate input/output (in total 105 kg ha−1 yr−1) and in‐stream removal (8% of mean nitrate load) were in comparable ranges with literature, respectively. The new mHM‐Nitrate model is capable of providing detailed spatial information on nitrate concentrations and fluxes, which can motivate more specific catchment investigations on nitrate transport processes and provide guidance on spatially differentiated agricultural practices and measures.

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{O F}_{m u l t i - v} = min ({}|, w_{q} \cdot {O F}_{m u l t i - s}^{q} + w_{n} \cdot {O F}_{m u l t i - s}^{n}),

where

w_{q} = 0.9

and

w_{n} = 0.1

denote weights for discharge and

N - {N O}_{3}^{-}

concentration objectives, respectively.…”

Section: Methodsmentioning

confidence: 99%

A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale

Yang

Jomaa

Zink

et al. 2018

Water Resources Research

104

View full text Add to dashboard Cite

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“…This suggests that the quantity and quality of water should be given equal importance, rather than separately calibrating their modeling effects in uncertainty analysis, as is commonly practiced. Zhang et al (2016) provided a similar recommendation for considering high interactions of runoff and NH 4 + -N concentration prediction in an integrated water system model using a combined auto-calibration multi-process approach. The stream water N loading prediction was highly constrained by parameter uncertainty, indicating the importance of optimizing the parameter set, while other sources of uncertainty also augment the total uncertainty.…”

Section: Main Factors Controlling Multi-source Uncertainties In N Loading Simulationmentioning

confidence: 96%

Partitioning multi-source uncertainties in simulating nitrogen loading in stream water using a coherent, stochastic framework: Application to a rice agricultural watershed in subtropical China

Xiong

Yong

et al. 2018

Science of The Total Environment

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Uncertainty is recognized as a critical consideration for accurately predicting stream water nitrogen (N) loading, but identifying the relative contribution of individual uncertainty sources within the total uncertainty remains unclear. In this study, a powerful method, referred to as the Bayesian inference combined with analysis of variance (BayeANOVA) was adopted to detect the timing and magnitude of multiple uncertainty sources and their relative contributions to total uncertainty in simulating daily loadings of three stream water N species (ammonium-N: NH-N, nitrate-N: NO-N and total N: TN) in a rice agricultural watershed (the Tuojia watershed) as influenced by non-point source N pollution. Five sources of uncertainty have been analyzed in this study, which arise from model structure, parameters, inputs, interaction effects between parameters and inputs, and internal variability (induced by random errors of model or environment). The results show that uncertainty in parameters relating to the processes of both N and hydrologic cycles contributed the largest fractions of total uncertainty in N loading simulations (58.83%, 63.48% and 61.64% for NH-N, NO-N and TN loading, respectively). Additionally, three of the largest uncertainties (i.e. parameters, inputs and interaction effects) in all three simulated N loadings were on average significantly greater in the rice-growing season relative to the fallow season, primarily due to the excess fertilization application during the rice-growing season. The predicted TN uncertainty was mainly attributed to the inaccuracy of NO-N simulation, which contributed to 75.48% of predicted TN uncertainty. It is concluded that reducing the parameter uncertainty of NO-N loading simulation during the rice-growing season is the key factor to improving stream water N modeling precision in rice agricultural watersheds.

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“…There are some examples of the use of EMO algorithms for multicriteria calibration of computational models [38], [39], [40], [41], [42], [43], [44]. Many of them are focused in the calibration of hydrological models, such as the soil and water assessment tool [45], [46], [47], [48], the rainfall-runoff models [41], empirical hydrological models for streamflow forecasting [39], and an integrated water system model [44]. The thorough review of these contributions reveals that their usual approach relies on employing the NSGA-II for running the calibration process, probably because it is the most popular EMO algorithm.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Multiobjective Optimization for Automatic Agent-Based Model Calibration: A Comparative Study

2021

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Complex problems can be analyzed by using model simulation but its use is not straight-forward since modelers must carefully calibrate and validate their models before using them. This is specially relevant for models considering multiple outputs as its calibration requires handling different criteria jointly. This can be achieved using automated calibration and evolutionary multiobjective optimization methods which are the state of the art in multiobjective optimization as they can find a set of representative Pareto solutions under these restrictions and in a single run. However, selecting the best algorithm for performing automated calibration can be overwhelming. Therefore, we propose to deal with this issue by conducting an exhaustive analysis of the performance of several evolutionary multiobjective optimization algorithms when calibrating several instances of an agent-based model for marketing with multiple outputs. We analyze the calibration results using multiobjective performance indicators and attainment surfaces, including a statistical test for studying the significance of the indicator values, and benchmarking their performance with respect to a classical mathematical method. The results of our experimentation reflect that those algorithms based on decomposition perform significantly better than the remaining methods in most instances. Besides, we also identify how different properties of the problem instances (i.e., the shape of the feasible region, the shape of the Pareto front, and the increased dimensionality) erode the behavior of the algorithms to different degrees.INDEX TERMS Model calibration, agent-based modeling, evolutionary multiobjective optimization.

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A balanced calibration of water quantity and quality by multi-objective optimization for integrated water system model

Cited by 23 publications

References 74 publications

A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale

A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale

Partitioning multi-source uncertainties in simulating nitrogen loading in stream water using a coherent, stochastic framework: Application to a rice agricultural watershed in subtropical China

Evolutionary Multiobjective Optimization for Automatic Agent-Based Model Calibration: A Comparative Study

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