scholarly journals The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration

2020 ◽  
Vol 12 (19) ◽  
pp. 3241
Author(s):  
Cassandra Nickles ◽  
Edward Beighley ◽  
Dongmei Feng
Keyword(s):  
Surface Water ◽  
Model Calibration ◽  
Optimal Parameter ◽  
Uniform Space ◽  
Hydrologic Model ◽  
Space Time ◽  
Added Value ◽  
Model Parameters ◽  
Time Sampling ◽  
Hydrologic Model Calibration

The Surface Water and Ocean Topography (SWOT) satellite mission, expected to launch in 2022, will enable near global river discharge estimation from surface water extents and elevations. However, SWOT’s orbit specifications provide non-uniform space–time sampling. Previous studies have demonstrated that SWOT’s unique spatiotemporal sampling has a minimal impact on derived discharge frequency distributions, baseflow magnitudes, and annual discharge characteristics. In this study, we aim to extend the analysis of SWOT’s added value in the context of hydrologic model calibration. We calibrate a hydrologic model using previously derived synthetic SWOT discharges across 39 gauges in the Ohio River Basin. Three discharge timeseries are used for calibration: daily observations, SWOT temporally sampled, and SWOT temporally sampled including estimated uncertainty. Using 10,000 model iterations to explore predefined parameter ranges, each discharge timeseries results in similar optimal model parameters. We find that the annual mean and peak flow values at each gauge location from the optimal parameter sets derived from each discharge timeseries differ by less than 10% percent on average. Our findings suggest that hydrologic models calibrated using discharges derived from SWOT’s non-uniform space–time sampling are likely to achieve results similar to those based on calibrating with in situ daily observations.

scholarly journals How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?

2006 ◽  
Vol 10 (2) ◽  
pp. 289-307 ◽  
Author(s):  
Y. Tang ◽  
P. Reed ◽  
T. Wagener
Keyword(s):  
Model Calibration ◽  
Standardized Test ◽  
A Priori ◽  
Relative Effectiveness ◽  
Hydrologic Model ◽  
Ease Of Use ◽  
Science Literature ◽  
Significant Trial ◽  
Computationally Intensive ◽  
Hydrologic Model Calibration

Abstract. This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools' relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic Algorithm-II (ε-NSGAII), the Multiobjective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). This study uses three test cases to compare the algorithms' performances: (1) a standardized test function suite from the computer science literature, (2) a benchmark hydrologic calibration test case for the Leaf River near Collins, Mississippi, and (3) a computationally intensive integrated surface-subsurface model application in the Shale Hills watershed in Pennsylvania. One challenge and contribution of this work is the development of a methodology for comprehensively comparing EMO algorithms that have different search operators and randomization techniques. Overall, SPEA2 attained competitive to superior results for most of the problems tested in this study. The primary strengths of the SPEA2 algorithm lie in its search reliability and its diversity preservation operator. The biggest challenge in maximizing the performance of SPEA2 lies in specifying an effective archive size without a priori knowledge of the Pareto set. In practice, this would require significant trial-and-error analysis, which is problematic for more complex, computationally intensive calibration applications. ε-NSGAII appears to be superior to MOSCEM-UA and competitive with SPEA2 for hydrologic model calibration. ε-NSGAII's primary strength lies in its ease-of-use due to its dynamic population sizing and archiving which lead to rapid convergence to very high quality solutions with minimal user input. MOSCEM-UA is best suited for hydrologic model calibration applications that have small parameter sets and small model evaluation times. In general, it would be expected that MOSCEM-UA's performance would be met or exceeded by either SPEA2 or ε-NSGAII.

Targeted hydrologic model calibration to improve prediction of ecologically-relevant flow metrics

2019 ◽  
Vol 573 ◽  
pp. 546-556 ◽  
Author(s):  
Sarah R. Parker ◽  
Stephen K. Adams ◽  
Roderick W. Lammers ◽  
Eric D. Stein ◽  
Brian P. Bledsoe
Keyword(s):  
Model Calibration ◽  
Hydrologic Model ◽  
Hydrologic Model Calibration

scholarly journals A practical protocol for calibration of nutrient removal wastewater treatment models

2011 ◽  
Vol 13 (4) ◽  
pp. 575-595 ◽  
Author(s):  
Giorgio Mannina ◽  
Alida Cosenza ◽  
Peter A. Vanrolleghem ◽  
Gaspare Viviani
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Nutrient Removal ◽  
Model Calibration ◽  
Wastewater Treatment Plants ◽  
Optimal Parameter ◽  
Model Parameters ◽  
Activated Sludge Model ◽  
Treatment Models ◽  
Glue Method

Activated sludge models can be very useful for designing and managing wastewater treatment plants (WWTPs). However, as with every model, they need to be calibrated for correct and reliable application. Activated sludge model calibration is still a crucial point that needs appropriate guidance. Indeed, although calibration protocols have been developed, the model calibration still represents the main bottleneck to modelling. This paper presents a procedure for the calibration of an activated sludge model based on a comprehensive sensitivity analysis and a novel step-wise Monte Carlo-based calibration of the subset of influential parameters. In the proposed procedure the complex calibration issue is tackled both by making a prior screening of the most influential model parameters and by simplifying the problem of finding the optimal parameter set by splitting the estimation task into steps. The key point of the proposed step-wise procedure is that calibration is undertaken for sub-groups of variables instead of solving a complex multi-objective function. Moreover, even with this step-wise approach parameter identifiability issues may occur, but this is dealt with by using the general likelihood uncertainty estimation (GLUE) method, that so far has rarely been used in the field of wastewater modelling. An example from a real case study illustrates the effectiveness of the proposed methodology. Particularly, a model was built for the simulation of the nutrient removal in a Bardenpho scheme plant. The model was successfully and efficiently calibrated to a large WWTP in Sicily.

scholarly journals Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

2016 ◽  
Vol 20 (5) ◽  
pp. 1925-1946 ◽  
Author(s):  
Nikolaj Kruse Christensen ◽  
Steen Christensen ◽  
Ty Paul A. Ferre
Keyword(s):  
Electrical Resistivity ◽  
Hydraulic Conductivity ◽  
Prediction Error ◽  
Model Calibration ◽  
Hydrologic Model ◽  
Geophysical Data ◽  
Model Parameters ◽  
Calibration Data ◽  
Groundwater Model ◽  
And Inversion

Abstract. In spite of geophysics being used increasingly, it is often unclear how and when the integration of geophysical data and models can best improve the construction and predictive capability of groundwater models. This paper uses a newly developed HYdrogeophysical TEst-Bench (HYTEB) that is a collection of geological, groundwater and geophysical modeling and inversion software to demonstrate alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity (clay). The synthetic 3-D reference system is designed so that there is a perfect relationship between hydraulic conductivity and electrical resistivity. For this system it is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by (in most cases) geophysics-based regularization. For the studied system and inversion approaches it is found that resistivities estimated by sequential hydrogeophysical inversion (SHI) or joint hydrogeophysical inversion (JHI) should be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. The limited groundwater model improvement obtained by using the geophysical data probably mainly arises from the way these data are used here: the alternative inversion approaches propagate geophysical estimation errors into the hydrologic model parameters. It was expected that JHI would compensate for this, but the hydrologic data were apparently insufficient to secure such compensation. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysics in a joint or sequential hydrogeophysical model calibration. It is found that all calibrated models are good predictors of hydraulic head. When the stress situation is changed from that of the hydrologic calibration data, then all models make biased predictions of head change. All calibrated models turn out to be very poor predictors of the pumping well's recharge area and groundwater age. The reason for this is that distributed recharge is parameterized as depending on estimated hydraulic conductivity of the upper model layer, which tends to be underestimated. Another important insight from our analysis is thus that either recharge should be parameterized and estimated in a different way, or other types of data should be added to better constrain the recharge estimates.

scholarly journals Attribution of hydrologic forecast uncertainty within scalable forecast windows

2013 ◽  
Vol 10 (9) ◽  
pp. 11795-11828 ◽  
Author(s):  
L. Yang ◽  
F. Tian ◽  
Y. Sun ◽  
X. Yuan ◽  
H. Hu
Keyword(s):  
Model Calibration ◽  
Transition Period ◽  
Hydrologic Model ◽  
Dry Season ◽  
Forecast Skill ◽  
Model Parameters ◽  
Combined Effects ◽  
Wet Season ◽  
Streamflow Forecast ◽  
Monthly Streamflow

Abstract. Hindcasts based on the Extended Streamflow Prediction (ESP) approach are carried out in a typical rainfall-dominated basin in China, aiming to examine the roles of initial condition (IC), future atmospheric forcing (FC) and hydrologic model uncertainty (MU) in the streamflow forecast skill. The combined effects of IC and FC are explored within the framework of a forecast window. By implementing virtual numerical simulations without the consideration of MU, it is found that the dominance of IC could last up to 90 days in dry season, while its impact gives way to FC for lead times exceeding 30 days in the wet season. The combined effects of IC and FC on the forecast skill are further investigated by proposing a dimensionless parameter (β) that represents the ratio of the total amount of initial water storage and the incoming rainfall. The forecast skill increases exponentially with β, and varies greatly in different forecast windows. Moreover, the influence of MU on forecast skill is examined by focusing on the uncertainty of model parameters. Two different hydrologic model calibration strategies are carried out. The results indicate that the uncertainty of model parameters exhibits a more significant influence on the forecast skill in the dry season than in the wet season. The ESP approach is more skillful in monthly streamflow forecast during the transition period from wet to dry than otherwise. For the transition period from dry to wet, the low skill of the forecasts could be attributed to the combined effects of IC and FC, but less to the biases in the hydrologic model parameters. For the forecasting in dry season, the usefulness of the ESP approach is heavily dependent on the strategy of the model calibration.

scholarly journals Rainfall-runoff simulations in the Lukovska River Basin with the HEC-HMS model

2019 ◽  
pp. 33-60
Author(s):  
Ranka Eric ◽  
Andrijana Todorovic ◽  
Jasna Plavsic ◽  
Vesna Djukic
Keyword(s):  
River Basin ◽  
Optimal Parameter ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Runoff Generation ◽  
Hydrologic Models ◽  
Generation Mechanisms ◽  
Event Based

Hydrologic models are important for effective water resources management at a basin level. This paper describes an application of the HEC-HMS hydrologic model for simulations of flood hydrographs in the Lukovska River basin. Five flood events observed at the Mercez stream gauge were available for modelling purposes. These events are from two distinct periods and two seasons with different prevailing runoff generation mechanisms. Hence the events are assigned to either ?present? or ?past?, and ?spring? or ?summer? group. The optimal parameter sets of each group are obtained by averaging the optimal parameters for individual events within the group. To assess model transferability, its applicability for simulation of flood events which are not considered in the model calibration, a cross-validation is performed. The results indicate that model parameters vary across the events, and that parameter transfer generally leads to considerable errors in hydrograph peaks and volumes, with the exception of simulation of summer events with ?spring? parameters. Based on these results, recommendations for event-based modeling are given.

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