Irrigation
M.S. Fakhar; A. Kaviani
Abstract
Introduction
Achieving food security in the future with sustainable use of water resources will be a big challenge for the current and future generations. Population increase, economic growth and climate change intensifythe pressure on existing resources. Agriculture is a key consumer of water, and ...
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Introduction
Achieving food security in the future with sustainable use of water resources will be a big challenge for the current and future generations. Population increase, economic growth and climate change intensifythe pressure on existing resources. Agriculture is a key consumer of water, and it is necessary to closely monitor water productivity for it and explore opportunities to increase its productivity. Systematic monitoring of water productivity through the use of remote sensing techniques can help identifying the gaps in water productivity and evaluate appropriate solutions to address these gaps.
Materials and Methods
Qazvin plain is known as a hub of modern agriculture by providing about 5% of the country's agricultural products. Therefore, estimating water demand and water productivity in agricultural management in the region is considered important and necessary. In order to monitor water productivity through access to various data across Africa and the Middle East, the WaPOR database provides the possibility to examine the rate of evapotranspiration, biomass and gross and net biomass volume productivity based on the land use map in the period of years 2009 to 2021. In this database, it is possible to check the mentioned items at three levels with different spatial resolution, which according to the scope of the study, it is possible to check values with a spatial resolution of 250(m). In order to determine the efficiency and accuracy of the land cover classification map of the WaPOR database, the results obtained are examined and compared with the Dynamic World model, which represents a global model with high accuracy. For this purpose, the latest land use map related to 2021 Using the WaPOR database and Dynamic World in the GEE system, it was prepared and based on the classification of the region in order to check the accuracy of the user map of the WaPOR database and to determine the percentage of each class compared to each other. Finally, all estimable indicators were calculated and checked by the WaPOR database during the years 2009 to 2022.
Results and Discussion
The amount of evapotranspiration of the plants covered by the irrigation network in the period of 2009 to 2016 has been associated with a relatively stable trend, but this trend has decreased in 2017 onwards, which is one of the reasons for the decrease in the amount of evapotranspiration in this the period of time and can refer to the lack of water available to the plant due to the limited water resources in recent years. The investigation of the total amount of biomass in different lands shows that during the years 2009 to 2022, this index has been accompanied by a gradual increase in all uses, so that the amount of TBP index in 2020 was 17% more than in 2009. It shows the amount of biomass in different lands. The amount of biomass in the lands covered by the water network is 5 to 6 times higher than that of the rainfed lands. Among the influential parameters in estimating the TBP index, we can mention the amount of evaporation, transpiration, and transpiration, the increase or decrease of each of these parameters will have a significant impact on the estimated amount of biomass. The results showed that the amount of biomass production in the areas covered by the irrigation network largely depends on the high transpiration rate in these areas. From the beginning of 2009 to 2016, the gross amount of biomass water in the lands covered by the irrigation network has been accompanied by an increase, but in 2017, drastic changes in the process of underground changes will decrease the area of the lands covered by the network and many of these lands. It has been turned into fallow and rainfed lands. The analysis of NBWP index also showed that the amount of net productivity in rainfed lands is strongly dependent on the annual increase rate, and much of the crop yield in rainfed lands is dependent on the amount received. Among the influential parameters in estimating the total amount of biomass, we can mention the amount of evaporation, transpiration and transpiration, the increase or decrease of each of these parameters will have a significant impact on the amount of estimated biomass.
Conclusion
WaPOR database data can play an important role in estimating the rate of delayed transpiration and parameters related to water productivity in the region due to its ten-day spatial resolution and the absence of data gaps. In general, the WaPOR database can be used as a guide in the reliable determination of evapotranspiration values and planning related to water resources in the agricultural sector.
Soil science
Hamid Reza Matinfar; M. Jalali; Z. Dibaei
Abstract
Introduction: Understanding the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. Over the past two decades, the use of data mining approaches in spatial modeling of soil organic carbon using machine learning techniques ...
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Introduction: Understanding the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. Over the past two decades, the use of data mining approaches in spatial modeling of soil organic carbon using machine learning techniques to investigate the amount of carbon to soil using remote sensing data has been widely considered. Accordingly, the aim of this study was to investigate the feasibility of estimating soil organic matter using satellite imagery and to assess the ability of spectral and terrestrial data to model the amount of soil organic matter.Materials and Methods: The study area is located in Lorestan province, and Sarab Changai area. This area has hot and dry summers and cold and wet winters and the wet season starts in November and ends in May. A total of 156 samples of surface soil (0-30 cm) were collected using random sampling pattern. Data were categorized into two categories: 80% (117 points) for training and 20% (29 points) for validation. Three machine learning algorithms including Random Forest (RF), Cubist, and Partial least squares regression (PLSR) were used to prepare the organic soil carbon map. In the present study, auxiliary variables for predicting SOC included bands related to Lands 8 OLI measurement images, and in order to reduce the volume of data, the principle component analysis method (PCA) was used to select the features that have the greatest impact on quality.Results and Discussion: The results of descriptive statistics showed that soil organic carbon from 0.02 to 2.34% with an average of 0.56 and a coefficient of variation of 69.64% according to the Wilding standard was located in a high variability class (0.35). According to the average amount of soil organic carbon, it can be said that the amount of soil organic carbon in the region is low. At the same time, the high value of organic carbon change coefficient confirms its high spatial variability in the study area. These drastic changes can be attributed to land use change, land management, and other environmental elements in the study area. In other words, the low level of soil organic carbon can be attributed to the collection of plant debris and their non-return to the soil. Another factor in reducing the amount of organic carbon is land use change, which mainly has a negative impact on soil quality and yield. In general, land use, tillage operations, intensity and frequency of cultivation, plowing, fertilizing, type of crop, are effective in reducing and increasing the amount of soil organic carbon. Based on the analysis of effective auxiliary variables in predicting soil organic carbon, based on the principle component analysis for remote sensing data, it led to the selection of 4 auxiliary variables TSAVI, RVI, Band10, and Band11 as the most effective environmental factors. Comparison of different estimation approaches showed that the random forest model with the values of coefficient of determination (R2), root mean square error (RMSE) and mean square error (MSE) of 0.74, 0.17, and 0.02, respectively, was the best performance ratio another study used to estimate the organic carbon content of surface soil in the study area.Conclusion: In this study, considering the importance of soil organic carbon, the efficiency of three different digital mapping models to prepare soil organic carbon map in Khorramabad plain soils was evaluated. The results showed that auxiliary variables such as TSAVI, RVI, Band 10, and Band11 are the most important variables in estimating soil organic carbon in this area. The wide range of soil organic carbon changes can be affected by land use and farmers' managerial behaviors. Also, the results indicated that different models had different accuracy in estimating soil organic carbon and the random forest model was superior to the other models. On the other hand, it can be said that the use of remote sensing and satellite imagery can overcome the limitations of traditional methods and be used as a suitable alternative to study carbon to soil changes with the possibility of displaying results at different time and space scales. Due to the determination of soil organic carbon content and their spatial distribution throughout the region, the present results can be a scientific basis as well as a suitable database and data for the implementation of any field operations, management of agricultural inputs, and any study in sustainable agriculture with soil properties in this area. In general, the results of this study indicated the ability of remote sensing techniques and random forest learning model in simultaneous estimation of soil organic carbon location. Therefore, this method can be used as an alternative to conventional laboratory methods in determining some soil characteristics, including organic carbon.
Mirhassan Rasoulsiadaghiani; Vali Feiziasl; Ebrahim Sepehr; Mehdi Rahmati; Salman Mirzaee
Abstract
Introduction: In cereal crops, nitrogen is the most important element for maintaining growth status and enhancing grain yield. Nitrogen is an important constituent of the chlorophyll molecule and the carbon-fixing enzyme ribulose-1, 5-bis-phosphate carboxylase/oxygenase. Therefore, providing enough nitrogen ...
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Introduction: In cereal crops, nitrogen is the most important element for maintaining growth status and enhancing grain yield. Nitrogen is an important constituent of the chlorophyll molecule and the carbon-fixing enzyme ribulose-1, 5-bis-phosphate carboxylase/oxygenase. Therefore, providing enough nitrogen to achieve optimal yield is essential. Common chemical analyzes are used to determine the nutrient elements of plants using laboratory methods. Conventional laboratory techniques are expensive, laborious, and time-consuming. Determination of plant biochemical content by remote sensing could be used as an alternative method which reduce the problems of laboratory analyses. Expensive and time-consuming direct determination of the nutritional status of the plant play an important role in the quantitative and qualitative yield of the product. However, exposure to rainfed wheat nutrient stresses (in particular, nitrogen) compared to irrigated wheat resulting in attempts to evaluate these features with acceptable accuracy without the direct measurement. In this regard, remote sensing data and satellite images are of the basic dryland management and optimal wheat production methods. As such, it collects massive information periodically from the surface of the planet, and it is easy to use this timely information to identify the stresses and apply appropriate agronomic methods in order to counteract them or reduce their negative impact on the production of this strategic product. Therefore, the goal of this study was to determine the nitrogen concentration of dryland wheat in the laboratory and its fitting with ETM+ images, evaluate the accuracy of remote sensing in determining the total nitrogen content of the plant and establish a regression relationship to estimate the amount of canopy nitrogen in the plant.
Material and Methods: This research was undertaken in parts of the south of the West Azerbaijan Province in Iran. The sampling was done from 45 dryland wheat fields using a stratified random method in May 2016. The wheat canopy nitrogen was determined using the Kjeldahl method. Satellite images of the ETM+ were downloaded on the USGS website. Then the required pre-processing was performed on images to reduce systematic and non-systematic errors. Statistical analyses were performed by excel and SPSS. Descriptive statistics and correlations were obtained between reflectance data obtained from various satellite bands and nitrogen measured in the laboratory. Correlated variables among the reflectance data of different bands were analyzed by principal component to reduce repeat calculations. The regression relationship between the plant canopy nitrogen and the first principal component has been evaluated using the stepwise regression method. To draw the plant canopy nitrogen, map, the equation was obtained and the ETM+ image has been used for land uses. Finally, the map of canopy N distribution at the studied area was drawn.
Results and Discussion: The results showed that nitrogen content varied from 1.6% to 0.79%, with an average of 1.11%. The normality data was verified by the Shapiro Wilk test. The results of the Pearson correlation showed that the wheat canopy nitrogen has a high correlation with digital number values of all bands of satellite images except band 4, so that it has the highest and the least correlation with band 2 and band 4, respectively. The correlation between remote sensing data in different bands was also evaluated using bi-plot statistics, which results showed a high correlation between all bands except band 4 with the first one of the principal component (PC1). Therefore, only PC1 data has been used to study the regression relationships between wheat canopy nitrogen and remote sensing data. A regression equation between wheat canopy nitrogen and ZPC1 (R2= 0.71) was developed. ZPC1 is obtained according to the following formula: where ZPC1 is the standardized Z parameter, is the average of PC1 and the ????pc1 is the standard deviation of PC1. Finally, the map of canopy N distribution was drawn to the studied area. According to the results of this study, the application of remote sensing data such as Landsat ETM+ data is a very important variable for improving and managing the prediction of wheat canopy nitrogen.
Conclusion: Overall, the results indicated that the remote sensing data provide more accurate and timely information from the drylands of Iran to manage farm fertilization and prevent the decline in yields at critical points. However, proper management to avoid the fertilizer loss by precise and timely application of N-fertilizer is needed.
M. A. Mahmoodi; S. P. Naghshbandi
Abstract
Introduction: Soil erosion is a serious environmental threat leading to loss of nutrient from surface soil, increased runoff, lake and reservoir sedimentation, and water pollution. Thus, estimation of soil loss and identification of critical area for implementation of best management practice is central ...
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Introduction: Soil erosion is a serious environmental threat leading to loss of nutrient from surface soil, increased runoff, lake and reservoir sedimentation, and water pollution. Thus, estimation of soil loss and identification of critical area for implementation of best management practice is central to success of soil conservation programs. Soil erosion modeling is an efficient method to simulate soil erosion, to identify sediment source areas, and to evaluate soil conservation measures. One of the most widely applied empirical models for assessing the sheet and rill erosion is the Universal Soil Loss Equation (USLE). Originally, USLE was developed mainly for soil erosion estimation in croplands or gently sloping topography. The RUSLE is an extension of the original USLE with improvements in determining the factors controlling erosion. It is an empirical model commonly used to estimate soil loss potential by water from hillslopes across large areas of land. RUSLE is a linear equation that estimates the annual soil loss as the product of environmental factors include rainfall, soil erodibility, slope length, slope steepness, cover management and conservation practices as inputs. To implement RUSLE over large areas, detailed sets of spatially explicit data are needed for precipitation, soil type, topographic slope, land cover and land use type. Conventionally, the collection of all these data from field studies is time-consuming and expensive. The integration of field data and data provided by remote sensing technologies through the use of geographic information systems (GIS) offers potential to estimate spatially input data for RUSLE over large and relatively sparsely sampled areas. Keeping in view of the above aspects, the objectives of the present study were 1) to integrate the field data and data provided by Landsat Enhanced Thematic Mapper (ETM) imagery with RUSLE through the use of GIS to estimate spatial distribution of soil erosion at Gawshan dam basin in west of Iran and 2) to delineate soil erosion probability zones by reclassifying of the prepared soil erosion map.
Materials and Methods: The annual rainfall erosivity factor (R) was determined from monthly rainfall data of 11 years (2005-2015) for 7 rain gauge stations in the the study area. Spatial distribution of R was estimated using ordinary kriging method of interpolation. The soil erodibility factor (K) was estimated on the basis of soil map prepared from land survey and Landsat ETM remote sensing data. The physical and chemical parameters required to calculate K were measured in the different soil units, and its spatial distribution was coincident with the soil unit boundaries. The topographic factor (LS) was derived from digital elevation model (DEM) of 30 m resolution. The annual crop management factor (C) was calculated from normalized difference vegetation index (NDVI) derived from Landsat ETM imagery for different seasons. Since there is a lack of field data regarding the conservation practices that have been taken place in the study area, the conservation support practice factor (P) value was taken as 1. Finally, average annual soil loss was estimated as the product of the mentioned factors, and categorized into four classes viz., low, moderate, high and very high erosion.
Results and Discussion: The estimated R, K, LS and C range from 564 to 1311 MJ mm ha-1 h-1 y-1, 0.02 to 0.04 t h MJ-1 mm-1, 0 to 2436 and 0 to 1, respectively. The results indicate the estimated mean annual potential soil loss of about 2.35 t ha-1, however in the 50% of the basin area annual soil loss is lower than 0.92 t ha-1. Based on categorized soil erosion map about nearly 52.5% of the basin area produces low erosion of 0.43 t ha-1 annually, whereas very high probability zone covers about 4% of the basin area, located dominantly in the southwestern part of the basin. Our results showed that slope steepness factor is the most important factor that controls soil erosion rate in the basin.
Conclusion: This study demonstrates the integration of field data and Landsat ETM imagery data with RUSLE through the use of GIS to estimate spatial distribution of soil erosion in Gawshan dam basin. The results of this study can be helpful for identifying critical areas for implementation of conservation practice and provide options to policy makers for prioritization of different regions of the basin for treatment.
Ali Chavoshian; P.S. Katiraie-Boroujerdy
Abstract
Introduction: Precipitation has an important role not only in the variety of scientific applications including climate change, climate simulations, weather modeling, and forecasting but also in decision making such as water management, hydrology, agriculture, drought, and crisis management. Different ...
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Introduction: Precipitation has an important role not only in the variety of scientific applications including climate change, climate simulations, weather modeling, and forecasting but also in decision making such as water management, hydrology, agriculture, drought, and crisis management. Different temporal resolutions and coverages of data are required for this and other applications. For example, long term meteorological data are needed for monitoring the climate variability and trends and for climate simulation assessments in local and global scales. Also, present data are used to assimilate into forecast models to improve the predictions. Historical and present precipitation data are the main requirements to monitor and predict droughts which help to early warning system and water management decisions in a country. The recent rainfall data are also the primary input of hydrological models to flood forecast in a basin. The accurate estimation of precipitation amount is vital for these applications.
Materials and Methods: However, rainfall is discontinuous and varies greatly both in time and space which makes it parallel with difficulties in the actual measurements. The two main sources of observational precipitation datasets are ground-based rain gauge measurements and space-based remote sensing satellite estimations each one with its own limitations and strengths. Historically, rain-gauge measurements have been considered as the “ground truth”, but they have mostly limited to land surface, the measurements are sparse or nonexistent in some regions like deserts or high topographic areas. Although rain gauges measure rainfall directly, their data are only representative for a limited spatial extent and may be subjected to some errors caused by local effects such as topography or wind-induced undercatch. An alternative approach which can provide relatively homogenous estimates with complete coverage over most of the globe is based on using satellite observations. Therefore, satellite data are capable to estimate precipitation over the oceans and over remote areas where few or no ground measurements are available. The satellite-based precipitation estimates are derived mainly from visible, infrared (IR) and passive microwave (PMW) radiances which are measured by satellites. Although the visible channels cannot be used at night, the IR data are available in fine spatial resolution (about 3-4 km) with high temporal sampling (15 min) which are provided by geosynchronous satellites. Another source of data is PMW that can be used to estimate rainfall more directly. Low-altitude polar-orbiting satellites serve to measure the PMW data. Although, the microwave sensors can penetrate into the clouds and provide more information about the cloud characteristics such as water vapor, cloud particles, and structure of hydrometeors, but at the expense of temporal sampling. In recent years, different algorithms have been developed using the combination of the IR, Visible (VIS) and PWM observations to provide more accurate rainfall estimations in high spatial and temporal resolutions. To demonstrate the similarities and differences between the spatial distribution of different satellite-based and gauge-based precipitation datasets over Iran we compared seven different datasets. For comparisons all datasets are regridded to 0.25-degree latitude longitude spatial resolutions. Then the spatial distribution of the mean and relative standard deviations of annual precipitation of these datasets have been calculated. We also used more than 2000 rain gauges to evaluate the selected datasets. To reduce error only 228 pixels, include at least 3 rain gauges are used for comparisons of spatial average of monthly, seasonal and annual precipitation of gauge and seven datasets.
Results and Discussion: The results showed a large amount of differences in annual precipitation between seven selected datasets. The most differences pronounce in wet areas in the north of Alborz Mountain, in the semi-arid and arid regions of the central desert and in the high mountainous areas of the southern Zagros. The reason for these differences is that not only satellite-based but gauge-based datasets have large uncertainties estimating areal precipitation in such high topographic areas. The satellite products are prone to some errors arising from not fully understood physical process, sampling error and parameter estimation. Therefore, verification of precipitation datasets is one of the most important parts of the data development and refinements. In this paper, the spatial distribution of seven different global-observational precipitation datasets over Iran are compared for the period 2003-2007. At first all datasets were regridded to 0.25° spatial resolutions using linear interpolation method. Then, the mean and relative standard deviation of annual precipitation of the datasets were calculated to analyze the spatial discrepancies between datasets. The areal average of annual precipitation and the contribution of seasonal precipitation were calculated for comparison purposes. The results showed that areal average of annual and seasonal precipitation for 228 selected pixels for PERSIANN-CDR, TRMM, and GPCP which are satellite-based and gauge adjusted datasets are more similar to the rain gauge data than other datasets. The results for the above datasets are even better than CRU and APHRODITE which are gauge-based datasets.
Conclusion: The results showed that the satellite estimates are not capable to show the precipitation (detection and amount) over the coast of Caspian Sea and the high areas of the Zagros Mountain as well as other parts of the country. There are some useful recommendations for data users at the end of this paper. In fact, in this paper our spatial focus is on Iran and we introduced a web address which data users can access freely from one of the most popular and widely used satellite-based products in easy-to-use format only for Iran. The results show considerable differences between the datasets. The difference is about 0.8 times of mean annual precipitation (about 300 mm in a year) for the coast of Caspian Sea. The satellite-based estimations were less accurate over the coast of Caspian Sea and high mountainous area of the southwest of Zagros comparing to other parts of the country. While spring precipitation shows maximum contributions in annul precipitation for in-situ datasets, winter precipitation shows maximum contribution in annual precipitation for other datasets. The results showed that areal average of monthly, seasonal and annual precipitation over 228 selected pixels for PERIANN-CDR, TRMM and GPCP were consistent with rain gauge data. CMORPH and PERSIANN underestimate areal average of monthly and seasonal precipitation over the pixels.
Yousef Hasheminejhad; Mehdi Homaee; Ali Akbar Noroozi
Abstract
Introduction: Soil salinization is increasing across developing world countries and agricultural production is decreasing as a result of this stress. Climate change could adversely affect soil salinization trend through the decrease in rainfall and increased evapotranspiration in arid regions. Policy ...
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Introduction: Soil salinization is increasing across developing world countries and agricultural production is decreasing as a result of this stress. Climate change could adversely affect soil salinization trend through the decrease in rainfall and increased evapotranspiration in arid regions. Policy and decision makers require continuous and quantitative monitoring of soil salinity to adapt with the adverse effects of climate change and increasing need for food. Indices derived from near surface or satellite based sensors are increasingly applied for monitoring of soil salinity so a considerable number of these indices are introduced already for soil salinity monitoring. Different regression methods have been already used for modeling and verification of developed models amongst them multiple linear regression (including stepwise, forward selection and backward elimination) and partial least square regression are the most important methods.
Materials and Methods: To evaluate different approaches for modeling soil salinity against remotely sensed data, an area of about 50000 ha was selected in Sabzevar- Davarzan plain during 2013 and 2014 years. The locations of sampling points were determined using Latin Hypercube Sampling (LHS) strategy. Sampling density was 97 points for 2013 and 25 points for 2014. All points were sampled down to 90 cm depth in 30 cm increments. Totally 366 soil samples were analyzed in the laboratory for electrical conductivity of saturated extract. Electromagnetic induction device (EM38) was also used to measure bulk soil electrical conductivity for the sampling points at the first year and sampling points and 8 points around it at the second year. Totally 97 and 225 EM measurements were also recorded for first and second years respectively. Mean measured soil EC data were calibrated against the EM measurements. Finding the fair correlations, the EM and EC data could be converted to each other. 23 spectral indices derived from Landsat 8 images in the sampling dates along with DEM were used as independent variables. Multiple Linear Regression (MLR) and Partial Least Square Regression (PLSR) methods were evaluated for their fitness in predicting soil salinity from independent variables in different calibration and verification datasets.
Results and Discussion: Different multiple linear regression approaches using the first year data for training and second year data for testing the models and vice versa were evaluated which produced determination coefficients of about 22 to 88 percent in the training dataset but this regression did not reach to 29 percent in the test dataset. Due to the multiple co-linearity amongst the independent variables the multiple linear regression methods were not applicable to all variables. Excluding the co-linear variables, log- transforming and randomizing them into train and test datasets improved the determination coefficient of model and its validation at an acceptable level. Application of partial least square regression using the original and log- transformed data of first and second years as train and test datasets and vice versa introduced determination coefficients of about 39 to 85 percent in the training dataset but were not able to predict in the test dataset. Random dividing of all data into train and test datasets considerably increased the determination coefficient in the verification dataset. Repeating the randomization showed that the approach has the required consistency for predicting the coefficients of variables.
Conclusions: Wide range of independent variable could be used for predicting soil salinity from remotely sensed data and indices. On the other hand the independent variables generally show multi-colinearity amongst themselves. Correlation matrix, variance inflation factor and tolerance indices could be used to identify multi-colinearity. Removing or scaling the variable with high colinearity could improve the regression. Different data transformation methods including log- transformation could also significantly improve the strength of regression. In this research EM data showed more significant correlations with spectral indices in comparison with laboratorial measured EC data. As the EM38 device measures the reflectance in special range of spectrum this higher correlation could be expected. Such models should be calibrated and verified against ground truth data. Generally a part of data set is used for calibrating (making the model) and the remained for verifying (testing the model). Random dividing of the total data of 2 years into calibration (2/3 of data) and verification (1/3 of data) could significantly improve the regression in the verification data set. This procedure increases the range of variability for data used for calibration and verification and prevents outlier predictions.
mozhdeh Jamei; mohammad mousavi; Amin Alizadeh; parviz irannejad
Abstract
Introduction: Surface soil moisture is one of the most important variables in the hydrological cycle, and plays a key role in scientific and practical applications such as hydrological modelling, weather forecasting, climate change studies and water resources managements. Microwave radiometry at low ...
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Introduction: Surface soil moisture is one of the most important variables in the hydrological cycle, and plays a key role in scientific and practical applications such as hydrological modelling, weather forecasting, climate change studies and water resources managements. Microwave radiometry at low frequencies (1.4GHz) is an established technique for estimating global surface soil moisture with a suitable accuracy. In recent years, soil moisture measurements have become increasingly available from satellite-based microwave sensors. The ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009. It carries the first L-band 2-D synthetic aperture microwave radiometer to provide global estimates of soil moisture with an averaged ground resolution of 43 km over the field of view. The main objective of this research was to validateSMOS soil moisture retrievals over the west and south west of Iran.
Materials and Methods:The study area is located in the west and southwest of Iran which contains five areas belongingto the Ministry of Power. For the validation of SMOS dataover the study area, the SMOS soil moisture retrievals from MIR_SMUDP2 productswere compared with ground-based insitu measurements. The validation process was carried out using Collocation techniquefor the period 2012-2013. Collocation technique is a method used in the field of remote sensing to verify compliance measurements from two or more different instruments. In this study, the collocation codes were developed in Matlab Linux programming language. The ground-based in situ measurements included direct soil moisture measurements at the 5cm depth which were collected from five meteorological stations in the study area. We prepared a file for each station which contained daily soil moisture, date and time, geographical coordinates of metrological stations as input for validation model. The SMOS Level 2 Soil Moisture User Data Product (MIR_SMUDP2 files) version 551, which were provided through the ESA, contains the retrieved soil moisture and simulated TB, dielectric constants, etc. In this work, the ESA’s SMOS Matlab tool on RedHat Linux was used to read and derivesoil moisture data from MIR_SMUDP2 files.Four statistical metrics and Taylor diagram were used for the evaluation error of validation; the Root Mean Squared Difference (RMSD), the centered Root Mean Square Difference (cRMSD), the Mean Bias Error or bias and the correlation coefficient (R).
The Taylor diagrams wereused to represent three different statistical metrics (R, centered Root Mean Square Difference (cRMSD) and standard deviation) on two dimensional plots to graphically describe how closely SMOS dataset matched ground-based observations .
Results and Discussion: Based on the research algorithm and using MATLAB, the Validation model for SMOS soil moisture data was obtained. This model was appliedfor five metrological stations and the collocated soil moisture data from SMOS data and in situ data was saved as output of model to error evaluation. The results of validation errorshoweda good correlation between the SMOS soil moisture andin situ measurements. The highestand lowest correlation coefficientswere shown at Ahvaz (R=0.88) and Sarableh(R=0.75)stations, respectively.According to the bias values, the SMOS soil moisture retrievals had underestimation atAhvaz(MBE=0.04 m3m−3),Sararod(MBE=0.011 m3m−3), Sarableh(MBE=0.048 m3m−3) stations, whereas a slight overestimation of the SMOS product was detectedatthe Darab (MBE=-0.01 m3m−3) andEkbatan (MBE=-0.031 m3m−3) stations. In addition, the Root Mean Squared Difference (RMSD) values between the SMOS data and in situ data varied from 0.02 to 0.062 m3m−3 and at Ahvaz station withRMSD=0.048 m3m−3is close to the targeted SMOS accuracy of 0.04 m3m−3.Based on the Taylor diagrams, SMOS data had the highest correlation (R=0.88) with in situ measurements at Ahwaz stationand the lowest difference (cRMSD=0.008 m3m−3) between two data setswas found at Darab station.
Conclusions:The objective of this paper was to validateESA’s SMOS (Soil Moisture and Ocean Salinity) satellite products in the west and southwest of Iran for the period of 2012-2013. The validation of SMOS soil moisture retrievals from MIR_SMUDP2 products was done by using soil moisture measurements from five meteorological stations. The SMOS soil moisture retrievals showed underestimations at Ahvaz, Sararod andSarableh stations, whereas a slight overestimation werefound at Darab, Ekbatan stations. The validation results and Taylor diagrams showed thatthe SMOS soil moisture retrievals with R=0.88, RMSD=0.048 m3m−3, cRMSD=0.021 m3m−3at Ahvaz stationwasvery close to the targeted SMOS accuracy objectiveof 0.04 m3m−3 and then at Darab station SMOS data with R=0.82, RMSD=0.028 m3m−3,cRMSD=0.008 m3m−3indicateda good agreement with ground soil moisture measurements. Overall, the SMOS soil moisture data hadan acceptableaccuracy and agreement with in situ data at all stations. Therefore, we can use these data sets as a tool to derive soil moisture maps at study areas.
Yavar Pourmohamad; Mohammad Mousavi baygi; Amin Alizadeh; Alinaghi Ziaei; Mohammad Bannayan
Abstract
Introductionin current situation when world is facing massive population, producing enough food and adequate income for people is a big challenge specifically for governors. This challenge gets even harder in recent decades, due to global population growth which was projected to increase to 7.8 billion ...
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Introductionin current situation when world is facing massive population, producing enough food and adequate income for people is a big challenge specifically for governors. This challenge gets even harder in recent decades, due to global population growth which was projected to increase to 7.8 billion in 2025. Agriculture as the only industry that has ability to produce food is consuming 90 percent of fresh water globally. Despite of increasing for food demand, appropriate agricultural land and fresh water resources are restricted. To solve this problem, one is to increase water productivity which can be obtain by irrigation. Iran is not only exempted from this situation but also has more critical situation due to its dry climate and inappropriate precipitation distribution spatially and temporally, also uneven distribution of population which is concentrate in small area. The only reasonable solution by considering water resources limitation and also restricted crop area is changing crop pattern to reach maximum or at least same amount of income by using same or less amount of water. The purpose of this study is to assess financial water productivity and optimize farmer’s income by changing in each crop acreage at basin and sub-basin level with no extra groundwater withdrawals, also in order to repair the damages which has enforce to groundwater resources during last decades a scenario of using only 80percent of renewable water were applied and crop area were optimize to provide maximum or same income for farmers.
Materials and methodsThe Neyshabour basin is located in northeast of Iran, the total geographical area of basin is 73,000 km2 consisting of 41,000 km2 plain and the rest of basin is mountains. This Basin is a part of Kalshoor catchment that is located in southern part of Binaloud heights and northeast of KavirMarkazi. In this study whole Neyshabour basin were divided into 199 sub-basins based on pervious study.Based on official reports, agriculture consumes around 93.5percent of the groundwater withdrawals in Neyshabour basin and mostly in irrigation fields, surface water resources share in total water resource withdrawals is about 4.2percent, which means that groundwater is a primary source of fresh water for different purposes and surface water has a minor role in providing water supply services in the Neyshabour basin. To determine crop cultivation area, major crops divided into two groups. two winter crops (Wheat and Barley) and two summer crops (Maize and Tomato). To accomplish land classification by using supervised method, a training area is needed, so different farms for each crop were chosen by consulting with official agricultural organization expert and multiple point read on GPS for each crop. The maximum likelihood (MLC) method was selected for the land cover classification. To estimate the amount of precipitation at each 199 sub-basins, 13 station data for precipitation were collected, these stations are including 11 pluviometry stations, one climatology station and one synoptic station. Actual evapotranspiration (ETa) is needed to estimate actual yield (Ya). Surface Energy Balance Algorithm for Land (SEBAL) technique were applied on Landsat 8 OLI images. To calculate actual ETa, the following steps in flowchart were modeled as tool in ArcGIS 10.3 and a spreadsheet file. To estimate actual crop yield, the suggested procedure by FAO-33 and FAO-66 were followed. Financial productivity could be defined in differently according to interest. In this study several of these definition was used. These definitions are Income productivity (IP) and Profit productivity (PP). To optimize crop area, linear programing technique were used.
Results and discussionaverage actual evapotranspiration result for each sub-basin are shown in context. In some sub-basins which there were no evapotranspiration are shown in white. And it happens in those sub-basins which assigned as desert in land classification. In figures 8 and 9 minimum amount of income and profit productivity for wheat and barley is negative, this number means in those area the value of precipitation is higher than value of evapotranspiration, so lower part of eq. 21 and 22 would be negative and in result water productivity would be negative. Since most of precipitation occurs during cold season of the year these numbers are expected. Two sub-basins of 43 and 82 has the value of negative, it means in these two sub-basins groundwater are recharging during the year 2014-2015.The maximum value of income and profit productivity belong to wheat and barley which are winter crops and mostly rain fed, so amount applied water would be so low and in result productivity increased. Among the summer crops maize has the most income and profit income which can be interpret due to their growing period and the crop types. Maize has around 110 days to reach to maturity and harvest, on the other hand tomato needs 145 days to harvest. Some plant is C3 and some are C4. C4 plants produce more biomass than C3 crops with same amount of water which leads to more productivity. The results showed that tomato should have the most changes in area reduction (0.2) and maize should have no changes in both scenarios. Crop area should reduce to 66percent of current cultivation area to maintain ground water level and only 6percent reduction in cultivation area would result in 20percent groundwater recharging.
Conclusion to save groundwater resources or even retrieve the only water resource, cultivation area must reduce if the crop pattern will not change. In this study only four crops were studied. It seems best solution is to introduce alternative crop.
Bahman Farhadi Bansouleh; Alireza Karimi; Hoamyoun Hesadi
Abstract
Introduction: Evapotranspiration (ET) is one of the key parameters in water resource planning and design of irrigation systems. ET could have spatial variations in a plain due to the diversity of plant species and spatial variability of meteorological parameters. Common methods of ET measurement are ...
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Introduction: Evapotranspiration (ET) is one of the key parameters in water resource planning and design of irrigation systems. ET could have spatial variations in a plain due to the diversity of plant species and spatial variability of meteorological parameters. Common methods of ET measurement are mostly point based and generalization of their results to the regional level are costly, time consuming and difficult. During the last three decades, several algorithms have been developed to estimate regional ET based on remote sensing techniques. Verstraeten et al. (2008) classified remote sensing-based methods for ET estimation into four categories i) methods based on the surface energy balance, ii) Penman-Monteith equation, iii) water balance and iv) the relationship between surface temperature and vegetation indices. SEBS (Surface Energy Balance System), SEBAL, METRIC and SEBI are examples of the algorithms which is developed based on the surface energy balance approach. SEBS is developed by Su (2002) and has been evaluated by several researchers. However this algorithm has been examined in the several studies in the world,it has been used rarely in Iran. The aim of the current study was to assess the results of SEBS algorithm in Mahidasht, Kermanshah, Iran. The study area is located at the latitude of 34º 5' – 34º 32' N and longitude of 46º 31' - 47º 06' E.
Materials and Methods: A brief description of the SEBS algorithm (in Persian) as well as its procedure to calculate ET based on Landsat images were presented in this paper. All equations of the algorithm were coded in the ERDAS Imagine package software using model maker tools. Actual ET over the study area was estimated using SEBS algorithm during the growth period of grain maize in the year 2010. For this purpose, available LANDSAT TM satellite images during the growing season of maize in 2010 (25 June, 11 July, 27 July and 12 August) were downloaded free of charge from the http://glovis.usgs.gov website (last visited: 26 November 2015).
A Lysimetric study was carried out to obtain reliable amounts of ET to assess the accuracy of calculating actual ET by SEBS algorithm. Because of the absence of the weighing Lysimeters in the study area, Drainable Lysimeter was used. Since the maize was the major crop in the study area, 10 ha maize was planted on 15 May 2010 at the research farm of the Mahidasht agricultural research station. At the same time, maize was cultivated in the Drainable Lysimeter (1m*1.5m*1.5m) which was located almost in the middle of the research farm. Actual ET of maize was calculated with the Lysimeter for each irrigation interval (10 days) based on water balance equation.
The Results of the SEBS algorithm were evaluated on two levels (farm and regional). At the farm level, average of calculating ET at the pixels of research farm was compared with the average of measured ET at the Lysimeter. The absolute and relative differences between the calculated and measured values of ET was used to describe the accuracy of the algorithm. Due to the absence of regional ET measurement, maximum ET estimated by the SEBS algorithm in the plain was compared with the calculated potential crop reference evapotranspiration (ETO). ETO was calculated using the Penman - Monteith formula based on daily weather data obtained from Mahidasht weather station.
Results and Discussion: Results indicated that an average of ET in the study area increased from June to August which coincides with increasing air temperature and vegetation density in the irrigated fields of the study area. The highest and lowest values of actual ET over the study area were determined in the irrigated farms and mountainous area, respectively. The results of Lysimetric study indicated that daily actual ET of maize on 25 June, 11 July, 27 July and 12 August was 4.13, 7.74, 7.45 and 8.05 mm.day-1, respectively. The value of ET estimated by SEBS algorithm was less than actual measured ET by Lysimeter for the all mentioned dates. The maximum absolute difference between estimated ET by SEBS and measured ET with the Lysimeter was occurred on 27 July with the amount 0.34 mm.day-1. Considering the maximum relative difference of 4.56 % between calculated and measured ET, it could be concluded that estimated ET by SEBS algorithm can be acceptable.
Due to the absence of ground-based measurements of evapotranspiration at the regional level, the maximum amount of ET estimated by SEBS algorithm was compared with ETO. The highest and lowest ratio of maximum ET over ETO were calculated as 1.02 and 1.22 which are acceptable values for the crop coefficient (Kc) in the studied period. The maximum difference between estimated ET by SEBS algorithm with ETO was 1.53 mm.day-1 which is equal to 21.86% of ETO in the same date (12 August).
Conclusions: The results of the current study showed that the SEBS algorithm can estimate the actual ET of maize with the acceptable accuracy in the Mahidasht. In the absence of measured ET data at the regional level, it was difficult to have a reasonable judgment on the accuracy of the estimated values of ET by SEBS algorithm at this scale. It is recommended to do the same study on other remote sensing-based approaches of ET estimation.
F. Mahmoodi; R. Jafari; H. Karimzadeh; N. Ramezani
Abstract
Introduction: Use of remote sensing for soil assessment and monitoring started with the launch of the first Landsat satellite. Since then many other polar orbiting Earth-observation satellites such as the Landsat series, have been launched and their imagery have been used for a wide range of soil mapping. ...
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Introduction: Use of remote sensing for soil assessment and monitoring started with the launch of the first Landsat satellite. Since then many other polar orbiting Earth-observation satellites such as the Landsat series, have been launched and their imagery have been used for a wide range of soil mapping. The broad swaths and regular revisit frequencies of these multispectral satellites mean that they can be used to rapidly detect changes in soil properties. Arid and semi-arid lands cover more than 70 percent of Iran and are very prone to desertification. Due to the broadness, remoteness, and harsh condition of these lands, soil studies using ground-based techniques appear to be limited. Remote sensing imagery with its cost and time-effectiveness has been suggested and used as an alternative approach for more than four decades. Flood irrigation is one of the most common techniques in Isfahan province in which 70% of water is lost through evaporation. This system has increased soil salinization and desert-like conditions in the region. For principled decision making on agricultural product management, combating desertification and its consequences and better use of production resources to achieve sustainable development; understanding and knowledge of the origin, amount and area of salinity, the percentage of calcite, gypsum and other mineral of soil in each region is essential. Therefore, this study aimed to map the physical and chemical characteristics of soils in Vazaneh region of Isfahan province, Iran.
Materials and Methods : Varzaneh region with 75000 ha located in central Iran and lies between latitudes 3550234 N and 3594309 N and longitudes 626530 E to 658338 E. The climate in the study area is characterized by hot summers and cold winters. The mean daily maximum temperature ranges from 35°C in summer to approximately 17°C in winter and mean daily minimum temperature ranges from 5°C in summer to about -24.5°C in winter. The mean annual evaporation rate is 3265 mm. In this study, image processing techniquess including band combinations, Principal Component Analysis (PC1, PC2 and PC3), and classification were applied to a TM image to map different soil properties. In order to prepare the satellite image, geometric correction was performed. A 1:25,000 map (UTM 39) was used as a base to georegister the Landsat image. 40 Ground Control Points (GCPs) were selected throughout the map and image. Road intersections or other man-made features were appropriate targets for this purpose. The raw image was transformed to the georectified image using a first order polynomial, and then resampled using the nearest neighbour method to preserve radiometry. The final Root Mean Square (RMS) error for the selected points was 0.3 pixels. To establish relationships between image and field data, stratified random sampling techniques were used to collect 53 soil samples at the GPS (Global Positioning System) points. The continuous map of soil properties was achieved using simple and multiple linear regression models by averaging 9 image pixels around sampling sites. Different image spectral indices were used as independent variables and the dependent variables were field- based data.
Results and Discussion: The results of multiple regression analysis showed that the strongest relationships was between sandy soil and TM bands 1, 2, 3, 4, and 5, explaining up to 83% of variation in this component. The weakest relationship was found between CaCo3 and 3, 5, and 7 TM bands. In some cases, the multiple regressions was not an appropriate predicting model of soil properties, therefore, the TM and PC bands that had the highest relationship with field data (confidence level, 99%) based on simple regression were classified by the maximum likelihood algorithm. According to error matrix, the overall accuracy of classified maps was between 85 and 93% for chlorine (Cl) and silt componets, repectively.
Conclusions: The results indicated that the discretely classified maps had higher accuracy than regression models. Therefore, to have an overview of soil properties in the region, classification techniques appears to be more applicable than regression models. The findings of this study shows that the extracted maps of the physical and chemical characteristics of soils can be used as a suitable tool for field operations, cambating desertification and rehabilitation purposes and compared to maps that are created by traditional methods, our final maps have more economically and time saving advantages. Therefore, they can be used as an adjunct to field methods to aid the assessment and monitoring of soil condition in the arid regions of Isfahan province.
M. Fashaee; Seied Hosein Sanaei-Nejad; K. Davary
Abstract
Introduction: Numerous studies have been undertaken based on satellite imagery in order to estimate soil moisture using vegetation indices such as NDVI. Previous studies suffer from a restriction; these indices are not able to estimate where the vegetative coverage is low or where no vegetation exists. ...
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Introduction: Numerous studies have been undertaken based on satellite imagery in order to estimate soil moisture using vegetation indices such as NDVI. Previous studies suffer from a restriction; these indices are not able to estimate where the vegetative coverage is low or where no vegetation exists. Hence, it is essential to develop a model which can overcome this restriction. Focus of this research is on estimation of soil moisture for low or scattered vegetative land covers. Trapezoidal temperature-vegetation (Ts~VI) model is able to consider the status of soil moisture and vegetation condition. It can estimate plant water deficit for weak or no vegetation land cover.
Materials and Methods: Moran proposed Water Deficit Index (WDI) for evaluating field evapotranspiration rates and relative field water deficit for both full-cover and partially vegetated sites. The theoretical basis of this method is based on the energy balance equation. Penman-Monteith equation of energy balance was used to calculate the coordinates of the four vertices of the temperature-vegetation trapezoid also for four different extreme combinations of temperature and vegetation. For the (Ts−Ta)~Vc trapezoid, four vertices correspond to 1) well-watered full-cover vegetation, 2) water-stressed full-cover vegetation, 3) saturated bare soil, and 4) dry bare soil. WDI is equal to 0 for well-watered conditions and equals to 1 for maximum stress conditions. As suggested by Moran et al. to draw a trapezoidal shape, some field measurements are required such as wind speed at the height of 2 meters, air pressure, mean daily temperature, vapor pressure-temperature curve slope, Psychrometrics constant, vapor pressure at mean temperature, vapor pressure deficit, external radiation, solar radiation of short wavelength, longwave radiation, net radiation, soil heat flux and air aerodynamic resistance is included. Crop vegetation and canopy resistance should be measured or estimated. The study area is selected in the Mashhad plain in Khorasan Razavi province of I.R. Iran. Study area is about 1,200 square kilometers and is located around the Golmakan center of agricultural research. In this study, water deficit index (WDI) was zoning by MODIS images in subset of Mashhad plain during water year of 2011-2012. Then, based on the close relationship between WDI and soil moisture parameter, a linear relationship between these two parameters were fitted. Soil moisture is measured by the TDR and every 7 days at 5 depths of 5, 10, 20, 30 and 50 cm from the surface. Remote Sensing (RS) technology used as a tool for providing some of the data that is required. The moderate resolution imaging spectroradiometer (MODIS) instrument is popular for monitoring soil moisture because of its high spectral (36 bands) resolution, moderate spatial (250–1000 m) resolution and various products for land surface properties. MODIS products used in the present study include: MOD09A1 land surface albedo data, MOD11A1 land surface temperature data, and MOD13A1 vegetation data. Using ArcMap 9.2 and ERDAS IMAGINE 2010 softwares, WDI was calculated pixel by pixel for 18 days (non-cloudy days and simultaneous with measurement of soil moisture at the station).
Results and Discussion: The results showed that the northeastern region is predominantly rainfed and irrigated farmlands are under water stress. Conversely, the southwestern part of the area is mountainous with less water stress. Based on NDVI, there is also less crop cover in the southwestern part of the region during the year. The results showed that about 44% of the index values are in the range of 0.2-0.3. Then about 22% of the index values are in the range of 0.3-0.4. Thus it can be concluded that over 66% of the index values are in the range of 0.2-0.4. According to the maximum index value (WDI=0.59 on the 201th day of year) and the minimum values (WDI=0.0004 on the 129th day of year) during the time period of study, it seems that water stress in the study area in the six-month period of observation is moderate. To validate the results, changes in precipitation, relative humidity and WDI values were compared. As expected, after the occurrence of any significant rainfall, water stress is decreased and decreasing in relative humidity, coincided with increase in water stress. In the next step, the linear relationship between measured values of soil moisture and WDI values were fitted in 2 depth of 5 and 10 cm. It should be noted that the average values of WDI of four pixels surrounding the Golmakan station was used in calculation of the regression coefficients Similar research has shown that Ts~VI trapezoid based WDI can accurately capture temporal variation in surface soil moisture, but the capability of detecting spatial variation is poor for such a semi-arid region like Mashhad. The high correlation coefficient (93%) obtained from soil moisture (5 cm) and WDI regression showed the good mutual impacts of these two parameters on each other. The correlation coefficient between WDI index and soil moisture at a depth of 10 cm was equal to 83%. Reducing the value of the correlation coefficient was probably due to the delay in transferring the soil moisture changes to underlying depth.
Conclusion: The similarity of the mean values of rainfall and relative humidity of the air showed good compliance with the WDI. Good correlation coefficient (93%) between WDI and soil moisture (measured at depth of 5cm in the station) certifies the accuracy of the results obtained from WDI. The results showed that Ts~VI trapezoid based WDI can well capture temporal variation in surface soil moisture, while in this study, spatial zoning was avoided because of the lack of soil moisture data within the study area.
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.
seyed vahidodin rezvani
Abstract
Today, remote sensing is used in various sciences, such as: geography, biology, meteorology, agriculture, water resources management and etc. Easy and inexpensive data access and data precision, digital and extensive and comprehensive of images that having the frequency spectrum, are some advantages ...
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Today, remote sensing is used in various sciences, such as: geography, biology, meteorology, agriculture, water resources management and etc. Easy and inexpensive data access and data precision, digital and extensive and comprehensive of images that having the frequency spectrum, are some advantages of remote sensing, than other methods of providing information. Therefore, by using algorithms in remote sensing that having the evaporation and transpiration, you can have a big step in the management of water resources. Among of these algorithms, SEBAL is a remote sensing algorithm that calculating surface energy balance for each pixel of a satellite image at each moment. In this study, surface albedo, surface temperature and vegetation status index were calculated by using this algorithm and multi-spectral satellite data and meteorological information such as degree of temperature, hours of sunshine, wind, saturated vapor pressure, soil humidity and etc. Finally evapotranspiration of Miandarband plain (west of Iran) was determined and the evapotranspiration maps were prepared. Also, the actual evapotranspiration computed for wheat using FAO conventional method and was compared with SEBAL method. The results showed that there was a high correlation (0.84) between these two methods.
F. Fathian; S. Morid; S. Arshad
Abstract
The drawdown trend of the water level in Urmia Lake poses a serious problem for northwestern Iran that will have a negative impact on the agriculture and industry. This research investigated the possible causes of this adversity by estimating trends in the time series of hydro-climatic variables of the ...
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The drawdown trend of the water level in Urmia Lake poses a serious problem for northwestern Iran that will have a negative impact on the agriculture and industry. This research investigated the possible causes of this adversity by estimating trends in the time series of hydro-climatic variables of the basin as well as tracking changes in the land use of the study area, using satellite images. Four non-parametric statistical tests, the Mann-Kendall, Theil-Sen, Spearman Rho and Sen's T test, were applied to estimate the trends in the annual time series of streamflow, precipitation and temperature at 18 stations throughout the case study. Furthermore, by using the LANDSAT satellite images of 1976, 1989, 2002 and 2011, land use classification was determined using maximum likelihood, minimum distance and mahalanobis distance methods. The results showed significant increasing temperature trend throughout the region and an area-specific precipitation trend. The trend tests also confirmed a general decreasing trend in region streamflows that was more pronounced in the downstream stations. The results showed that the classification by the maximum likelihood method wass associated with minimum error. The results of processing the images showed that the irrigated crops, horticultural and dry lands have increased during last 35 years by 412, 485 and 672 percent, respectively. But, the pasture area is decreased by 34 percent. Finally, correlation between streamflow changes with simultaneous changes in climatic variables and land use showed it is significant in case of temperature and land use; and insignificant in case of precipitation. However, the determination coefficient of land use is higher than temperature.
