scholarly journals Monthly Reservoir Inflow Forecasting for Dry Period Using Teleconnection Indices: A Statistical Ensemble Approach

2020 ◽  
Vol 10 (10) ◽  
pp. 3470
Author(s):  
Donghee Lee ◽  
Hwansuk Kim ◽  
Ilwon Jung ◽  
Jaeyoung Yoon
Keyword(s):  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Data Driven ◽  
Statistical Ensemble ◽  
Support Vector ◽  
Ann Model ◽  
Ensemble Approach ◽  
Inflow Forecasting ◽  
Reservoir Inflow ◽  
Reservoir Watershed

Reliable long-range reservoir inflow forecast is essential to successfully manage water supply from reservoirs. This study aims to develop statistical reservoir inflow forecast models for a reservoir watershed, based on hydroclimatic teleconnection between monthly reservoir inflow and climatic variables. Predictability of such a direct relationship has not been assessed yet at the monthly time scale using the statistical ensemble approach that employs multiple data-driven models as an ensemble. For this purpose, three popular data-driven models, namely multiple linear regression (MLR), support vector machines (SVM) and artificial neural networks (ANN) were used to develop monthly reservoir inflow forecasting models. These models have been verified using leave-one-out cross-validation with expected error S as a measure of forecast skill. The S values of the MLR model ranged from 0.21 to 0.55, the ANN model ranged from 0.20 to 0.52 and the SVM from 0.21 to 0.56 for different months. When used as an ensemble, Bayesian model averaging was more accurate than simple model averaging and naïve forecast for four target years tested. These were considered to be decent prediction skills, indicating that teleconnection-based models have the potential to be used as a tool to make a decision for reservoir operation in preparing for droughts.

scholarly journals Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach

2013 ◽  
Vol 10 (3) ◽  
pp. 3467-3500 ◽  
Author(s):  
J.-G. Liu ◽  
Z.-H. Xie
Keyword(s):  
Soil Moisture ◽  
Seasonal Variations ◽  
Land Surface ◽  
Bayesian Model Averaging ◽  
Mainland China ◽  
Model Averaging ◽  
Meteorological Forcing ◽  
Community Land Model ◽  
Soil Moisture Data ◽  
Ensemble Approach

Abstract. The quality of soil moisture simulation using land surface models depends largely on the accuracy of the meteorological forcing data. The present study investigated how to reduce the uncertainty arising from meteorological forcings in a simulation by adopting a multiple meteorological forcing ensemble approach. Simulations by the Community Land Model version 3.5 (CLM3.5) over mainland China were conducted using four different meteorological forcings, and the four sets of soil moisture data related to the simulations were then merged using simple arithmetical averaging and Bayesian model averaging (BMA) ensemble approaches. Compared to in situ observations, the four simulations captured the spatial and seasonal variations of soil moisture in most cases with some mean bias. They performed differently when simulating the seasonal phases in the annual cycle, the interannual variation and the magnitude of observed soil moisture over different subregions of mainland China, but no individual meteorological forcing performed best for all subregions. The simple arithmetical average ensemble product outperformed most, but not all, individual members over most of the subregions. The BMA ensemble product performed better than simple arithmetical averaging, and performed best for all fields over most of the subregions. The BMA ensemble approach applied to the ensemble simulation reproduced anomalies and seasonal variations in observed soil moisture values, and simulated the mean soil moisture. It is presented here as a promising way for reproducing long-term, high-resolution spatial and temporal soil moisture data.

Combination of Multiple Data-Driven Models for Long-Term Monthly Runoff Predictions Based on Bayesian Model Averaging

2019 ◽  
Vol 33 (9) ◽  
pp. 3321-3338 ◽  
Author(s):  
Huaping Huang ◽  
Zhongmin Liang ◽  
Binquan Li ◽  
Dong Wang ◽  
Yiming Hu ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Data Driven ◽  
Multiple Data ◽  
Monthly Runoff

scholarly journals A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Gwo-Fong Lin ◽  
Tsung-Chun Wang ◽  
Lu-Hsien Chen
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Self Organizing Map ◽  
Support Vector Regression Model ◽  
Proposed Model ◽  
Forecasting Performance ◽  
Short Lead Time ◽  
Reservoir Inflow ◽  
Reservoir Watershed ◽  
Self Organizing

This study describes the development of a reservoir inflow forecasting model for typhoon events to improve short lead-time flood forecasting performance. To strengthen the forecasting ability of the original support vector machines (SVMs) model, the self-organizing map (SOM) is adopted to group inputs into different clusters in advance of the proposed SOM-SVM model. Two different input methods are proposed for the SVM-based forecasting method, namely, SOM-SVM1 and SOM-SVM2. The methods are applied to an actual reservoir watershed to determine the 1 to 3 h ahead inflow forecasts. For 1, 2, and 3 h ahead forecasts, improvements in mean coefficient of efficiency (MCE) due to the clusters obtained from SOM-SVM1 are 21.5%, 18.5%, and 23.0%, respectively. Furthermore, improvement in MCE for SOM-SVM2 is 20.9%, 21.2%, and 35.4%, respectively. Another SOM-SVM2 model increases the SOM-SVM1 model for 1, 2, and 3 h ahead forecasts obtained improvement increases of 0.33%, 2.25%, and 10.08%, respectively. These results show that the performance of the proposed model can provide improved forecasts of hourly inflow, especially in the proposed SOM-SVM2 model. In conclusion, the proposed model, which considers limit and higher related inputs instead of all inputs, can generate better forecasts in different clusters than are generated from the SOM process. The SOM-SVM2 model is recommended as an alternative to the original SVR (Support Vector Regression) model because of its accuracy and robustness.

scholarly journals Comparison of Three Statistical Downscaling Methods and Ensemble Downscaling Method Based on Bayesian Model Averaging in Upper Hanjiang River Basin, China

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jiaming Liu ◽  
Di Yuan ◽  
Liping Zhang ◽  
Xia Zou ◽  
Xingyuan Song
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Support Vector ◽  
Simulation Accuracy ◽  
Downscaling Method ◽  
Hanjiang River

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables to assess the hydrological impacts of climate change. To improve the simulation accuracy of downscaling methods, the Bayesian Model Averaging (BMA) method combined with three statistical downscaling methods, which are support vector machine (SVM), BCC/RCG-Weather Generators (BCC/RCG-WG), and Statistics Downscaling Model (SDSM), is proposed in this study, based on the statistical relationship between the larger scale climate predictors and observed precipitation in upper Hanjiang River Basin (HRB). The statistical analysis of three performance criteria (the Nash-Sutcliffe coefficient of efficiency, the coefficient of correlation, and the relative error) shows that the performance of ensemble downscaling method based on BMA for rainfall is better than that of each single statistical downscaling method. Moreover, the performance for the runoff modelled by the SWAT rainfall-runoff model using the downscaled daily rainfall by four methods is also compared, and the ensemble downscaling method has better simulation accuracy. The ensemble downscaling technology based on BMA can provide scientific basis for the study of runoff response to climate change.

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