Spatially distributed model calibration of a highly managed hydrological system using remote sensing-derived ET data

Becker, Rike; Koppa, Akash; Schulz, Stephan; Usman, Muhammad; Beek, Tim aus der; Schüth, Christoph

doi:10.1016/j.jhydrol.2019.123944

Cited by 66 publications

(42 citation statements)

References 47 publications

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“…DDS is designed for computationally expensive optimization problems and has been widely used in hydrology (Becker et al, 2019;Seiller et al, 2017;Shafii & Tolson, 2015;Zink et al, 2018). Although Tolson and Shoemaker (2007) originally introduce DDS for solving optimization (model calibration) problems with continuous decision variables, Tolson et al (2009) modify DDS to solve optimization problems with discrete or integer-valued decision variables.…”

Section: Mixed-integer Optimization For Amsimentioning

confidence: 99%

Automatic Model Structure Identification for Conceptual Hydrologic Models

Spieler

Mai

Craig

et al. 2020

Water Resources Research

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Choosing (an) adequate model structure(s) for a given purpose, catchment, and data situation is a critical task in the modeling chain. However, despite model intercomparison studies, hypothesis testing approaches with modular modeling frameworks, and continuous efforts in model development and improvement, there are still no clear guidelines for identifying a preferred model structure. By introducing a framework for Automatic Model Structure Identification (AMSI), we support the process of identifying (a) suitable model structure(s) for a given task. The proposed AMSI framework employs a combination of the modular hydrological model RAVEN and the heuristic global optimization algorithm dynamically dimensioned search (DDS). It is the first demonstration of a mixed-integer optimization algorithm applied to simultaneously optimize model structure choices (integer decision variables) and parameter values (continuous decision variables) in hydrological modeling. The AMSI framework is thus able to sift through a vast number of model structure and parameter choices for identifying the most adequate model structure(s) for representing the rainfall-runoff behavior of a catchment. We demonstrate the feasibility of the approach by reidentifying given model structures that produced a specific hydrograph and show the limits of the current setup via a real-world application of AMSI on 12 MOPEX catchments. Results show that the AMSI framework is capable of inferring feasible model structure(s) reproducing the rainfall-runoff behavior of a given catchment. However, it is a complex optimization problem to identify model structure and parameters simultaneously. The variance in the identified structures is high due to near equivalent diagnostic measures for multiple model structures, reflecting substantial model equifinality. Future work with AMSI should consider the use of hydrologic signatures, case studies with multiple types of observation data, and the use of mixed-integer multiobjective optimization algorithms.

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Section: Mixed-integer Optimization For Amsimentioning

confidence: 99%

Automatic Model Structure Identification for Conceptual Hydrologic Models

Spieler

Mai

Craig

et al. 2020

Water Resources Research

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“…However, increased accuracy in ET comes at the cost of degrading SM and SF estimates. In the absence of SF estimates, calibration with observed ET offers the best alternative for reliably simulating SF [42,43]. ET-SM-SF: Incorporating all the water balance components (ET, SM, SF) for calibration provides the best compromise solution to preserve the accuracies in simulating each of the three components.…”

Section: Discussionmentioning

confidence: 99%

Improving the Applicability of Hydrologic Models for Food–Energy–Water Nexus Studies Using Remote Sensing Data

Koppa¹,

Gebremichael²

2020

Remote Sensing

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Food, energy, and water (FEW) nexus studies require reliable estimates of water availability, use, and demand. In this regard, spatially distributed hydrologic models are widely used to estimate not only streamflow (SF) but also different components of the water balance such as evapotranspiration (ET), soil moisture (SM), and groundwater. For such studies, the traditional calibration approach of using SF observations is inadequate. To address this, we use state-of-the-art global remote sensing-based estimates of ET and SM with a multivariate calibration methodology to improve the applicability of a widely used spatially distributed hydrologic model (Noah-MP) for FEW nexus studies. Specifically, we conduct univariate and multivariate calibration experiments in the Mississippi river basin with ET, SM, and SF to understand the trade-offs in accurately simulating ET, SM, and SF simultaneously. Results from univariate calibration with just SF reveal that increased accuracy in SF at the cost of degrading the spatio-temporal accuracy of ET and SM, which is essential for FEW nexus studies. We show that multivariate calibration helps preserve the accuracy of all the components involved in calibration. The study emphasizes the importance of multiple sources of information, especially from satellite remote sensing, for improving FEW nexus studies.Author Contributions: Conceptualization, A.K.; methodology, A.K.; formal analysis, A.K.; investigation, A.K. and M.G.; resources, M.G.; data curation, A.K.; writing-original draft preparation, A.K.; writing-review and editing, A.K. and M.G.; visualization, A.K.; supervision, M.G.; funding acquisition, M.G. All authors have read and agreed to the published version of the manuscript.

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“…Previous studies involving ET RS calibration for hydrological modeling have largely adopted a lumped calibration method, whereby the modeled basin average ET is tuned toward spatially averaged ET RS observations. Relatively few studies have used the spatially distributed ET RS calibration method with all grid cells or sub-basins being treated individually [30,32,33]. The lumped ET RS calibration method relies heavily on the seasonal pattern of ET RS observations.…”

Section: Methodsmentioning

confidence: 99%

“…This reflects both the limitations in the ET RS observations used for model ET calibration and the precipitation inputs. Effective spatial calibration of the parameter fields will be constrained as long as the errors in ET RS and the precipitation inputs remain larger than the variability in the calibrating parameters [32]. However, in all river basins, the model streamflow performance (i.e., KGE metrics) increases or decreases consistently with the gradient in ET bias.…”

Section: Uncertainty In the Calibrated Model Parametersmentioning

confidence: 99%

Satellite-Based Evapotranspiration in Hydrological Model Calibration

Jiang

Tao

et al. 2020

Remote Sensing

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Hydrological models are usually calibrated against observed streamflow (Qobs), which is not applicable for ungauged river basins. A few studies have exploited remotely sensed evapotranspiration (ETRS) for model calibration but their effectiveness on streamflow simulation remains uncertain. This paper investigates the use of ETRS in the hydrological calibration of a widely used land surface model coupled with a source–sink routing scheme and global optimization algorithm for 28 natural river basins. A baseline simulation is a setup based on the latest model developments and inputs. Sensitive parameters are determined for Qobs and ETRS-based model calibrations, respectively, through comprehensive sensitivity tests. The ETRS-based model calibration results in a mean Kling–Gupta Efficiency (KGE) value of 0.54 for streamflow simulation; 61% of the river basins have KGE > 0.5 in the validation period, which is consistent with the calibration period and provides a significant improvement over the baseline. Compared to Qobs, the ETRS calibration produces better or similar streamflow simulations in 29% of the basins, while further significant improvements are achieved when either better ET or precipitation observations are used. Furthermore, the model results show better or similar performance in 68% of the basins and outperform the baseline simulations in 90% of the river basins using model parameters from the best ETRS calibration runs. This study confirms that with reasonable precipitation input, the ETRS-based spatially distributed calibration can efficiently tune parameters for better ET and streamflow simulations. The application of ETRS for global scale hydrological model calibration promises even better streamflow accuracy as the satellite-based ETRS observations continue to improve.

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Spatially distributed model calibration of a highly managed hydrological system using remote sensing-derived ET data

Cited by 66 publications

References 47 publications

Automatic Model Structure Identification for Conceptual Hydrologic Models

Automatic Model Structure Identification for Conceptual Hydrologic Models

Improving the Applicability of Hydrologic Models for Food–Energy–Water Nexus Studies Using Remote Sensing Data

Satellite-Based Evapotranspiration in Hydrological Model Calibration

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