Soil Moisture Monitoring By Using BUDGET Model In A Changing Climate (Case Study: Isfahan-Iran)

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1 International Conference on Water Resources (ICWR 9) May 9 \ Bayview Hotel, Langkawi. Kedah,Malaysia Soil Moisture Monitoring By Using BUDGET Model In A Changing Climate (Case Study: Isfahan-Iran) Sayed Masood Mostafavi Darani a,gholam ali Kamali b,mohammad Rahimi a a Senior expert of Agro-meteorology- I.R.IRAN meteorological organization b Associate professor- Atmospheric science and meteorological research center-i.r.iran meteorological organization Sayeddarani@yahoo.com Abstract The city of Isfahan is located in arid and semi-arid climate zone similar to most parts of the Islamic republic of Iran. More than 94 percent of water resources in IRAN are consumed by agricultural sector. Implementation of GCM projections in a ing framework in the area for two time period, -39 and 7-99 has showed that this area may face with percent of decrease in annual precipitation. As soil moisture plays an important role in the hydrological system and agricultural practices, its measurement and monitoring is very important in water resource management. Hence, the aim of this study is using ing technique for measuring soil moisture in Isfahan. The BUDGET (version 6.) designed by Professor Dirk Raes was used for measurement and monitoring soil moisture in a winter wheat experimental field at Kabootarabad agro-meteorological station that located in east of the Isfahan, from 6 to 7 and the results were compared with gravitational method. Sampling was taken in 4 points 4 times during the growth season in tillering and flowering stages from soil surface to cm depth by auger. The soil water content was calculated by oven drying at degree of centigrade to constant weight. Furthermore, 16 samples were taken from soil surface to cm. The volumetric soil water content was calculated for each sample. The soil water content as simulated by BUDGET is nearly in line with the observed data and it seems that the can simulate soil surface water content (-cm) better than other depths (-cm). The value of correlation coefficient (.6) significant at 1% and confidence interval between.17 and.8 showed that there is a positive and strong correlation between and observations in (-cm) depth. The values of statistical criteria such as RMSE (root mean square error), MAE (mean absolute error) and NS (Nash-Sutcliffe index) were 7.33,.49 and.16 respectively. On the basis of suggested, under conditions similar to Isfahan BUDGET can simulate soil moisture with the acceptable accuracy and confidence limit. Key words: BUDGET, soil monitoring, soil measurement, drought monitoring 1. Introduction Located in the mid-latitude belt of arid and semi arid region of the earth, more than 6 percent of Iran is covered by arid and semi arid regions.the average rainfall of Iran is 24 mm and 1/4 of the world average rainfall. On the other hand More than 94 percent of water resources of the country are consumed by agricultural sector. Implementation of GCM projections in a ing framework in the area for two time period, -39 and 7-99 has showed that this area may face with percent of decrease in annual precipitation [1]. So irrigation scheduling has an important role in water management in the area. Soil moisture is a major component of water cycle and its measurement and monitoring is very important in water resource management under rain-fed and irrigated condition. Soil water content may be considered as a key variable in many fundamental processes such as monitoring agricultural drought. The purpose of this study was using and simulation technique for measuring and monitoring soil moisture in Kabootarabad agro-meteorological station (Isfahan- IRAN) Methods and applications of Soil moisture measurements Methods of soil moisture measurement Contact methods, calculation methods, and contactless (remote sensing) methods are three

2 major methods in soil moisture measurement. The contact methods are divided into two direct and indirect methods. Direct method such as gravimetric method is the most reliable technique but takes more time and it is physically tiring [2]. The indirect methods are electrical, tensiometric and radiometric approaches such as using resistance blocks, tensiometer, TDR.and etc. Calculation methods, assessment of soil moisture imply plant-atmosphere- soil interactions and are sophisticated and because of variety of involved factors, different procedures are represented. The common calculation method is water balance equation. Contactless or remote sensing method divides into three categories of measuring thermal diffusion by radiometers, using UHF radiation and using gamma radiation Applications of soil moisture measurement Some common uses of soil moisture measurements are as follows: Increasing irrigation efficiency, drought monitoring (like Palmer method), and application of remote sensing data, improvement of climate classification, monitoring and forecasting crop yield, water balance determination, calculation of soil infiltration coefficient and application in plant water requirement assessment and flood management[3]. By determination of soil water content, the calibration and use of satellite data in soil water assessment will be possible Agro-meteorological concept Making soil moisture forecast is to analyze and predict variations in the soil water storage in a given period. Based on the concept of water equilibrium in the field soil, the future variations in soil moisture can be identified, considering the current soil moisture and the potential impact of future weather condition and water consumption for crop growth. According to the initial soil moisture and its forecast in the certain timeframe as well as the water demand for crop growth at certain soil moisture conditions, assessment is made on the impact of soil moisture on agricultural production and early warning of the drought severity is issued. Accordingly, irrigation options and other responsive measures are also proposed [4]. 2. Experimental Details 2.1. Materials and Methods This study has been carried out in a winter wheat experimental field located in Kabootarabad agrometeorological station 22 Km east of Isfahan( 32,31 N, 1,1E) during Nov6-Jun7.The annual average temperature of this area is 1 degree of centigrade and the mean annual precipitation is 1 mm[]. The soil texture is silty clay loam from soil surface to.6 m and clay loam from.6 to 1 m. Winter wheat (var. Sepahan) planting date was 11 th Nov 6 and the length of different growth stages are shown in table (1). The growth period was 232 days and plant was harvested on 3 th Jun7 table (2). The field was irrigated 7 times during the growth season table (3) and water quality was 2 d s /m[6][7]. In this study gravimetric method was used 21 times during growth season. This method involves drying a sample of soil, weighting before and after the drying and expressing the moisture content in terms of the percentage of water held by a unit mass of dry soil. The fields' area was 1 ha and sampling was taken in 4 points 4 times during the growth season in tillering and flowering stages from soil surface to cm depth by auger. The soil water content was calculated by oven drying at degree of centigrade to constant weight. Furthermore 16 samples were taken from soil surface to cm. The volumetric soil water content was calculated for each sample by following formula [8]: Gravimetric soil moisture content (W g ) = (W 1 - W 2 ) /W 2 Volumetric soil moisture content (W v ) = W g. d W 1 = Weight before drying W 2 = Weight after drying Bulk density (d) = Dry weight of soil in depth interval /Volume of soil in depth interval Table1. Different growth stages of winter wheat (var. Sepahan) in Kabootarabad No Stages Length(day) 1 Initial stages 3 2 Crop developing stage 7 3 Mid season stage 9 4 Late season stage 4 Table2. Phenological stages of winter wheat (assumption of % completion) Phenological stage Day number Planting 1 Emergence 36 3 leaf stage 92 Tillering 13 Earing 18 Flowering 19 Milk stage 8 Dough stage 224 Physiological maturity 232 * Day number was calculated considering planting date as No. 1. Table3. Time and amount of net irrigation application (efficiency %) Day number* Application (mm) depth Growth stage

3 1 144 Initial 132 Initial Mid season 14 4 Mid season Mid season Mid season 121 Late season %Vol * Day number was calculated considering planting date as No. 1. th Nov6 8th Dec6 3rd Feb7 24th Feb7 th Mar7 17th Mar7 22th Apr7 1th May7 26th May7 16th Jun7 29th Jun Model Description The "BUDGET" is a soil water and salt balance created and developed by Prof. Dirk Raes in K. U. Leuven university of Belgium. The is composed of a set of validated subroutines describing the various processes involved in water extraction by plant roots and water movement in the soil profile. During periods of crop water stress the resulting yield depression is estimated by means of yield response factors. The climate data consists of daily or monthly ET (reference crop evapotranspiration) and rainfall observations. The soil profile may be composed of several soil layers each with their specific characteristics. The contains a complete set of default characteristics that can be selected and adjusted for various types of soil layers. By calculating the water content in a soil profile as affected by input and withdrawal of water during the simulation period, the is suitable to asses crop water stress, evaluate irrigation strategies and design irrigation schedule. The input consists of climatic data (ET and rainfall), crop and soil parameters and irrigation data. The FAO Penman-Monteith equation was used for calculating reference crop evapotranspiration and climatic data (air temperature, air humidity, wind speed and radiation) were used during growth period (11th Nov6 3 th Jun7) for running the. In the soil water balance BUDGET the various processes such as surface runoff, infiltration, internal drainage, soil evaporation, crop transpiration and drainage from the root zone that affected the soil water content in the root zone were considered. Fig1. Simulated and observed soil moisture (- cm) Fig (1) indicates that the soil water content as simulated by BUDGET is nearly in line with the observed data. It seems that the can simulate soil surface water content (-cm) better than other depths (-cm) as shown in fig (2-) th Mar7 th Mar7 1th May7 24th May7 Fig.2. Measured and observed soil moisture (- cm) th Mar7 th Mar7 1th May7 24th May7 Fig.3. Measured and observed soil moisture (- 3cm) Results and discussion 1 The simulated and observed soil water contents in the root zone (-cm) for winter wheat cultivated in Kabootarabad (Isfahan-IRAN) during 6-7 growing period are plotted in fig(1). 13th Mar7 th Mar7 1th May7 24th May7 Fig.4. Measured and observed soil moisture (3-4cm)

4 This goodness of fit test shows the tendency of the to under or over-predicts the observed values. It is very similar to RMSE but less sensitive to error. When data is limited, it is preferable to other estimators. 4.Nash-Sutcliffe index(ns) 13th Mar7 th Mar7 1th May7 24th May7 Fig.. Measured and observed soil moisture (4- cm) When comparing predictions with observed data, qualitative as well as quantitative techniques should be employed. While qualitative techniques are typically based on visual inspection of the results (Fig.1), Visual comparison of the two time series can reveal patterns which cannot be detected by the statistical methods.quantitative techniques try to express the agreement between and data numerically in terms of the outcomes of performance measures. It is often difficult or timeconsuming to judge the significance of these outcomes objectively. Moreover the various measures typically only highlight specific aspects of the system and the. Therefore a judicious combination of several techniques should be employed for a thorough assessment. In this study the following statistical goodness of fit criteria were used. 1. Correlation coefficient Pi and Oi denote the predicted value and observed value i and the value of correlation coefficient.6 significant at %1 (df=19) and confidence interval (%9) between.17 and.8 showed that there is a positive and large correlation between and observations. 2. Root Mean Square Error(RMSE) The RMSE is a statistical estimator to show how much the over or under-estimates the measurements. It compares the predictions Pi and observations Oi on an individual level. It measures Pi-Oi in a quadratic sense, therefore it is rather sensitive to outliers [9]. The result is better when it is closer to zero. 3. Mean Absolute Error The most important criterion is the coefficient of efficiency, Nash-Sutcliffe index []. It is in concern of the ing efficiency measure and quantifies the relative improvement of the employed.a Nash-Sutcliffe index of 1 means that the produces discharge data which are exactly coinciding with the measured data. Different statistical criteria used in this study are presented in table (4). Table 4. Values of different statistical criteria r MAE RMSE NS value Conclusion and recommendations According to the obtained results it seems that the is capable of soil water content simulation in the semi-arid climate of Isfahan (IRAN) and can be used for different purposes in the changing climate of this region. Visual comparison of figure (1) reveals that coincidence of simulated and observed soil moisture from th Mar (Tillering) till 2 nd Jun (Milk stage) is very high. From physiological point of view, in this period the root system of winter wheat is established well. Before tillering the root system of plant is not expanded and after flowering roots growth is halted. So it seems that the performance is better when root system is expanded well. Following recommendations would be useful in future: In order to obtain more precision results, it is necessary to getting more samples in different depths. Using modern techniques such as using TDR for soil moisture measurement will be useful. The period of study should be extended to different years and because of routine agenda of agrometeorological stations and having archives in soil moisture sampling, It can be extended and examined over the country. In order to establish an early warning system for irrigation schedule and agricultural drought monitoring and having soil moisture forecast, it is necessary to couple BUDGET with numerical weather prediction (NWP) s such

5 as MM. These steps are very essential for calibration of remote sensing data in future too.. References 1. S. Morid, A. R. Massah Bavani, Exploration of potential adaptation strategies to climate change in the Zayandeh rud irrigation system, Iran, Irrigation and drainage, John Wiley & sons, J. Wieringa, J. Lomas, Lecture notes for training agricultural meteorological personnel, 2 nd ed., WMO-N. 1, J. Bodaghjamali, et.al., The necessity of soil moisture monitoring in order to increasing water efficiency in agriculture, Proceedings of the 11 th conference of irrigation and drainage, Tehran, Iran, 4, No., pp K. Stigter, Personal communication, 9.. S.M. Mostafavi Darani, Kabootarabad agrometerological bulletin (6-7), I.R.Iran Meteorological Organization. 6. A. Alizadeh, Gh. A. Kamali, Crop water requirements in Iran, Imam reza university publication, 1991, 214 pages. 7. A. Mamanpoosh, F. Abbasi, F. Moosavi, Assessment of water use efficiency in surface irrigation in some fields of Isfahan province, J. Agr. Eng. Res. 9(2) A. A. Mahboobi, A. A. Naderi, Applied soil physics, Avicenna university publication, 1991, 214 pages. 9. M. Azadi, Gh. A. Vakili, Statistical criteria for calibration of MM, 9, not published.. X. Zhang, Test and application of hydrological s with a spatial ing language (PCRaster) for the discharge simulation of a wetland dominated catchment in northern Germany, 6,