Signature-based multi-modelling and multi-objective calibration of hydrologic models: Application in flood forecasting for Canadian Prairies

Sahraei, Shahram; Asadzadeh, Masoud; Unduche, Fisaha

doi:10.1016/j.jhydrol.2020.125095

Cited by 36 publications

(17 citation statements)

References 58 publications

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“…4) show that these formulations are superior to the results of single-objective approaches. This finding demonstrates that the inclusion of FDC signatures in addition to discharge-based performance measures within a multi-objective calibration leads to improved discharge simulation, which is in agreement with previous studies (Pokhrel and Yilmaz 2012, Hrachowitz et al 2014, Shafii and Tolson 2015, Pfannerstill et al 2017, Chilkoti et al 2018, Sahraei et al 2020).…”

Section: Model Performance In Streamflow Simulationsupporting

confidence: 92%

“…The application of signatures related to FDC provides more information about the hydrological behaviours of the modelled basin (Hrachowitz et al 2014) and their underlying processes (Yilmaz et al 2008, Gupta et al 2009. FDC has often been used for model evaluation (Yilmaz et al 2008, Pokhrel and Yilmaz 2012, Hrachowitz et al 2014, Pfannerstill et al 2014 and lately as an objective in model calibration (Shafii and Tolson 2015, Chilkoti et al 2018, Sahraei et al 2020. These studies have demonstrated that signature-based calibration approaches (using discharge and FDC) lead to a more accurate discharge simulation and the reduction of predictive uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…FDC signatures) and remote sensing data (e.g. LAI) in the calibration of hydrological models (Shafii and Tolson 2015, Chilkoti et al 2018, Ha et al 2018, Rajib et al 2018, Sahraei et al 2020. However, to the best of our knowledge, no previous study has taken into account both datasets for hydrological model calibration.…”

Section: Introductionmentioning

confidence: 99%

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Towards a more consistent eco-hydrological modelling through multi-objective calibration: a case study in the Andean Vilcanota River basin, Peru

Fernández-Palomino

Hattermann

Krysanova

et al. 2020

Hydrological Sciences Journal

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Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve-FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography.

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Section: Model Performance In Streamflow Simulationsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Towards a more consistent eco-hydrological modelling through multi-objective calibration: a case study in the Andean Vilcanota River basin, Peru

Fernández-Palomino

Hattermann

Krysanova

et al. 2020

Hydrological Sciences Journal

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“…The goal of hydrologic modelers is to reduce uncertainties associated with the model outputs upon which decision about hydrologic fluxes are based. There have been numerous studies focusing on different ways to improve model performance by exploring various input data characteristics that affects the predictions in conceptual and physically distributed models [17][18][19][61][62][63]. Models like mHM are spatially distributed since they consist of equations involving one or more space coordinates, which are used for simulation of spatial variation in hydrological variables within a catchment as well as simple outflows and bulk storage volumes.…”

Section: Discussionmentioning

confidence: 99%

Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

Demirel

Newton

2021

Water

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Effective management of water resources entails the understanding of spatiotemporal changes in hydrologic fluxes with variation in land use, especially with a growing trend of urbanization, agricultural lands and non-stationarity of climate. This study explores the use of satellite-based Land Use Land Cover (LULC) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

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“…Several studies use signature‐based calibration to search for models that achieve “hydrologic consistency,” that is, that reproduce multiple flow signatures (Martinez & Gupta, 2011; Pechlivanidis, Jackson, McMillan, & Gupta, 2014; Pokhrel, Yilmaz, & Gupta, 2012; Sahraei, Asadzadeh, & Unduche, 2020; Shafii & Tolson, 2015). The hope is that these models provide a realistic representation of a range of hydrologic processes.…”

Section: Applications Of Hydrologic Signaturesmentioning

confidence: 99%

A review of hydrologic signatures and their applications

McMillan

2020

WIREs Water

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Hydrologic signatures are quantitative metrics or indices that describe statistical or dynamical properties of hydrologic data series, primarily streamflow. Hydrologic signatures were first used in eco-hydrology to assess alterations in flow regime, and have since seen wide uptake across a variety of hydrological fields. Their applications include extracting biologically relevant attributes of streamflow data, monitoring hydrologic change, analyzing runoff generation processes, defining similarity between watersheds, and calibrating and evaluating hydrologic models. Hydrologic signatures allow us to extract meaningful information about watershed processes from streamflow series, and are therefore seeing increasing use in emerging information-rich areas such as global-scale hydrologic modeling, machine learning, and large-sample hydrology. This overview paper describes the background and development of hydrologic signature theory, reviews hydrologic signature use across a variety of applications, and discusses ongoing hydrologic signature research including current challenges. This article is categorized under: Science of Water > Science of Water K E Y W O R D S flow indices, flow metrics, hydrologic signatures 1 | INTRODUCTION 1.1 | Discovering the information in streamflow data Streamflow timeseries show patterns: flood peaks and low flow periods, daily changes and seasonal cycles. These patterns are examples of information in streamflow data. The information might describe how the stream reacts to changes in weather, or what magnitudes and rates of change of flow are usual for the stream. Streamflow patterns depend on the physical characteristics of the watershed, telling a story about the path of water from precipitation to streamflow. Flow patterns in turn affect the stream's environment, informing us about riparian conditions and habitats. This paper describes how hydrologists use hydrologic signatures to extract this wealth of information from streamflow. 1.2 | What is a hydrologic signature? Hydrologic signatures are quantitative metrics that describe statistical or dynamic properties of streamflow. They are also known as hydrologic metrics, hydrologic indices, or diagnostic signatures. Hydrologic signatures range from simple

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Signature-based multi-modelling and multi-objective calibration of hydrologic models: Application in flood forecasting for Canadian Prairies

Cited by 36 publications

References 58 publications

Towards a more consistent eco-hydrological modelling through multi-objective calibration: a case study in the Andean Vilcanota River basin, Peru

Towards a more consistent eco-hydrological modelling through multi-objective calibration: a case study in the Andean Vilcanota River basin, Peru

Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

A review of hydrologic signatures and their applications

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