Annual Precipitation Concentration Index Research Articles

Assessment of rainfall erosivity for Bundelkhand region of central India using long-term rainfall data

Prevention of soil erosion requires long term assessment of rainfall erosivity and other related variables of the region. In the present study, four variables related to rainfall erosivity i.e. modified Fournier index (MFI), rainfall erosivity (R) factor, erosivity density (ED) and precipitation concentration index (PCI) were calculated. Long-term (1901-2021) daily rainfall data of Bundelkhand region (Central India) were used in the analysis. The above variables were assessed for spatial and temporal variability on annual and seasonal scale. The R-factor values range from 3010.61 MJ.mm ha-1 h-1 to 5346.53 MJ.mm ha-1 h-1, showing the region belongs to moderate to severe erosivity class. The mean annual R-factor, MFI, ED and PCI values for the Bundelkhand region were calculated as 4072.86 MJ.mm ha-1 h-1, 270.55 mm, 19.13 MJ ha-1 h-1 and 28.88, respectively. This study provides the insights of soil erosion problems of Bundelkhand region and would help in adopting the preventive measures and watershed development activities.

Spatiotemporal variability of precipitation concentration in Pakistan

AbstractPrecipitation is a meteorological variable that plays a vital role in determining water resources and managing water risks. The intensity and frequency of precipitation extremes have increased in recent years; however, the changes in precipitation regimes across Pakistan remain unclear. This study analysed spatial and temporal changes in the precipitation concentration index (PCI) and concentration index (CI) from 100 meteorological stations across Pakistan from 1981 to 2017. Using innovative trend analysis (ITA) and Pettitt's test, we detected trends and change points in the annual and wet season PCI and CI. Additionally, we examined the relationship between PCI/CI values and latitude and altitude. The results revealed that the CI and PCI ranged between 0.48–0.78 and 10–55, respectively, with higher values being more prevalent in the dry and hot zones of the country. The study established a linear relationship between PCI/CI values, latitude and altitude, with higher values at lower altitudes and a decrease in values from south to north. Most stations presented a significant positive ITA trend for PCI and CI from 1981 to 2017, with a change between 1985 and 1995, indicating an overall increase in heavy precipitation events in the area. These findings are crucial for disaster preparedness, water resource planning, the mitigation of drought and flood risks, and conservation efforts.

The Precipitation Concentration in the Cheliff Watershed, Algeria: A Critical Analysis for Sustainable Water Resource Management

In this study, a statistical analysis of historical and projected values of the annual, seasonal, and supra-seasonal precipitation concentration index (PCI) was conducted for different stations in the Cheliff watershed. Statistical methods such as breakpoint and trend analysis using Pettitt and Mann–Kendall tests were used for the PCI values of annual, monthly, and supra-seasonal precipitation data between 1950 and 2014. The PCI values varied between 14.24% and 24.86% annually, between 10.75% and 18.74% seasonally, and between 11.23% and 26.98% supra-seasonally. According to the Pettitt and Mann–Kendall tests, there was an insignificant change in precipitation distribution during the study period. Spatial analysis using the inverse distance weighting (IDW) method confirmed the minor variability in precipitation distribution in the study area. The Pearson correlation coefficient between oscillation indices and PCI values at different scales showed significant values with the Mediterranean Oscillation index (MOi), North Atlantic Oscillation (NAO), and Western Mediterranean Oscillation index (WeMOi), highlighting their potential influence on annual PCI values. For the climate projection scenarios, projected PCI values align with historical PCI values. Application of Pettitt and Mann-Kendall tests to the projected PCI series shows that there will not be a significant change in future precipitation distribution.

Rainfall Spatial-Temporal Variability and Trends in the Thamirabharani River Basin, India: Implications for Agricultural Planning and Water Management

Rainfall is critical to agricultural and drinking water supply in the Thamirabharani river basin. The upper catchment areas of the Thamirabharani basin are located in high-elevated forest regions, and rainfall variability affects dam inflow and outflow. The well-known methods for rainfall analysis such as the coefficient of variation (CV), the precipitation concentration index (PCI), and trend analysis by Mann-Kendall and Sen’s slope test, as well as the Sen’s graphical innovative trend method (ITA) recently reported in several studies, were used. Rainfall data from gauge stations and the satellite-gridded Multisource Weighted Ensemble Precipitation (MSWEP) dataset were chosen for analysis at the annual and four-season time scales, namely, the Southwest Monsoon, Northeast Monsoon, winter, and summer seasons from 1991 to 2020. The mean annual PCI value reflects irregular monthly rainfall distribution (PCI > 20) in all gauge stations. The spatial monthly rainfall distribution of PCI values remarkedly shows a moderate distribution in the western and an anomalous distribution in the eastern part of the basin. The annual mean rainfall ranges from 718.4 to 2268.6 mm/year, decreasing from the high altitude zone in the west to the low plains and coastal regions in the east. Seasonal rainfall contributes about 42% from the NEM, 30.6% from the SWM, 22.8% from summer, and 3.9% from winter, with moderate variability (CV less than 30%). Ground stations experienced extremely high interannual variability in rainfall (more than 60%). Trend analysis by the MK, TFPW-MK, and ITA methods shows increasing annual rainfall in the plains and coastal regions of the basin; particularly, more variations among the seasons were observed in the Lower Thamirabharani sub-basin. The NEM and summer season rainfall are statistically significant and contribute to the increasing trend in annual rainfall. The ITA method performed better in the annual and seasonal scale for detecting the rainfall trend than the MK and TFPW-MK test. The Lower Thamirabharani sub-basin in the eastern part of the basin receives more rain during the NEM than in other areas. To summarize, the low plains in the central and coastal regions in the southeast part experience an increase in rainfall with irregular monthly distribution. This study helps farmers, governments, and policymakers in effective agricultural crop planning and water management.

Spatiotemporal variations of precipitation concentration influenced by large-scale climatic factors and potential links to flood-drought events across China 1958–2019

Understanding the irregularity of precipitation variations at both temporal and spatial scales is critical for assessing flood and drought risks. In this study, the statistical characteristics of precipitation variations in eight subregions were detected based on daily precipitation records from 798 meteorological sites during 1958–2019, and Long-term precipitation Concentration Index (LCI), Annual precipitation Concentration Index (ACI) and Seasonal precipitation concentration index (SPCI) values were used to analyse temporal heterogeneity of precipitation concentrations. Results showed that the LCI and ACI values were generally characterized by “high in southwest, low in Tibet, high in summer, and low in winter.” Specifically, ACI presented significant increasing trends in 5 regions (Northeast, North, Tibetan Plateau, Western Northwest, and Eastern Northwest), insignificant increasing trends in 2 regions (Jianghuai and Southwest), and a significant decreasing trend in 1 region (South), indicating the potential of increased drought and flood risks. SPCI exhibited an increasing trend in the spring months in Northeast China (p < 0.01), and significant increasing trends in the Tibetan Plateau and Western Northwest China during autumn months (p < 0.05). Attribution analyses on 11 large-scale climatic factors showed that the El Nino-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and Arctic Oscillation (AO) strongly influenced the precipitation concentration indices with distinct zonal characteristics. ENSO, which is the most significant factor affecting the precipitation concentrations, had the strongest positive effect in South China and coastal areas. Meanwhile, the NAO presented a notable negative influence on the ACI, while the AMO exhibited a positive impact in Northern China. Our work added to the body of evidence that the extreme rainfall events have been significantly increasing in the Tibetan Plateau, Western Northwest China, and Eastern Northwest China (p < 0.05), and that the north-western regions of China have been experiencing significant wetting trends. Furthermore, climate change would potentially lead to more extreme drought-flood events in the future.

Spatiotemporal Variability, Trends, and Potential Impacts of Extreme Rainfall Events in the Sudano-Sahelian Region of Cameroon

The Sudano-Sahelian region of Cameroon is mainly drained by the Benue, Chari, and Logone rivers; which are very useful for water resources; especially for irrigation, hydropower generation, and navigation. Long-term changes in mean and extreme rainfall events in the region may be of crucial importance in understanding the impacts of climate change. Daily and monthly rainfall data from fifteen climate stations in the study area from 1980 to 2018 and measurements from the Expert Team on Climate Change Detection and Indices (ETCCDI) were estimated using the non-parametric innovative trend analysis (ITA) and the Sen slope estimator. The precipitation concentration index (PCI), the precipitation concentration degree (PCD), and the precipitation concentration period (PCP) were used to explore the spatiotemporal variations in the characteristics of rainfall concentrations. The results showed complex spatial patterns of the annual average PCI values across the Sudano-Sahelian region; which varied from values lower in the south to higher in the far north, which were the characteristic of seasonality and a strong seasonal precipitation distribution throughout the year; respectively. The PCD results revealed that the annual rainy days in a year decreased from the south to the far north. Additionally, the PCP results indicated a slightly later occurrence of precipitation. A significant trend toward more intense–extreme rainfall events was observed in most parts of the study area, but a significant trend toward more humid days was observed in the southeastern part. Trends in dry days significantly increased in the central and southwestern parts of the study area. This could mean that the prevalence of flood and drought risks are higher in the study area. Overall, the increase in annual rainfall could benefit the hydro-power sector, agricultural irrigation, the availability of potable water sources, and food security.

Spatio-Temporal Rainfall Variability and Concentration over Sri Lanka

Changes in precipitation patterns significantly affect flood and drought hazard management and water resources at local to regional scales. Therefore, the main motivation behind this paper is to examine the spatial and temporal rainfall variability over Sri Lanka by Standardized Rainfall Anomaly Index (SRAI) and Precipitation Concentration Index (PCI) from 1990 to 2019. The Mann–Kendall (MK) trend test and Sen’s slope (SS) were utilized to assess the trend in the precipitation concentration based on PCI. The Inverse Distance Weighting (IDW) interpolation method was incorporated to measure spatial distribution. Precipitation variability analysis showed that seasonal variations are more than those of annual variations. In addition, wet, normal, and dry years were identified over Sri Lanka using SRAI. The maximum SRAI (2.27) was observed for the year 2014 for the last 30 years (1990–2019), which shows the extremely wet year of Sri Lanka. The annual and seasonal PCI analysis showed moderate to irregular rainfall distribution except for the Jaffna and Ratnapura areas (annual scale-positive changes in Katugastota for 21.39% and Wellawaya for 17.6%; seasonal scale-Vavuniya for 33.64%, Trincomalee for 31.26%, and Batticaloa for 18.79% in SWMS). The MK test, SS-test, and percent change analyses reveal that rainfall distribution and concentration change do not show a significant positive or negative change in rainfall pattern in Sri Lanka, despite a few areas which experienced significant positive changes. Therefore, this study suggests that the rainfall in Sri Lanka follows the normal trend of precipitation with variations observed both annually and seasonally.

Regional Characteristics of Precipitation in the Nanpan River Basin, China

Nanpan River is the source of the Pearl River in China, and thus, the exploitation of water resources in the Nanpan River Basin directly affects development in its middle and lower reaches. In the present study, the availability of water resources in the Nanpan River Basin and their differences were investigated. Sixteen statistical variables including the 25th and 75th percentiles, the coefficient of variation of the seasonal and annual precipitation and the annual precipitation concentration index were examined using monthly precipitation data collected in 33 stations in the Nanpan River Basin from 1956 to 2016. This paper studies the relationship between the Spearman’s rank correlation coefficient and the distance between stations, and uses principal component analysis (PCA) and cluster analysis to identify the homogeneous precipitation regions in the Nanpan River Basin. The results reveal the following: 1) The Spearman’s rank correlation coefficients for the monthly, seasonal and annual precipitation of the stations exhibit negative correlations with the interstation distance; the stronger the spatial correlation between both parameters, the shorter the time scale. 2) The factors controlling the spatial patterns of precipitation in the basin are its interannual and intra-annual variations. 3) Precipitation for the Nanpan River Basin produce two homogenous regions, which are associated with the influences of the South Asian monsoon, the North Atlantic Oscillation and the South Branch Trough. The first region is mainly to the east of longitude 104°E, while the second is principally to the west.

Assessments of multiple gridded‐rainfall datasets for characterizing the precipitation concentration index and its trends in India

AbstractRainfall is time concentric in nature. The spatial and temporal distribution of rainfall is changing over the Earth with recent anthropogenic warming. The study explores various characteristics of annual and seasonal concentration of rainfall across India using the precipitation concentration index (PCI) and its trends during the period of 1986–2015, based on a high‐resolution gauge‐based rainfall data (0.25 × 0.25°), obtained from the India Meteorological Department (IMD). An intercomparison is made with 11 other gridded rainfall datasets to infer whether these datasets can reasonably reproduce the spatiotemporal distribution of PCI and its trends in various homogenous rainfall regions of India or not. These datasets are categorized into gauge‐based (APHRODITE, GPCC, CPC), satellite‐derived (CHIRPS, PERSIANN‐CDR), and reanalysis (JRA‐55, MERRA‐2, NCEP‐2, PGF, ERA‐Interim, ERA‐5). On annual scale, about 8.52, 24.1, and 67.38% of area of India are under moderate, irregular, and strongly irregular rainfall distribution, respectively. Spatial variation of PCI in India is influenced by geographical factors such as latitude, longitude, and elevation. Significant increasing and decreasing trends in annual PCI have been observed in the northeast, eastern, and western coasts of India, respectively. Gridded data intercomparison suggests that the gauge‐based APHRODITE, GPCC, and satellite‐derived CHIRPS datasets better perform in capturing the temporal and spatial variation of PCI across India when compared to the IMD gridded dataset, whereas the ERA‐5 performs better among the reanalysis datasets. However, the rainfall datasets exhibited marked differences with the IMD while estimating the annual and seasonal trend and its magnitude across various regions of India. The JRA‐55 overestimated areas of positive trend and its magnitude on annual and seasonal scale. These findings have practical implications for hydroclimatic studies.

Assessing Impact of Climate Variability in Southwest Coastal Bangladesh Using Livelihood Vulnerability Index

This study analyzed the variability of rainfall and temperature in southwest coastal Bangladesh and assessed the impact of such variability on local livelihood in the last two decades. The variability analysis involved the use of coefficient of variation (CV), standardized precipitation anomaly (Z), and precipitation concentration index (PCI). Linear regression analysis was conducted to assess the trends, and a Mann–Kendall test was performed to detect the significance of the trends. The impact of climate variability was assessed by using a livelihood vulnerability index (LVI), which consisted of six livelihood components with several sub-components under each component. Primary data to construct the LVIs were collected through a semi-structed questionnaire survey of 132 households in a coastal polder. The survey data were triangulated and supplemented with qualitative data from focused group discussions and key informant interviews. The results showed significant rises in temperature in southwest coastal Bangladesh. Though there were no discernable trends in annual and seasonal rainfalls, the anomalies increased in the dry season. The annual PCI and Z were found to capture the climate variability better than the currently used mean monthly standard deviation. The comparison of the LVIs of the present decade with the past indicated that the livelihood vulnerability, particularly in the water component, had increased in the coastal polder due to the increases in natural hazards and climate variability. The index-based vulnerability analysis conducted in this study can be adapted for livelihood vulnerability assessment in deltaic coastal areas of Asia and Africa.

Precipitation concentration index (PCI) a tool to evaluate the distribution of Rainfall, Barishal, Bangladesh

The present research work reveals the mean annual rainfall of Barishal is 2087.34 mm for the investigated period. The maximum annual rainfall was 3390 mm in the year of 1960 and minimum annual rainfall was recorded as 1277 mm in the year of 1964. The annual rainfall is inversely correlated with time. The maximum monthly rainfall is recorded in the month of July. The amount of annual rainfall is strongly significantly positively correlated with the monthly rainfall of May, June, July, August and September. In Barishal, the value of skewness for all rainfall data are positive that indicate the data are skewed to the right. The positive value of kurtosis of the eleven months of the year (except July) means a peaked distribution and a negative value in the month of July reveals the flat distribution with the same mean and standard deviation. The annual PCI value is inversely proportional to the annual rainfall. The analyses of seasonal precipitation concentration index (SPCI) reveals that the rainfall is uniformly distributed in summer monsoon whereas the winter rainfall shows the dominance of strong irregularity in precipitation distribution. Â

Spatiotemporal variations of precipitation concentration and their potential links to drought in mainland China

Climate warming triggers increased frequency and intensity of extreme climate events, and due to disequilibrium precipitation at spatial and temporal scales, droughts or floods may occur frequently. Therefore, it is necessary to assess spatiotemporal variation of precipitation concentration and its potential impact on drought. The study calculated the Concentration Index (CI) and Precipitation Concentration Index (PCI) of 780 stations during 1961–2016 in mainland China, and monthly temperature and precipitation data were adapted to calculate the Standardized Precipitation Evapotranspiration Index (SPEI). Changepoint method was used to determine the turn and change points of CI and PCI, and Sen’s slope method was used to calculate the trend before and after the year of turn points (YTPs) and the overall trend of drought was also analyzed. The Pearson correlation method was employed to evaluate the correlation between CI/PCI and SPEI to explore potential influence of precipitation concentration on drought from qualitative and quantitative perspectives. The results demonstrate that the annual CI ranges from 0.56 to 0.75, and the 25% rainiest days account for 68.35%–85.92% of total precipitation. The summer CI was the largest and the winter CI was the smallest among all seasons. 9.74% and 9.62% of stations were observed with change points for annual CI and PCI, respectively. Meanwhile, 80.13% and 69.36% of stations for annual CI and PCI showed a decreasing trend, respectively, and 73.21% of stations exhibited a trend of drought. Annual SPEI was positively correlated with CI and PCI, accounting for 88.59% and 70.77% of stations in mainland China, respectively, indicating that the variation of drought was more sensitive to CI than to PCI. The results can provide a basis for decision-making in water resources and drought risk management in China.

Watershed-Based Rainfall variability and trends of extreme rainfall events in South East Awash Basin, Ethiopia

This study presents analysis of Rainfall variability and trends of extreme rainfall events in the Oda Gunufeta -Cherecha -Dechatu watershed, Awash Drainage Basin, Eastern Ethiopia. The study employed the coefficient of variation and the Precipitation Concentration Index (PCI) as statistical descriptors of rainfall variability. The indices at the five stations were subjected to non-parametric Mann-Kendall test to detect the trend over the period between 1985 to 2014. The results of the study revealed that, the watershed experiences moderate inter-annual rainfall variability. The Belg rainfall shows high variability than Kiremt rainfall. Highest Belg &amp; Kiremt rainfall variability is observed in Dire Dawa with coefficient of variation of 46% and 40% respectively. The annual PCI for the watershed in all the stations under investigation during the record periods showed that 100% of the years for which the annual PCI was estimated fell within the irregular precipitation distribution range or high precipitation concentration. The irregular precipitation distribution also extended to all the stations in short rainy season (Belg rainfall) and in two stations in the main rainy season (Kiremt season). With regard to the rainfall trend, the annual rainfall has showed a negative trend in most of the stations for the period 1985-2014. The Mann–Kendall trend test during the Kiremt season shows a positive trend in Dengego, Dire Dawa, Combolcha and Haramaya and the increasing tendency is significant at p&lt;0.1 in Degego, p&lt;0.05 in Dire Dawa, p &lt;0.05 in Combolcha and p &lt;0.01 in Haramaya. The heavy rainfall events, the 90th &amp; 95th percentiles, in all the five stations showed an increasing pattern but except in Combolcha the trends are not statistically significant. This implies that the watershed has been under increased rainfall intensity and this in turn has the potential cause for high risk of flood occurrences.

Various characteristics of precipitation concentration index and its cause analysis in China between 1960 and 2016

The precipitation concentration index (PCI) is a powerful indicator for temporal precipitation distribution and is also very useful for the assessment of seasonal precipitation changes. The primary objectives of this study are to investigate and analyse the temporal–spatial variability patterns of annual and seasonal PCI values based on monthly precipitation data. These data were collected from 597 meteorological stations located throughout China, for the time period of 1960–2016, and were used to assess the impacts of geographical parameters (latitude, longitude, and altitude) on the PCI. Additionally, the possible teleconnection with the large‐scale circulation pattern was investigated. Our results reveal that the variation trend of annual PCI values has decreased significantly at a rate of −.234/10 year (α = .01) in China over the past 57 years. For all studied station records, 434 (72.7%) stations showed decreasing trends of PCI values, and these stations are distributed over large areas in China. On an annual scale, the average PCI value ranged from 11 in Hunan province to 44 in Qinghai province. The precipitation concentration in China can be described as strongly irregular in the western and northern parts of the northwest and in the northern region of the Tibetan Plateau, while it is irregular in the southwest and the north of China, and moderately irregular in some parts of the middle‐lower regions of the Yangtze River and southern China. The regularity of the annual precipitation pattern significantly decreased in spring, autumn, and winter from southeastern to northwestern China, and was the most in winter. However, the summer precipitation dispersion and the pattern in the considered period were more regular than those of the other seasons. Furthermore, changes in the PCI appear to be rather complex and possibly related to global atmospheric characteristics as well as geographical factors (latitude, longitude, and altitude). The results presented in this study indicate that the PCI is an essential feature for water resource planning, prediction of risk due to droughts or floods, and the management of natural resources.

Responses of normalized difference vegetation index (NDVI) to precipitation changes on the grassland of Tibetan Plateau from 2000 to 2015.

Precipitation change is an important factor in the inter-annual variation of grassland growth on the Tibetan Plateau. The total amount, distribution pattern and concentration time are three basic characteristics of precipitation change. The temporal and spatial characteristics of precipitation change were analyzed based on climate data of 145 meteorological stations on the Tibetan Plateau and nearby areas from 2000 to 2015. The total precipitation amount was characterized by annual precipitation, distribution pattern of precipitation during the year was characterized by improved precipitation concentration index (PCI), and precipitation centroid (PC) was defined to indicate the change in precipitation concentrated time. To better illustrate the response of grassland to precipitation change, vegetation growth status was characterized by the maximum value of normalized difference vegetation index (NDVImax). Results indicated that the annual precipitation and PCI had an apparent gradient across the whole plateau and the latest PC occurred in the southern plateau. NDVImax of alpine shrub grassland was significantly correlated with the change of PCI,increased with even distribution of precipitation during growth period, and limited by the total annual precipitation. Alpine meadow did not show significantly correlations with these three indices. The inter-annual variability of NDVImax of steppe was controlled by both PCI and PC. NDVImaxof alpine desert grassland was mainly controlled by annual precipitation. In addition to annual total amount of precipitation, the distribution characteristics of precipitation should be further considered when the influence of precipitation change on different types of vegetation on the Qinghai Tibet Plateau was studied.

Modeling the Spatial and Temporal Variability of Precipitation in Northwest Iran

Spatial and temporal variability analysis of precipitation is an important task in water resources planning and management. This study aims to analyze the spatial and temporal variability of precipitation in the northeastern corner of Iran using data from 24 well-distributed weather stations between 1991 and 2015. The mean annual rainfall, precipitation concentration index (PCI), and their coefficients of variation were mapped to examine the spatial variability of rainfall. An artificial neural network (ANN) in association with the inverse distance weighted (IDW) method was proposed as a hybrid interpolation method to map the spatial distribution of the detected trends of mean annual rainfall and PCI over the study region. In addition, principal component analysis (PCA) was applied to annual precipitation time series in order to verify the results of the analysis using the mean annual rainfall and PCI data sets. Results show high variation in inter-annual precipitation in the west, and a moderate to high intra-annual variability over the whole region. Irregular year-to-year precipitation concentration is also observed in the northeastern and northwestern parts. All in all, the highest variations in inter-annual and intra-annual precipitation occurred over the western and northern parts, while the lowest variability was observed in the eastern part (i.e., the coastal region).

Changes in temporal distribution of precipitation in a Mediterranean area (Tuscany, Italy) 1955–2013

ABSTRACTThis study investigates changes in precipitation regimes in Tuscany (central Italy) over the 1955–2013 period, by using the precipitation concentration index (PCI) and rainfall amount. The PCI is a dimensionless indicator of the temporal concentration of the precipitation. Different statistical methods were applied over the whole period and two independent sub‐periods (1955–1984 and 1985–2013). A p‐value &lt;0.05 was chosen as level of significance for all the statistical tests.Main results, coming from 35 weather stations, did not show statistically significant trends of the PCI on annual basis and during winter. In spring and summer statistically significant increases were found, respectively, in two stations in northern Apennine (+1.6/50 years) and in four stations in central southern inner areas (+2.3/50 years). In autumn one station in northern Apennine showed a significant decrease (−1.9/50 years).Sub‐periods analysis mainly showed higher values of PCI in the recent sub‐period on annual scale (statistically significant along the coast), in spring (significant in northwest and northern Apennine), in summer (significant in central southern inner areas) and during winter (not significant).Considering the year‐to‐year variability of the annual PCI, statistically significant increasing trends were found in the northern part of Tuscany, while decreasing tendencies were observed in the southern one.Concerning annual rainfall, six stations showed statistically significant decreases (−19%/50 years). On monthly basis, two stations exhibited significant decreasing trends in March and two in December (respectively −48% and −37%/50 years). Two significant decreases were observed in both June and July (respectively −53% and −63%/50 years). One station in northern coast showed instead a significant increase in September (+72%/50 years).This investigation represents a further tile in the larger mosaic of analysis of climate change and its different impact on precipitation regime at regional level and supports studies concerning water management in the areas characterized by strong seasonality.

Spatiotemporal pattern of precipitation concentration and its possible causes in the Pearl River basin, China

The spatiotemporal pattern of precipitation concentration is a key factor in a hydrological analysis. This study attempts to explore the spatiotemporal variations and the causes of the variations in precipitation concentration. Firstly, the Mann-Kendall statistical test and Sen’s slope method are used to detect the temporal patterns of the annual precipitation concentration index (ACI), and the inverse distance weighted (IDW) interpolation method is used to analyze the spatial changes in the long period precipitation concentration index (LCI) as well as the temporal variation trend of the ACI based on daily precipitation at 42 sites during 1960–2012 in the Pearl River basin. Secondly, a possible link between precipitation concentration and elevation is investigated by calculating the correlation coefficient between LCI and elevation. Finally, the random forest algorithm (RF) is applied to identify the contributions of seven associated circulation influencing factors on the CI. The results show that: 1) The northwest of the Pearl River basin that is further from the sea and has a higher elevation, exhibits a lower LCI. The southeast part that is closer to the sea and has a lower elevation exhibits a higher LCI, indicating that extreme precipitation events will occur more frequently at lower elevations and closer to the sea areas. 2) The interannual variation of the ACI in the Pearl River basin is not obvious; the northwest part exhibits a decreasing trend while the southeast part has an increasing trend, and the spatial distribution of the ACI’s change trend is likely affected by elevation and the distances from the sea. 3) The precipitation concentration is negatively correlated with elevation with a correlation coefficient of −0.92 at the 99% confidence level. 4) The significance analysis based on RF shows that the East Asian Summer Monsoon (EASM) is the most significant factor affecting the precipitation concentration of the Pearl River basin among seven associated circulation influencing factors.

Analysis of Rain Variability Using the Daily and Monthly Concentration Indexes in Southeastern Brazil

Daily and monthly rainfall was analyzed by the Concentration Index (CI) of [1] and the Annual Precipitation Concentration Index (PCI) of [2] in southeastern Brazil. About 120 rainfall stations were used in the period from 1976 to 2010. Data were obtained from the National Water Agency (ANA). These data were selected by observing their spatial and temporal distribution in the study area. In general, the Concentration Index and the Precipitation Concentration Index were consistent for the study of rainfall variability in the study area. High percentages of annual rainfall concentration in a few very rainy days can increase the risk of flooding and soil instability. In this work, a survey of the spatial and temporal patterns of daily and monthly rainfall concentration in southeast Brazil was conducted. The results showed a relatively homogeneous temporal distribution of daily rainfall in the study area. In addition, the Mann-Kendall test showed a clear trend towards a seasonal rainfall distribution.

Analysis of Precipitation Concentration Index (PCI) for Awka Urban Area, Nigeria

Causes and consequences of climate variability of which the fluctuation in rainfall amounts over time represent an important indicator is indeed an area that has continued to generate research attention. The aim of this paper is, therefore, to evaluate the concentration and variability of rainfall in time using Precipitation Concentration Index (PCI) based on monthly precipitation over 38 years period (1976–2013) and its implications for flooding and water supply in the town. Data for the study were collected from NIMET, Lagos. The PCI was calculated for annual, seasonal and decadal scales using annual, wet and dry season equations. Result shows that annual PCI value range from the lowest of 12 in 1976 to the highest of 20 in 1987. About 78% of the years were characterized by PCI values that fall within moderate range, an indication of moderate precipitation distribution within the 38-year period, while 24% of the years recorded annual value within the irregular range indicating an irregular rainfall distribution and concentration across the months. Again, the fact that many months have PCI values within ≥ 16.7 is a further manifestation that almost 90% of the rainfall occurred in 6 months. These results show that the area is having reasonable floods with consequent pollution of the surface water bodies as well as soil erosion as observed in the town. It is, therefore, recommended that the Awka Capital Territory Development Authority (ACTDA) should take cognizance of this result in the planning of the new layouts and redevelopment of the old areas of the town.