scholarly journals Effect of Baseflow Separation on Uncertainty of Hydrological Modeling in the Xinanjiang Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kairong Lin ◽  
Yanqing Lian ◽  
Yanhu He
Keyword(s):  
Hydrological Modeling ◽  
Threshold Value ◽  
Efficiency Index ◽  
Uncertainty Interval ◽  
Xinanjiang Model ◽  
Shuffled Complex Evolution ◽  
Interval Width ◽  
Behavioral Parameter ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation

Based on the idea of inputting more available useful information for evaluation to gain less uncertainty, this study focuses on how well the uncertainty can be reduced by considering the baseflow estimation information obtained from the smoothed minima method (SMM). The Xinanjiang model and the generalized likelihood uncertainty estimation (GLUE) method with the shuffled complex evolution Metropolis (SCEM-UA) sampling algorithm were used for hydrological modeling and uncertainty analysis, respectively. The Jiangkou basin, located in the upper of the Hanjiang River, was selected as case study. It was found that the number and standard deviation of behavioral parameter sets both decreased when the threshold value for the baseflow efficiency index increased, and the high Nash-Sutcliffe efficiency coefficients correspond well with the high baseflow efficiency coefficients. The results also showed that uncertainty interval width decreased significantly, while containing ratio did not decrease by much and the simulated runoff with the behavioral parameter sets can fit better to the observed runoff, when threshold for the baseflow efficiency index was taken into consideration. These implied that using the baseflow estimation information can reduce the uncertainty in hydrological modeling to some degree and gain more reasonable prediction bounds.

scholarly journals Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1641 ◽  
Author(s):  
Phuong Cu Thi ◽  
James Ball ◽  
Ngoc Dao
Keyword(s):  
Threshold Value ◽  
Uncertainty Estimation ◽  
Design Flow ◽  
Design Flood ◽  
Bayesian Techniques ◽  
Flood Estimation ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Parameter Values ◽  
The Impact

In the last few decades tremendous progress has been made in the use of catchment models for the analysis and understanding of hydrologic systems. A common application involves the use of these models to predict flows at catchment outputs. However, the outputs predicted by these models are often deterministic because they focused only on the most probable forecast without an explicit estimate of the associated uncertainty. This paper uses Bayesian and Generalized Likelihood Uncertainty Estimation (GLUE) approaches to estimate uncertainty in catchment modelling parameter values and uncertainty in design flow estimates. Testing of join probability of both these estimates has been conducted for a monsoon catchment in Vietnam. The paper focuses on computational efficiency and the differences in results, regardless of the philosophies and mathematical rigor of both methods. It was found that the application of GLUE and Bayesian techniques resulted in parameter values that were statistically different. The design flood quantiles estimated by the GLUE method were less scattered than those resulting from the Bayesian approach when using a closer threshold value (1 standard deviation departed from the mean). More studies are required to evaluate the impact of threshold in GLUE on design flood estimation.

scholarly journals Multi-site evaluation to reduce parameter uncertainty in a conceptual hydrological modeling within the GLUE framework

2013 ◽  
Vol 16 (1) ◽  
pp. 60-73 ◽  
Author(s):  
Kairong Lin ◽  
Pan Liu ◽  
Yanhu He ◽  
Shenglian Guo
Keyword(s):  
Parameter Uncertainty ◽  
Hydrological Modeling ◽  
Yellow River ◽  
Practical Importance ◽  
Yellow River Basin ◽  
Xinanjiang Model ◽  
Runoff Series ◽  
Modeling And Forecasting ◽  
Flow Information ◽  
Behavioral Parameter

Reducing uncertainty of hydrological modeling and forecasting has both theoretical and practical importance in hydrological sciences and water resources management. This study focuses on reducing parameter uncertainty by multi-sites validating for the conceptual Xinanjiang model. The generalized likelihood uncertainty estimation (GLUE) method was used to conduct the uncertainty analysis with Shuffled Complex Evolution Metropolis (SCEM-UA) sampling. The discharge criterion of interior gauge station was added to select the behavioral parameters, and then two comparable schemes were established to illustrate how well the uncertainty can be reduced by considering the observations of the interior sites’ flow information. The Dongwan watershed, a sub-basin of the Yellow River basin in China, was selected as the case study. The results showed that the number and standard deviation of behavioral parameter sets decreased, and the simulated runoff series by the Xinanjiang model with the behavioral parameter sets can fit better with the observed runoff series when setting the threshold value at the interior sites. In addition, considering the interior sites’ flow information allows one to derive more reasonable prediction bounds and reduce the uncertainty in hydrological modeling and forecasting to some degree.

scholarly journals Uncertainty Assessment of Urban Hydrological Modelling from a Multiple Objective Perspective

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1393 ◽  
Author(s):  
Bo Pang ◽  
Shulan Shi ◽  
Gang Zhao ◽  
Rong Shi ◽  
Dingzhi Peng ◽  
...  
Keyword(s):  
Flood Management ◽  
Uncertainty Assessment ◽  
Multiple Objective ◽  
Hydrological Models ◽  
Validation Period ◽  
Urban Flood ◽  
Calibration Period ◽  
Storm Water Management Model ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation

The uncertainty assessment of urban hydrological models is important for understanding the reliability of the simulated results. To satisfy the demand for urban flood management, we assessed the uncertainty of urban hydrological models from a multiple-objective perspective. A multiple-criteria decision analysis method, namely, the Generalized Likelihood Uncertainty Estimation-Technique for Order Preference by Similarity to Ideal Solution (GLUE-TOPSIS) was proposed, wherein TOPSIS was adopted to measure the likelihood within the GLUE framework. Four criteria describing different urban stormwater characteristics were combined to test the acceptability of the parameter sets. The TOPSIS was used to calculate the aggregate employed in the calculation of the aggregate likelihood value. The proposed method was implemented in the Storm Water Management Model (SWMM), which was applied to the Dahongmen catchment in Beijing, China. The SWMM model was calibrated and validated based on the three and two flood events respectively downstream of the Dahongmen catchment. The results showed that the GLUE-TOPSIS provided a more precise uncertainty boundary compared with the single-objective GLUE method. The band widths were reduced by 7.30 m3/s in the calibration period, and by 7.56 m3/s in the validation period. The coverages increased by 20.3% in the calibration period, and by 3.2% in the validation period. The median estimates improved, with an increase of the Nash–Sutcliffe efficiency coefficients by 1.6% in the calibration period, and by 10.0% in the validation period. We conclude that the proposed GLUE-TOPSIS is a valid approach to assess the uncertainty of urban hydrological model from a multiple objective perspective, thereby improving the reliability of model results in urban catchment.

Uncertainty analysis of the global hydrological model WGHM using the GLUE method for multi-type variables in the Mississippi basin

2020 ◽  
Author(s):  
somayeh shadkam ◽  
Mehedi Hasan ◽  
Christoph Niemann ◽  
Andreas Guenter ◽  
Petra Döll
Keyword(s):  
Hydrological Model ◽  
Latin Hypercube Sampling ◽  
Large River ◽  
Observation Data ◽  
Parameter Uncertainties ◽  
Trade Offs ◽  
Behavioral Parameters ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Likelihood Measures

<p>In this research we evaluated the WaterGAP Global Hydrological Model (WGHM) parameter uncertainties and predictive intervals for multi-type variables, including streamflow, total water storage anomaly (TWSA) and snow cover based on the Generalized Likelihood Uncertainty Estimation (GLUE) method, for a large river basin in North America, the Mississippi basin. The GLUE approach is built on Monte Carlo concept, in which simulations are performed for all the parameter sets. The parameter sets are sampled from a prior range of the parameters using the Latin Hypercube Sampling. The Nash-Sutcliffe efficiency was used as likelihood measure in case of all variables. The behavioral set of models were selected as those which result likelihood measures above the pre-specified thresholds for all three variables or subsets. These behavioral parameters set were used to analyze different parameters uncertainties, trade-offs among the variables, and the influence of each individual observation data on constraining other variables.</p>

Multi-Parameters Water Quality Model Uncertainty Analysis

2013 ◽  
Vol 807-809 ◽  
pp. 301-307
Author(s):  
Yan Li ◽  
Zi Ming Wang ◽  
Long Jiang Zhang ◽  
De Guan Wang
Keyword(s):  
Water Quality ◽  
Total Nitrogen ◽  
Coupling Model ◽  
Quality Model ◽  
Value Range ◽  
Glue Method ◽  
Function Module ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Volume Method ◽  
Sensitive Parameters

Based on the water quality index interaction of WASP model and the finite volume method, two-dimensional coupling model of water quantity and water quality was established. Then a random function module was added into the model having Generalized Likelihood Uncertainty Estimation (GLUE) function. Using GLUE method analyzes the uncertainty and sensitivity of the established model. The results show that organic sedimentation rateVS3is the most sensitive to total nitrogen changes, and its sensitive value range is 0.03-0.07m/d, while the influence of other parameters isnt obvious. By using the combinations of obtained sensitive parameters, the total nitrogen variation of Taihu Lake is simulated. The results are all within the 95% confidence interval, which explains that the model is reasonable.

scholarly journals Appraisal of the generalized likelihood uncertainty estimation (GLUE) method

2008 ◽  
Vol 44 (12) ◽  
Author(s):  
Jery R. Stedinger ◽  
Richard M. Vogel ◽  
Seung Uk Lee ◽  
Rebecca Batchelder
Keyword(s):  
Uncertainty Estimation ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation

scholarly journals An Evaluation of Catchment Transit Time Model Parameters: A Comparative Study between Two Stable Isotopes of Water

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 318
Author(s):  
Samuel Bansah ◽  
Samuel Ato Andam-Akorful ◽  
Jonathan Quaye-Ballard ◽  
Matthew Coffie Wilson ◽  
Solomon Senyo Gidigasu ◽  
...  
Keyword(s):  
Time Series ◽  
Transit Time ◽  
Mean Transit Time ◽  
Model Parameters ◽  
Flow Process ◽  
Time Model ◽  
Shallow Subsurface ◽  
Behavioral Parameter ◽  
Stable Isotopes Of Water ◽  
Generalized Likelihood Uncertainty Estimation

Using δ18O and δ2H in mean transit time (MTT) modeling can ensure the verifiability of results across catchments. The main objectives of this study were to (i) evaluate the δ18O- and δ2H-based behavioral transit time distributions and (ii) assess if δ18O and δ2H-based MTTs can lead to similar conclusions about catchment hydrologic functioning. A volume weighted δ18O (or δ2H) time series of sampled precipitation was used as an input variable in a 50,000 Monte Carlo (MC) time-based convolution modeling process. An observed streamflow δ18O (or δ2H) time series was used to calibrate the model to obtain the simulated time series of δ18O (or δ2H) of the streamflow within a nested system of eight Prairie catchments in Canada. The model efficiency was assessed via a generalized likelihood uncertainty estimation by setting a minimum Nash–Sutcliffe Efficiency threshold of 0.3 for behavioral parameter sets. Results show that the percentage of behavioral parameter sets across both tracers were lower than 50 at the majority of the studied outlets; a phenomenon hypothesized to have resulted from the number of MC runs. Tracer-based verifiability of results could be achieved within five of the eight studied outlets during the model process. The flow process in those five outlets were mainly of a shallow subsurface flow as opposed to the other three outlets, which experienced other additional flow dynamics. The potential impacts of this study on the integrated use of δ18O and δ2H in catchment water storage and release dynamics must be further investigated in multiple catchments within various hydro-physiographic settings across the world.

scholarly journals Influence of Parameter Sensitivity and Uncertainty on Projected Runoff in the Upper Niger Basin under a Changing Climate

2017 ◽  
Author(s):  
Ganiyu Titilope Oyerinde ◽  
Bernd Diekkrüger
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Meteorological Data ◽  
Global Climate Models ◽  
Low Flow ◽  
Rainfall Runoff ◽  
Moisture Deficit ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Sensitivity And Uncertainty Analysis

Hydro-climatic projections in West Africa are attributed with high uncertainties that are difficult to quantify. This study assesses the influence of the parameter sensitivities and uncertainties of three rainfall runoff models on simulated discharge in current and future times using meteorological data from 8 Global Climate Models. The IHACRES Catchment Moisture Deficit (IHACRES-CMD) model, the GR4J and the Sacramento model were chosen for this study. During model evaluation, 10,000 parameter sets have been generated for each model and used in a sensitivity and uncertainty analysis using the Generalized Likelihood Uncertainty Estimation (GLUE) method. Out of the three models, IHACRES-CMD recorded the highest Nash-Sutcliffe Efficiency (NSE) of 0.92 and 0.86 for the calibration (1997-2003) and the validation (2004-2010) period respectively. The Sacramento model was able to adequately predict low flow patterns on the catchment while the GR4J and IHACRES-CMD over and under estimate low flow respectively. The use of multiple hydrological models to reduce uncertainties caused by model approaches is recommended along with other methods of sustainable river basin managements.

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