Agricultural Meteorology
S. Bayati; Kh. Abdollahi
Abstract
Introduction: Rainfall data are required for planning, designing, developing and managing water resources projects as well as hydrological studies. Some previous studies have suggested increasing the density of the rain gauge network to reduce the estimation error. However, more operational stations ...
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Introduction: Rainfall data are required for planning, designing, developing and managing water resources projects as well as hydrological studies. Some previous studies have suggested increasing the density of the rain gauge network to reduce the estimation error. However, more operational stations require more installation costs and monitoring. Some common techniques including statistical methods, spatial interpolation, information-based theory and combination are used to evaluate and design the network. Chaharmahal va Bakhtiari province is a mountainous region; hence, a denser rainfall network is expected in this mountainous environment. The aim of this study was to evaluate the condition of rain gauge stations in Chaharmahal va Bakhtiari province using two approaches, i.e. geostatistical methods and entropy theory.
Materials and Methods: The main required data set for this study is a time series of rainfall data. These data were collected on a daily scale from the Regional Water Company of Chaharmahal va Bakhtiari. After performing statistical tests, the annual data series was prepared for 46 rain gauge stations. A statistical period of 2000 to 2016 was used. The homogeneity of data was investigated by double mass test and histogram drawing methods using Excel and SPSS software, and the existence of trend in the time series of data was investigated by applying a Spearman test. Then, the adequacy of rain gauges in the gauging network was investigated. Annual rainfall interpolation maps and their standard error maps were prepared using the kriging method. Contribution of each station in reducing or increasing the error in the rain gauge network was investigated by removing each station in a cross validation procedure. The efficiency of the rain gauge network was evaluated using the concept of discrete entropy and the values of entropy indices. The value of keeping the rain gauge stations was determined using the net exchange information index.
Results and Discussion: There was no homogeneity problem and significant trend in the data series. Considering the permissible error percentage of 5%, there is a need to add 15 new rain gauge stations to the network. To apply the geostatistical method, we applied it once without deleting any station; then, the kriging interpolation error was calculated for the precipitation data. Then, only one station was removed at each stage, and both the error and the contribution of each station in increasing or decreasing the error compared to the case without Station deletion were obtained. The results indicated that Ab-Turki, Shahrekord, Borujen and Barez stations were more important than other stations. Two stations namely Chaman-Goli and Ben stations can also be considered as the influential stations in error due to the density of stations in the region and error maps. Similarly, the results of the entropy theory method were found effective in evaluating the design of the rain gauge network. The highest value of H(x) was observed in the data of Armand station (3.26) and the lowest value was observed in Abbasabad station (2.28). Since H(x) shows the uncertainty of measuring data, the maximum and minimum uncertainty were found for Armand and Abbasabad sites, respectively. Based on the Net Exchange Information Index, Bardeh, Bareh Mardeh and Dezkabad stations were ranked 1 to 3, respectively, indicating that they transmit and receive more information than other stations. On the other hand, a number of stations including Dorak anari, Abtorki and Chelo stations had the lowest values.
Conclusion: Due to the vast extent of the area and also considering the permissible error percentage of 5%, the number of the stations in this area was found to be insufficient. Thus, although calculating the kriging error maps showed that some stations do not have a significant share in increasing the error, removing the stations is not recommendable. Regarding the new stations, new 15 rain gauge stations are needed to check out the error maps. According to the field observations, the higher priority should be given to the northwestern area (which had the largest interpolation error) in the first place. For the regions with lower error, such as northeast, east, southeast, west and southwest that do not have rain gauge stations, additional rain gauge stations should be constructed.
Ghamar Fadavi; Javad Bazarafshan
Abstract
Introduction: As the statistical time series are in short period and the meteorological station are not distributed well in mountainous area determining of climatic criteria are complex. Therefore, in recent years interpolation methods for establishment of continuous climatic data have been considered. ...
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Introduction: As the statistical time series are in short period and the meteorological station are not distributed well in mountainous area determining of climatic criteria are complex. Therefore, in recent years interpolation methods for establishment of continuous climatic data have been considered. Continuous daily maximum temperature data are a key factor for climate-crop modeling which is fundamental for water resources management, drought, and optimal use from climatic potentials of different regions. The main objective of this study is to evaluate different interpolation methods for estimation of regional maximum temperature in the Isfahan province.
Materials and Methods: Isfahan province has about 937,105 square kilometers, between 30 degree and 43 minutes to 34 degree and 27 minutes North latitude equator line and 49 degree and 36 minutes to 55 degree and 31 minutes east longitude Greenwich. It is located in the center of Iran and it's western part extend to eastern footage of the Zagros mountain range. It should be mentioned that elevation range of meteorological stations are between 845 to 2490 in the study area. This study was done using daily maximum temperature data of 1992 and 2007 years of synoptic and climatology stations of I.R. of Iran meteorological organization (IRIMO). In order to interpolate temperature data, two years including 1992 and 2007 with different number of meteorological stations have been selected the temperature data of thirty meteorological stations (17 synoptic and 13 climatologically stations) for 1992 year and fifty four meteorological stations (31 synoptic and 23 climatologically stations) for 2007 year were used from Isfahan province and neighboring provinces. In order to regionalize the point data of daily maximum temperature, the interpolation methods, including inverse distance weighted (IDW), Kriging, Co-Kriging, Kriging-Regression, multiple regression and Spline were used. Therefore, for this allocated data (24 days for each year and 2 days for each month) were used for different interpolation methods. Using difference measures viz. Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE) and Correlation Coefficient (r), the performance and accuracy of each model were tested to select the best method.
Results and Discussion: The assessment of normalizing condition of data was done using Kolmogrov-Smirnov test at ninety five percent (95%) level of significance in Mini Tab software. The results show that distribution of daily maximum temperature data had no significant difference with normal distribution for both years. Weighed inverse distance method used for estimation daily maximum temperature, for this purpose, root mean square error (RMSE) for different status of power (1 to 5) and number of station (5,10,15 and20) was calculated. According to the minimum RMSE, power for 2 and number of station for 15 in 2007 and power for 1 and number of station for 5 in 1992 were obtained as optimum power and number of station. The results also show that in regression equation the correlation coefficient were more than 0.8 for the most of the days. The regression coefficient of elevation (h) and latitude (y) were almost negative for all the month and the regression coefficient of longitude (x) was positive, showing that decreasing temperature with increasing elevation and increasing temperature with increasing longitude. The results revealed that for Kriging method the Gussian model had the best semivariogram and after that spherical and exponential were in the next order, respectively for 2007 year. In the year 1992, spherical and Gussian models had better semivariogram among others. Elevation was the best variable to improve Co-kriging method as auxiliary data. such that The correlation coefficient between temperature and elevation was more than 0.5 for all days. The results also show that for Co-Kriging method the spherical model had the best semivariogram and after that the exponential and Gussian were in the next order, respectively for 2007 year. In the year 1992, the best model of semivariogram was the linear model and after that the spherical and Gussian models had better semivariogram in the next order.
Conclusion: The results revealed that the application of multiple regression method for interpolation produced less errors between observed and estimated maximum temperature in 1992 (RMSE ranges from 1.41 to 4.03, MAE ranges from 0.98 to 2.55, and r ranges from 0.61 to 0.95). For 2007 year, the best estimation was performed by multiple regression and Kriging-Regression (RMSE=ranges from 0.99 to 3.98, MAE ranges from0.77 to 2.92, and r ranges from 0.32 to 0.97). Kriging, Co-Kriging, IDW, Spline methods were also reasonably performed well and could be used as the second order of priority .In addition, with increasing number of stations in 2007 as compared to 1992, the overall accuracy of model performance in estimation of daily maximum temperature have been improved.
A. Mianabadi; A. Alizadeh; Seied Hosein Sanaei-Nejad; M. Bannayan Awal; A. Faridhosseini
Abstract
Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation ...
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Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation of daily areal rainfall can be obtained by spatial interpolation of rain gauges data. However, direct application of these techniques may produce inaccurate results. In the last years, applying the remote sensing for estimation of rainfall have got so popular all around the word and many techniques have been developed based on the satellite data with high temporal and spatial resolution. In this paper, CMORPH model was validated for precipitation estimation over the northeast of Iran. Results showed that this model could not estimate precipitation accurately in daily scale, but in monthly and seasonal scale the estimation was more accurate. Farooj and Namanloo station had the highest correlation equal to 0.31 in daily scale. The highest correlation in monthly scale was equal to 0.62 for Barzoo, Namanloo and Se yekAb station. In Seasonal scale Gholaman station had the highest correlation which was equal to 0.63. Also, the probability of detection has been estimated accurately by CMORPH. But this technique did not have an accurate estimation for wet and dry days, mean annual precipitation and the number of non-rainy days.
Kh. Ghorbani
Abstract
So far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is geographically weighting regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local regressions. In order to evaluate annually precipitation ...
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So far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is geographically weighting regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local regressions. In order to evaluate annually precipitation spatial variation, this study was conducted in Gilan province which precipitation is distributed non-uniform due to different environmental conditions. The results of geographically weighting regression method were compared with another interpolation methods including global polynomial, local polynomial, inverse distance weighting (IDW), spiline, kriging and co-kriging and . In this study, average of 20 years annually precipitation data of 185 meteorological observations over Gilan Province and its neighboring stations was used for modeling of spatial distribution variations of mean annual precipitation by using other variables like elevation and position of points to the sea level. Cross validation technique was used to assessment accuracy of each interpolation methods. The result showed that geographically weighting regression method had minimum error with RMSE=147 and had significant difference with the kriging method which was in the second rank with RMSE=187. Finally the best method for mapping isohyets in Gilan province is geographically weighting regression method.
