leila jalali; J. Bazrafshan; A.R. Tavakoli
Abstract
Introduction: There have been several indices for agricultural drought monitoring such as Palmer Drought Severity Index (PDSI), Crop Moisture Index (CMI) and Reconnaissance Drought Index (RDI). These indices model the general conditions of soil moisture as a function of climatic parameters such as temperature ...
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Introduction: There have been several indices for agricultural drought monitoring such as Palmer Drought Severity Index (PDSI), Crop Moisture Index (CMI) and Reconnaissance Drought Index (RDI). These indices model the general conditions of soil moisture as a function of climatic parameters such as temperature and rainfall and they are not appropriate to any specific crop. Crop- Specific Drought Index (CSDI) is among few indices which directly take into account evapotranspiration for drought monitoring. This index is defined based on the ratio of actual evapotranspiration (ETa) to potential evapotranspiration (ETp). Literature review of agricultural drought monitoring in Iran reveals that was mainly used Reconnaissance Drought Index (RDI) and in some cases Drought Severity Index Palmer (PDSI) which have less associated with the growth or performance of the specific crop or not. In this paper, a Crop Specific Drought Index (CSDI) model was evaluated for rain-fed wheat in a cold-temperate climate. Then, it’s correlation with RDI was assessed.
Materials and Methods: In this study, using 9 years of data of meteorology, soil, and crop yield and phenology, a CSDI model has been calibrated and validated for rain-fed wheat. A two-layer model of daily soil water balance was used to CSDI calculation. The first layer is the current root growth zone which its depth increases with time. The second layer is between the first layer and maximum depth of root growth which its depth decreases by root growth and the thickness of this layer becomes zero when the root growth is Maximum. Actual daily Evapotranspiration (ETa) was calculated based on two-layer model of daily soil water balance. For this, we used the moisture content of the first layer (active), potential evapotranspiration and daily rainfall values. The statistical indices of error analysis like RMSE, MAE and Index of Agreement was used for assessment of CSDI model. Then, to investigate the correlation possibility of crop specific drought severity, Reconnaissance Drought Index (RDI) was used which is based on two variables of potential evapotranspiration and precipitation. In fact, RDI considers the precipitation as a factor of moisture input and the potential evapotranspiration as a factor of moisture exhaust.
Results Discussion: At Kermanshah station high coefficient of determination (0.95), relatively high index of agreement (0.747), and low error values (RMSE =0. 098 and MAE =0. 068) was obtained. Sensitivity coefficients during vegetative and productivity stages were obtained 1.31 and -0.0542, respectively. As a result, crop yield at vegetative stage severely affected by aridity stress while at productivity stage there was no sensitivity. In fact, water demand supply is vital at vegetative stage .Range of RDI at Kermanshah station was between 1.13 to -2.59. This threshold is the condition between "moderately wet" and "extreme drought". Correlation between the two index was started from March (R2 =0. 467) and persisted to September (R2 =0. 717). But the highest coefficients of determination were related to July to august (0. 738). Although CSDI didn’t affect by drought stress during October to February, it affected by moisture of March onward. RDI incorporating precipitation and potential evapotranspiration is one of the most recent developments for the assessment of drought severity through drought indices. That is why this index is chosen to investigate the relationship with CSDI. Actually, both indices get evapotranspiration factor in the agricultural drought monitoring. Based on the results, there is a good correlation between two indices. Since the CSDI is relay on ETa to ETp ratio and RDI is based on the P/ ETp ratio, it can be concluded that there is a possible to replacement of ETa with rainfall (R) in the CSDI equation.
Conclusion: Many indices and indicators are available to assist in the quantitative assessment of drought severity, and these should be evaluated carefully for their application to each region or location and sector. This paper presents a CSDI model which compared with RDI. Based on the results of this analysis, CSDI model was performed well in high values of coefficients of determination and Index of Agreement, and low values of errors. Therefore, the CSDI seems to be a reliable index to assess agricultural drought. Furthermore, it is observed a reliable relation between CSDI and RDI during crop growth period. Due to good correlation of CSDI and RDI, it can be proposed to replacement of rainfall (R) instead of ETp in the CSDI equation.
M. Ghamghami; J. Bazrafshan
Abstract
Today, there arevarious statistical models for the discrete simulation of the rainfall occurrence/non-occurrence with more emphasizing on long-term climatic statistics. Nevertheless, the accuracy of such models or predictions should be improved in short timescale. In the present paper, it is assumed ...
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Today, there arevarious statistical models for the discrete simulation of the rainfall occurrence/non-occurrence with more emphasizing on long-term climatic statistics. Nevertheless, the accuracy of such models or predictions should be improved in short timescale. In the present paper, it is assumed that the rainfall occurrence/non-occurrence sequences follow a two-layer Hidden Markov Model (HMM) consist of a hidden layer (discrete time series of rainfall occurrence and non-occurrence) and an observable layer (weather variables), which is considered as a case study in Khoramabad station during the period of 1961-2005. The decoding algorithm of Viterbi has been used for simulation of wet/dry sequences. Performance of five weather variables, as the observable variables, including air pressure, vapor pressure, diurnal air temperature, relative humidity and dew point temperature for choosing the best observed variables were evaluated using some measures oferror evaluation. Results showed that the variable of diurnal air temperatureis the best observable variable for decoding process of wet/dry sequences, which detects the strong physical relationship between those variables. Also the Viterbi output was compared with ClimGen and LARS-WG weather generators, in terms of two accuracy measures including similarity of climatic statistics and forecasting skills. Finally, it is concluded that HMM has more skills rather than the other two weather generators in simulation of wet and dry spells. Therefore, we recommend the use of HMM instead of two other approaches for generation of wet and dry sequences.
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.
sajjad ebrahimzadeh; javad bazrafshan
Abstract
Drought can affects by reduced water resources, agricultural productivity, change in vegetation cover, and accelerate the desertification of areas. In order to drought monitoring, we need to quantify drought effects by using drought indices. These indices based on type of available data are divided ...
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Drought can affects by reduced water resources, agricultural productivity, change in vegetation cover, and accelerate the desertification of areas. In order to drought monitoring, we need to quantify drought effects by using drought indices. These indices based on type of available data are divided into two general categories of ground- and satellite- based indices. The aim of this study was to compare the capability of detection and classification of vegetation changes occurred due to the drought, between one ground-based drought index (Standardized Precipitation Index (SPI)) and four satellite drought indices derived from AVHRR-NOAA (normalized difference vegetation index (NDVI), temperature condition index (TCI), ratio vegetation index (RVI), standardized vegetation index (SVI) in the Kermanshah province. To do this, the change vector (CV) analysis was used as one of the important change detection algorithms. In this method, the change occurred in vegetation has been shown by two components, change magnitude and change direction. The results of implementation of the CVA on the maps of drought indices during the growing season (March to August) in selected years (two normal years, one wet year, and one drought year) showed the best response to the drought in the study years (except the wet year 1992), obtained by SVI. The lowest similarity was obtained between the SPI and TCI, for wet and normal years. Finally, the study suggests mostly the satellite indices based on the vegetation conditions, rather than the temperature indices, for assessing the effect of drought on vegetation cover.
J. Rahimi; A. Khalili; J. Bazrafshan
Abstract
The estimation of effective rainfall is an important technique for exploiting the rainwater resource, planning for irrigation in irrigated agriculture, and determining changes in dryland wheat products for country's macroeconomic management. In this research, a two-layer soil-water balance model based ...
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The estimation of effective rainfall is an important technique for exploiting the rainwater resource, planning for irrigation in irrigated agriculture, and determining changes in dryland wheat products for country's macroeconomic management. In this research, a two-layer soil-water balance model based on a new approach has been formulated to estimate the effective rainfall in dryland wheat crop. In this model, it is assumed that, in addition to the soil moisture retained on root zone on before day, also that part of water saved between the previous and current root-zone development add to the effective rainfall at current day. Greater ability of current approach to determine changes in wheat yields, which can be justified by changes in effective rainfall, confirms superiority of the current approach. Besides that, In order to estimate the effective rainfall in dryland wheat crop by this approach, daily climatic records during statistical period (1999-2000 till 2008-2009) from 12 meteorological stations of Fars province were collected. Results indicated that among the studied stations the amount of mean annual effective rainfall in dryland wheat crop changes from 296.4 (mm) in Doroodzan dam station to 112.6 (mm) in Abadeh station. Also, it is assumed that production of dryland wheat crop at north and northwest of Fars province is more success than other parts, due to satisfactory supply of effective rainfall.
