scholarly journals Development of multi-model ensemble for projection of extreme rainfall events in Peninsular Malaysia

2019 ◽  
Vol 50 (6) ◽  
pp. 1772-1788 ◽  
Author(s):  
Muhammad Noor ◽  
Tarmizi Ismail ◽  
Shamsuddin Shahid ◽  
Mohamed Salem Nashwan ◽  
Shahid Ullah
Keyword(s):  
Bias Correction ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Correction Method ◽  
Peninsular Malaysia ◽  
Model Ensemble ◽  
Random Forest Regression ◽  
Maximum Increase ◽  
Extreme Rainfall Events ◽  
Rainfall Extremes

Abstract Possible changes in rainfall extremes in Peninsular Malaysia were assessed in this study using an ensemble of four GCMs of CMIP5. The performance of four bias correction methods was compared, and the most suitable method was used for downscaling of GCM simulated daily rainfall to the spatial resolution (0.25°) of APHRODITE rainfall. The multi-model ensemble (MME) mean of the downscaled rainfall was developed using a random forest regression algorithm. The MME projected rainfall for four RCPs were compared with APHRODITE rainfall for the base year (1961–2005) to assess the annual and seasonal changes in eight extreme rainfall indices. The results showed power transformation as the most suitable bias correction method. The maximum changes in most of the annual and seasonal extreme rainfall indices were observed for RCP8.5 in the last part of this century. The maximum increase was observed for 1-day and 5 consecutive days' rainfall amount for RCP4.5. Spatial distribution of the changes revealed higher increase of the extremes in the northeast region where rainfall extremes are already very high. The increase in rainfall extremes would increase the possibility of frequent hydrological disasters in Peninsular Malaysia.

Extreme rainfall events during and following heatwaves

2021 ◽  
Author(s):  
Christoph Sauter ◽  
Christopher White ◽  
Hayley Fowler ◽  
Seth Westra
Keyword(s):  
Natural Hazards ◽  
Rainfall Intensity ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Rainfall Extremes ◽  
Average Rainfall ◽  
Time Of Year ◽  
The Impact

<p>Heatwaves and extreme rainfall events are natural hazards that can have severe impacts on society. The relationship between temperature and extreme rainfall has received scientific attention with studies focussing on how single daily or sub-daily rainfall extremes are related to day-to-day temperature variability. However, the impact multi-day heatwaves have on sub-daily extreme rainfall events and how extreme rainfall properties change during different stages of a heatwave remains mostly unexplored.</p><p>In this study, we analyse sub-daily rainfall records across Australia, a country that experiences severe natural hazards on a frequent basis, and determine their extreme rainfall properties, such as rainfall intensity, duration and frequency during SH-summer heatwaves. These properties are then compared to extreme rainfall properties found outside heatwaves, but during the same time of year, to examine to what extent they differ from normal conditions. We also conduct a spatial analysis to investigate any spatial patterns that arise.</p><p>We find that rainfall breaking heatwaves is often more extreme than average rainfall during the same time of year. This is especially prominent on the eastern and south-eastern Australian coast, where frequency and intensity of sub-daily rainfall extremes show an increase during the last day or the day immediately after a heatwave. We also find that although during heatwaves the average rainfall amount and duration decreases, there is an increase in sub-daily rainfall intensity when compared to conditions outside heatwaves. This implies that even though Australian heatwaves are generally characterised by dry conditions, rainfall occurrences within heatwaves are more intense.</p><p>Both heatwaves and extreme rainfall events pose great challenges for many sectors such as agriculture, and especially if they occur together. Understanding how and to what degree these events co-occur could help mitigate the impacts caused by them.</p>

Impacts of convectively coupled equatorial waves on rainfall extremes in Java, Indonesia

2021 ◽  
Author(s):  
Muhamad Reyhan Respati ◽  
Sandro W. Lubis
Keyword(s):  
Heavy Rainfall ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Moisture Flux ◽  
Kelvin Waves ◽  
Equatorial Waves ◽  
Multiple Wave ◽  
Extreme Rainfall Events ◽  
Convectively Coupled Equatorial Waves ◽  
Rainfall Extremes

<p>Rainfall extremes cause significant socioeconomic impacts in Indonesia, as they are often followed by disastrous events, such as floods and landslides. Of particular interest is Java Island, the most populated region in Indonesia, which is prone to damaging flooding as a result of heavy rainfall. The prediction of rainfall extremes in this region has mainly been focused on the effects of seasonal and intraseasonal variability, such as monsoons and the Madden–Julian Oscillation. Here, using an extensive station database from 1987 to 2017 and the gridded Asian Precipitation‐Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) product from 1980 to 2007, we show that severe weather conditions associated with rainfall extremes in Java during the rainy season (November to April) can also be attributed to convectively coupled equatorial waves (CCEWs) that occur on a shorter time scale.</p><p>Evidence is presented that CCEWs, including Kelvin, equatorial Rossby (ER), and mixed Rossby‐gravity (MRG) waves, significantly modulate daily rainfall extremes over Java Island. Of these three types, the Kelvin waves have the greatest influence on heavy rainfall over Java Island. The convectively active (suppressed) phases of Kelvin waves increase (decrease) the probability of extreme rain events over land regions by up to 60% (50%) of the baseline probability. On the other hand, the convectively active phases of ER (MRG) waves increase the probability by up to 45% (40%), while the suppressed phases decrease this by up to 40% (30%). In terms of the mechanism of rainfall extremes, CCEWs modulate moisture flux convergence, leading to the enhancement of local convection over the region. In addition, the analysis of multiple wave events indicates that positive (negative) interferences of the CCEWs lead to an amplification (suppression) of extreme rainfall probability. Overall, the results suggest that equatorial waves provide an important source of the predictability for daily extreme rainfall events over Java Island.</p><p><strong><span>Reference:</span></strong></p><p><span>Lubis, SW</span>, <span>Respati, MR</span>. <span>Impacts of convectively coupled equatorial waves on rainfall extremes in Java, Indonesia</span>. <em>Int J Climatol</em>. <span>2020</span>; <span>1</span>– <span>23</span>. </p><p> </p>

ANALYSIS OF EXTREME RAINFALL INDICES IN PENINSULAR MALAYSIA

2016 ◽  
Vol 78 (9-4) ◽  
Author(s):  
Nur Shazwani Muhammad ◽  
Amieroul Iefwat Akashah ◽  
Jazuri Abdullah
Keyword(s):  
East Coast ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Peninsular Malaysia ◽  
Rainfall Data ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Daily Rainfall Data ◽  
Extreme Rain ◽  
Increasing Trends

Extreme rainfall events are the main cause of flooding. This study aimed to examine seven extreme rainfall indices, i.e. extreme rain sum (XRS), very wet day intensity (I95), extremely wet day intensity (I99), very wet day proportion (R95), extremely wet day proportion (R99), very wet days (N95) and extremely wet days (N99) using Mann-Kendall (MK) and the normalized statistic Z tests. The analyses are based on the daily rainfall data gathered from Bayan Lepas, Subang, Senai, Kuantan and Kota Bharu. The east coast states received more rainfall than any other parts in Peninsular Malaysia. Kota Bharu station recorded the highest XRS, i.e. 648 mm. The analyses also indicate that the stations in the eastern part of Peninsular Malaysia experienced higher XRS, I95, I99, R95 and R99 as compared to the stations located in the western and northern part of Peninsular Malaysia. Subang and Senai show the highest number of days for wet and very wet (N95) as compared to other stations. Other than that, all stations except for Kota Bharu show increasing trends for most of the extreme rainfall indices. Upward trends indicate that the extreme rainfall events were becoming more severe over the period of 1960 to 2014. 

Bias Correction Method of High-Resolution Satellite-Based Precipitation Product for Peninsular Malaysia

2021 ◽  
Author(s):  
Zafar Iqbal ◽  
Shamsuddin Shahid ◽  
Kamal Ahmed ◽  
Xiaojun Wang ◽  
Tarmizi Ismail ◽  
...  
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Daily Rainfall ◽  
Skill Score ◽  
Correction Method ◽  
Peninsular Malaysia ◽  
Rainfall Events ◽  
Bias Correction Method ◽  
Rainfall Occurrence ◽  
Classification And Regression

Abstract Satellite-based precipitation (SBP) is emerging as a reliable source for high-resolution rainfall estimates over the globe. However, uncertainty in SBP is still significant, limiting their use without evaluation and often without bias correction. The bias correction of SBP remained a challenge for atmospheric scientists. In this study, the performance of six SBPs, namely, SM2RAIN-ASCAT, IMERG, GsMap, CHIRPS, PERSIANN-CDS and PERSIANN-CSS in replicating observed daily rainfall at 364 stations over Peninsular Malaysia was evaluated. The bias of the most suitable SBP was corrected using a novel machine learning (ML)-based bias-correction method. The proposed bias-correction method consists of an ML classifier to correct the bias in estimating rainfall occurrence and an ML regression model to correct the amount of rainfall during rainfall events. The performance of different widely used ML algorithms for classification and regression were evaluated to select the suitable algorithms. IMERG showed better performance, showing a higher correlation coefficient (R2) of 0.57 and Kling-Gupta Efficiency (KGE) of 0.5 compared to the other products. The performance of random forest (RF) was better than the k-nearest neighbourhood (KNN) for both classification and regression. RF classified the rainfall events with a skill score of 0.38 and estimated the rainfall amount of a rainfall event with the modified Index of Agreement (md) of 0.56. Comparison of IMERG and bias-corrected IMERG (BIMERG) revealed an average reduction in RMSE by 55% in simulating observed rainfall. The proposed bias correction method performed much better when compared with the conventional bias correction methods such as linear scaling and quantile regression. The BIMERG could reliably replicate the spatial distribution of heavy rainfall events, indicating its potential for hydro-climatic studies like flood and drought monitoring in the study area.

The Influence of Geographical Factors on Extreme Rainfalls in Lampung Province

2019 ◽  
Vol 1 (1) ◽  
pp. 33
Author(s):  
M Welly
Keyword(s):  
Geographical Location ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Design Flood ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Ungauged Basin ◽  
Flood Discharge ◽  
Design Rainfall ◽  
Geographical Factors

Many people in Indonesia calculate design rainfall before calculating the design flooddischarge. The design rainfall with a certain return period will eventually be convertedinto a design flood discharge by combining it with the characteristics of the watershed.However, the lack of a network of rainfall recording stations makes many areas that arenot hydrologically measured (ungauged basin), so it is quite difficult to know thecharacteristics of rain in the area concerned. This study aims to analyze thecharacteristics of design rainfall in Lampung Province. The focus of the analysis is toinvestigate whether geographical factors influence the design rainfall that occurs in theparticular area. The data used in this study is daily rainfall data from 15 rainfallrecording stations spread in Lampung Province. The method of frequency analysis usedin this study is the Gumbel method. The research shows that the geographical location ofan area does not have significant effect on extreme rainfall events. The effect of risingearth temperatures due to natural exploitation by humans tends to be stronger as a causeof extreme events such as extreme rainfall.Keywords: Influence, geographical, factors, extreme, rainfall.

scholarly journals TRMM rainfall estimative coupled with Bell (1969) methodology for extreme rainfall characterization

2015 ◽  
Vol 369 ◽  
pp. 163-168 ◽  
Author(s):  
E. Schiavo Bernardi ◽  
D. Allasia ◽  
R. Basso ◽  
P. Freitas Ferreira ◽  
R. Tassi
Keyword(s):  
Temporal Distribution ◽  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Extreme Rainfall ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Remote Sensors ◽  
Indirect Measures ◽  
Equation Error ◽  
Convective Cells

Abstract. The lack of rainfall data in Brazil, and, in particular, in Rio Grande do Sul State (RS), hinders the understanding of the spatial and temporal distribution of rainfall, especially in the case of the more complex extreme events. In this context, rainfall's estimation from remote sensors is seen as alternative to the scarcity of rainfall gauges. However, as they are indirect measures, such estimates needs validation. This paper aims to verify the applicability of the Tropical Rainfall Measuring Mission (TRMM) satellite information for extreme rainfall determination in RS. The analysis was accomplished at different temporal scales that ranged from 5 min to daily rainfall while spatial distribution of rainfall was investigated by means of regionalization. An initial test verified TRMM rainfall estimative against measured rainfall at gauges for 1998–2013 period considering different durations and return periods (RP). Results indicated that, for the RP of 2, 5, 10 and 15 years, TRMM overestimated on average 24.7% daily rainfall. As TRMM minimum time-steps is 3 h, in order to verify shorter duration rainfall, the TRMM data were adapted to fit Bell's (1969) generalized IDF formula (based on the existence of similarity between the mechanisms of extreme rainfall events as they are associated to convective cells). Bell`s equation error against measured precipitation was around 5–10%, which varied based on location, RP and duration while the coupled BELL+TRMM error was around 10–35%. However, errors were regionally distributed, allowing a correction to be implemented that reduced by half these values. These findings in turn permitted the use of TRMM+Bell estimates to improve the understanding of spatiotemporal distribution of extreme hydrological rainfall events.

scholarly journals Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Lu Ye ◽  
Saadya Fahad Jabbar ◽  
Musaddak M. Abdul Zahra ◽  
Mou Leong Tan
Keyword(s):  
Sea Level ◽  
Daily Rainfall ◽  
Peninsular Malaysia ◽  
Gradient Boosting ◽  
Sea Level Pressure ◽  
Extreme Rainfall Events ◽  
Inference System ◽  
Rainfall Events ◽  
Level Pressure ◽  
Extreme Gradient Boosting

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error (NRMSE) of 0.678 compared with HNFIS (NRMSE = 0.708), BART (NRMSE = 0.784), xgBoost (NRMSE = 0.803), and ELM (NRMSE = 0.915). Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably.

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