Using Bayesian model averaging to estimate terrestrial evapotranspiration in China

Chen, Yang; Yuan, Wenping; Xia, Jiangzhou; Fisher, Joshua B.; Dong, Wenjie; Zhang, Xiaolin; Liang, Shunlin; Ye, Aizhong; Cai, Wenju; Feng, Jinming

doi:10.1016/j.jhydrol.2015.06.059

Cited by 64 publications

(37 citation statements)

References 91 publications

(104 reference statements)

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“…Recently, Ershadi et al (2014) showed that even the simple averaging (SA) method performed better than any individual model in estimating ET across the 20 selected FLUXNET sites. Also, the Bayesian model averaging (BMA) approach has been used to merge a range of satellite-based models for regional/global ET estimations (Vinukollu et al, 2011;Mueller et al, 2011;Yao et al, 2014;Chen et al, 2015). The results indicated that the BMA method can generally outperforms the best individual model, and provides a useful tool for generating a long-term regional/global terrestrial ET product (Yao et al, 2014;Chen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Also, the Bayesian model averaging (BMA) approach has been used to merge a range of satellite-based models for regional/global ET estimations (Vinukollu et al, 2011;Mueller et al, 2011;Yao et al, 2014;Chen et al, 2015). The results indicated that the BMA method can generally outperforms the best individual model, and provides a useful tool for generating a long-term regional/global terrestrial ET product (Yao et al, 2014;Chen et al, 2015). However, the success of the BMA method depends on the skill and performance of the individual members of the ensemble (Vrugt and Robinson, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Multi-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging

Zhu

Zhang

et al. 2016

Hydrol. Process.

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Using high‐quality dataset from 12 flux towers in north China, the performance of four evapotranspiration (ET) models and the multi‐model ensemble approaches including the simple averaging (SA) and Bayesian model average (BMA) were systematically evaluated in this study. The four models were the single‐layer Penman–Monteith (P–M) model, the two‐layer Shuttleworthe–Wallace (S–W) model, the advection–aridity (A–A) model, and a modified Priestley–Taylor (PT‐JPL). Based on the mean value of Taylor skill (S) and the regression slope between measured and simulated ET values across all sites, the order of overall performance of the individual models from the best to the worst were: S–W (0.88, 0.87), PT‐JPL (0.80, 1.17), P–M (0.63, 1.73) and A–A (0.60, 1.68) [statistics stated as (Taylor skill, regression slope)]. Here, all models used the same values of parameters, LAI and fractional vegetation cover as well as the forcing meteorological data. Thus, the differences in model performance were mainly attributed to errors in model structure. To the ensemble approach, the BMA method has the advantage of generating more skillful and reliable predictions than the SA scheme. However, successful implementation of BMA requires accurate estimates of its parameters, and some degradation in performance were observed when the BMA parameters generated from the training period were used for the validation period. Thus, it is necessary to explore the seasonal variations of the BMA parameters according the different growth stages. Finally, the optimal conditional density function of half‐hourly ET approximated well by the double‐exponential distribution. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging

Zhu

Zhang

et al. 2016

Hydrol. Process.

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“…Consequently, we focus here on comparing the improved versions (DSRC-R, DSRC-D, and DSRC-RD) of the DSRC method with the original version. Performance was evaluated using several measures, including the Nash-Sutcliffe Efficiency (NSE) coefficient (Nash & Sutcliffe, 1970;Schaefli & Gupta, 2007;Yapo et al, 1996), and more comprehensively, the Kling-Gupta efficiency (KGE; Gupta et al, 2009) and its components: the correlation coefficient r, standard deviation ratio α, and mean ratio β, for which mathematical definitions are provided in Appendix C. Ideally, when there are no simulation errors, we would have NSE = 1 and KGE = 1, these being achievable only when r = 1, α = 1, and β = 1 (Chen et al, 2015;Gupta et al, 2009).…”

Section: Synthetic Data Studymentioning

confidence: 99%

Improved Dynamic System Response Curve Method for Real‐Time Flood Forecast Updating

Gupta

Bao

et al. 2019

Water Resources Research

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The dynamic system response curve (DSRC) method has been shown to effectively use error feedback correction to obtain updated areal estimates of mean rainfall and thereby improve the accuracy of real‐time flood forecasts. In this study, we address two main shortcomings of the existing method. First, ridge estimation is used to deal with ill‐conditioning of the normal equation coefficient matrix when the method is applied to small basins, or when the length of updating rainfall series is short. Second, the effects of spatial heterogeneity of rainfall on rainfall error estimates are accounted for using a simple index. The improved performance of the method is demonstrated using both synthetic and real data studies. For smaller basins with relatively homogeneous spatial distributions of rainfall, the use of ridge regression provides more accurate and robust results. For larger‐scale basins with significant spatial heterogeneity of rainfall, spatial rainfall error updating provides significant improvements. Overall, combining the two strategies results in the best performance for all cases, with the effects of ridge estimation and spatially distributed updating complementing each other.

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“…In this method, consensus predictions are derived from multiple competing predictors, and MSPD is generated to exploit the strength of every prediction. BMA method assigns weights to each member based on its predictive performance [27,42,43]. It provides a more convincing portrayal of the predictive uncertainty that considers both within-model and between-model variances and also outperformed other merging techniques by producing reliable and precise results [40,42,43].…”

mentioning

confidence: 99%

“…BMA method assigns weights to each member based on its predictive performance [27,42,43]. It provides a more convincing portrayal of the predictive uncertainty that considers both within-model and between-model variances and also outperformed other merging techniques by producing reliable and precise results [40,42,43]. This method has also been successfully applied in hydrological modeling and streamflow simulations [44][45][46].…”

mentioning

confidence: 99%

An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan

et al. 2019

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Merging satellite precipitation products tends to reduce the errors associated with individual satellite precipitation products and has higher potential for hydrological applications. The current study evaluates the performance of merged multi-satellite precipitation dataset (daily temporal and 0.25 • spatial resolution) developed using the Dynamic Bayesian Model Averaging algorithm across four different climate regions, i.e., glacial, humid, arid and hyper-arid regions, of Pakistan during 2000-2015. Four extensively evaluated SPPs over Pakistan, i.e., Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Prediction Center MORPHing technique (CMORPH), and Era-Interim, are used to develop the merged multi-satellite precipitation dataset. Six statistical indices, including Mean Bias Error, Mean Absolute Error, Root Mean Square Error, Correlation Coefficient, Kling-Gupta efficiency, and Theil's U coefficient, are used to evaluate the performance of merged multi-satellite precipitation dataset over 102 ground precipitation gauges both spatially and temporally. Moreover, the ensemble spread score and standard deviation are also used to depict the spread and variation of precipitation of merged multi-satellite precipitation dataset. Skill scores for all statistical indices are also included in the analyses, which shows improvement of merged multi-satellite precipitation dataset against Simple Model Averaging. The results revealed that DBMA-MSPD assigned higher weights to TMPA (0.32) and PERSIANN-CDR (0.27). TMPA presented higher skills in glacial and humid regions with average weights of 0.32 and 0.37 as compared to PERSIANN-CDR of 0.27 and 0.25, respectively. TMPA and Era-Interim depicted higher skills during pre-monsoon and monsoon seasons, with average weights of 0.31 and 0.52 (TMPA) and 0.25 and 0.21 (Era-Interim), respectively. Merged multi-satellite precipitation dataset overestimated precipitation in glacial/humid regions and showed poor performance, with the poorest values of mean absolute error (2.69 mm/day), root mean square error (11.96 mm/day), correlation coefficient (0.41), Kling-Gupta efficiency score (0.33) and Theil's U (0.70) at some stations in glacial/humid regions. Higher performance is observed in hyper-arid region, with the best values of 0.71 mm/day, 1.72 mm/day, 0.84, 0.93, and 0.37 for mean absolute error, root mean square error, correlation coefficient, Kling-Gupta Efficiency score, and Theil's U, respectively. Merged multi-Satellite Precipitation Dataset demonstrated significant improvements as compared to TMPA across all climate regions with average improvements of 45.26% (mean bias error), 30.99% (mean absolute error), 30.1% (root mean square error), 11.34% (correlation coefficient), 9.53% (Kling-Gupta efficiency score) and 8.86% (Theil's U). The ensemble spread and variation of DBMA-MSPD calculated using ensem...

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Using Bayesian model averaging to estimate terrestrial evapotranspiration in China

Cited by 64 publications

References 91 publications

Multi-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging

Multi-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging

Improved Dynamic System Response Curve Method for Real‐Time Flood Forecast Updating

An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan

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