Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin

Evsukoff, Alexandre G.; Lima, Beatriz Souza Leite Pires de; Ebecken, Nelson F. F.

doi:10.1007/s11269-010-9736-3

Cited by 14 publications

(6 citation statements)

References 23 publications

(30 reference statements)

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“…Abudu et al [8] used a stochastic hybrid modeling approach for forecasting monthly streamflow in the Rio Grande headwaters basin, and the input variables were antecedent runoff, precipitation and snow water equivalent. Evsukoff et al [9] presented the development of a recurrent fuzzy system model for the Iguaçu River basin in southern Brazil, and the input variable was only rainfall. Talei et al [10] applied a Takagi-Sugeno neuro-fuzzy model with online learning for runoff forecasting for three different catchments, and the input variable was the antecedent rainfall.…”

Section: Introductionmentioning

confidence: 99%

Improved Medium- and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information

Chen

Wei

et al. 2017

Water

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Medium-and long-term runoff forecasting is essential for hydropower generation and water resources coordinated regulation in the Yellow River headwaters region. Climate change has a great impact on runoff within basins, and incorporating different climate information into runoff forecasting can assist in creating longer lead-times in planning periods. In this paper, a multimodel approach was developed to further improve the accuracy and reliability of runoff forecasting fully considering of large-scale and local-scale climatic factors. First, with four large-scale atmospheric oscillations, sea surface temperature, precipitation, and temperature as the predictors, multiple linear regression (MLR), radial basis function neural network (RBFNN), and support vector regression (SVR) models were built. Next, a Bayesian model averaging (BMA)-based multimodel was developed using weighted MLR, RBFNN, and SVR models, and the performance of the BMA-based multimodel was compared to those of the MLR, RBFNN, and SVR models. Finally, the high-runoff performance of these four models was further analyzed to prove the effectiveness of each model. The BMA-based multimodel performed better than those of the other models, as well as high-runoff forecasting. The results also revealed that the performance of the forecasting models with multiple climatic factors were generally superior to that without climatic factors. The BMA-based multimodel with climatic factors not only provides a promising, reliable method for medium-and long-term runoff forecasting, but also facilitates uncertainty estimation under different confidence intervals.

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Section: Introductionmentioning

confidence: 99%

Improved Medium- and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information

Chen

Wei

et al. 2017

Water

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“…Considerable research has been conducted on the relationship between local‐scale climate variables and runoff (Risbey & Entekhabi, ; Tang, Crosby, Wheaton, & Piechota, ; Zhang et al, ). In addition, the variabilities of global‐scale climate variables and their relationships with runoff have received considerable attention in recent years (Chen, Guo, Xu, & Singh, ; Evsukoff, de Lima, & Ebecken, ; Wang, Chau, Qiu, & Chen, ). For example, Pekarova and Pekar () analysed possible teleconnections of the quasi‐biennial oscillation, southern oscillation, North Atlantic oscillation, and Arctic oscillation with long‐term streamflow fluctuation in two mountainous basins in Slovakia (Central Europe).…”

Section: Introductionmentioning

confidence: 99%

Investigation of the relationship between runoff and atmospheric oscillations, sea surface temperature, and local‐scale climate variables in theYellow Riverheadwaters region

Chen

Wei

et al. 2018

Hydrological Processes

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The Yellow River headwaters region (YRHR) contributes nearly 40% of total flow in the Yellow River basin, which is suffering from a serious water shortage problem. Investigation of the relationship between runoff and climate variables is important for understanding the variation trend of runoff in the YRHR under global climate change. Global and local climate variables, including the West Pacific subtropical high; northern hemisphere polar vortex (NH); Tibetan Plateau Index B (TPI‐B); southern oscillation index; sea surface temperature; and precipitation, evaporation, and temperature, were fully considered to explore the relationship with runoff at Jimai, Maqu, and Tangnaihai stations from 1956 to 2014. The results reveal that runoff had a decreasing trend, which will likely be maintained in the future, and there was a significant change in runoff around 1995 at all stations. Correlation analysis indicated that runoff was dominated by precipitation, NH, temperature, and TPI‐B, and a substantial correlation was observed with sea surface temperature and evaporation, but there was little correlation with West Pacific subtropical high and southern oscillation index. Furthermore, impacts of climate change on runoff variations were distinctly different at different temporal scales. Three dominant runoff periodicities were identified by a singular spectrum analysis‐multitaper method and continuous wavelet transform, that is, 1.0‐, 6.9‐, and 24.8‐year runoff periodicities. In addition, runoff was positively correlated with temperature at a 1‐year periodicity, negatively correlated with TPI‐B at a 6.9‐year periodicity, and positively correlated with NH at a 24.8‐year periodicity, that is, temperature, TPI‐B, and NH‐controlled runoff at annual, interannual, and interdecadal scales. Further, all analyses of the stations in the YRHR showed excellent consistency. The results will provide valuable information for water resource management in the YRHR.

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“…O desenvolvimento de modelos com estruturas recorrentes têm atraído grande interesse e se mostrado uma abordagem promissora para problemas envolvendo sistemas dinâmicos não lineares [4]- [16]. Na década de 90, Gorrini e Bersini [11] apresentaram o desenvolvimento dos sistemas fuzzy recorrentes (SFR) como uma extensão da versão tradicional dos sistemas fuzzy do tipo Takagi-Sugeno-Kang (TSK).…”

Section: Introductionunclassified

Development of Recurrent Fuzzy Systems with External Feedback

Santos

Espíndola

Evsukoff

2015

IEEE Latin Am. Trans.

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This paper investigates the use of three external feedback connections in the development of new recurrent fuzzy models for prediction of nonlinear dynamic systems. These models are formulated by state space equations. The state transition function is a TSK recurrent fuzzy system with an external feedback connection and adaptive delayed operators, and the output function is a polynomial function of the states. The identification of the model's parameters is carried out by a canonical differential evolution algorithm. The model performance was evaluated in benchmark problems found in the literature and the results demonstrated that the aiding of external feedback enhanced the recurrent fuzzy system quality, yielding models with good performance.

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Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin

Cited by 14 publications

References 23 publications

Improved Medium- and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information

Improved Medium- and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information

Investigation of the relationship between runoff and atmospheric oscillations, sea surface temperature, and local‐scale climate variables in theYellow Riverheadwaters region

Development of Recurrent Fuzzy Systems with External Feedback

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