Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model

Barzkar, Ali; Najafzadeh, Mohammad; Homaei, Farshad

doi:10.1007/s11069-021-05019-7

Cited by 26 publications

(11 citation statements)

References 55 publications

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“…But there is no single standard to determine which evaluation metric is the most accurate assessment method. Therefore, to estimate the performance of the proposed model, the evaluation metrics, such as bias index (BIAS) (Najafzadeh & Sattar 2015;Barzkar et al 2021), scatter index (SI) (Najafzadeh et al 2020), mean absolute percentage error (MAPE), root-mean-square error (RMSE), mean absolute error (MAE) and deterministic coefficient (DC) (Yue et al 2020a(Yue et al , 2020b, are applied. Among them, four common evaluation metrics are adopted in this paper, including MAE, MAPE, RMSE and DC.…”

Section: Evaluation Metricsmentioning

confidence: 99%

A novel medium- and long-term runoff combined forecasting model based on different lag periods

Song

Xiong

et al. 2022

Journal of Hydroinformatics

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The accuracy of medium- and long-term runoff forecasting plays a signiﬁcant role in several applications involving the management of hydrological resources, such as power generation, water supply and ﬂood mitigation. Numerous studies that adopted combined forecasting models to enhance runoff forecasting accuracy have been proposed. Nevertheless, some models do not take into account the effects of different lag periods on the selection of input factors. Based on this, this paper proposed a novel medium- and long-term runoff combined forecasting model based on different lag periods. In this approach, the factors are initially selected by the time-delay correlation analysis method of different lag periods and further screened with stepwise regression analysis. Next, an extreme learning machine (ELM) is adopted to integrate each result obtained from the three single models, including multiple linear regression (MLR), feed-forward back propagation-neural network (FFBP-NN) and support vector regression (SVR), which is optimized by particle swarm optimization (PSO). To verify the effectiveness and versatility of the proposed combined model, the Lianghekou and Jinping hydrological stations from the Yalong River basin, China, are utilized as case studies. The experimental results indicate that compared with MLR, FFBP-NN, SVR and ridge regression (RR), the proposed combined model can better improve the accuracy of medium- and long-term runoff forecasting in the statistical indices of MAE, MAPE, RMSE, DC, U95 and reliability.

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Section: Evaluation Metricsmentioning

confidence: 99%

A novel medium- and long-term runoff combined forecasting model based on different lag periods

Song

Xiong

et al. 2022

Journal of Hydroinformatics

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“…Recall that the shorelines were visible in the remote-sensed slackwater sediments (yellow lines in Figure 5). Next, we evaluated the Index of Agreement (IA), Root Mean Square Error (RMSE), Mean Absolute Percentage of Error (MAPE), Bias Index (BIAS) and Scatter Index (SI) of η (see details in Barzkar et al [50]):…”

Section: Verification Of the Simulated Water Level And Rainfall Databasesmentioning

confidence: 99%

Flood Hazard Mapping with Distributed Hydrological Simulations and Remote-Sensed Slackwater Sediments in Ungauged Basins

Moral-Erencia

Bohorquez

Jimenez-Ruiz

et al. 2021

Water

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We present a basin-scale method to assimilate hydrological data from remote-sensed flood evidence and map civil infrastructures with risk of flooding. As in many rural areas with a semi-arid climate, the studied catchments do not contain stream gauge, and precipitation data does not capture the spatial variability of extreme hydrological events. Remote-sensed flood evidence as slackwater sediments were available at the whole basin, allowing the paleohydrological reconstruction at many sites across the catchment. The agreement between the predicted and observed inundation area was excellent, with an error lower than 15% on average. In addition, the simulated elevations overlapped the observed values in the flooded areas, showing the accuracy of the method. The peak discharges that provoked floods recorded the spatial variability of the precipitation. The variation coefficients of the rainfall intensity were 30% and 40% in the two studied basins with a mean precipitation rate of 3.1 and 4.6 mm/h, respectively. The assumption of spatially uniform precipitation leads to a mean error of 20% in evaluating the local water discharges. Satellite-based rainfall underpredicted the accumulated precipitation by 30–85.5%. Elaborating an inventory of the civil infrastructures at risk was straightforward by comparing the water surface elevation and transport network. The reconstructed maps of rainfall rate were used in the distributed hydrological model IBERPLUS to this end. Recent flood events that overtopped the infrastructures at risk verified our predictions. The proposed research methods can be easily applied and tested in basins with similar physical characteristics around the Mediterranean region.

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“…The major factor that is responsible for drought is rainfall, but temperature also plays a significant role to find the severity of drought over a region (Barzkar et al 2022). It is imperative to perform climate change studies of meteorological variables such as precipitation and temperature over the central India region, Bundelkhand, due to frequent drought events observed in the past decades (Vishwakarma et al 2020).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Assessment of CMIP5 and CORDEX-SA experiments in representing multiscale temperature climatology over central India

Vishwakarma

Choudhary

Chauhan

2022

Water Practice and Technology

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The central India region has been seriously affected by repeated droughts in recent decades due to climate change, which is the main reason for conducting this research. It is still uncertain how the numerous climate models could precisely estimate the future climate for central India. The study mainly focuses on the forcing global climate models (GCMs) and the regional climate models (RCMs). The models have been checked using the coefficient of correlation (r2), Nash Sutcliffe efficiency (NSE) and an improved method, Skill score (SS). The performance is also spatially checked on ArcGIS using the kriging interpolation. The bias-corrected GCMs performed more authentic than the CORDEX RCMs in signifying maximum and minimum temperatures for the Bundelkhand region in central India. Bias-corrected GCMs, EC-EARTH, CCSM4 and GFDL-ESM-2M affirmed the best models on multiple time scales for maximum and minimum temperature in the study region. Maximum NSE and r2 have been observed for seasonal minimum temperature. GCM-EC-EARTH has shown 97% to 98% accuracy, while GCM-GFDL-ESM-2M has demonstrated 84% to 97% accuracy among other selected models. The research outcomes will also assist policymakers in developing strategies and policies for the future climate of central India with the help of more precise projected climatic data.

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Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model

Cited by 26 publications

References 55 publications

A novel medium- and long-term runoff combined forecasting model based on different lag periods

A novel medium- and long-term runoff combined forecasting model based on different lag periods

Flood Hazard Mapping with Distributed Hydrological Simulations and Remote-Sensed Slackwater Sediments in Ungauged Basins

Assessment of CMIP5 and CORDEX-SA experiments in representing multiscale temperature climatology over central India

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