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

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Yuan Liu ◽  
Dongchun Yan ◽  
Anbang Wen ◽  
Zhonglin Shi ◽  
Taili Chen ◽  
...  
Keyword(s):  
Heavy Precipitation ◽  
Resource Planning ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
Precipitation Concentration Index ◽  
Intensity Index ◽  
Precipitation Concentration ◽  
Reference Information ◽  
Concentration Degree ◽  
The Lorenz Curve

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.

scholarly journals Spatial variability of concentration and aggressiveness of precipitation in North-East of Algeria

2018 ◽  
Vol 36 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Hanene Bessaklia ◽  
Abderrahmane Nekkache Ghenim ◽  
Abdessalam Megnounif ◽  
Javier Martin-Vide
Keyword(s):  
Concentration Index ◽  
Annual Precipitation ◽  
Monthly Precipitation ◽  
Study Region ◽  
Precipitation Concentration Index ◽  
Precipitation Concentration ◽  
North East ◽  
Mk Test ◽  
High Precipitation ◽  
Extreme North

AbstractIn this study, the spatial variation of daily and monthly concentration precipitation index and its aggressiveness were used in 23 rainfall stations in the extreme north-east of Algeria over the period 1970–2010. The trend was analysed by the Mann–Kendall (MK) test. The results show that daily precipitation concentration index (CI) values are noticeably higher in places where the amount of total precipitation is low, the results of MK test show that areas of high precipitation concentration tend to increase. The seasonality and aggressiveness of precipitation are high in the eastern and western parts of the study region (eastern and central coastal of Constantine catchments), whereas a moderately seasonal distribution with low aggressiveness is found in the middle of the study area (plains and central Seybouse catchment). As a result, the modified Fournier index (MFI) has a significant correlation with annual precipitation, whereas the CI and monthly precipitation concentration index (PCI) show an opposite correlation in relation to annual precipitation.

scholarly journals Precipitation concentration changes in Spain 1946–2005

2011 ◽  
Vol 11 (5) ◽  
pp. 1259-1265 ◽  
Author(s):  
M. de Luis ◽  
J. C. González-Hidalgo ◽  
M. Brunetti ◽  
L. A. Longares
Keyword(s):  
Spatial Pattern ◽  
Concentration Index ◽  
Storm Track ◽  
Temporal Distribution ◽  
Monthly Precipitation ◽  
Wet Season ◽  
Mean Values ◽  
Precipitation Concentration Index ◽  
Precipitation Concentration ◽  
Concentration Changes

Abstract. An analysis was made of the Precipitation Concentration Index using the new MOPREDAS database of monthly precipitation in Spain (Monthly Precipitation Data base of Spain). The database was compiled after exhaustive quality control of the complete digitalized Spanish Meterological Agency (AEMet) archives and contains a total set of 2670 complete and homogeneous monthly precipitation series from 1946 to 2005. Thus, MOPREDAS currently holds the densest information available for the 1946–2005 period for Spain and ensures a high resolution of results. The Precipitation Concentration Index (PCI) is a powerful indicator of the temporal distribution of precipitation, traditionally applied at annual scales; as the value increases, the more concentrated the precipitation. Furthermore PCI is a part of the well-known Fournier index, with a long tradition on natural system analyses, as for example soil erosion. In this paper, the mean values of annual, seasonal and wet and dry periods of PCI in the conterminous Spain and for two normal periods (1946–1975 and 1976–2005) were studied. Precipitation in Spain follows a general NW-SE spatial pattern during the wet (months) period due to the Atlantic storm track, while during the dry (months) period, it follows a predominantly N-S spatial pattern. As a result, the annual values of PCI combine the two patterns and show a SW-NE PCI gradient. The analyses of the two sub-periods show significant changes in the precipitation occurred in conterminous Spain from 1946 to 2005, and precipitation concentration increased across most of the IP. At an annual scale, PCI increases mostly due to an increase in precipitation concentration during the wet season. At a seasonal scale significant changes were detected between 1945–1975 and 1976–2005, particularly in autumn (increase of PCI values), while changes in winter, spring and summer were mostly localized and not generalized (both increase and decrease). Changes in PCI seem to be complex and appear to be related to global atmospheric features and synoptic and local factors affecting precipitation trends. We discuss the possible explanation linked to the atmospheric pattern and monthly trends and their implications.

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

2021 ◽  
Author(s):  
Arijit De ◽  
Srishty Shreya ◽  
Neel Sarkar ◽  
Animesh Maitra
Keyword(s):  
Time Series ◽  
Concentration Index ◽  
Kendall Test ◽  
Drought Severity ◽  
Tropical Region ◽  
Monthly Precipitation ◽  
Monsoon Period ◽  
Precipitation Concentration Index ◽  
Precipitation Concentration

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.

scholarly journals Estimation of Temporal R-factor Based on Monthly Precipitation Data

2021 ◽  
Vol 2070 (1) ◽  
pp. 012210
Author(s):  
Narendra Kumar Maurya ◽  
Prakash Singh Tanwar
Keyword(s):  
Heavy Precipitation ◽  
Rainfall Amount ◽  
Annual Rainfall ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
R Factor ◽  
R Value ◽  
Monthly Precipitation Data ◽  
Precipitation Events

Abstract This study assesses temporal variation in rainfall erosivity of Gurushikhar, Rajasthan, (India) on a monthly precipitation basis in the form of the USLE/RUSLE R-factor. The objective of the paper is to theoretically calculate rainfall erosivity when the unavailability of high temporal resolution pluviographic rainfall data such as Indian condition. In the study, the rainfall erosivity has been calculated using the Modified Fourier Index. The results show that the annual rainfall erosivity factor (R) value highest in the year 2017 and lowest in 1974. Conferring to an examination through NASA, earth’s global superficial temperatures in 2017 ranked as second warmest since 1880. Therefore, the rainfall amount was more in 2017 compared to past years, and also rainfall erosivity value suddenly increased in 2017, achieved the highest value. They concluded that the heavy precipitation events in the year are lead to an increase in rainfall erosivity value and risk of soil erosion.

scholarly journals Spatiotemporal Changes in Precipitation Concentration Indicators Over Iran

2021 ◽  
Author(s):  
Mohammad Darand ◽  
Farshad Pazhoh
Keyword(s):  
Change Point ◽  
Spatiotemporal Variability ◽  
Precipitation Concentration Index ◽  
South Eastern ◽  
Precipitation Concentration ◽  
Oman Sea ◽  
The North ◽  
Daily Precipitation Data ◽  
Concentration Degree ◽  
Period Of Occurrence

Abstract 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.

scholarly journals Long-Term Precipitation Analysis and Estimation of Precipitation Concentration Index Using Three Support Vector Machine Methods

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Milan Gocic ◽  
Shahaboddin Shamshirband ◽  
Zaidi Razak ◽  
Dalibor Petković ◽  
Sudheer Ch ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Concentration Index ◽  
Predictive Accuracy ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Linear Regression Method ◽  
Monthly Precipitation ◽  
Precipitation Concentration Index ◽  
Precipitation Concentration

The monthly precipitation data from 29 stations in Serbia during the period of 1946–2012 were considered. Precipitation trends were calculated using linear regression method. Three CLINO periods (1961–1990, 1971–2000, and 1981–2010) in three subregions were analysed. The CLINO 1981–2010 period had a significant increasing trend. Spatial pattern of the precipitation concentration index (PCI) was presented. For the purpose of PCI prediction, three Support Vector Machine (SVM) models, namely, SVM coupled with the discrete wavelet transform (SVM-Wavelet), the firefly algorithm (SVM-FFA), and using the radial basis function (SVM-RBF), were developed and used. The estimation and prediction results of these models were compared with each other using three statistical indicators, that is, root mean square error, coefficient of determination, and coefficient of efficiency. The experimental results showed that an improvement in predictive accuracy and capability of generalization can be achieved by the SVM-Wavelet approach. Moreover, the results indicated the proposed SVM-Wavelet model can adequately predict the PCI.

scholarly journals Estimating the rainfall erosivity factor from monthly precipitation data in the Madrid Region (Spain)

2015 ◽  
Vol 63 (1) ◽  
pp. 55-62 ◽  
Author(s):  
David Hernando ◽  
Manuel G. Romana
Keyword(s):  
Mean Squared Error ◽  
Rain Gauge ◽  
Accurate Estimate ◽  
Coefficient Of Determination ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
Precipitation Concentration Index ◽  
R Factor ◽  
Record Length

Abstract The need for continuous recording rain gauges makes it difficult to determine the rainfall erosivity factor (Rfactor) of the Universal Soil Loss Equation in regions without good spatial and temporal data coverage. In particular, the R-factor is only known at 16 rain gauge stations in the Madrid Region (Spain). The objectives of this study were to identify a readily available estimate of the R-factor for the Madrid Region and to evaluate the effect of rainfall record length on estimate precision and accuracy. Five estimators based on monthly precipitation were considered: total annual rainfall (P), Fournier index (F), modified Fournier index (MFI), precipitation concentration index (PCI) and a regression equation provided by the Spanish Nature Conservation Institute (RICONA). Regression results from 8 calibration stations showed that MFI was the best estimator in terms of coefficient of determination and root mean squared error, closely followed by P. Analysis of the effect of record length indicated that little improvement was obtained for MFI and P over 5- year intervals. Finally, validation in 8 additional stations supported that the equation R = 1.05·MFI computed for a record length of 5 years provided a simple, precise and accurate estimate of the R-factor in the Madrid Region.

scholarly journals CLIMATE SEASONALITY AND ITS RELEVANCE FOR SOIL EROSION DURING SUMMER IN EXTRA-CARPATHIAN MOLDOVA

2020 ◽  
Vol 14 (2) ◽  
pp. 193-205
Author(s):  
Mădălina Mega ◽  
Andreea-Diana Damian
Keyword(s):  
Soil Erosion ◽  
Soil Aggregates ◽  
Aridity Index ◽  
General Level ◽  
Rainfall Erosivity ◽  
Climate Conditions ◽  
Precipitation Concentration Index ◽  
Precipitation Concentration ◽  
Summer Climate ◽  
The Impact

Rainfall erosivity is one of the most important topics, linking geomorphological and climatological studies. Rainwater plays an important role in triggering soil erosion. Thus, the impact of raindrops during significant torrential episodes causes the destabilization of soil aggregates and leads to erosion. Romania is a country where dry periods alternate with the rainy ones and studying rainfall erosivity is essential due to its importance for agriculture. The highest intensity of erosion occurs during violent rainfall episodes in the summer months, this being triggered by the energy of the torrential rains, but also by the liquid runoff. The extra-Carpathian Moldova is located in the northeastern and eastern part of Romania. In this study, The Suceava Plateau, The Moldavian Plain, The Moldavian Subcarpathians and The Bârlad Plateau were considered as part of the extra-Carpathian Moldova. Four indices were used to assess the climate seasonality on a general level with focus on the summer climate conditions: De Martonne Aridity Index (IdM), Lang Factor (L), Precipitation Concentration Index (PCI) and Angot Index (K). Our results indicate moderate seasonality in precipitation concentration along the year which underlines that the region cannot be considered climaticaly prone to massive soil erosion induced by rainfall erosivity.

scholarly journals Analysis of Seasonality Precipitation Concentration in Northern of Iraq

2021 ◽  
Vol 05 (1) ◽  
pp. 50-67
Author(s):  
Surah Hussain ◽  
Safa Khalil
Keyword(s):  
Concentration Index ◽  
The Other ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
High Concentration ◽  
Wet Season ◽  
Precipitation Concentration Index ◽  
Precipitation Concentration ◽  
The North ◽  
Monthly Precipitation Data

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.

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