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Hamideh Roshani, Raoof Mostafazadeh, Abazar Esmali-Ouri, Mohsen Zabihi,
Volume 7, Issue 4 (2-2021)
Volume 7, Issue 4 (2-2021)
Abstract
Introduction and objective:
Temporal and spatial variability of rainfall is one of the determining factors for water resources management, agricultural production, drought risk, flood control and understanding the effect of climate change. The impact of spatiotemporal patterns of precipitation on flood/drought hazard and available water resources is an undeniable issue in water resources management. Precipitation concentration (PCI) and Seasonality (SI) indices are the important indicators to determine the distribution of precipitation in a region which can lead to identify and manage before occurring natural hazards including flood and drought and hydro-meteorological storms. Several methods available to study the spatial and temporal distribution of rainfall. Indicators of rainfall concentration and seasonality are among the methods of studying rainfall dispersion that depend on the distribution of rainfall patterns at different time scales. Accordingly, the study and understanding of temporal and spatial changes in rainfall can lead to sound management policies in the field of water and soil resources by planners and decision makers.
Methodology:
The precipitation concentration index is presented as a powerful indicator for determining the temporal distribution of precipitation to show the distribution of precipitation and rain erosion. The increase in the value of this indicator indicates a low dispersion and a higher concentration of rainfall, which is closely related to the intensity of rainfall. Seasonality index as one of the key factors in detecting seasonal variation in the variables of natural ecosystems, measures the time distribution of hydrological components at different times of the year and uses each hydrological variable to classify different hydrologic variable regimes. In this regard, the present research aimed to investigate the spatial and temporal distribution and trend analysis of PCI and SI for 41 rain gauge stations of Golestan province (38-year study period) in annual, seasonal and dry and wet time scales. The Mann-Kendall test was used to determine the trend of time changes in PCI and SI indices during the study period in all selected rain gauge stations in Golestan province. Mann-Kendall test is one of the non-parametric tests to determine the trend in hydroclimate time series. The advantages of this method include its suitability for use in time series without a specific statistical distribution, as well as the effectiveness of this method in data with extreme values in time series. In order to determine the spatial pattern of PCI and SI indices in different time scales (annual, seasonal, and dry and wet periods), the method of inverse distance weighting was employed in GIS environment. In this method, a weight has been assigned to each point that decreases with increasing distance from the known value point. On the other hand, the effectiveness of the known point in estimating the unknown point and calculating the mean also decreases. In this regard, the best results are obtained when the behavior of the mathematical function is similar to the behavior of the observed phenomenon. The study area in terms of extent, topographic diversity, type of land use has a high heterogeneity that affects the characteristics and temporal and spatial occurrence of dry and wet periods. The average annual rainfall varies from about 150 to 750 mm over the study area.
Results:
According to the results, the average of PCI for annual, spring, summer, autumn, winter, dry and wet periods in the research area were obtained 13.15, 11.96, 13.15, 10.72, 9.96, 14.72, and 1072, respectively. Also, Chat station with 0.79 (seasonal distribution with dry and wet seasons) and Shastkalateh station with 0.47 (mainly seasonal distribution with short dry season) had the maximum and minimum of SI in the Golestan province, respectively. In addition, 27 and 14 of studied stations had the increasing (Significant and no-significant) and decreasing (Significant and no-significant) trend for PCI and SI.
Conclusions:
Non-compliance of precipitation in Golestan province with a single temporal and spatial pattern is another achievement of the present study. The results of the current research can be used as a roadmap for water resources planning and policy making in the study area. It is noteworthy that the PCI and SI indices do not emphasize the cumulative values of precipitation and address the pattern of rainfall distribution, which can be a better criterion for assessing changes in precipitation patterns at different time scales. In this regard, determining the priority of areas for protection and management of water and soil resources, and spatial pattern of agricultural crops. The trend of changes in PCI and SI indicators and its relationship with important climatic components can be considered in assessing the changes in pattern of precipitation and climatic variables.
Temporal and spatial variability of rainfall is one of the determining factors for water resources management, agricultural production, drought risk, flood control and understanding the effect of climate change. The impact of spatiotemporal patterns of precipitation on flood/drought hazard and available water resources is an undeniable issue in water resources management. Precipitation concentration (PCI) and Seasonality (SI) indices are the important indicators to determine the distribution of precipitation in a region which can lead to identify and manage before occurring natural hazards including flood and drought and hydro-meteorological storms. Several methods available to study the spatial and temporal distribution of rainfall. Indicators of rainfall concentration and seasonality are among the methods of studying rainfall dispersion that depend on the distribution of rainfall patterns at different time scales. Accordingly, the study and understanding of temporal and spatial changes in rainfall can lead to sound management policies in the field of water and soil resources by planners and decision makers.
Methodology:
The precipitation concentration index is presented as a powerful indicator for determining the temporal distribution of precipitation to show the distribution of precipitation and rain erosion. The increase in the value of this indicator indicates a low dispersion and a higher concentration of rainfall, which is closely related to the intensity of rainfall. Seasonality index as one of the key factors in detecting seasonal variation in the variables of natural ecosystems, measures the time distribution of hydrological components at different times of the year and uses each hydrological variable to classify different hydrologic variable regimes. In this regard, the present research aimed to investigate the spatial and temporal distribution and trend analysis of PCI and SI for 41 rain gauge stations of Golestan province (38-year study period) in annual, seasonal and dry and wet time scales. The Mann-Kendall test was used to determine the trend of time changes in PCI and SI indices during the study period in all selected rain gauge stations in Golestan province. Mann-Kendall test is one of the non-parametric tests to determine the trend in hydroclimate time series. The advantages of this method include its suitability for use in time series without a specific statistical distribution, as well as the effectiveness of this method in data with extreme values in time series. In order to determine the spatial pattern of PCI and SI indices in different time scales (annual, seasonal, and dry and wet periods), the method of inverse distance weighting was employed in GIS environment. In this method, a weight has been assigned to each point that decreases with increasing distance from the known value point. On the other hand, the effectiveness of the known point in estimating the unknown point and calculating the mean also decreases. In this regard, the best results are obtained when the behavior of the mathematical function is similar to the behavior of the observed phenomenon. The study area in terms of extent, topographic diversity, type of land use has a high heterogeneity that affects the characteristics and temporal and spatial occurrence of dry and wet periods. The average annual rainfall varies from about 150 to 750 mm over the study area.
Results:
According to the results, the average of PCI for annual, spring, summer, autumn, winter, dry and wet periods in the research area were obtained 13.15, 11.96, 13.15, 10.72, 9.96, 14.72, and 1072, respectively. Also, Chat station with 0.79 (seasonal distribution with dry and wet seasons) and Shastkalateh station with 0.47 (mainly seasonal distribution with short dry season) had the maximum and minimum of SI in the Golestan province, respectively. In addition, 27 and 14 of studied stations had the increasing (Significant and no-significant) and decreasing (Significant and no-significant) trend for PCI and SI.
Conclusions:
Non-compliance of precipitation in Golestan province with a single temporal and spatial pattern is another achievement of the present study. The results of the current research can be used as a roadmap for water resources planning and policy making in the study area. It is noteworthy that the PCI and SI indices do not emphasize the cumulative values of precipitation and address the pattern of rainfall distribution, which can be a better criterion for assessing changes in precipitation patterns at different time scales. In this regard, determining the priority of areas for protection and management of water and soil resources, and spatial pattern of agricultural crops. The trend of changes in PCI and SI indicators and its relationship with important climatic components can be considered in assessing the changes in pattern of precipitation and climatic variables.
- Mahmoud Roshani, - Mohammad Saligheh, - Bohlol Alijani, - Zahra Begum Hejazizadeh,
Volume 8, Issue 4 (1-2021)
Volume 8, Issue 4 (1-2021)
Abstract
In this study, the synoptic patterns of the warm period of the year that lead to the cessation of rainfall and the creation of short to long dry spells were identified and analyzed. For this purpose, the rainfall data of 8 synoptic stations were used to identify the dry spells of the warm season for 30 years (1986 to 2015). The average daily rainfall of each station was used as the threshold value to distinguish between wet and dry spells. Then, according to the effects of dry spells, they were defined subjectively and objectively with different durations. Thus, 5 numerical periods of 12 to 15, 15 to 30, 30 to 45, 45 to 60 and more than 60 days were identified. By factor analysis of Geopotential height data at 500 hPa, 4 components were identified for each period and a total of 20 components for 5 dry spells. Therefore, 5 common patterns control the stable weather conditions of dry spells. Most dry days are caused by subtropical high-pressure nuclei, which have a wide, even, dual-core, triple-core arrangement. The effect of subtropical high pressure on the dryness of the southern coast of the Caspian Sea is quite evident. Other dry days were caused by southerly currents, weakening of northern currents, and the trough Anticyclones’ area. Also, the anomaly map of the components days at the 500 hPa level showed that the anticyclones and cyclones correspond to the positive and negative phases of the anomalies, respectively.
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