Statistical Postprocessing of Water Level Forecasts Using Bayesian Model Averaging With Doubly Truncated Normal Components

Baran, Sándor; Hemri, Stephan; Ayari, Mehrez El

doi:10.1029/2018wr024028

Cited by 17 publications

(7 citation statements)

References 59 publications

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“…More recently, post‐processing techniques such as machine learning (ML) have emerged as a popular alternative method for enhancing the performance of hydrological simulation (He et al., 2021). ML post‐processing contributes to the reduction of systematic errors and improves the accuracy of climate or hydrological simulations and projections (Baran et al., 2019; Gong et al., 2022; Hemri et al., 2015). ML can enhance the modeling performance of physical processes (Bonavita et al., 2021; Ma et al., 2020), enabling researchers to acquire new insights on climate change impacts.…”

Section: Introductionmentioning

confidence: 99%

Observation‐Constrained Projection of Flood Risks and Socioeconomic Exposure in China

et al. 2023

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As the planet warms, the atmosphere's water vapor holding capacity rises, leading to more intense precipitation extremes. River floods with high peak discharge or long duration can increase the likelihood of infrastructure failure and enhance ecosystem vulnerability. However, changes in the peak and duration of floods and corresponding socioeconomic exposure under climate change are still poorly understood. This study employs a bivariate framework to quantify changes in flood risks and their socioeconomic impacts in China between the past (1985–2014) and future (2071–2100) in 204 catchments. Future daily river streamflow is projected by using a cascade modeling chain based on the outputs of five bias‐corrected global climate models (GCMs) under three shared socioeconomic CMIP6 pathways (SSP1‐26, SSP3‐70, and SSP5‐85), a machine learning model and four hydrological models. We also utilize the copula function to build the joint distribution of flood peak and duration, and calculate the joint return periods of the bivariate flood hazard. Finally, the exposure of population and regional gross domestic product to floods are investigated at the national scale. Our results indicate that flood peak and duration are likely to increase in the majority of catchments by 25%–100% by the late 21st century depending on the shared socioeconomic pathway. China is projected to experience a significant increase in bivariate flood risks even under the lowest emission pathway, with 24.0 million dollars/km2 and 608 people/km2 exposed under a moderate emissions scenario (SSP3‐70). These findings have direct implications for hazard mitigation and climate adaptation policies in China.

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Section: Introductionmentioning

confidence: 99%

Observation‐Constrained Projection of Flood Risks and Socioeconomic Exposure in China

et al. 2023

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“…Among them, BMA is one of the most commonly and widely used methods to generate reliable model prediction. In recent years, BMA has been widely used in runoff simulation and prediction [ 11 , 32 , 33 ]. Huo et al [ 34 ] used the numerical model of physical mechanism and BMA to simulate the flood process in semi humid areas.…”

Section: Introductionmentioning

confidence: 99%

Improved runoff forecasting based on time-varying model averaging method and deep learning

et al. 2022

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In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.

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“…Selain melakukan koreksi bias dengan gQM, tahapan utama pada penelitian ini adalah menerapkan BMA pada RAW model ECS4. BMA adalah metode post processing statistik yang menghasilkan prediksi probabilistik dari prediksi ensemble dalam bentuk fungsi kepadatan peluang atau Probability Density Function (PDF) prediktif (Abraham and Puthiyidam, 2016; Baran et al, 2019;Ji et al, 2019;Liu et al, 2019;Song et al, 2018;Xu et al, 2019 Normal (Raftery et al, 2005), curah hujan harian dengan gamma nol (Sloughter et al, 2007), dan kecepatan angin dengan gamma (Sloughter et al, 2010). Menurut Gneiting (2014) dan Sloughter et al (2007), distribusi PDF prediktif BMA untuk curah hujan harian adalah gamma nol.…”

Section: Kalibrasi Prediksi Ensemble Dengan Bayesian Model Averagingunclassified

Calibration Of Rainfall Ensemble Prediction Of ECMWF System 4 Using Bayesian Model Averaging

2020

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Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles in the form of predictive PDFs. It is known that BMA is able to improve the reliability of probabilistic forecast of short range and medium range rainfall forecast. This study aims to develop the application of BMA to calibrate seasonal forecast (long range) in order to improved quality of seasonal forecast in Indonesia. The seasonal forecast used is monthly rainfall from the output of the ensemble prediction system European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4) and it is calibrated against observational data at 26 stations of the Agency for Meteorology Climatology and Geophysics of Republic of Indonesia (BMKG) in Java Island in 1981 – 2018. BMA predictive PDFs is generated with Gamma distribution approach which is obtained based on sequential training windows (JTS) and conditionals training windows (JTC). BMA-JTS approach is done by selecting the width of the 30-month training window as the optimal training period while the BMA-JTC is carried out with a cross-validation scheme for each month. In general, Both of BMA-JTS and BMA-JTC better than RAW models. BMA-JTC calibration results are varying according to spatial and temporal, but in general the result is better in the dry season and during the El Nino phase. BMA is able to improve the distribution characteristics of the RAW model ECS4 prediction which is shown by: a smaller value of Continuous Rank Probability Score (CRPS), a larger value of the Continuous Rank Probability Skill Score (CRPSS) and more flat form of the Verification Rank Histogram (VRH) than the RAW model. BMA also increases the skill, esolution and reliability of prediction of probability Below Normal (BN) and Above Normal (AN), which is known from the increasing Brier Skill Score (BSS), and the increasing area under curve of Relative Operating Characteristics (ROC) compared to the RAW model. Furthermore, the reliability of BN and AN of BMA results also has the category of “still very useful” and “perfect” compared to RAW models that are in the “dangerous”, “not useful” and “marginally useful” categories. The reliability of BMA results with the category “still very useful” and “perfect” show that the probabilistic forecast of BN and AN events can be used in making decisions related to seasonal forecast.

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Statistical Postprocessing of Water Level Forecasts Using Bayesian Model Averaging With Doubly Truncated Normal Components

Cited by 17 publications

References 59 publications

Observation‐Constrained Projection of Flood Risks and Socioeconomic Exposure in China

Observation‐Constrained Projection of Flood Risks and Socioeconomic Exposure in China

Improved runoff forecasting based on time-varying model averaging method and deep learning

Calibration Of Rainfall Ensemble Prediction Of ECMWF System 4 Using Bayesian Model Averaging

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