Event-based model calibration approaches for selecting representative distributed parameters in semi-urban watersheds

Awol, Frezer Seid; Coulibaly, Paulin; Tolson, Bryan A.

doi:10.1016/j.advwatres.2018.05.013

Cited by 22 publications

(12 citation statements)

References 63 publications

(72 reference statements)

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“…Other limitations of assimilating soil water data in models have also been reported for predicting end‐of‐season crop yield (Nearing et al., 2012), assessing extreme drought events over long‐term periods (Bolten, Crow, Zhan, Jackson, & Reynolds, 2010), and estimating agricultural irrigation schedules and crop water use (Wang & Cai, 2007). Further research is needed to refine assimilation datasets in order to reduce the above uncertainties, as discussed by Beven and Freer (2001), by including data collection during high‐intensity rainfall events (Awol, Coulibaly, & Tolson, 2018) and from different irrigation treatments (such as in our current study) in order to improve the use of data assimilation in cropping system models.…”

Section: Resultsmentioning

confidence: 99%

Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit‐irrigated maize

Sima

Fang

et al. 2020

Agronomy Journal

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Correct soil water simulation is critical for water balance and plant growth in agricultural systems. Crop production simulation errors have often been attributed to a lack of accuracy in soil water content (SWC) estimates. However, only a few studies have quantified the effects of SWC estimate errors on crop production and evapotranspiration (ET), especially under different irrigation treatments. The objective of this study was to investigate the impacts of direct assimilation of measured SWC during model calibration for deficit irrigated maize (Zea mays L.) on simulated ET, leaf area index (LAI), biomass, and yield. The CERES-Maize model within the Root Zone Water Quality Model (RZWQM) was calibrated using the automatic parameter estimation (PEST) software. Simulation results showed that, using PEST-optimized crop parameters, RZWQM was able to adequately predict crop yield (relative root mean squared error, rRMSE, of 4.8%) and biomass (rRMSE of 7.1%) in response to irrigation levels, in spite of the bias in SWC and ET simulation. However, with the same crop parameters but replacing simulated SWC with measured data, simulations of crop yield and biomass became worse, with higher rRMSE values (14.5% for yield and 21.5% for biomass). This unexpected model performance with SWC assimilation was mainly associated with the water addition and removal from the soil, which was improved only by recalibration of both soil and crop parameters. This study suggested compensating effects between soil and crop parameters during model calibration. Caution should be applied when using measured SWC as model inputs, especially under water stress conditions. Abbreviations: ET, evapotranspiration; LAI, leaf area index; PEST, parameter estimation software; RMSE, root mean squared error; rRMSE, relative root mean squared error; RZWQM, root zone water quality model; SWC, soil water content.

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Section: Resultsmentioning

confidence: 99%

Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit‐irrigated maize

Sima

Fang

et al. 2020

Agronomy Journal

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“…Modeling components that may influence the output hydrographs of the studied basin are: the results pointed out greater uncertainty for sub-basins with imperviousness higher than 80% [66]. Different methodologies of applying areal precipitation may also affect the final rain distribution per sub-watershed.…”

Section: Discussionmentioning

confidence: 97%

Hydrological Analysis of Extreme Rain Events in a Medium-Sized Basin

2021

Self Cite

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The hydrological response of a medium-sized watershed with both rural and urban characteristics was investigated through event-based modeling. Different meteorological event conditions were examined, such as events of high precipitation intensity, double hydrological peak, and mainly normal to wet antecedent moisture conditions. Analysis of the hydrometric features of the precipitation events was conducted by comparing the different rainfall time intervals, the total volume of water, and the precedent soil moisture. Parameter model calibration and validation were performed for rainfall events under similar conditions, examined in pairs, in order to verify two hydrological models, the lumped HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System model) and the semi-distributed HBV-light (a recent version of Hydrologiska Byråns Vattenbalansavdelning model), at the exit of six individual gauged sub-basins. Model verification was achieved by using the Nash–Sutcliffe efficiency and volume error index. Different time of concentration (Tc) formulas are better applied to the sub-watersheds with respect to the dominant land uses, classifying the Tc among the most sensitive parameters that influence the time of appearance and the magnitude of the peak modeled flow through the HEC-HMS model. The maximum water content of the soil box (FC) affects most the peak flow via the HBV-light model, whereas the MAXBAS parameter has the greatest effect on the displayed time of peak discharge. The modeling results show that the HBV-light performed better in the events that had less precipitation volume compared to their pairs. The event with the higher total precipitated water produced better results with the HEC-HMS model, whereas the rest of the two high precipitation events performed satisfactorily with both models. April to July is a flood hazard period that will be worsened with the effect of climate change. The suggested calibrated parameters for severe precipitation events can be used for the prediction of future events with similar features. The above results can be used in the water resources management of the basin.

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“…In some hydrologic modeling attempts, the calibrated model does not necessarily yield the best results for the verification period (Beven & Freer 2001;Madsen 2003;Brocca et al 2011), and it is important to address this issue also for quantitative models of water resources system management. In most previous studies, distributed models for catchments were calibrated based on the hydrographic statistics related to a catchment outlet point (Awol et al 2018). Some hydrologic models of catchment basins have been calibrated with data from several hydrometric stations scattered across the basin using an optimization algorithm (Zhang et al 2010;Leta et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Decentralized calibration process for distributed water resources systems using the self-adaptive multi-memory melody search algorithm

Ashrafi

Mahmoudi

2021

Journal of Hydroinformatics

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Having systematic simulation and optimization models with high computational accuracy is one of the most important problems in developing decision support systems. In the present research, a specific methodology was proposed for decentralized calibration of complex water resources system models by using the structural capabilities of the melody search algorithm. This methodology was implemented in the framework of a self-adaptive simulation–optimization model that helps fine-tune complex water resources models by introducing a new definition of the way sub-memories are related to each. The introduced structure aims to achieve the highest possible level of consistency, which is estimated by using different criteria, between model results and observed data at several control points of surface flows. The introduced strategy was put to the test in developing a water resources model for the Great Karun Watershed, Iran, and was found to produce accurate results compared to some other well-known optimization algorithms such as GA, HS, PSO, SGHS, EMPSO, and SaMeS. In an attempt to determine the effect of calibration on water resources system modeling, 16 calibration models of different dimensions are developed and their computational costs are compared in terms of their computation time and effects on the accuracy of the results.

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Event-based model calibration approaches for selecting representative distributed parameters in semi-urban watersheds

Cited by 22 publications

References 63 publications

Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit‐irrigated maize

Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit‐irrigated maize

Hydrological Analysis of Extreme Rain Events in a Medium-Sized Basin

Decentralized calibration process for distributed water resources systems using the self-adaptive multi-memory melody search algorithm

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