Extreme Rainfall Characteristics Research Articles

Improvement of extreme rainfall characteristics for disaggregation of rainfall using MMRC with machine learning based DBSCAN clustering algorithm

Improvement of extreme rainfall characteristics for disaggregation of rainfall using MMRC with machine learning based DBSCAN clustering algorithm

Effect of climate change to characteristic of extreme rainfall over Batanghari watershed

Climate change will cause extreme rainfall, both wet and dry. Extreme rainfall will cause catastrophic floods and droughts. Therefore, it is important to analyse extreme rainfall and flood events in the future. Disasters and environmental issues in the Batanghari watershed include flooding, drought, forest and land fires, sedimentation, and water quality degradation. Therefore, the Batanghari watershed is selected as a study area in this research. Research on climate change in the Batanghari watershed is crucial to understanding the trend of extreme conditions now and in the future. Characteristics of extreme rainfall will be analysed using the climate index that was developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Results show that climate change in the Batanghari watershed is indicated by increasing 5 of 6 extreme rainfall indices: R95p, R99p, Rx1day, Rx5day, and SDII. An increase in extreme rainfall is correlated with an increase in flood events in the Batanghari watershed in similar periods. The extreme rainfall index that most influences flooding in the Batanghari watershed is PRCPTOT and R99p. Flooding is expected to occur more frequently in the future than it does now. The Batanghari Hilir Sub-watershed is more vulnerable to extreme flooding in the future than other sub-watershed.

Applicability of statistical and deep-learning models for rainfall disaggregation at metropolitan stations in India

Study regionMetropolitan stations in India with distinct climatic conditions, namely, Delhi, Chennai, Kolkata, and Mumbai were selected for this study. Study focusRainfall disaggregation models were studied based on four models, namely Neyman-Scott Rectangular pulse (NSRP) process, Microcanonical Multiplicative Random Cascade (MMRC) process and its variant MMRC-K, and one Deep-Learning based process (ANN-K), using metrics like dry periods, event rainfall volumes, extreme rainfall characteristics, etc. New hydrological insights for the regionThe study successfully established individual rainfall volume, event rainfall volume, and event durations are all within 10% of each other for the four stations. Delhi due to its continental climate showed a higher percentage of dry periods and longer dry periods, which were most successfully modelled by MMRC and MMRC-K. Kolkata and Mumbai stations displayed a higher number of extremely intense and cloudburst types of rainfall, which were modeled effectively by the Deep-Learning Model. Chennai has a different rainfall pattern due to returning monsoon which was also captured by the models. Generally, for extreme rainfall parameters, the ANN-K model performs significantly better, successfully reproducing the characteristics at all quantiles, especially with rainfall above 100 mm/hour intensity or cloud bursts, while 50% of the models overestimated these. NSRP on the other hand performs reasonably well for most considered parameters, without being exceptional at any of them. MMRC and MMRC-K most accurately modeled the dry period parameters.

Analysis of Extreme Rainfall Characteristics in 2022 and Projection of Extreme Rainfall Based on Climate Change Scenarios

In this study, we analyzed the characteristics of the heavy rainfall events that occurred in Seoul in 2022 and compared them with the projections of the representative concentration pathway (RCP). The analysis results indicated that climate change is ongoing. In this era of climate crisis, based on the shared socioeconomic pathway (SSP) data, we projected the 20-year frequency rainfall for South Korea at intervals of 1 day/24 h. Our results indicate that the maximum rainfall (with a 24 h duration) will increase by ~18% in the second half of the 21st century, compared to the current maximum rainfall. Finally, we projected the intensity–duration–frequency (IDF) curve for the infrastructure design of Seoul. According to the projected IDF curve, across all durations, the rainfall intensity was the strongest in the early 21st century, indicating that at present, we are in the midst of a climate crisis. Thus, it is important to develop and implement effective urban and river flood management measures to mitigate the current effects of climate change. Notably, our study can serve as a reference for future studies on climate change and help policymakers and decision-makers develop relevant policies and mitigation strategies related to the effects of climate change.

Characteristics of Water Vapor Transport for Extreme Summer Precipitation in the Eastern Southwest China and Its Impact Mechanism

To improve understanding of the characteristics of extreme summer rainfall and its water vapor transport in the eastern part of southwestern China (ESWC), this study analyzed data on daily precipitation from 118 meteorological stations in the ESWC from 1979 to 2020, as well as daily reanalysis data from ERA5 and daily reanalysis data from NCEP/NCAR. The study employed polynomial fitting, correlation, regression, clustering, and mixed single-particle Lagrangian trajectory (HYSPLITv5.0) modeling methods to simulate extreme summer precipitation and its water vapor transport characteristics in the ESWC and its possible formation mechanism. The results show that: (1) The contribution rate of extreme precipitation in the ESWC from 1979 to 2020 varied significantly on the interannual time scale. When the number of extreme precipitation days is high (low), the contribution rate of extreme precipitation is also high (low), while the contribution rate of general precipitation (the percentage of the sum of general precipitation to the total summer precipitation of that year) is often low (high). (2) When extreme precipitation occurs in the ESWC, compared with general precipitation, the high-level potential vortices are stronger, and the cold air from higher latitude is more likely to move southward. Meanwhile, the amount of water vapor input to the region is significantly larger than that of general precipitation. (3) There are four channels of water vapor sources in the ESWC during the period of extreme precipitation: the Bay of Bengal, the Arabian Sea, the western Pacific, and the northwest. The contribution of water vapor from the Bay of Bengal is the highest. The number of extreme summer precipitation days in the ESWC is significantly negatively correlated with the water vapor budget of the eastern boundary and positively correlated with Indian Ocean Basin-Wide (IOBW) index in the previous winter. (4) When the winter SST is high in the IOBW mode, it can cause the western Pacific subtropical high and the South Asian high to be stronger and shifted southward in summer, resulting in an increase in the number of extreme precipitation days in the ESWC.

Non-stationarity in extreme rainfalls across Australia

Future flooding is likely to exceed current design flood levels which are based on historical extreme rainfall characteristics. The Clausius-Clapeyron relationship explains the intensification of extreme rainfalls as approximately 7% per one degree warming as atmospheric water holding capacity increases with temperature. Therefore, to prepare for a future warmer climate, we need to develop methodologies to project future rainfall intensities across the range of durations and exceedance probabilities used in engineering design. However, the studies that have investigated changes in extreme rainfalls across Australia have had disparate results and are not spatially or temporally comprehensive – hampering our understanding of changes in extreme rainfalls across different durations and exceedance probabilities.This study investigates the impact of climate change on rainfalls from the annual maximum to the 1 in 100-year rainfall across a range of storm durations for the continent of Australia. We find increases in short duration (<1 h) annual maximum rainfall are greater than increases in long duration (>1 h) annual maxima across Australia from 1967 to 2021. These results are consistent regardless of the data period or data set chosen for analysis. We estimate events rarer than the annual maxima through fitting non-stationary Generalize Extreme Value models. We find that events of rarer severity have increased more than frequent events. Further, we identify the parameterisation of a model with non-stationary location and scale parameters to capture the changes in historic design quantiles that are consistent with our physical understanding of rainfall intensification, empirical quantile changes, and historical trends. We conclude that trends in annual maxima are best represented by non-stationary models that incorporate changes in both location and scale parameters, not by solely varying either location or scale parameters.

A gauge-based sub-daily extreme rainfall climatology for western Europe

We present here the first climatology of extreme sub-daily rainfall for a large part of western Europe, developed using a newly developed set of sub-daily rainfall indices, derived from a recently created global sub-daily rainfall (GSDR) dataset. The indices describe sub-daily (3-hourly) rainfall extremes in terms of their intensity, frequency and timing. Analysis of the frequency index (number of 3hr periods with >20 mm rain (R3hr20mm)) and a suite of intensity indices including the annual maxima (Rx3hr) and values of the 99.9th percentile at each gauge (R99.9p3hr) indicate a peak in the frequency and intensity of short-duration rainfall extremes in summer across most of western Europe, while areas around the north-west Mediterranean experience the highest intensities and frequencies of 3-hr rainfall in autumn. The index of contribution from 3-h annual maxima to the daily total (Rx3hrP) indicates that these events are often produced by very short-duration or very peaked storms, with a tendency towards a late afternoon or evening peak in the time of occurrence. There are also clear differences in the spatiotemporal occurrence of the sub-daily extremes when compared to extreme daily rainfall indices, which could have repercussions for flood management. Additional analysis of the sub-daily rainfall indices within the context of Köppen-Geiger climate zones indicates Mediterranean-type climate zones experience more intense and more frequent sub-daily extremes, while the intensity of rainfall within cooler climate zones is lower and the most intense events are restricted to summer. The climate zones are found to be a relatively good indicator of the extreme rainfall characteristics that can be expected in a region. Being able to compare sub-daily rainfall characteristics across a wide region in this manner greatly enhances our ability to investigate future variability and change in sub-daily extremes and will aid in high-resolution climate model validation.

Spatial and temporal characteristics of extreme rainfall: Added benefits with sub‐kilometre‐resolution climate model simulations?

AbstractLocal, short‐duration extreme precipitation events can cause floodings and have massive economic consequences. The climate change impact on such events is of great interest, but due to the small spatio‐temporal scales involved, these are challenging to properly represent in climate models. This study analyses a new sub‐kilometre (750 m) HARMONIE‐Climate model simulation driven by ERA5 reanalysis data. Three convection‐permitting models at 750 m, 3 and 5 km grid distance are analysed and compared with driving reanalyses, intermediate model simulations and a dense rain gauge network. The representation of convective events is analysed by a range of metrics categorised as spatial, temporal and event‐focused. Precipitation events are analysed at both hourly and sub‐hourly scales and a clear difference between model performance on these scales is found. Overall, we find a better performance for HCLIM750m for most metrics, yet the added benefits of the computationally intensive sub‐kilometre scale simulation seems limited compared to the convection‐permitting models at 3 and 5 km.

Spatiotemporal Variation of Hourly Scale Extreme Rainstorms in the Huang-Huai-Hai Plain and Its Impact on NDVI

This paper utilizes high-resolution ERA5 hourly data from 1980 to 2020 and long-term normalized difference vegetation index (NDVI) time series obtained from remote sensing and applies trend analysis, correlation analysis, lag analysis, and other methods to study the spatiotemporal characteristics of extreme rainfall at daily and hourly scales in the Huang-Huai-Hai Plain. The paper explores the NDVI’s variability and its relationship with extreme hourly precipitation and analyzes the main factors affecting it. The study made the following observations: (1) The extreme daily precipitation in the Huang-Huai-Hai Plain shows a decreasing trend, with a 13.6 mm/yr reduction rate. In contrast, the proportion of extreme rainfall to total precipitation generally exceeds 20%, and the intensity of extreme rain has gradually increased. The spatial distribution pattern of extreme rainfall follows the distribution pattern of China’s rain belts, with the terrain being an important influencing factor. The high-incidence areas for extreme rainfall are the Huaihe River region and the Shandong Peninsula. (2) The observed significant increase in hourly extreme precipitation events in the Shandong and Henan provinces of the Huang-Huai-Hai Plain has led to an increased risk of flooding, while the corresponding events in the northwest region of the Plain have exhibited a gradual weakening trend over time. (3) The extreme hourly precipitation in the Huang-Huai-Hai plain shows a frequent and scattered pattern, with decreasing intensity over time. Extreme precipitation mainly occurs in the first half of the night, especially between 19:00 and 21:00, with extreme hourly rainfall intensity fluctuating between 0.2 and 0.25 and the proportion of rainfall to total precipitation reaching as high as 10%. The spatial distribution of extreme hourly rainstorms during the peak period (19:00–21:00) exhibits a high rainfall volume, intensity, and frequency pattern in the eastern region, while the western part exhibits low rainfall volume, intensity, and frequency. (4) The incidence of extremely heavy rainfall in an hour has exhibited a more significant increase compared to extreme daily events in the Huang-Huai-Hai Plain, primarily in the form of backward-type precipitation. Hourly extreme precipitation events in the Huang-Huai-Hai Plain are affected by terrain and land use/cover change (LUCC), with the micro-topography of hilly areas leading to a concentrated distribution of precipitation and LUCC suppressing extreme precipitation events in arid climates. (5) At the ten-day scale, the spatial distribution of the NDVI shows a gradually increasing trend from northwest to southeast, with the highest NDVI value reaching up to 0.6 in the southern part of the study area. For extreme hourly precipitation, there is no significant change observed at the multi-year ten-day scale; while the NDVI in the northern and central parts of the Huang-Huai-Hai Plain shows a significant decreasing trend, in contrast, it presents a significant increasing trend in the southern region. (6) Finally, the correlation between NDVI at the ten-day scale and extreme hourly precipitation exhibits a decreasing pattern from north to south, with a correlation coefficient decreasing from 0.48 to 0.08. The lagged correlation analysis of extreme hourly rainfall and NDVI for one, two, and three ten-day periods shows that the lagged effect of extreme hourly precipitation on NDVI is negligible. Analyzing the correlation between extreme hourly rainfall and NDVI for different months, the impact of extreme hourly precipitation on NDVI is predominantly negative, except for June, which shows a positive correlation (0.35), passing the significance test. This study offers a scientific foundation for enhancing disaster warning accuracy and timeliness and strengthening the research on disaster reduction techniques.

Introduction of k-means clustering into random cascade model for disaggregation of rainfall from daily to 1-hour resolution with improved preservation of extreme rainfall

Temporal disaggregation of rainfall has been of particular focus because of the non-availability of higher-resolution rainfall data for a long-duration period. Fine temporal resolution rainfall is used in a multitude of hydrological applications. Researchers have proposed various disaggregation models to disaggregate coarse temporal resolution rainfall. In this paper, firstly, the microcanonical multiplicative random cascade (MMRC) model is applied for disaggregation from daily rainfall to a one-hour scale. The model is applied in four different rainfall stations for disaggregation having varying rainfall patterns and characteristics. It is observed that the MMRC model can generate statistically reliable rainfall time series; however, the extreme rainfall characteristics are not well conserved by the model for all the stations.This paper describes a new model based on a random multiplicative cascade process where classification and parameter generation is done by k-means clustering such that it can better conserve extreme rainfall conditions and generate a reliable rainfall time series (MMRC-K). K-means clustering is a vector quantization method that divides the observations into a particular number of clusters based on the nearest mean called cluster centroid. The novel approach is tested with the same four Indian cities. The use of k-means clustering has made the classification and parameter generation of the model robust such that it can work with data sets of varying characteristics. It is found that MMRC-K provides improved conservation of extreme rainfall characteristics compared to the MMRC model for all four stations. The MMRC-K model reproduces the IDF curves of Delhi and Mumbai stations quite well; however, a little discrepancy was observed at higher resolution and larger return periods in Kolkata and Chennai stations. Extreme rainfall at finer resolution is used in various hydrological analyses and design problems like urban drainage design, stormwater management, etc. The overall superior conservation of the extreme rainfall characteristics in the model-generated rainfall time series by the MMRC-K model compared to the MMRC model supports the potential applicability of the model for temporal disaggregation.

Exploring the Future of Rainfall Extremes Over CONUS: The Effects of High Emission Climate Change Trajectories on the Intensity and Frequency of Rare Precipitation Events

AbstractThe impacts of climate change on extreme rainfall characteristics at fine spatiotemporal scales are governed by substantial uncertainty, primarily due to the systematic error components inherited from conventional numerical prediction systems, and/or the intrinsic assumptions of the selected modeling schemes. Here, we attempt to robustly evaluate the effects of future climate scenarios on intensity‐duration‐frequency (IDF) curves over the entire Contiguous United States, while accounting for the nonstationary nature of the rainfall process across adequately fine spatiotemporal resolutions. To do so, we apply a parametric approach to statistically downscaled climate model outputs that reflect the Representative Concentration Pathway 8.5, which are offered by the North American Coordinated Regional Downscaling Experiment. Compared to traditional IDF estimation techniques, the employed framework is based on multifractal (MF) scaling arguments and assumes that the statistical structure of rainfall at interannual scales can be approximated by sequential realizations of a stationary MF process with parameters that vary slowly across (not within) realizations. The obtained results show that return period estimates exhibit significant downward trends over most of the domain, which slowly dampen with time, as the effects of climate change are more pronounced at lower exceedance probability levels. Given the observed rate of changes in the frequency and intensity of extreme rainfall for the remainder of the century, we argue that future infrastructure design should be strategically tailored to account for a wide range of potential outcomes.

Spatial analysis of historical extreme rainfall characteristics using regionalization in the Lake Biwa and Yodo River Basin, Japan

AbstractThis research investigated the historical changes in basin-scale rainfall characteristics and their spatial distributions in the Lake Biwa and Yodo River Basin. Nine rainfall indices including two general and seven extreme rainfall indices and the probable rainfall according to 50- and 100-year return periods were evaluated based on the data gathered from 61 rain gauge stations. The regionalization of the rainfall indices and probable rainfall was then examined using spatial interpolation via the Kriging method. The results of the spatial analysis for the rainfall indices showed that there is a potentially high risk of extreme rainfall events and pluvial floods, particularly in the northern, western, and southern areas. The 50- and 100-year probable rainfall has historically increased in the north through to the western area of the basin. The return periods of the 50- and 100-year rainfall events decreased in the northern area of Lake Biwa and the western area of the basin. The findings of this research suggest that the local flood management plan needs to be updated depending on the regional differences in extreme rainfall characteristics. In basins/areas with sparsely distributed rain gauge stations, regionalization can provide useful information as part of local flood management planning.

Assessing the Impact of the Urban Landscape on Extreme Rainfall Characteristics Triggering Flood Hazards

This study configures the Weather Research and Forecasting (WRF) model with the updated urban fraction for optimal rainfall simulation over Kampala, Uganda. The urban parameter values associated with urban fractions are adjusted based on literature reviews. An extreme rainfall event that triggered a flood hazard in Kampala on 25 June 2012 is used for the model simulation. Observed rainfall from two gauging stations and satellite rainfall from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) are used for model validation. We compared the simulation using the default urban fraction with the updated urban fraction focusing on extreme rainfall amount and spatial-temporal rainfall distribution. Results indicate that the simulated rainfall is overestimated compared to CHIRPS and underestimated when comparing gridcell values with gauging station records. However, the simulation with updated urban fraction shows relatively better results with a lower absolute relative error score than when using default simulation. Our findings indicated that the WRF model configuration with default urban fraction produces rainfall amount and its spatial distribution outside the city boundary. In contrast, the updated urban fraction has peak rainfall events within the urban catchment boundary, indicating that a proper Numerical Weather Prediction rainfall simulation must consider the urban morphological impact. The satellite-derived urban fraction represents a more realistic urban extent and intensity than the default urban fraction and, thus, produces more realistic rainfall characteristics over the city. The use of explicit urban fractions will be crucial for assessing the effects of spatial differences in the urban morphology within an urban fraction, which is vital for understanding the role of urban green areas on the local climate.

Analyzing the Spatiotemporal Characteristics of Extreme Rainfall Using CAPE and GNSS-Derived ZTD Across China

The power-law relationship between precipitable water vapor (PWV)/convective available potential energy (CAPE), and extreme rainfall (ER) has been explored. However, the retrieval of PWV is reliant on the zenith total delay (ZTD) of the Global Navigation Satellite System (GNSS), and errors are introduced when converting ZTD to PWV. In this study, we propose the ZCER model, a comprehensive analysis model that integrates ZTD, CAPE, and ER, for investigating long-term variation in ER intensity and frequency, which expresses their close relationships in a novel approach and expands the application area of GNSS-derived ZTD. Daily ZTD, CAPE, and rainfall data were collected from 219 GNSS stations in China from 2011 to 2020 (10 years). Time- and frequency-domain information encapsulated in the three variables was extracted using wavelet coherence, proving that both ZTD and CAPE contributed to rainfall. The relationships between ZTD/log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> (CAPE) and log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> (ER) were investigated at the annual, seasonal, and monthly scales. The results revealed that the contribution of ZTD/CAPE to ER varied spatially and temporally. Furthermore, the synergistic contributions of ZTD and CAPE to ER were further investigated. Statistical results showed that CAPE and ZTD not only complemented each other on the geographical scales to ER in China but also on the seasonal and monthly scales. Moreover, the qualitative relationships between the ZTD, CAPE, and ER frequency were elucidated. Our findings validate the strong links between high ZTD, CAPE, and ER intensity and frequency in China on geographical and temporal scales.

Deep learning application for disaggregation of rainfall with emphasis on preservation of extreme rainfall characteristics for Indian monsoon conditions

Deep learning application for disaggregation of rainfall with emphasis on preservation of extreme rainfall characteristics for Indian monsoon conditions

Identifikasi Karakteristik Spasio-Temporal Hujan Ekstrem di Tanjungpinang Menggunakan Google Earth Engine (GEE)

In early January 2021, Tanjungpinang was hit by floods due to extreme rainfall intensity, causing the affected communities to suffer material and non-material losses. The flood has occurred from 2011 to 2021. To minimize the impact of flood events, it is necessary to study the characteristics of rainfall both temporally and spatially. This is considered important because with this information the public can anticipate the impact of extreme rainfall at a certain time and location. This study aims to identify the Spatio-temporal characteristics of extreme rainfall in Tanjungpinang City which often causes floods. However, to identify the characteristics of rainfall, long data is needed. This study uses CHIRPS (Climate Hazards InfraRed Precipitation with Station) satellite data because the rainfall observation data at the Raja Haji Fisabilillah station is not available for 2012. The identification results show that the correlation criterion (R) value between rainfall observation data and CHIRPS satellite data is 0.688. This value is interpreted as "strong" to be used as an alternative study data. GEE (Google Earth Engine) as a cloud-based platform is used to identify the spatial and temporal characteristics of extreme rainfall. From the results of temporal identification, it is known that extreme rainfall occurs in January, April, and December. The results of spatial identification indicate that the area that has the highest average maximum rainfalls at the Bukit Bestari District area of 156.60 mm/day. This can then be confirmed by looking at the documentation of flood events that often occur in the area.

Changes in extreme daily rainfall characteristics in South Africa: 1921–2020

Many areas in South Africa are prone to localized flooding. With climate change already said to affect the intensity of rainfall, there is a need to investigate if there is a change in the probability of significant to extreme daily rainfall across South Africa. This was investigated through the analysis of the daily time series of 70 manual rainfall stations, over the period 1921 to 2020. The analysis period was divided into two equal periods of 50 years for comparison. With the application of the gamma distribution, it is shown that most stations experienced an increase in the probability of receiving more than 50 mm per day, defined as significant rainfall, in the latter half of the analysis period. Also, most stations showed an increase in their 1:50- and 1:100-year return period values, with some stations over the eastern parts showing increases of over 100 mm. There was also an increase in the probability of “heavy rainfall” (>75 mm) and “very heavy rainfall” events (>115 mm) between the first and second half of the analysis period for most stations over the country when applying the Peak-Over-Threshold approach. In summary, the results indicate that, although the number of rain days has remained near-constant over the 1921–2020 period, the probability of experiencing significant and extreme daily rainfall events has increased generally for most regions in South Africa. This is of concern as rainfall of this nature can have serious consequences in terms of flooding, erosion, and damage to agriculture and infrastructure.

Rainwater chemical evolution driven by extreme rainfall in megacity: Implication for the urban air pollution source identification

Atmospheric pollution has become a global environmental issue, which caused a number of human health threats. It is therefore vital to understand the source-sink processes of air pollutants and their mechanisms. Rainwater is not only the major sink (removal process) of air pollutants, but also the good source tracer of atmospheric components. On the background of global climate changes, the extreme rainfall exerts a serious influence in megacity. However, the extreme rainfall driven chemical evolution of rainwater and its reflection on air pollution are rarely focused, even though it plays a significant role in urban-surficial ecosystem. To better understand the chemical evolution of urban extreme rainfall and their potential environmental effects, rainwater samples were collected in Beijing, a typical megacity, during the extreme rainfall period in 2021. Based on rainwater stoichiometry and historical comparison, the scouring process of air substance, the neutralizing process of rainwater and the ion sources were revealed. The findings showed that NH4+, NO3−, SO42−, and Ca2+ were four primary rainwater ions with distinct daily variations which were well removed by rainwater scouring process. The high values of NF (neutralization factor, ∼2.2) and NP/AP (ratio of neutralizing to acidifying potential, ∼1.8) suggested a relatively high level of rainwater neutralization. Source identification revealed that rainwater SO42− (94.9%) and NO3− (99.9%) were primarily originated from anthropogenic input, particularly the mobile emission sources (transportation), while sea salt input represented the major Cl− source (86.9%) and all Na+ source. By contrast, crust dust input was the main contributor of rainwater K+ (94.0%), Mg2+ (92.3%) and Ca2+ (98.2%), whereas NH4+ was considered only the contribution of human input (e.g., municipal feces and fossil fuel burning). This study clarified the chemical characteristics of extreme rainfall in megacity and highlighted the significant impact on urban environment, which will benefit the urban environmental management in the context of global climate change.

Analisis Karakteristik Hujan Ekstrim Menggunakan Model Iklim di Wilayah Gunung Merapi

Extreme rainfall is one of the trigger factors for debris floods in stratovolcanos. It caused by volcanic materials will be easily eroded in large quantity with surface water flow as the result of extreme rainfall. Extreme rainfall is avnatural phenomenon which is often related with climate change. In the future, changes in extreme rainfall characteristics may occur. Therefore, it’s necessary to conduct extreme rainfall analysis for historical and future periods. In this study, the characteristics of rainfall analyzed were the variability of extreme rain as shown by trend analysis of extreme rain indices namely RTOT. Hourly rainfall data at eight rain stations used as input. Future rainfall data was projected using the global climate model CanESM2 (RCP4.5 and RCP8.5 and downscaling process using Statistical Downscaling Model (SDSM). Comparison of the projection rainfall with historical rainfall shows a different trend at each station. Increasing trend occurred at four stations including Plosokerep, Pucanganom, Sopalan, and Talun stations, with the highest increasing trend occurring at Sopalan stations. In addition, there was also a decreasing trend that occurred at Ngandong station for both scenarios and at Sorasan station in the RCP8.5 scenario. The Jrakah and Randugunting stations show a steady trend.

Analysis of changes in extreme rainfall characteristics over Ambica river basin, India

ABSTRACT Significant variations are detected in extreme rainfall characteristics over Ambica river basin, Gujarat. The frequent flooding in Ambica basin has become a persistent phenomenon over last decade. Based on new knowledge and understanding in Extreme Value Theory (EVT), the discrepancies in extreme rainfall characteristics over entire basin are analyzed considering the daily rainfall series of 25 rain gauge stations for period of 1961–2017. At each station, the threshold value was selected as 90th percentile rainfall for computing the excesses of Peak over Threshold (POT) series. To examine the variations in rainfall pattern, basic rainfall indices viz. total annual rainfall (PRCPTOT), rainy days (RD) and simple daily intensity index (SDII) are calculated. To assess the non-uniformity of extreme rainfall, coefficient of variation (Cv) has been found. Non-parametric tests like Modified Mann-Kendall (MMK) test and Sen’s slope test were used for evaluating the trend. The western parts of the basin show a declining trend in PRCPTOT and SDII, wherein stationarity has been observed for RD. The stations in the central region show a significantly declining trend in total annual rainfall. The SDII and RD are found to be higher and lower respectively for the stations situated in eastern parts of the basin. It has been found that 20 %, 4 % and 17 % of stations exhibit significant decreasing trends in PRCPTOT, RD and SDII respectively. The western basin has lesser values of coefficient of variation (Cv) as compared to stations in eastern part. At the majority of stations, extreme rainfall duration and frequency were found to be stationary, whereas intensity of extreme rainfall is found to be non-stationary. Also, it has been found that 16 % of stations exhibit significant decreasing trends in extreme rainfall intensity and duration, which demonstrates the consequences to water availability in near future.