arash zandkarimi; Davood Mokhtari; Shaida Zandkarimi
Abstract
Extended Abstract Introduction The prediction of the occurrence of floods and the reduction of damages caused by it is strongly influenced by the modeling of physical phenomena and the spatial-temporal distribution of precipitation. The purpose of the research was to optimize the rainfall gauging network ...
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Extended Abstract Introduction The prediction of the occurrence of floods and the reduction of damages caused by it is strongly influenced by the modeling of physical phenomena and the spatial-temporal distribution of precipitation. The purpose of the research was to optimize the rainfall gauging network in Kurdistan province using Kriging estimation variance and taking into account the topography of the area. In this study, to optimize the rain gauging network in Kurdistan province, rainfall data of the rain gauging, synoptic, and climatology stations were used. In order to reduce the costs, stations close to each other that are located in the same height range and also have the same error variance, were removed from the existing network. In order to reduce the maintenance cost of the stations, after clustering of the area, 8 stations whose removal had little impact on the accuracy of the data, were identified in the province. Then. In order to strengthen the network, the optimization of new stations was put on the agenda and 28 points were set as the proposed stations. Materials and methods After reviewing the existing stations’ data, 145 stations were selected for the analysis and optimization of the existing network. After selecting the normal data and spatializing them, due to the large extent of the area and the variability of the average annual precipitation, Kurdistan province is divided into smaller regions with less variations in the average rainfall. The regional division or clustering of stations is carried out using the functions available in the ArcGIS 10.2.2 software and based on the main catchment basins. In the next step, the spatial distribution of rainfall and the variance of the errors in all clusters are calculated separately. Given the importance of highlands in receiving rainfall and supplying water, the distribution of rain gauges on elevation layers has been studied. At this stage, redundant stations were eliminated, and stations which are located in close proximity of each other, and are located in the same elevation range and also have the same error variance, can be eliminated too. At the final stage, adding new stations and strengthening the network took place. At this stage, the priority is to build the station for areas where the variance of the errors is high. After adding each station, the error variance of the whole system is calculated again. Adding a new station to the network will continue as long as the network error reaches its minimum. Discussion 1-Normality test of data After spatializing the rainfall data, their normal distribution was investigated using the Kolmogorov – Smirnov test. The results show that the distribution of data at 95% level does not have a significant difference with a normal distribution. 2- Division of the region and clustering of stations In this study, using the region’s digital elevation map, and based on the analyses made in the software ArcGIS 10.2.2, clustering of stations and division of the region was carried out. The entire area of interest is divided into 8 clusters. 3- Calculating the Kriging error of the existing network The amounts of the rainfall data error can be obtained by calculating the Kriging error of the existing network. As mentioned in the previous sections, the calculation of the error in the Kriging method is a function of semi-variogram (spatial structure) of the variable and this feature increases the estimation accuracy of the variable error. 4- Distribution of the stations on elevation layers and determination of the redundant stations By studying the distribution of the stations on altitudes, stations which had no impact on the accuracy of data extraction were removed. The candidate stations for removal were located in a same range of elevation, and showed similar error values. In order to be sure of the decision taken, by eliminating each station, the overall error of the network in each cluster is calculated, and an increase in the error values represents the wrong station is being removed. 5- Adding the proposed stations and calculating the variance of the new network error Adding new stations to the network is done based on the Kriging variance. The priority of the station construction is for areas that display a high error. In the Kriging error variance method, adding a new station to the network is done based on Eó2 (error variance), in a way that points with equal error variance or greater than the value of data variance is considered as the first priority for the construction of the station. The points whose error variances are between the variance of data and ½ of the variance of data, is the second priority and finally, the third priority belongs to the points whose variances are between ½ and ¼ of the variance. In this research, based on Kriging variance, 28 stations have been proposed to strengthen the rain gauging network in Kurdistan Province. Conclusion Given that precipitation is considered as the main entrance to the planning of sustainable water resources development, in this study, the optimization of rain gauging station network in Kurdistan province was investigated using the Kriging error variance. In previous studies, generally, entropy has been considered as the main model for network modification, therefore, due to the limitations of these methods in not using the semi-variogram features, in this research, the geo-statistic method based on kriging error variance was used due to its high accuracy. The amount accuracy increase in this method depends to a large extent on the semi-variogram features (spatial structure) of the precipitation, which can be used to calculate the error variance rate for the new station before the construction and inventory of the station. In order to strengthen the network, the optimization of new stations was put on the agenda and 28 points were set as the location of the proposed stations. For practical comparison of the results, the error variance values were calculated before and after the addition of the proposed stations, the average error variance of the annual precipitation in the province decreased by 11%, with the largest decrease belonging to the central part of the province with 24.03%.
Seyyed Yaser Hakimdoust; Alimohammad Pourzeidi; Mohammad Saleh Gerami
Abstract
Introduction
Precipitation is an atmospheric factor, its quantity and distribution vary considerably in different parts of the planet, and is one of the most influential climatic elements that has always been influenced by the climate. Its amount changes in time and place continuously.Knowing the temporal ...
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Introduction
Precipitation is an atmospheric factor, its quantity and distribution vary considerably in different parts of the planet, and is one of the most influential climatic elements that has always been influenced by the climate. Its amount changes in time and place continuously.Knowing the temporal and spatial distribution of rainfall is a useful tool for understanding how non-uniform distribution of water resources and vegetation in each region takes place.Precipitation occurs when the wet weather and the climb factor exist both in the region.In other words, the wet air must rise to a certain height so that it can reach the saturation point due to the subsequent cooling down, and in the next, the cloud produces precipitation.The absence of any of these two factors prevents the occurrence of precipitation.
Rainfall variation is considered as a key factor in the structure and functioning of ecosystems, but its impact on scale and magnitude is much less than its spatial variation.The climatic element, especially precipitation, has significant changes in time periods.Therefore, the recognition of the element of precipitation as one of the two elements of the climate and its changes in different times and places allows the optimal utilization of the natural environment.The amountand spatial distribution of rainfall is a fundamental factor for decision making, design and evaluation of hydrological models as well as water management and planning.Temporal spatial variations have diverse and varied impacts on the management and planning of water resources along a water basin.Climate change is one of the factors affecting the change of water resources.Precipitation, as a highly variable element, has always been a concern for climatologists and waterologistsas a fundamental factor in the blue balance. The extreme variability of rainfall along the time-space has a variety of study approaches.The purpose of this research is to identify the conditions of rainfall in Mazandaran province. Therefore, the location of rainfallin this province was investigated.In this regard, identification of the effective factors of the occurrence of these rainfall in different seasons and their role in the province has been addressed and its results will be available as a scientific and practical solution.
Materials and Methods
In this study, for the purpose of identifying the rainfall in the province of Mazandaran, five years of rainfall from 2006 to 2010 have been used from a total of 12 synoptic stations.Using extracted data from precipitation graphs, rainfall of more than 10 mm was extracted in the studied area.Then the data were categorized into four parts: spring, summer, autumn, and winter of the year. To create the database, they entered the SPSS and ARC GIS10 software.In the spatial analysis of the data, the semi-modification of these models has been used, which was calculated using ARC GIS10 software.The methods used in the zoning of Kriging and IDW models for fitting include: IDW with three potentials of 1,2,3, and the Kriging method with spherical, circular, exponential, Gaussian, and spherical models, which is performed with conventional Kriging technique.Also, for statistical comparison of models, root mean square error of RMSE, MAE, RMSE and their correlation coefficient were used.Then, optimal mapping based on multivariate regression was fitted based on the simulation method and the recursive method of six variables in rainfall generation including latitude and longitude, number of rainfall days, elevation, relative humidity and dew point temperature. The effects of these factors on rainfall in the province will be evaluated in different seasons and annually.
ResultsandDiscussion
The results of the spring survey show that there were 5 stations out of 12 stations without rainfall.These stations are located in the plain and in the mountain range of the region.The analysis showed that the correlation coefficient between variables is R^2= 967, which indicates a strong relationship between the set of independent variables and the dependent variable.85.8% of rainfall in the spring season in Mazandaran province depend on these variables. In the summer, only 2 stations in the province did not experience rainfall ranges, both of which were at high altitudes and include the station Alasht and Kyasar.Variables show a very strong relationship in the summer with a correlation coefficientof R^2=0.995 which is 0.9. 9%of rainfall in Mazandaran province depends on these six variables.The fall season is one of the high seasons in the province of Mazandaran. Only one station (Siahbisheh) has been registered from 12 storm rainfall stations.Estimates show that the six variables analyzed in this chapter with a correlation coefficient of R^2 = 0.983 represent a strong correlation.The results of the winter season show that all stations in Mazandaran province have rainfall, although it includes fewer days than theautumn season.All stations experience at least one day at Alasht Station for up to 7 days in Ramsar.The results of the analysis show that in winter, the correlation coefficient is R^2 = 0.996.
Conclusion
For zoning of the study area, the IDW method with three potentials of 1, 2, 3 and the Kriging method have been used with spherical, circular, exponential and Gaussian models. The evaluation and determination of the best model and verification of the produced maps was carried out. Also, for statistical comparison of the models, the root mean square errors of RMS, MAE, RMSE and their correlation coefficient were used, which, the best model for zoning was the IDW model with two potentials of 1,3 and ordinary circular kriging. Optimal mapping was done by multivariate regression based on the model of synchronous and retrograde method, and six variables that have the greatest effect on rainfall, including latitude and longitude, rainfall days, elevation, relative humidity and dew point temperature were studied.The results show that the correlation values of these six variables are 0.97 in spring, 0.99 in summer, 0.98 in autumn, 0.99 in winter and 0.99 in annual rainfall which indicates a strong relationship between these six variables in the rainfall ofMazandaran province.
