Using uncertainty and sensitivity analysis for finding the best rainfall-runoff model in mountainous watersheds (Case study: the Navrood watershed in Iran)

Adib, Arash; Lotfirad, Morteza; Haghighi, Ali

doi:10.1007/s11629-018-5010-6

Cited by 27 publications

(10 citation statements)

References 31 publications

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“…To evaluate the performance of the models, the statistical indices of NS and root-mean-square error (RMSE) were used to determine the accuracy and error of the modeling (Nash & Sutcliffe 1970;Adib et al 2019).…”

Section: Evaluation Criteriamentioning

confidence: 99%

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Lotfirad

Esmaeili-Gisavandani

Adib

2021

Journal of Water and Climate Change

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The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficient of SPI and SPEI was 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing of time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.

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Section: Evaluation Criteriamentioning

confidence: 99%

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Lotfirad

Esmaeili-Gisavandani

Adib

2021

Journal of Water and Climate Change

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“…The sources of runoff prediction errors mainly include model structure error, measurement data error and system initial state error, etc. The size of prediction error will directly affect the scheduling mode of hydropower stations [7][8] . By comparing and analyzing the output plan of each cascade power station and the actual output in the power system, it is not difficult to find the important position of peak load balancing.…”

Section: Runoff Error Regression Model Is Constructedmentioning

confidence: 99%

“…This part of compensation benefit is the externality generated by the construction and operation of upstream hydropower stations to downstream hydropower stations. It is necessary to quantify the compensation benefits of construction and operation of controlled hydropower stations without considering joint operation: 8) In formula (8), n represents outbound flow and  represents inbound and outbound flow. In the model, the incoming water of hydropower stations is described by a deterministic process, which is usually predicted according to the historical runoff data and meteorological information.…”

Section: Optimizing the Cooperative Scheduling Modementioning

confidence: 99%

Coordinated operation method of cascade hydropower stations considering runoff error

You

2022

J. Phys.: Conf. Ser.

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The traditional collaborative scheduling method of cascade hydropower stations group has the problem of imperfect regression model, which leads to excessive water consumption rate. A collaborative scheduling method of cascade hydropower stations group considering runoff error is designed. The influence factors of runoff forecast were obtained, the network parameters were called, and the regression model was built based on the runoff error according to the total power generation of cascade hydropower stations in the degree period. The objective function of hydropower system with hydropower stations as the center was established to optimize the cooperative dispatching mode. Experimental results: The average water consumption rates of the coordinated operation method of cascade hydropower stations group in this paper and the other two kinds of coordinated operation method of cascade hydropower stations group are 34.86%, 57.26% and 59.47% respectively, indicating that the coordinated operation method of cascade hydropower stations group with runoff error is more perfect.

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“…The error measures used to evaluate the accuracy of the models included the root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NS), coefficient of determination (R 2 ), and Kling-Gupta efficiency (KGE) (McCuen et al 2006;Gupta et al 2009;Knoben et al 2019;Adib et al 2019). These measures are defined as:…”

Section: Performance Criteriamentioning

confidence: 99%

Improving the performance of rainfall-runoff models using the gene expression programming approach

Esmaeili-Gisavandani

Lotfirad

Sofla

et al. 2021

Journal of Water and Climate Change

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In this study, five hydrological models, including the soil and water assessment tool (SWAT), identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow (IHACRES), Hydrologiska Byråns Vattenbalansavdelning (HBV), Australian water balance model (AWBM), and Soil Moisture Accounting (SMA), were used to simulate the flow of the Hablehroud River, north-central Iran. All the models were validated based on the root mean square error (RMSE), coefficient of determination (R2), Nash-Sutcliffe model efficiency coefficient (NS), and Kling-Gupta efficiency (KGE). It was found that SWAT, IHACRES, and HBV had satisfactory results in the calibration phase. However, only the SWAT model had good performance in the validation phase and outperformed the other models. It was also observed that peak flows were generally underestimated by the models. The sensitivity analysis results of the model parameters were also evaluated. A hybrid model was developed using gene expression programming (GEP). According to the error measures, the ensemble model had the best performance in both calibration (NS = 0.79) and validation (NS = 0.56). The GEP combination method can combine model outputs from less accurate individual models and produce a superior river flow estimate.

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Using uncertainty and sensitivity analysis for finding the best rainfall-runoff model in mountainous watersheds (Case study: the Navrood watershed in Iran)

Cited by 27 publications

References 31 publications

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Coordinated operation method of cascade hydropower stations considering runoff error

Improving the performance of rainfall-runoff models using the gene expression programming approach

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