Precipitation Concentration Index Values 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.

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.

Spatiotemporal variability of monthly precipitation concentration in Argentina

Studies of the spatial and temporal variability of precipitation concentration are necessary. This variability is a significant climate element and also a critical socioeconomic factor. This study aims to contribute to the knowledge of rainfall in Argentina using records of monthly precipitation for 64 stations (period 1991–2021) to calculate the monthly precipitation concentration index (PCI). Precipitation in Argentina, given its vast territory, presents a great variability with a wide range of rainfall regimes; the range of PCI values is 10.6–27.3. Values of PCI range from uniform distribution (less than 10) to strong irregularity of precipitation distribution (greater than 20). The north-west of the country shows a high concentration and the south has a low concentration. Analysis shows that the majority of the stations have positive trends in PCI (although not significant), and this increase in concentration of precipitation could become a risk depending on the evolution of the associated rain.

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 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.

Analysis of Spatio-temporal rainfall trends and rainfall variability in Botswana between 1958 and 2019

Environments with a climatic characteristic of semi-aridity exhibit high rainfall variability, increasing significantly as the climate changes. In this study, rainfall concentration and Spatio-temporal trends in annual and seasonal (November to March) rainfall in Botswana were analyzed. The satellite data (1958-2019) is obtained from the Southern African Science Service Centre for Climate Change and Adaptive Land Management and the Climate Toolbox. The Mann–Kendall trend test (MK), autocorrelation function (ACF), relative percentage change (RPC), precipitation concentration index (PCI), and Theil–Sen's slope estimator (β) methods were adopted for data analysis. The regions include Gaborone, Maun, Francistown, Serowe, Kasane, Tsabong, Ghanzi, and Pandamatenga. The results indicated that the magnitude of change of change of the significant downward trends in the annual rainfall was found to be -1.11 mm/year at Maun, -1.62 mm/year at Ghanzi, -0.33 mm/year at Pandamatenga, and -0.25 mm/year at Tsabong. The magnitude of change of change of the significant downward trends in the seasonal rainfall was between -0.60 mm/year at Pandamatenga and -0.19 mm/year at Tsabong. All these regions, except Ghanzi, experienced a downward trend in the rainfall distribution. This is owing to the synoptic characteristic of the below-average geopotential heights over Ghanzi. Which might exacerbate the formation of convective systems, leading to a positive rainfall trend at Ghanzi. The annual calculated PCI values are divided into three classes; PCI>10≤15, PCI>16≤20, and PCI>20. The Mann-Kendall analysis of the PCI values indicates that all the regions experienced a downward rainfall trend, implying that it is heading toward a uniform to a moderate distribution. There were distinct patterns that emerged from the Pandamatenga region, indicating a strongly irregular distribution. The regions of Kasane, Maun and Francistown, demonstrated moderate to an irregular distribution. For Gaborone and Francistown, results showed occurrences of a moderate to strongly irregular distribution.

Precipitation pattern changes and response of vegetation to drought variability in the eastern Hungary

BackgroundRecently, many parts of Hungary, as well as central Europe, have been hit by successive extreme climatic events. The main aim of this research was to analyze hydrological and agricultural drought episodes in the Debrecen Region in eastern Hungary from 1950 to 2010. Thus, data of monthly precipitation were collected and tested. After that, three indices were applied, the Precipitation Concentration Index (PCI), dry Precipitation Concentration Index (dPCI), and the Standardized Precipitation Index (SPI). Meanwhile, the correlation between SPI and the normalized difference vegetation index (NDVI) was calculated.ResultsThe results showed that the PCI values ranged between 9.44 and 15.8 with an average of 11.8, while the dPCI values ranged between 8.6 and 20.7 with an average of 10.9, which indicates heterogeneity in rainfall distribution from year to year. More than 103 events of severe agricultural drought were detected for the whole studied period. In connection to this, severe hydrological drought was recorded in 16% of the total monthly rainfall events from 1950 to 2010. Interestingly, a significant correlation was obtained between SPI-3, -6 (i.e., drought), and NDVI in the summer season.ConclusionsThe SPI index could provide an easy tool for drought monitoring meanwhile drought evaluation and monitoring should be taken seriously in the central Europe.

Trend of extreme precipitation indices and analysis of long-term climate variability in the Upper Awash basin, Ethiopia

Assessing the spatial and temporal variability of climate data is one of the basic requirements to solve the hydrological and water management problems. In this manuscript, a comprehensive assessment of data quality, precipitation concentration index (PCI), Mann-Kendall (MK) trend test, and extreme climate change indices analyses were performed. The standard normal homogeneity test (SNHT), the Pettitt test, the Buishand range test, and the Von Neumann ratio test were used for homogeneity analysis, and most of the climate stations exhibited homogenous precipitation series. The annual mean and dry season PCI values demonstrated higher spatial and temporal variability or irregular distribution of precipitation, whereas PCI in the wetter seasons indicated low precipitation concentration in the Upper Awash basin. The MK trend test exhibited a significantly increasing trend of maximum temperature; however, the trend of mean annual and extreme precipitation events was insignificant for the majority of stations. Moreover, the magnitude and trend of ten main extreme precipitation indices were constructed. Unlike the mean annual precipitation, larger values of the extreme 1-day (Rx1day) and 5-day (Rx5day) annual precipitation were obtained in the downstream areas of the basin which could signify the prevalence higher flood risk in this portion of the basin. The highest predicted 100-year annual maximum daily (AMD) precipitation was obtained at Zikuala station as 128.5 mm. Statistical analysis of the magnitude and trend of extreme precipitation indices and spatial variability of climate data is imperative for the integrated water resource planning and management decisions.

Spatial and temporal climate variability and change in the bilate catchment, central Rift Valley lakes region, Ethiopia

ABSTRACT The paper explores spatial and temporal variability of rainfall and temperature in Bilate catchment based on monthly data of 11 meteorological stations. Precipitation Concentration Index (PCI) and Pearson product moment correlation were used to estimate monthly concentrations and associations between annual and seasonal rainfall. Similar methods were used to analyze the association of ENSO with rainfall variability using Eastern Equatorial Pacific (Niño 3.4) data, Sea Surface Temperature (SST), and Southern Oscillation Index (SOI). Rainfall and temperature series were tested for trends using the Mann–Kendall test. Rainfall in the catchment is highly seasonal and concentrated in Kiremt (June–September), followed by Belg (February–May). Belg rainfall was highly variable. Annual rainfall was relatively stable in the upper catchment and variable in the lower catchment. PCI values are expected to increase as annual and seasonal rainfall decrease. The middle catchment had significant negative trends in annual and Belg rainfall. November–March SST is strongly associated with Belg rainfall variations throughout the catchment, and ~49% of the variability can be explained by Equatorial Pacific SST. Annual and seasonal temperature significantly increased in middle and lower parts of the catchment in the past 30 years. Warming was ~0.05°C/yr to 0.17°C/yr.

Long-term rainfall characteristics in the Mzingwane catchment of south-western Zimbabwe

Rainfall characteristics during the annual rainy season are explored for the Mzingwane catchment of south-western Zimbabwe, for both historic period (1886–1906) and more recent times (1950–2015), based on available daily and monthly precipitation series. Annual and seasonal rainfall trends are determined using the modified Mann-Kendall test, magnitude of trends test and Sen’s slope estimator. Rainfall variability is quantified using the coefficient of variation (CV), precipitation concentration index (PCI) and standard precipitation index (SPI). Results suggest that contemporary mean annual rainfall may not have changed from that measured during the historic period of 1886–1906. However, the number of rainy days (≥ 1 mm) has decreased by 34%, thus suggesting much more concentrated and increased rainfall intensity. A notable shift in both the onset and cessation dates of the rainy season is recorded, particularly during the twenty-first century, which has resulted in a significantly reduced (p < 0.05) length of the rainy season. The combination of a reduced number of rainy days (≥ 1 mm) and a shortened rainy season suggests that long intra-season dry spells have become more common through time and have considerable negative consequences for agriculture and wetland ecosystem in the region. In addition, high spatio-temporal rainfall variability and seasonal PCI values indicate strong seasonality in the rainy season. Based on the SPI results, the El Nino Southern Oscillation (ENSO) strongly influences rainfall variability. The results further suggest high uncertainty in rain season characteristics, which requires effective planning for water needs.

Statistical analysis of rainfall in Bihar

Availability of water is largely dependent on the amount of precipitation received by an area. Fluctuations in precipitation pattern exercises significant impact on the livelihood of the people especially in areas where rainfed agriculture has predominance. In the present study, temporal variation of rainfall in Bihar, India has been analysed. Trend analysis for monthly, seasonal as well as annual rainfall series for the duration of 1950–2016 was performed using non-parametric Mann–Kendall. Parametric linear regression test was also used to check the trends. Deficiency in yearly total rainfall was identified using Index of Wetness and standardized rainfall anomaly. Precipitation Concentration Index (PCI) was also estimated to check the monthly distribution of rainfall. Both parametric and non-parametric tests predict declining trend in rainfall for almost all the months except for the month of May, which is reflected as a decline in total annual rainfall in the study area. Analysis of rainfall data revealed very high intra and inter annual variability [average coefficient of variation (CV) = 91.26]. PCI values too revealed very high nonuniformity of rainfall. Analyses of data confirmed high concentration (84%) of rainfall in monsoon months. Rainfall depth with 90% dependability has been found to be over 180 mm and 160 mm for the months of July and August, respectively. Significant decline has been observed in total rainfall over the last three decades (1986–2016) highlighting the need of rainwater harvesting and management to avoid acute shortage of water in near future.

Toward coupling hydrological and meteorological drought characteristics in Lake Urmia Basin, Iran

Investigation of precipitation characteristics on daily, monthly, and annual time scales can contribute to gaining important information related to temporal and spatial distribution of precipitation or even flow rate challenges (e.g., hydrological droughts). The low levels of long-term precipitation and high variability in different time scales are considered the main inherent characteristics of climate in Iran. Due to the direct effects of precipitation on water resources, especially on the river flow rate, it is necessary to assess the efficient indices to visualize the variations in the components of water resources. One of the main indices is the precipitation concentration index (PCI) which is known as a strong indicator of the precipitation distribution generally used on annual and seasonal scales. In this study, drought analysis in the Lake Urmia Basin (LUB) located in northwest of Iran was performed with the daily river flow rate and monthly precipitation values within the period of 1984–2013. The results of changes in precipitation indicated that the irregularity of precipitation distribution had grown in spring months. Also, due to the diminishing precipitation trend on the annual time scale, PCI index also increased. It is concluded that LUB detected a significant descending trend on the annual, spring, and winter time scales in the last 30 years. The PCI values were proved high irregularity in summer with PCI amount of 20.1 and most regularity in winter with PCI amount of 10.4. This paper also aims to assess the effects of PCI on the river flow rate along with the flow shortness volume values using hydrometric and rain gauge stations within LUB. The results obtained from the changes in river flow rate and flow shortness volume revealed that the river flow rate has mostly a falling trend. Finally, it was observed that the time when the river flow rate data changed happened after beginning of changes in the precipitation data. A decrease in inflow from 900 million cubic meters up to 14 billion cubic meters with high flow shortness volume may happen in worst conditions. These results highlighted the importance of applying water resources management in LUB.

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.

Changes and variability of precipitation and temperature in the Guna Tana watershed, Upper Blue Nile Basin, Ethiopia

This paper studied the spatial and temporal variability of the statistical structures of precipitation Guna Tana watershed, Upper Blue Nile, Ethiopia, by analyzing the time series of temperature and precipitation from six weather stations during the period from 1990 to 2016. Inverse distance weight, precipitation concentration index and MK test statics were used to detect annual and seasonal precipitation concentrations and the associated spatial patterns. The results show that precipitation concentration index values were mainly observed in Guna Tana watershed in which about 83.33% and 16.67% were the uniform concentration of precipitation and strong irregularity of precipitation distribution was observed in the kiremt seasons’ rainfall. The demonstration using Mann–Kendall trend test depicted that most parts of Guna Tana watershed are characterized by variability of precipitation and temperature. The results reveal that significant trends in average rainfall were observed (both positive and negative trends). Those significantly decreasing trends of average monsoon rainfall have the highest value of decreasing slope (i.e., − 1.88 mm/year) for Luwaye station and percentage change of − 31.07% in Bega season. Decreasing slopes (− 0.14 °C/year) and percentage change (− 22.79%) were observed in Luwaye station at the average annual minimum temperature and increasing Sen’s slope temperature recorded at 24.64 °C/year and percentage changes at 0.136% in 10% level of significance at Woreta station in average annual minimum temperature.

On the analysis of the temporal precipitation distribution over Greece using the Precipitation Concentration Index (PCI): annual, seasonal, monthly analysis and association with the atmospheric circulation

The investigation and analysis of precipitation concentration on an annual and seasonal basis, over Greece, motivated the present study. To achieve this objective, the Precipitation Concentration Index (PCI) was implemented for various stations over the domain of interest, for a 43-year period starting from 1958 up to 2000. On an annual basis, the results showed that northern Greece is characterized by a moderate precipitation concentration while moving to the southern parts of the country, PCI values increase. The stations over the western and central Greece present a moderate concentrated distribution, while in the Cyclades areas and southeastern Aegean Sea, precipitation amounts are concentrated in only a few months of the year with a prolonged dry period. In winter and spring, the whole Greece is characterized, in general, by a moderate concentrated distribution, and the seasonal amounts are relatively even distributed among the three season’s months. Contrarily, in summer, the results are less coherent and the index values are much higher. Finally in autumn, according to the PCI values, continental Greece is characterized by moderate concentrated distribution while the islands of the Aegean Sea by a high concentrated distribution. Overall, Greece seems to be shifting during the time period under study to a more concentrated precipitation contribution especially regarding the annual, winter, spring, and autumn totals. This change can be partially related to the changes of the atmospheric circulation over the country due to the decrease of the frequency of the wettest circulation types on an annual basis.

Precipitation Concentration in Bangladesh over Different Temporal Periods

Precipitation concentration is an important component of climate, and an unbalanced distribution of precipitation can yield excess or scarcity of water resources, which in turn can influence plant growth, flood risk, and water resource use. The precipitation concentration index (PCI) is a well-known indicator for the measurement of temporal precipitation in a short or long area. The purpose of this study was to analyze precipitation concentration rates in different regions of Bangladesh using the precipitation concentration index (PCI) and the inverse distance weighting method. In this study, the rainfall data from 30 meteorological observatory stations across Bangladesh were collected for the period 1980 to 2011. We defined periods of varying lengths (i.e., annual, supraseasonal, seasonal, and three- and two-month rainfall concentrations) and compared their PCI values. The results showed that precipitation concentrations were mostly irregular when rainfall was concentrated within two to four months of the year. Higher PCI values were mainly identified in the eastern region and have strong seasonal influences, whereas lower PCI values were mostly observed in the northern region. The analyses of periodic variation and precipitation in Bangladesh generally follow through the SW–NE direction due to the summer monsoon, while during the winter monsoon, they follow the N–S direction where JAS and JFM showed higher and lower PCI values. We observed variations in PCI among different regions using the Kruskal–Wallis test of the mean PCI on a decadal scale (1980–1989, 1990–1999, and 2000–2011). The result showed that significant changes in the precipitation occurred during the period of 1980–2011. At a two-month scale, significant changes were identified during transition periods where PCI values were lower from 2000 to 2011 than those in the earlier decades.

Analysis of Rainfall Distribution, Temporal Trends, and Rates of Change in the Savannah Zones of Nigeria

ABSTRACT The impact of climate change is often demonstrated by rainfall and its attributes. Consequently, this study analyzes rainfall concentration, temporal trends, and rates of change in the savannah zones of Nigeria. Rainfall data were acquired from the archives of the Environmental Management Programme, Federal University of Technology, Minna, for 13 synoptic stations at annual, seasonal, and monthly time scales for the 1970–2016 period. The precipitation concentration index (PCI), Mann–Kendall trend test, Theil–Sen’s slope estimator (β), and relative percentage change methods were adopted for data analysis. The findings reveal that PCI calculated on an annual scale falls into three categories 11–15, 16–20, and PCI > 20. Two distinct patterns emerged from the calculated PCI indicating that stations in the Guinea savannah zone (Bida, Yola, Minna, Jos, Bauchi, and Kaduna) have moderate, irregular, and strongly irregular rainfall concentrations, whereas stations in the Sudano-Sahelian savannah zone (Kano, Gusau, Maiduguri, Yelwa, Nguru, Sokoto, and Katsina) have irregular and strongly irregular rainfall concentrations. The Mann–Kendall analysis of the PCI values reveals that 8 of the 13 stations (62%) experienced downward trends. This implies that rainfall is sliding toward a moderate to uniform distribution. The trends, and consequently the variability in the annual and seasonal rainfall, reveal that, with the exception of Yola and Jos stations, where the trends were downward, the overall rainfall was increasing significantly in some areas and insignificantly in others. The magnitude of the significant upward trends in the annual rainfall was found to be 3.59 mm yr−1 at Yelwa station, 9.84 mm yr−1 at Bauchi station, 17.13 mm yr−1 at Kano station, 3.98 mm yr−1 at Sokoto station, and 3.11 mm yr−1 at Katsina station. It is understood that the changes in rainfall distribution and trends have positive effects on water availability for crops, and this should facilitate enhanced productivity in rain-fed farming.

The prediction of spatial and temporal distribution of precipitation regime in Iran: the case of Fars province

Precipitation regime and its spatial and temporal distribution are essential requirements of environmental planning. Precipitation Concentration Index (PCI) and geographical weight regression method are two practical methods for the prediction of the temporal and spatial distribution of precipitation variables. For this purpose, the precipitation regime data for 13 synoptic stations in Fars province in Iran were used since their foundation in 2014. Then, the data were interpolated using Inverse Distance Weighting (IDW) method and precipitation pixels were prepared corresponding to the Digital Elevation Model (DEM), slope and aspect. The results of the study showed that the precipitation regime of the northern, northwest, and western parts of Fars province with PCI values of 13–20 with a maximum rainfall in winter was different from that of the southern and eastern parts of the studied area. In addition, the PCI values higher than 22 (PCI > 22) in the southwestern and eastern parts of the region suggested significant changes of rainfall throughout the year. Summer is the dry season of the area, and except for a very small part of southern parts of the province, the other areas were without rainfall. The results of Geographically Weighted Regression (GWR) also indicated that the prediction of precipitation regime with topographic elements shows much better results than the Ordinary Least Squares (OLS) regression, in a way that the relationship between altitude and rainfall in the western, northwestern, and central parts of the province were positive and significant, and the areas of eastern parts of Fars province showed a negative relationship in terms of topographic elements (aspect and altitude). Finally, the results of the prediction of GWR model showed that the mentioned model was able to assess more than 80% of the existing relationship between observe and predict the monthly precipitation amount based on the studied spatial factors successfully.

English

The precipitation concentration index (PCI) of Indian region using monthly data for 1951 to 2015 was estimated on an annual and seasonal scale to show the changes in temporal and spatial distribution of rainfall over the 34 meteorological sub-divisions. As a result, annual values of PCI showed more erratic nature than the uniform to moderate behaviour of seasonal (Jun-Sept) PCI values. Computation of PCI will be useful in hydrological, water resources as well as flood and drought management programs. As floods are more common in north and northeast India, PCI may serve as a warning tool in disaster control management in relation to floods and soil erosion since PCI can help in the analysis of soil erosion and in the calculation of erodibility of land and loss of soil due to it. Key words: Temporal and spatial distribution of rainfall, precipitation concentration index, meteorological sub-divisions of India, floods and erosion.

Spatial and Temporal Variability in the Precipitation Concentration in the Upper Reaches of the Hongshui River Basin, Southwestern China

The statistical characteristics of precipitation play important roles not only in flood and drought risk assessments but also in water resource management. This paper implements a statistical analysis to study the spatial and temporal variability in precipitation in the upper reaches of the Hongshui River basin (UHRB), southwestern China, by analysing time series of daily precipitation from 18 weather stations during the period of 1959 to 2015. To detect precipitation concentrations and the associated patterns, three indices, the precipitation concentration index (PCI), precipitation concentration degree (PCD), and precipitation concentration period (PCP), were used. The relationships between the precipitation concentration indices (PCI, PCD, and PCP) and geographic variables (latitude, longitude, and elevation), large-scale atmospheric circulation indices, and summer monsoon indices were investigated to identify specific dependencies and spatial patterns in the precipitation distribution and concentration. The results show that high PCI values were mainly observed in the northeastern portion of the basin, whereas low PCI values were mainly detected in the southwest. The Mann-Kendall test results demonstrate that the majority of the UHRB is characterized by nonsignificant trends in the PCI, PCD, and PCP from 1959 to 2015. The PCP results reveal that rainfall in the UHRB mainly occurs in summer months, and the rainy season arrives earlier in the eastern UHRB than in the western UHRB. Additionally, the PCD results indicate that the rainfall in the western UHRB is more dispersed throughout the year than that in the eastern UHRB. Compared with other geographical factors, longitude is the most important variable that governs the spatial distribution and variations in annual precipitation and the precipitation concentration indices. Due to a combination of topography, the Indian subtropical high, and monsoon weakening, precipitation may be more concentrated in one period, especially in the eastern part of the basin, which increases the risk of drought.