Estimation of the added value of using rainfall–runoff transformation and statistical models for seasonal streamflow forecasting

Sittichok, Ketvara; Seidou, Ousmane; Djibo, Abdouramane Gado; Rakangthong, Neeranat Kaewprasert

doi:10.1080/02626667.2018.1445854

Cited by 7 publications

(9 citation statements)

References 55 publications

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“…Hindcast accuracy, however, is substantially lower for the process-based approach. This result 5 is well in line with findings of other studies that simple statistical model approaches often perform equally well or even better than complex process-based prediction systems, especially in tropical regions due to well exploitable correlations among meteorological and hydrological variables (Block and Rajagopalan, 2009;Hastenrath, 2012;Sittichok et al, 2018). It has to be noted, however, that the process-based approach with the WASA-SED model achieved acceptable results on monthly (hindcasts) and even daily (calibration metrics) time scales whereas former studies in NEB reported passable results only 10 aggregated over seasonal scales (Galvão et al, 2005;Alves et al, 2012).…”

Section: Model Comparisonsupporting

confidence: 81%

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Seasonal drought prediction for semiarid northeast Brazil: About the added value of a process-based hydrological model

Pilz¹,

Delgado²,

Voss³

et al. 2018

Preprint

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Abstract. The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher 10 sensitivity of model prediction performance towards rainfall forecast quality.Based on the findings of this study, we recommend using a statistical approach for merely predicting reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model is worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like 15 data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.

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Section: Model Comparisonsupporting

confidence: 81%

“…Therefore, statistical models relating meteorological or SST indices with streamflow or a combination of statistical and process-based models are applied in many 25 dryland regions in the world to provide seasonal forecasts (e.g. Schepen and Wang, 2015;Seibert et al, 2017;Sittichok et al, 2018).…”

mentioning

confidence: 99%

Seasonal drought prediction for semiarid northeast Brazil: About the added value of a process-based hydrological model

Pilz¹,

Delgado²,

Voss³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Weather forecasts are a result of field observation and general circulation models (GCM). However, due to the small amount of observed data and inconveniences of the MCG scale, it is not possible to satisfactorily answer key questions about the interrelation of atmosphere-ocean-land [11,12]. As a result, statistical models are more popular for applications that require a high spatial and temporal resolution scale [13].…”

Section: Introductionmentioning

confidence: 99%

“…However, currently used PRESASS forum's forecasts and models are not precise enough, and drought and flooding catch authorities and people off guard. That is why it is necessary to develop better models [12,13].…”

Section: Introductionmentioning

confidence: 99%

Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection

et al. 2022

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The high variability of rainfall in the Sahel region causes droughts and floods that affect millions of people every year. Several rainfall forecasting models have been proposed, but the results still need to be improved. In this study, linear, polynomial, and exponential models are developed to forecast rainfall in the Bani and Senegal River basins. All three models use Atlantic sea surface temperature (SST). A fourth algorithm using stepwise regression was also developed for the precipitation estimates over these two basins. The stepwise regression algorithm uses SST with covariates, mean sea level pressure (MSLP), relative humidity (RHUM), and five El Niño indices. The explanatory variables SST, RHUM, and MSLP were selected based on principal component analysis (PCA) and cluster analysis to find the homogeneous region of the Atlantic with the greatest predictive ability. PERSIANN-CDR rainfall data were used as the dependent variable. Models were developed for each pixel of 0.25° × 0.25° spatial resolution. The second-order polynomial model with a lag of about 11 months outperforms all other models and explains 87% of the variance in precipitation over the two watersheds. Nash–Sutcliffe efficiency (NSE) values were between 0.751 and 0.926 for the Bani River basin and from 0.175 to 0.915 for the Senegal River basin, for which the lowest values are found in the driest area (Sahara). Results showed that the North Atlantic SST shows a more robust teleconnection with precipitation dynamics in both basins.

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“…Previous studies have statistically predicted the regional climate (e.g., temperature, precipitation) of a target site based on global scale-climate variables, such as sea surface temperature (SST) and geopotential height (GPH), through teleconnection. Statistical analysis methods, such as multiple regression analysis and machine learning techniques, have been mainly applied to predict climate variables such as temperature and precipitation based on teleconnection (Asong et al, 2018, Cho et al, 2016, Kim et al, 2018, Sittichok et al, 2018. In recent years, the studies have also been conducted on El Niño-Southern-oscillation (ENSO) teleconnection as a climate variability factor that affects the global climate for seasonal forecasting (Amarasekera et al, 1997, Bonsal et The Korean Peninsula is located on the western border of the North Paci c Ocean and is in uenced by the El Niño and La Niña phenomena.…”

Section: Introductionmentioning

confidence: 99%

Predicting Temperature And Precipitation During The Flood Season Based On Teleconnection

Jung¹,

Kim

2021

Preprint

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In recent years, the damages resulting from abnormal hydrometeorological climate have substantially increased over the world due to the climate variability and change. Especially, the flood damage has been severely occurred during the flood season almost every year in Korea. For an example, we had the localized heavy rainfalls for 54 days in flood season of 2020 and had huge property damage and loss of life. Therefore, the study needs to be conducted to improve the predictive power of seasonal time-scale forecasts spanning one to several months for the damage reduction and prevention. In this regard, this study aims to provide a priori predictions (several months ahead) of the climate variable at target sites with a statistical method based on teleconnection with global climatic conditions. Herein, the paradigm of the temperature and precipitation prediction in the Geum river basin in Korea is presented. The purposes of the study are also (1) to analyse the characteristics of summer temperatures and precipitation according to the occurrence of El Niño/La Niña and (2) to suggest a seasonal prediction model that can consider the effects of the occurrence of El Niño and La Niña during the flood season. The model is constructed by classifying the data period into El Niño, La Niña, and neutral status. Then we have shown that the prediction model improves the predictive power for the predictions of climate variables such as temperature and precipitation at midlatitude stations which Korea is located. Therefore, this study demonstrates the possibility of improving the predictive power for forecasting temperature and precipitation by the prediction model considering climate variability.

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Estimation of the added value of using rainfall–runoff transformation and statistical models for seasonal streamflow forecasting

Cited by 7 publications

References 55 publications

Seasonal drought prediction for semiarid northeast Brazil: About the added value of a process-based hydrological model

Seasonal drought prediction for semiarid northeast Brazil: About the added value of a process-based hydrological model

Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection

Predicting Temperature And Precipitation During The Flood Season Based On Teleconnection

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