Relationship between Precipitation Characteristics at Different Scales and Drought/Flood during the Past 40 Years in Longchuan River, Southwestern China

In this study, the temporal and spatial patterns of rainfall in the Longchuan River basin from 1977 to 2017 were analyzed, to assess the feature of precipitation. Based on the daily precipitation time series, the Lorenz curve, precipitation concentration index (PCI), precipitation concentration degree (PCD), and the precipitation concentration period (PCP) were used to evaluate the precipitation distribution characteristics. The PCI, PCD and PCP in five categories, defined by the fixed thresholds, were proposed to investigate the concentrations, and the average values indicated the higher concentrations in the higher intensities. The indices showed strong irregularity of daily and monthly precipitation distributions in this basin. The decrease in the PCD revealed an increase in the proportion of precipitation in the dry season. The rainy days of slight precipitation in the upper and lower basins with significant downward trends (−13.13 d/10 a, −7.78 d/10 a) led to longer dry spells and an increase in the risk of drought, even severe in the lower area. In the upper basin, the increase in rainfall erosivity was supported by the upward trend in the PCIw of heavy precipitation and the simple daily intensity index (SDII) of extreme precipitation. Moreover, the PCP of light precipitation, moderate precipitation, and heavy precipitation concentrated earlier at the end of July. The results of this study can provide beneficial reference information to water resource planning, reservoir operation, and agricultural production in the basin.

Time Series Trend Analysis of Rainfall and Temperature over Kolkata and Surrounding Region

Study of long term variability of temperature and rainfall in the context of climate change is of much importance particularly in the region where rainfed agriculture is predominant. Long term trends of temperature and rainfall have been investigated over Kolkata, India, a tropical region using gridded monthly precipitation and temperature data obtained from Global Precipitation and Climate Centre (GPCC V7) with 0.5° X 0.5° resolution for the period 1901 to 2014. Precipitation concentration index, coefficient of variation, rainfall anomaly have been calculated and Palmer drought severity index data have been analyzed. Furthermore, Mann-Kendall test and sen’s slope estimator have been used to detect time series trend. Annual temperature and rainfall have been increased with a rate of 0.0082°C/ year and 0.03 mm/ year respectively. Statistically significant increasing trend has been observed for most of the months for temperature and rainfall. Winter and monsoon period shows highest and lowest inter-annual variability respectively. Rainfall with high precipitation concentration index (16-20) has been observed for the period 1951-1975 and 1976-2000. It has been observed that the number of years with dry conditions have been increased. However, the intensity of dryness is very near to zero. The information from this study will be helpful for the farmers to plan for resilient farming.

Spatiotemporal Changes in Precipitation Concentration Indicators Over Iran

This study was conducted to investigate the spatiotemporal variability in precipitation concentration over Iran. For that purpose, daily precipitation data with a spatial resolution of 0.25° × 0.25° from the Asfazari database for the period from 01/01/1962 to 31/12/2019 were used. Three indices including the precipitation concentration index (PCI), precipitation concentration period (PCP), and precipitation concentration degree (PCD) were utilized for examination of the variability in precipitation concentration over the country. The results demonstrated that the central, south-eastern, and eastern parts of the country exhibited maximum temporal precipitation concentration, while the least precipitation concentration could be observed over the Caspian coasts and the northern half of the country. The year 1998 was selected as the change point due to the considerable difference in the values of the examined indices, and the long-term statistical period was divided into two sub-periods before and after the change. During the sub-period after the change point (1999-2019), precipitation concentration has increased in the western, central, eastern, and south-eastern parts of Iran, according to PCI and PCD, and has decreased in the North and Northeast and along the northern coastline of Oman Sea. Furthermore, there have been great spatial differences in the period of occurrence of precipitation along the Northern coasts, according to PCP, varying from November, along the Caspian coasts, to August, along the northern foothills of Alborz Mountains. The PCP index has increased during the sub-period after the change point along the northern coastlines of Persian Gulf and Oman Sea and in parts of the North (along Alborz Mountains), indicating a shift in the period of precipitation from winter to the warm seasons of spring and summer. Moreover, the decrease in PCP in the Northwest and Northeast suggested that the period of occurrence of precipitation has shifted from the second half of winter toward early winter and late fall. After the year of change point, the frequency of rainy days and precipitation have decreased, and PCI and PCD have increased.

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

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 understandings of precipitation variations, which are meaningful for regional flood risk management under climate change.

Precipitation extremes and anomalies of the Indian Sundarban 1984-2018

Gridded precipitation data products of 0.5º × 0.5º spatial resolution were analysed to understand the climatic variability in a spatial and temporal context. Data reliability of processed gridded data products were examined in the absence of gauge station data observations in the study area. However, the implementations of comparative analysis of the spatial and temporal data products in this study area are missing. The NASA Power Data (NPD) and Climate Research Unit (CRU TS 4.03) Data were scrutinized from 1984-2018. The data products were selected, compared, and interpreted grid wise. Annual and monsoonal precipitation pattern was also studied. Data variability has been analyzed using the Coefficient of Variation (CV), Anomaly, and Precipitation Concentration Index (PCI). The statistical analysis of R2, MAE, RMSE, MAPE and BIAS was performed to quantify the error and differences. Considering the independent grid point, the MAPE and BIAS indicate that only grid 4 performed better than the rest with 12.7% and 17%, respectively. The results regarding the data products illustrate significant differences both in averaged and grid wise context. The NPD shows an increasing trend, whereas CRU represents a decreasing trend from the year 1984-2018. Before the implementation of any processed secondary gridded data products in complex terrain, the critical evaluation and quantification of the magnitude of error is a prerequisite, like the Sundarbans, where the gauge stationed data is unavailable.

Analysis of Seasonality Precipitation Concentration in Northern of Iraq

This research is about analysis seasonality of precipitation concentration in the north of Iraq, by using multiple methods of precipitation concentration Index .The first is the standard vectors method that determines the date of concentration and the number of the rainy months, the second, precipitation concentration index (PCI) that classify the degree of (PCI) annually, supra-seasonal, seasonal depending on monthly precipitation data from nine metrological stations For 36 years (1979-2014), using Excel, Arc map 10.8 and Oriana software in calculates and representation of precipitation concentration. the result shows that all stations in the study area share the same date (Jan.-Feb.) and the stations differ in the length of the rainy season (7-9) month. and for PCI results, PCI annual shows denote a moderate concentration in the whole study area, PCI supra-seasonal value shows (in the wet season uniform rain distribution, the dry season value shows high concentration, PCI seasonal Shows (autumn) moderate concentration, winter: low concentration in all stations, in the spring: PCI value shows the moderate concentration in Erbil, Kirkuk, Sulaymaniyah, Salaheddin, and the other stations shows uniform rain distribution. Keywords: seasonal rain concentration, mathematical vector, PCI.

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.

Trend and Variability Analysis of Rainfall and Temperature Trends in Ilorin Metropolis, Kwara State, Nigeria

ABSTRACT Rainfall and temperature are the most important physical parameters that influence climate. This paper examines the trend and variability of rainfall and temperature of Ilorin township in Nigeria between2010- 2018, using standard statistical descriptors. Rainfall had an increasing trend (positive slope value of 5.30), moderate precipitation concentration index of 12.15 percent and extremely high degree of variability with a coefficient of variation ranging between 33.54 percent and 155.73 percent. Temperature also had a slight warming or increasing trend (positive slope value of 0.012) with minimal degree of variability of coefficient of variation between 5.49 percent and 7.31 percent. The oscillating structure of both rainfall and temperature anomalies further confirm yearly fluctuations as well as change in the distribution and characteristics. It is recommended that government and non-government agencies should formulate plans and policies that will accommodate changes in rainfall and temperature patterns to successfully manage the environment.

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.