Daily Precipitation Concentration Research Articles

Daily precipitation concentration and Shannon’s entropy characteristics: spatial and temporal variability in Iran, 1966–2018

Daily precipitation concentration and Shannon’s entropy characteristics: spatial and temporal variability in Iran, 1966–2018

Analysis of daily rainfall concentration in northeastern Algeria 1980–2012

This article analyzes the spatial distribution of the values of six daily precipitation concentration indexes in the northeastern part of Algeria. The used indexes were the concentration index (CI) and the Gini index (GI) with the resolution of precipitation amounts 1, 5, and 10 mm. The values of the six indexes are calculated for the period 1980–2012 with 22 stations, and on the other hand, the correlations of the CI1 concentration index with geographical and rainfall variables are analyzed. The values of CI1, CI5, and CI10 and IG1, IG5, and IG10 have been mapped using the tools of the ArcGis10.02 programs. The CI1 concentration index correlates significantly with all the geographic and rainfall variables considered. A negative linear correlation was observed with a linear correlation coefficient r = − 0.67 (p value = 0.0006) between CI1 and altitude. The correlation between CI1 and the distance to sea with a linear correlation coefficient r = − 0.47 (p value = 0.026), the correlation between CI1 and latitude is positive with r = + 0.40 (p value = 0.032). On the other hand, CI1 is significantly positively correlated with average annual precipitation, the coefficient of variation, and the number of rainy days in the same period: r = 0.52 (p value = 0.023); r = − 0.25 (p value = 0.049) and r = 0.49 (p value = 0.022), respectively.

Changing Properties of Daily Precipitation Concentration in the Hai River Basin, China

Understanding the spatiotemporal pattern of precipitation concentration is important for the assessment of flood and drought risk and utilization of water resources. In this study, the daily precipitation concentration index in the Hai River basin in China was calculated based on the Gini coefficient obtained from the observed data of 51 meteorological stations from 1951 to 2018 and spatiotemporal pattern variations were investigated. The trends and abrupt changes of DPCI were tested by the Mann–Kendall and the Pettitt methods, respectively. The relationships among DPCI, percentage of precipitation contributed by the rainiest days, and disaster losses were discussed by the linear correlation analysis. The results showed that the DPCI value ranges between 0.6471 and 0.7938, decreases westward and northward, which is negatively related to latitude and elevation, and is positively related to longitude. Negative trends of the DPCI were found at most stations, and the PCI trends of more than 80% stations were statistically significant. Abrupt changes of the DPCI have a postponement trend from west to east with time. Daily heterogeneity of the rainfall in a year is highly correlated with the heavy rainfall amount of the 25% rainiest days. For a year, higher DPCI coupled with more precipitation is easy to cause a flood disaster; conversely, higher DPCI along with less precipitation is easy to cause a drought disaster. In the future, the risk of flood disasters would be reduced, but the drought disasters would be increased in the Hai River basin.

Spatiotemporal variation of daily precipitation concentration and its potential relationship with climatic factors and land use types in the Haihe River basin, China

AbstractPrecipitation concentration is a key climatic factor in the hydrologic system. The purpose of this study was to explore the spatiotemporal variation in precipitation concentration and its possible relationship with large‐scale atmospheric circulation and land use types. Based on daily precipitation recorded at 254 stations during 1961–2019 throughout the Haihe River basin (HRB), linear slope and Manner–Kendall trend analysis were used to analyse the spatial variations and trends of the annual daily precipitation concentration index (CI). The Pearson correlation coefficient and cross wavelet analysis were used to evaluate potential correlations between CI and eight climatic factors. Meteorological stations were classified into three land use types: urban type (UT), farmland type (FT), and natural type (NT) using hierarchical clustering, and the impacts of land use types on CI variations and trends were investigated. Annual CI in HRB showed a significant downward trend with a linear rate of −0.0058/decade, and about 210 of the 254 stations had a downtrend, in which 42 stations were at the significance level of 0.05 but did not reach the significance level of 0.01, and 52 stations are at the 0.01 significance level. The East Asian monsoon index (EASMI), South Asian monsoon index (SASMI), South China Sea monsoon index (SCSMI), and Southern Oscillation Index (SOI) were positively correlated with CI, whereas the Pacific Decadal Oscillation (PDO), Multivariate ENSO index (MEI), and Western Pacific index (WP) were negatively correlated. The Sunspot index (SS) had a significant resonance period of 9–14‐year with CI. EASMI and ENSO events were the dominant factors driving CI trends. The negative CI trends of UT were more significant than those of FT and NT, with linear slope gaps of −0.0041/decade (UT versus FT) and −0.0064/decade (UT vs. NT), respectively.

Temporal variability of daily precipitation concentration in Japan for a century: Effects of air temperature rises on extreme rainfall events

Climate change induced by air temperature rises may have a significant impact on change in rainfall patterns. Indeed, flood disasters due to downpours have increased over the last few decades in Japan. This study considers the Lorenz Curve, a concept used in economic theory, to describe spatial-temporal variability in the daily time series of precipitation concentration. The results reveal that annual average air temperature has risen at 10 rural sites and 5 urban sites over the period from 1901 to 2020 (120 years). The obtained trends are positive, and statistically meaningful at the 1% level. Whereas, annual precipitation has not changed significantly in the period (1901–2020) since obtained trends at all 15 sites are non-statistically meaningful (p > 0.01). In the meantime, annual maximum daily precipitation has increased for 1 of 15 sites, i.e. Ishigaki, while annual maximum hourly precipitation has increased for 4 of 15 sites, i.e. Choshi, Sapporo, Tokyo and Fukuoka. Calculated Gini coefficient, i.e. the precipitation concentration index (CI) has increased at all 15 sites in the period (1901–2020). The trends are positive, and statistically significant (p < 0.01). This indicates that rainfall has become more concentrated into a fewer rainy days throughout a year as the air temperature has risen for the 120 year period. The correlation between the precipitation CI and warming trends of individual monthly average air temperatures are weak, i.e. almost no correlation, for April, May, June, and November. Whereas, the obtained Spearman's rank correlation coefficient ρs = 0.78 and 0.72 indicate a strong correlation between the trend in air temperature rises and the trend in precipitation CI for July and August. This suggests that air temperature rises during summer (July and August) play an important role in the change in precipitation characteristics throughout Japan.

Spatiotemporal pattern variations of daily precipitation concentration and their relationship with possible causes in the Yangtze River Delta, China

Abstract Understanding the spatiotemporal pattern of precipitation concentration is important in the water cycle under changing environments. In this study, the daily precipitation concentration index in the Yangtze River Delta in China is calculated based on the Lorenz curves obtained from the observed data of 36 meteorological stations from 1960 to 2017, and spatiotemporal pattern variations and their possible causes are investigated. The driving forces of elevation, SUNSPOT, El Niño-Antarctic Oscillation, Pacific Decade Oscillation, and Arctic Oscillation are detected with correlation and wavelet analysis. Results show that, the daily precipitation concentration index ranges from 0.55 to 0.62 during the study period, 22 of 36 stations (accounting for 61%) show increasing trends, while three stations increase significantly at the 95% significant level. Relationship analysis indicates that the daily precipitation concentration shows a slightly negative correlation with elevation, while the relationships with SUNSPOT, El Niño-Antarctic Oscillation, Pacific Decade Oscillation, and Arctic Oscillation are complicated and diverse, there are different correlations and significance levels in different years. Further analysis shows that SUNSPOT is significantly correlated with El Niño-Antarctic Oscillation, Pacific Decade Oscillation, and Arctic Oscillation, which suggests that SUNSPOT may be an important factor that drives the changes of the three large-scale atmosphere circulation factors and causes precipitation concentration changing indirectly. These results provide further understanding of precipitation variations, which is meaningful for regional flood risk management under climate change.

Reanalysis of the daily precipitation concentration index in the Río Grande of Morelia basin

The daily precipitation concentration index is a useful tool for the analysis of the statistical structure of rainfall. However, it is necessary to be careful with the data used for its calculation since this index can be sensitive to both the temporal location of the data and the length of the records. Thus, the objective of this work consisted of a re-analysis of the daily precipitation concentration index (CI) in the Río Grande de Morelia basin to determine how the temporal location of the data and the length of the records affect the CI values. In addition to analyzing the behavior of the CI under three different conditions. The results show that the temporary location of the records can affect the value of the CI, increasing it if the last available years are analyzed or decreasing it if records before 1980 are taken. Regarding the length of the records, it was found that the value of this index tends to stabilize in most of the cases studied for record lengths greater than 20 years. The behavior of the CI at the annual level is within that reported in the literature, and at the decade level and for the 30-year, a decrease in its variability was observed. Therefore, it is recommended that in studies of this type, a common recording period be chosen for the analyzes and a length according to the objectives pursued.

Daily precipitation concentration in Central Coast Vietnam

Empirical frequency distribution of daily precipitation amounts can be fitted by a negative exponential distribution, because anywhere there are many small daily totals and few large ones. Therefore, the cumulative percentages of days with precipitation, sorted in increasing order according to their amounts, against the cumulative percentage of the rainfall amounts that they contribute are fitted by positive exponential curves Y = aXebx, a and b constants. Based on these curves, the Concentration Index (CI) evaluates the contribution of the rainiest days to the total amount. In this study the CI has been calculated for 15 meteorological stations in Da Nang city and Quang Nam province in Central Coast Vietnam, for the 1979–2016 period. The results show high values of CI, ranging from 0.62 to 0.72. Conversely, the linear correlation between altitude and CI is negative (R = -0.60, p < 0.01). There are no correlations between the latitude nor the annual mean number of precipitation days and the CI. CI change for the sub-periods of 1979–1997 and 1998–2016 is also analyzed.

Spatial Characteristics of Precipitation in the Greater Sydney Metropolitan Area as Revealed by the Daily Precipitation Concentration Index

In this study; the spatial distribution of the Daily Precipitation Concentration Index (DPCI) has been analyzed inside the Greater Sydney Metropolitan Area (GSMA). Accordingly, the rainfall database from the Australian Bureau of Meteorology archive was utilized after comprehensive quality control. The compiled data contains a set of 41 rainfall stations indicating consistent daily precipitation series from 1950 to 2015. In the analysis of the DPCI across GSMA the techniques of Moran’s Spatial Autocorrelation has been applied. In addition, a cross-covariance method was applied to assess the spatial interdependency between vector-based datasets after performing an Ordinary Kriging interpolation. The results identify four well-recognized intense rainfall development zones: the south coast and topographic areas of the Illawarra district characterized by Tasman Sea coastal regions with DPCI values ranging from 0.61 to 0.63, the western highlands of the Blue Mountains, with values between 0.60 and 0.62, the inland regions, with lowest rainfall concentrations between 0.55 and 0.59, and lastly the districts located inside the GSMA with DPCI ranging 0.60 to 0.61. Such spatial distribution has revealed the rainstorm and severe thunderstorm activity in the area. This study applies the present models to identify the nature and mechanisms underlying the distribution of torrential rains over space within the metropolis of Sydney, and to monitor any changes in the spatial pattern under the warming climate.

Analysis of behaviour and relationship of four daily precipitation concentration indices according to Mexico's precipitation

AbstractIn recent years, the field of precipitation concentration has garnered significant research interest owing to the importance of precipitation for economic activities related to agriculture, energy production, and drinking water supply management. For the study and characterization of precipitation concentration, different approaches have been defined, such as consideration of the concentration index (CI), Theil index (TI), Q index (QI), and precipitation concentration degree (PCD) index. However, in the literature, there is only one study that evaluates the performance of the CI and TI in conjunction on a spatial basis. Another study evaluates the behaviour of the QI and PCD indices on a temporal basis. Thus, the objective of this work was to analyse the behaviour of the four indices both on temporal and spatial scales according to Mexico's precipitation. Results show that the four indices can detect, in a similar manner, the concentration degree of regions with different rainfall regimes, each within its own variability. A lower range of variability was found for the TI, while the highest was found for the CI and the PCD index. In addition, the PCD index showed high sensitivity within its concentration degree when calculated for different time intervals. The results of the temporal and spatial correlation analysis show a very high correlation between the CI and TI, and moderate and low correlations among the other indices. Low correlations can be attributed to the differences in the theoretical bases of each index. Although low correlations were shown for some indices, the results indicate that these can be used in a complementary manner to improve the precipitation concentration analysis on temporal and spatial scales.

Spatial Distribution and Temporal Trends in the Daily Precipitation Concentration across the Yarlung Tsangpo River Basin: Eastern Himalaya of China

Understanding the temporal inequality in precipitation is of great importance for water resource management, environmental risk management, and ecological conservation. This study investigated the spatial patterns and trends of the daily precipitation concentration over the Yarlung Tsangpo River Basin using the concentration index (CI) and the Lorentz asymmetric coefficient (LAC). A Mann–Kendall test and Hurst’s rescaled range analysis were used to detect the change in CI trends. The CI ranged from 0.58 to 0.65, suggesting that a quarter of the rainiest days contributed approximately 69–78% of the total precipitation. The LAC analysis indicated that the nonuniform distribution of precipitation was mainly attributed to a large proportion of days with light rainfall. Compared with that of the central region, the daily precipitation in the western and eastern regions was more irregular. At a seasonal scale, the dry season had a less homogeneous spatial distribution of CI compared to that of the wet season. Most areas exhibited no significant trends in CI from 1970 to 2017. A quarter of the stations presented a significant downward trend in CI, which were primarily found in the central and northern regions. In addition, the future trends of CI in most areas mostly agree with those of the current state; however, the majority of stations exhibited an uneven precipitation distribution in the dry season.

Spatial and temporal variability of daily precipitation concentration in southeastern South America

AbstractThe study of the spatial and temporal variability of the precipitation concentration is necessary, due to its high environmental and socioeconomic impact. For this reason, this study aims to contribute to the knowledge of daily rainfall in southeastern South America. Daily records of precipitation for 48 stations are used (period 1979–2018) to calculate the concentration index (CI). High CI is linked to rapid physical processes such as convection, and low CI can be interpreted as a consequence of regular patterns such as the continuous passage of fronts. The spatial and temporal distribution of CI brings information about frequency and changes in heavy rainfall days. The spatial distribution of the CI at an annual level has a range of variation between 0.54 and 0.68. The CI temporal variation shows different regional behaviour. In Brazil, there is an increase in the CI, while in Uruguay and the north‐central part of Argentina negative trends are present in the four seasons.

Spatial and temporal characteristics of the daily precipitation concentration index over China from 1979 to 2015

Abstract Irregular precipitation has a nontrivial influence on hydrological processes and regional agriculture. The precipitation concentration index provides convenient quantitative characterizations of precipitation variability. To explore the spatial and temporal distribution of the precipitation concentration index, the long-term concentration index (LCI) and the annual concentration index (ACI) during 1979–2015 were calculated based on the China Meteorological Forcing Dataset. The results are as follows: (1) The LCI in China ranged from 0.4571 to 0.9197, and the values between 0.6 and 0.7 accounted for 61.61% of the dataset. The highest and lowest LCI values were both recorded in Northwest China, which features low precipitation levels. Additionally, there are high LCI values (greater than 0.6) in Southeast China, which features high precipitation levels. (2) Application of the Mann-Kendall test (M-K test) and Sen's slope revealed that more than 88% of the grids exhibited nonsignificant positive or negative ACI trends and that more than 10% of the grid ACI values exhibited positive trends, with approximately 2.8% showing significant changes at the 0.1 significance level. (3) Application of the Pettitt test revealed that approximately 11.9% of the grid ACI values exhibited an abrupt change at the 0.5 significance level, with abrupt changes occurring in 1991, 1992 and 1993, together accounting for 45.89% of all grids with abrupt changes.

Spatiotemporal variability of the precipitation concentration and diversity in Central Asia

More extreme precipitation is expected to occur in the arid and wet regions due to climate warming. This intensification will increase the risk of floods, soil erosion, and droughts in the world's dry regions. Exploring the precipitation's irregularity and the daily precipitation concentration in Central Asia (CA) has significant implications for regional agricultural development and ecosystem services. However, the irregularity of precipitation was poorly understood throughout the entire CA due to the sparse meteorological observations. Using the daily Multi-Source Weighted-Ensemble Precipitation (MSWEP-V2.2) from 1979 to 2016 across CA, this study investigated the spatiotemporal variations of the daily precipitation concentration index (CI) and the precipitation diversity and analyzed their attributes and implications. The results demonstrated that the CI was significantly negatively correlated with the precipitation frequency and amount. The distribution of the precipitation frequency diversity (Hfre) and the precipitation amount diversity (Hamount) showed an opposite pattern with the CI, implying that the low Hfre and Hamount values were corresponding to a high precipitation concentration. The high CI values were found in the Tarim Basin and near the Aral Sea, revealing a strong irregularity in precipitation. The decrease regarding the light precipitation frequency or increase in the torrential precipitation frequency generated the increasing precipitation frequency diversity, corresponding to a widespread significant reduction of the CI in CA. The decreased CI and increased precipitation diversity over past decades were likely related to the warming phase of the Atlantic Multidecadal Oscillation. The shift from light to extreme precipitation will trigger the risk of flooding and soil erosion and bring about big challenges for water resources management in the arid and semi-arid regions.

Analysis of precipitation variation in the northern strip of Iran

The present study aimed at studying the spatial–temporal behavior of precipitation received over a long period of time in the northern strip in Iran. To this end, the available data related to the precipitation in the 50 years (1957–2007) were extracted from APHRODITE with a spatial resolution of 0.25° × 0.25°. Some statistical analyses such as dispersion statistics and the analysis of trend were run on the extracted data to obtain the precipitation changes in a decade, using MATLAB software. The results of the analyses showed that the precipitation changes in decade in the northern strip in Iran followed a high clustering pattern due to the physiography of the region, convection and the influence of weather systems, and over time, precipitation is concentrated in this region of the north of Iran. Statistical analyses showed that precipitation pattern had a severe disorder. The simulation of the precipitation density function also showed a decrease in precipitation variance and a sharpening of the Gaussian function, indicating the absence of constant precipitation conditions in the northern strip of the country. Moreover, the assessment of trends through non-parametric method showed that the annual precipitation has a decreasing trend in the region. Analysis of the daily precipitation concentration demonstrated a widespread distribution, and, confirmed the irregularity and inequality of daily precipitation across the northern strip of Iran.

Spatiotemporal variability of daily precipitation concentration and its relationship to teleconnection patterns over the Mediterranean during 1975–2015

AbstractThis study has addressed the spatiotemporal distribution of the daily rainfall concentration and its relation to the teleconnection patterns across the Mediterranean (MR). Daily concentration index (CI) and the ordered n index ( nor) are used at annual time scale to reveal the statistical structure of precipitation across the MR based on 233 daily rainfall series for the period 1975–2015. Eight teleconnection patterns, North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO), Western Mediterranean Oscillation (WeMO), Upper‐Level Mediterranean Oscillation index (ULMO), East Atlantic (EA) pattern, East Atlantic/West Russia (EATL/WRUS) pattern, Scandinavia (SCAND) pattern and Southern Oscillation (SO) at annual time scale are selected. The spatiotemporal patterns in precipitation concentration indices, annual precipitation and their teleconnections with previous large‐scale circulations are investigated. Results show a strong connection between the CI and the nor (r = 0.70, p &lt; .05) which present the same relative areas of high and low concentration. The annual values range from 0.57 to 0.70 for CI and 0.49 to 0.71 for nor index which show a high daily precipitation concentration across the MR. Trend analysis demonstrated mostly significant increasing trends for both indices. This increase is mainly found in south France, northern coastlands of the Iberian Peninsula (IP), Greece and Tunisia. An inverse relationship between the number of rainy days and concentration indices is evident. Both of WeMO and MO can play an important role in modulating rainfall in the northwest Mediterranean. The positive EATL/WRUS phase is mainly connected with positive precipitation mean anomalies in the eastern Mediterranean and vice versa in the west. The high daily precipitation concentration values over south France, northeast Spain, Croatia and Tunisia are linked to the low values of WeMO and high values of EA. These results could pave the way for new possibilities regarding the projection of precipitation concentration and precipitation irregularity in downscaling techniques.

Spatial and temporal variability of daily precipitation concentration in the Sardinia region (Italy)

AbstractRecent studies evidenced that extreme daily rainfall often increases in spite of a decrease of the total rainfall. For this reason, the analysis of daily precipitation concentration is an important issue in climate research and can be useful to evaluate risks linked to extreme rainfall events. In this paper, an analysis of the spatial and temporal variability of the daily precipitation concentration in the Sardinia region, the second largest island in the Mediterranean Sea, has been performed for the period 1922–2011 by applying the Concentration Index. First, the CI has been evaluated at annual and seasonal scale. Then, the coefficient of variation, and the 5th and 95th percentiles of the annual CI values were calculated and mapped as a set of descriptors of variability and extreme characterization of precipitation concentration across the study area. Finally, in order to evaluate the temporal evolution of the CI, a trend analysis has been also performed using the Mann–Kendall test. Results show CI values ranging from a minimum of 0.477 to a maximum of 0.723. Spatially, the region can be divided in two parts with opposite behaviours: the western side characterized by a more uniform temporal distribution of rainfall intensity and the eastern side with the most critical intensity and aggressiveness of rainfall. From a seasonal point of view, this spatial gradient was evidenced in winter, spring and autumn while summer showed a more homogeneous spatial distribution of CI. The spatial distribution of the coefficient of variation did not show any spatial gradient between the two sides of the region, both at annual and seasonal scale. As regards the trend analysis, a marked positive trend of the CI has been detected, thus indicating a tendency towards more intense and aggressive rainfall. Copyright © 2019 John Wiley &amp; Sons, Ltd.

Rainfall distribution and trends of the daily precipitation concentration index in northern Morocco: a need for an adaptive environmental policy

A national priority at the Moroccan level is to understand the spatiotemporal rainfall irregularity to anticipate suitable strategies for water and agriculture management. The most northern mountainous region shows intense sensitivity to extreme hydroclimatic hazards which may compromise the future of socioeconomic acceleration and further weaken environmental balances. However, planning and decision making require accurate data, on a detailed scale and with good spatial distribution which is not the case in several south-Mediterranean countries where rainfall data and/or well-established hydro-meteorological network is lacking. In this paper, abundant rain gauge measurements combined with time-series open data (1958–2015) were used to study the rainfall variability. Results show an annual rainfall increase trend since 1996 with frequent, intense and abundant rains causing violent torrents and floods. This humid trend alternates with short but severe dry periods. Spatial distribution shows a rainfall increase from eastern to western area and from the coastal plains to high mountains. Based on a continuous daily rainfall dataset (from 1988 to 2012) from six scattered stations, it appears that the pattern of heavy and extreme wet days takes the form of an irregular interannual cycle. Trends of the annual precipitation daily concentration indicate a significant positive trend in the center than in the East of the study area. Considering these observations, a review of water needs/obligations priorities seems crucial so that a new conscious and adaptative environmental policy would approve new procedures for a sustainable management.

Spatial and temporal variations of precipitation concentration and their relationships with large-scale atmospheric circulations across Northeast China

Understanding the trends of precipitation concentration play a key role in watershed development and management. In this study, spatiotemporal and abrupt changes in the concentration index (CI) and their relationships with summer large-scale atmospheric circulations over Northeast China (NEC) were discovered. We used daily precipitation data from 71 meteorological stations and five large-scale atmosphere circulation indices for the period 1961–2016 across NEC. The results show that annual daily precipitation concentration in NEC decreased slightly in most areas. The spatial distribution of the CI ranges from 0.64 to 0.7 and the regions of the highest value were in Liaoning Province. The correlation between CI and different large-scale atmospheric indices show significant differences and mainly depend on the regional differences. Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) are negatively correlated with the CI in most regions of NEC, in contrast to the East Asian summer monsoon index (EASMI) and Multivariate ENSO Index (MEI). The Southern Oscillation Index (SOI) is negatively correlated with the CI in Liaoning Province and positivity correlated with the CI in Jilin and most of Heilongjiang Province. Compared to before 1975, a Eurasian continent anticyclonic circulation anomaly caused more clear days and higher solar radiation during 1976–2016.

Assessing precipitation concentration in the Amazon basin from different satellite‐based data sets

Daily precipitation concentration in the Amazon basin (AB) is characterized using concentration index (CI), which is computed from HYBAM Observed Precipitation (HOP) data set, for 1980–2009 period. The ability of four satellite precipitation data sets (TMPA V7, TMPA RT, CMORPH and PERSIANN) to estimate CI is evaluated for 2001–2009 period. Our findings provide new information about the spatial irregularity of daily rainfall distribution over the AB. In addition, the spatial distribution of CI values is not completely explained by rainfall seasonality, which highlights the influence of different weather systems over the AB. The results of rainfall concentration indicate that the distribution of daily rainfall is more regular over northwest (northern Peru) and central Andes. Conversely, Roraima region and a large area of Bolivian Amazon register the highest irregularity in the daily rainfall. Bolivian Amazon also represents regions where the large percentage of total rainfall arises from extreme events (&gt;90th percentile). Heavy rainfall episodes over Roraima region are induced by humidity influx come from Caribbean region, while heavy rainfall events over Bolivian Amazon and Andes region are induced by the northwards propagation of cold and dry air along both sides of Andes Mountains, but only propagate in all tropospheric levels for the Andes. The results also show that PERSIANN and TMPA7 data sets better estimates the daily precipitation concentration for whole AB, but with a relative error 8%. CI estimated from satellites does not agree well with HOP over the Andes and northern Peruvian Amazon. On the other hand, the temporal variability of CI can partly be detected using CMORPH and TMPAV7 data sets over the Peruvian Andes, and central and southern Brazil. Errors in CI estimating might be related to inaccurate estimation of daily rainfall. Finally, we conclude that satellite‐based precipitation data sets are useful for analysing rainfall concentration in some regions of AB.