جستجوی مقالات مرتبط با کلیدواژه « spi » در نشریات گروه « کشاورزی »

Today, the phenomenon of climate change is one of the problems that has always received attention in the world. One of the reasons for climate change is the increase of greenhouse gases, which have a significant effect on extreme phenomena. Drought forecasting is one of the management strategies that helps to plan properly for using limited water resources. It is also important to examine drought changes based on climate changes from various aspects; in many long-term plans, it is necessary to develop a vision of the future state of rainfall and drought periods for the region. The SPEI index can calculate drought in different time scales and can also include the effects of temperature changes in drought assessment and be used to monitor current and future climate changes based on climate scenarios. Also, the Standard Precipitation Index (SPI) is one of the most important indicators in drought analysis. This index is known as the most appropriate index for drought analysis due to the simplicity of calculations, the use of available rainfall data, and the ability to calculate for any desired time scale. The recent increase in droughts in the Karun 3 watershed has caused water shortages and economic and social damages. Therefore, investigating the effect of climate change and analyzing the frequency of drought is very important. In line with this goal, in this research, firstly, simulation of rainfall, maximum temperature, and minimum temperature of eight meteorological stations using three climate models EC-EARTH, CNRM-CM5, and GFDL-ESM2M under two release scenarios RCP4.5 and RCP8 5. It was forecated for the period (2055-2030).

In this research, firstly, the temperature data (maximum and minimum) and precipitation output of EC-EARTH, CNRM-CM5 and GFDL-ESM2M climate models under the CORDEX project for RCP4.5 and RCP8.5 emission scenarios with the help of CMhyd Raz software Scaled and time series of maximum, minimum and precipitation temperatures for eight meteorological stations in the future period (2055-2030) were produced. Then, the temperature and precipitation changes in the future period compared to the base period (1995-2020) were investigated. Finally, the investigation of drought (frequency and continuity) in the future period under two release scenarios of RCP4.5 and RCP8.5 by (SPI) and (SPEI) indicators in three-time scales of three, six, and twelve months using DIP software was performed. To evaluate the efficiency and accuracy of three EC-EARTH, CNRM-CM5, and GFDL-ESM2M general circulation models for simulating climate data, the monthly average of climate variables in the period (1990-2005) obtained from the output of the model was compared with the corresponding observational values. For this purpose, Nash Sutcliffe (NS), bias index and coefficient of determination (R^2) were used.

The results showed that the maximum temperature increase is related to Shushtar station. In this station, the maximum temperature will increase by 1.46 degrees Celsius in the RCP8.5 emission scenario and one degree Celsius in the RCP4.5 emission scenario compared to the base period. Masjid Sulaiman station has the highest increase in minimum temperature. At this station, the minimum temperature increase under the RCP8.5 release scenario is 2.41 degrees Celsius and under the RCP4.5 scenario, it is 1.92 degrees Celsius. Also, the rainfall in the whole range has been decreasing in the coming period. The decrease of rainfall in Kohrang in the RCP8.5 scenario is 146.3 mm and in the RCP4.5 scenario, it is 53.6 mm, which is about 11.7 and 4.3 percent, respectively. In terms of the frequency of drought in the statistical period (2055-2030) on a three-month scale, in the RCP4.5 scenario, Izeh station has the highest frequency with 14 droughts, on a six-month scale, in the RCP4.5 scenario, the maximum number of droughts in Shahrekord with 16 times and on a twelve-month scale, in the RCP4.5 scenario, Kohrang station shows the highest frequency with 15 times. In the RCP8.5 scenario, Izeh station has the highest frequency with 20 drought experiences. Also, in the upcoming period, under the RCP4.5 release scenario, on the scale of three, six, and twelve, respectively, Shahrekord, Yasouj, and Lordegan stations will have eight, 11, and 27 months, and in the RCP8.5 scenario, the drought will continue the most at Masjid Sulaiman, Shushtar, and Lordegan, respectively.

The results showed that the annual long-term average of the maximum and minimum temperature in the future period compared to the base period in all eight stations had an upward trend. Also, the results showed that the maximum temperature under the 8.5RCP scenario increases more than the 4.5RCP release scenario. Also, the results showed that the Kohrang station affected by climate change will have the largest decrease in rainfall in the study area during the coming period. Also, the results showed that in all three-time scales of three, six, and 12 months in the future period, compared to the base period, there are many fluctuations in the time series of SPI and SPEI drought indices. Also, extreme drought conditions will increase in the coming period. It is expected that in the coming period, the number of dry months will be less than the number of months with normal and wet conditions. Also, when examining the drought situation using the SPEI index, it can be seen that, like the SPI index, the pattern of changes in the drought period can be recognized in the stations. It should be mentioned that the intensity of droughts calculated using the SPEI index is rarely higher than the numerical value of two. By comparing the duration and frequency of drought in the studied stations and in different time comparisons, it was found that the SPI index shows the continuity and frequency of drought in most cases compared to the SPEI index.

Drought, as one of the natural disasters, has significant impacts on the environment. Although its occurrence is inevitable, its negative effects can be mitigated by taking measures. Freshwater groundwater, due to its sustainability and high quality, plays an important role in providing drinking water and socio-economic development, especially in dry and semi-arid regions. In this study, drought in Mahneshan County was investigated using various indices. 30 years of rainfall data from Mahneshan station were analyzed. To investigate climatic drought, the SPI index was used, to determine wet and dry periods, the 5 and 7 year moving averages of annual rainfall were used, and to investigate hydrological drought, well data and the GRI index were used. The findings show that climatic and hydrological droughts have occurred in the Mahneshan region and that there is a significant correlation between the SPI index and groundwater level changes. According to the findings, drought is one of the main challenges in the Mahneshan region. The use of various indices to investigate drought is necessary and its negative effects can be mitigated with proper planning. This study only focuses on a specific region and other factors affecting drought and ways to reduce its effects are not investigated. The aim of this study is to provide an overview of the drought situation in the Mahneshan region and the need to pay attention to this phenomenon in future planning and actions.

This study is of an applied nature with a descriptive-analytical approach. To collect data, two methods were used: library studies and field surveys. In the field section, various methods were used, such as questionnaires, interviews, and observations, to collect the opinions and views of the local residents, expert information from specialists, and field surveys of water resources and water transmission systems. To complete the information, inquiries were made from relevant organizations such as the Rural Affairs Deputy and the Rural Water and Sewerage Administration. Also, by visiting the region in the field, information related to water resources, water allocation, and other drought-related matters were collected and analyzed and interpreted using scientific methods. To investigate climatic drought in the region, the Standard Precipitation Index (SPI) was used. This index is calculated using long-term rainfall data (30 years) and can show wet, dry, and normal periods in the region. In addition to the SPI index, the 5 and 7 year moving averages of annual rainfall were also used to determine wet and dry periods. In this method, the average rainfall in a specific period of time (5 or 7 years) is calculated and compared with the long-term average rainfall. To investigate hydrological drought in the region, data from wells with appropriate distribution and long-term data were used. Changes in groundwater level in these wells were calculated by the Groundwater Recharge Index (GRI). The GRI indicates the amount of water entering the groundwater and can be used to assess the status of groundwater resources in the region.

Analysis of SPI values at different time scales and 5 and 7 year moving averages of rainfall indicates a significant decrease in rainfall in the Mahneshan region over the past 20 years. This downward trend raises concerns about its negative consequences for water resources, agriculture and other water-dependent sectors in the region.Analysis of the GRI index on monthly and annual time scales indicates a decline in groundwater levels in the Mahneshan region. This decline has been exacerbated due to the imbalance between recharge and withdrawal from the groundwater table, especially in recent years with consecutive droughts.Overexploitation of groundwater resources has threatened the sustainability of these resources.Correlation analysis between the SPI and GRI indices indicates a significant correlation between meteorological and hydrological droughts in the Mahneshan region. This correlation is stronger at longer time scales. The findings show that meteorologicaldroughts can lead to hydrological droughts in the region.Analysis of the relationship between meteorological and hydrological droughts with time lags of one, two and three years shows that there is a significant correlation between these two types of droughts with lags of one and two years. This correlation shows that meteorological droughts with lags of one and two years lead to hydrological droughts in the Mahneshan region.

The Mahneshan region has faced a decline in groundwater levels and frequent and severe droughts over the past two decades. Studies show that:The average annual decline in groundwater level has varied over the years. This is due to the diversity in formation type, permeability, hydraulic gradient, and the amount of groundwater withdrawal in different regions. In the 20-year period (1375 to 1395), the groundwater level in the Mahneshan region has dropped by an average of 45 cm annually. This indicates the excessive and unsustainable withdrawal of groundwater resources. Repeated droughts have also exacerbated the trend of groundwater decline. In addition to rainfall, other factors such as overexploitation of groundwater resources also affect groundwater decline. A comparison of the SPI (climatic drought) and GRI (groundwater resources) indices shows that there is a significant relationship between these two indices on monthly and annual time scales. This relationship shows that climatic drought, with a 2-year lag, affects groundwater decline in the Mahneshan region. The lag time between climatic drought and groundwater decline can vary in different parts of the basin, depending on the diversity of formation type and geological characteristics and different groundwater withdrawals.Overall, the results of this study show that the Mahneshan region faces a serious challenge of water scarcity and drought. Overexploitation of groundwater resources and repeated droughts have exacerbated this challenge.

A combined assessment of drought risk and associated impacts on crop production based on a probabilistic approach seems appropriate to understand the multivariate nature of drought risk in agriculture. To overcome the problems caused by drought impact detection, several approaches have been developed in recent decades. Among the multivariate analysis approaches, copula functions are very popular. Copulas use univariate marginal distributions to form a joint distribution. The joint distribution can be described by the corresponding marginal distributions and copula functions that describe the dependency structure. In this research, using the statistical precipitation data in the Tabriz plain in eastern part of Lake Urmia basin, and yield of rainfed wheat in this area, a model based on copula functions was developed to determine the diffrent probabilities of yield risk and different drought conditions. Also, the application of copula functions related to rainfed wheat yield in this region was performed for the first time, and the presented method will be applicable to other areas and other crop cultivation.

In the case of meteorological drought, the basis for calculating the degree of drought is determined by comparing the amount of precipitation with the long-term average or its normal values. The SPI index is considered to be an appropriate and powerful index to use as a time scale droughts monitoring. Basically, SPI was created to detect the lack of precipitation on multiple time scales. Among the reasons that make the use of this index so popular, we can mention the standard nature of this index as it can be used in regional studies and establish a temporal relationship between drought events in different parts of the same area. The SPI index is a dimensionless index and its more negative values show the more severe the drought.
Analysis of variables individually is easy and can be analyzed by statistical distribution functions; but statistical analysis joint variables is very complicated and impossible in most cases. If the correlation criterion of these variables is known, their joint probability distribution can be obtained using copula functions. Using copula functions for modeling has a high degree of flexibility as it is possible to choose different marginal distributions to create a multivariate model. Copulas are functions that form a bivariate or multivariate distribution based on two or more univariate marginal functions. Several copula functions can be used to construct a two-dimensional joint distribution of hydrological and agricultural variables, among which Archimedean and elliptical copula families are the most commonly used. In the present study, six copula functions are used and the parameters of the paired functions are determined using the two-stage maximum likelihood method, which estimates the parameters of the marginal distribution and the copula function by forming two likelihood functions. In order to investigate joint probability of rainfed wheat yield and drought index, the time series of rainfed wheat yield in the Tabriz Plain region and SPI index during the last 30 years from 1990 to 2020 was applicated in this study.

In general, the improvement in agricultural methods, investments and technological advances during this period have led to a continuous increase in crop yield, however, a sharp drop in crop yield is clearly evident at times during the reporting period. In this research, to ensure that the observed trend does not affect the results, the Copula model was built using the detrended time series data by removing the trend in the values and the variance in the yield data. Based on the results obtained, crop production decreases dramatically during severe droughts, so such sensitivity to moisture deficits caused by low rainfall after several wet years can be attributed to farmers' expectations and management policies driven by high productivity during the previous ones wet years were determined.
To use series of standardized yield and growing season SPI in copula, the most appropriate distributions were selected and used, logistic and normal, respectively. Also, according to the calculated Kendall correlation (0.35), the best fit joint was Claytons function with AIC = -11.10, RMSE = 0.036 and used to construct the joint probability distribution of the standardized yield series of rainfed wheat and SPI of growth Period. Results showed that the cumulative probability of yield risk event in mild, moderate, severe, and very severe drought was 18.42, 7.82, 4.26, and 2.32 percent, respectively,and for an overall rainfed wheat yield risk and drought condition is about 33%. Meanwhile, the probability of rainfed wheat yield risk and non-drought events is only about 7% and joint probability of yield risk and SPI>1 is so close to zero.

In the current research, the probability of occurrence of the rainfed wheat yield risk and drought conditions was extracted by copula functions. Based on the results obtained, most changes in yield occur when the drought index is in the range greater than -1.5. In other words, in the conditions of severe and very severe drought, the yield of rainfed wheat does not show noticeable changes in probability. The opposite situation can also be observed for the situation of very wet years, that when there is high amounts of precipitation and the drought index show values higher than 1.5, the yield of the crop does not show much change. Thus, the SPI must be greater than one in order to achieve the desired yield and not be at risk of rainfed wheat yield, which in this study is considered to be a standardized yield values greater than zero. Because in this range of SPI, the joint probability of yield risk is estimated to be very close to zero. Therefore, this threshold can be introduced as the rainfed wheat yield safety threshold, but depending on the situation of the region and the drought index, the drought threshold (SPI<0) contains cumulative probability about 33% of the rainfed wheat yield risk.

Drought can have serious negative effects on the quality of water needed for irrigated agriculture. The geological formations of the region increase the concentration of minor and rare toxic elements in the waters, and on the other hand, human activities lead to water pollution in the nearby areas. In this research, the effect of drought on the quality of surface water, the effect of various factors such as geological formations and the decrease or increase of rainfall and air temperature on the quality of water resources, how the quality of water changes, determining the limits of water consumption in the drinking sector and agriculture should be paid.

The Tirah River Basin with an area of 3243.6 square kilometers is considered as one of the sub-basins of the Dez River and a subset of the Karun Basin. The Standardized Precipitation Index (SPI), Standard StreamFlow Index (SSI) and Decimal Index (DI) were used to calculate standard drought indices. Elements and compounds of electrical conductivity (Ec), Total dissolved solids (TDS), pH and Anions of chlorine (Cl-), Sulfate (SO42-), Bicarbonate (HCO3-), Sodium (Na), Calcium (Ca) and Magnesium cations (Mg) were investigated. To draw the pattern of droughts, 3 hydrometric stations of Teng Mohammad Haji (upstream), Rahimabad Silakhor (middle) and Tir-Droud (outlet) stations were tested.

The most severe hydrological droughts with a duration of 2 years and a minimum SSI value of -1.23 in the Teng Mohammad Haji Station in the water years of 2009 and 2010 and the Tir-Droud Station with a duration of 4 years (-1.19) and the Rahimabad Silakhor Station with a duration of 3 years (-1.16) occurred for the water years 2012, 2013, 2014 and 2015. The analysis of discharge and precipitation data of Teir sub-basins showed that between the discharge and precipitation (SPI and SSI) of Tang Mohammadhaji and Rahimabad Stations, there was an average positive correlation at a significant level of 0.05 (R2=0.526). Examining the relationship between annual discharge and water quality parameters showed that there was a negative correlation for most parameters such as TDS, EC, chlorine, the total of anions and cations, so that the maximum concentration of quality parameters occurred in droughts. In Tang Mohammadhaji Station, the highest percentage of changes was related to sodium and chlorine, respectively, with 62.55 and 39.70% in dry years compared to the long-term average. The results of this research showed that the percentage of changes in calcium, magnesium, sodium, carbonate and chloride
 increased in dry years compared to the long-term average in the studied stations in the Tirah River Basin. All the stations in the Tirah Basin have passed the period of relatively dry hydrological drought (-0.84 to -1.28) and none of them have suffered severe hydrological drought.

The analysis of discharge and precipitation data of dark sub-basins shows that between discharge and precipitation (SPI and SSI) of Tang Mohammad Haji Station, which is an upstream sub-basin of dark, there is an average positive correlation at a significant level of 0.05 and in Rahim Abad Station, which is in the middle of the basin It is located in Tire, and according to Pearson, it has a correlation at a significant level of 0.05 at the exit station of Tire basin, no significant correlation is observed between discharge and precipitation variables, which can be caused by snowfall and the persistence of snow in the heights from the previous water year, the construction of Meruk Dam. In the upper part of the basin, as well as the water intake of Bishehdalan Silakhor Wetland in droughts and abundant water harvesting from the Tirah River for agriculture. The upstream basin of Tang Mohammadhaji Station is completely calcareous, which has caused changes in parameters of calcium, TDS, EC, etc. In Rahimabad Silakhor Station and Tire outlet, half of the area of the basin is dedicated to intrusive rocks such as granite and granodiorite, which increases potassium and calcium. In the studied stations in Tirah River Basin, the percentage of changes related to calcium, magnesium and sodium, and carbonate and chlorine anions have increased in dry years compared to the long-term average. In general, it can be concluded that drought can have negative effects on water quality parameters in Tirah River Basin.

Drought is one of the destructive phenomena with adverse impacts on water resources and water needs. Machine-learning models are among the helpful tools in time-series prediction that can provide suitable results without the requirements for basic information about a system. In this study, adaptive neuro-fuzzy inference system (ANFIS) and least square support vector regression (LSSVR) models were utilized to predict the standardized precipitation index (SPI) as a meteorological drought indicator and streamflow drought index (SDI) as a hydrological drought indicator for a period (2001-2019). Ajabshir, located in the northwest of Iran, was selected as the study area, where the data of Qaleh Chay meteorological and hydrological stations were used to calculate SPI and SDI, respectively. The precipitation and flow rate data were considered input variables of the machine-learning models in predicting the SPI and SDI, respectively. The results revealed that during the period under review, meteorological drought was more severe in 2004-2011. While in this period, hydrological drought was more severe in 2007-2011 (SPI<-3). Moreover, the prediction results of the indices showed that the performance of the LSSVR model was better than that of ANFIS for both indicators. Using LSSVR, the RMSE and MAPE error evaluation criteria for SPI were 0.74 and 0.59, respectively, while these values for SDI were obtained as 0.62 and 0.46, respectively. The findings of this study show that machine-learning models are suitable tools for predicting drought indicators. Therefore, it is suggested to use such models in predicting drought indicators in other similar regions.

The present study was conducted to investigate NPP changes and its relationship with drought. The monthly time series of NPP for the country's rangelands was prepared from the sum of net photosynthesis production (PSN) of the eight-day MODIS sensor (MOD17A2H) with a spatial scale of 500 m for 2000 to 2022.The monthly rainfall data of 165 synoptic stations were obtained from the National Meteorological Organization to calculate the Standard Precipitation Index (SPI) and monitor droughts in this period. Box plots of precipitation and NPP were prepared and showed that the highest values of NPP with an average of more than 50 gC.m-2month-1 occur during spring and early summer in humid areas. Results showed that NPP is increasing in Iran's rangeland ecosystems, and on average, the NPP of all rangeland ecosystems in the country is about 112.6 ± 13 gC.m-2.year-1  ,in other words, about 76 million tons of carbon per year. It is absorbed by the plants in the rangeland ecosystems of the country. The trend of changes in the monthly series of NPP was not significant due to the influence of seasonal characteristics, but the annual changes of NPP showed a significant trend of increase in all regions.

Drought as a climatic phenomenon is considered as one of the most important natural hazards, which is known for its slow onset and may have multiple effects on hydrology, agriculture and socio-economic conditions. Drought monitoring includes its severity, spatial distribution, and duration, and becomes an essential input for establishing drought risk management and mitigation plans. Many drought indicators have been introduced and applied in recent decades in regions with different climatic characteristics.

In this study, the meteorological and hydrological drought of Tajan watershed in Mazandaran province was evaluated and monitored using standardized precipitation index (SPI) and standardized streamflow index (SSI). For this purpose, firstly, the data of six stations Rain gauges and eight hydrometric stations were used in the period of 1990-2020, after removing the statistical deficiencies and completing the data of rainfall, flow rate, homogeneity and normality of the data. Finally, the meteorological and hydrological drought process was analyzed with the mentioned two indicators using Mann-Kendall test and Sen slope estimator in MAKESENS software.

The results showed that the SPI index has the highest response to rainfall on a one-month scale, so that its lowest value was at Kordkheil station with a value of -3.31, the most severe drought that occurred in April 2011. On the other hand, droughts have occurred in all rain gauge stations and their intensity has increased over time. According to the SSI index, hydrological drought has also occurred in all hydrometric stations with different intensities. The trend of these indicators also shows the increase in intensity and frequency of drought with the passage of time in the studied watershed.

In Darabkola and Kordkheil stations, the SPI index has the highest number of years of occurrence and the highest intensity of drought. In Darabkola station, the frequency of moderate, severe and very severe droughts are relatively high. Very, very severe drought in Edareh Sari station has the highest amount in terms of frequency, which indicates that there is the greatest risk of drought occurrence in the plains and plains of the basin. The results of the trend with the Mann-Kendall test indicate an increase in drought and a decrease in rainfall in the Tajan watershed. Due to climate changes, increase in demand due to increase in population and decrease in rainfall, the occurrence of drought in the future may occur with more frequency and intensity, which can cause great damage to agriculture and other parts of the region. The SSI index in hydrometric stations indicates the decrease in flow and the occurrence of hydrological drought in the basin.

The use of simulations combined with the drought index enhances forecast accuracy. The exploratory approach provides the scope for drought warning and the opportunity for financial support and insurance mechanisms for local communities. The most important part related to the reduction of the effects of drought is the improvement of the precision of forecasting systems.

In this research, the precision of the new Anfis Simulation combined with the meta-heuristics of ant colonies in drought prediction is compared to the normal ANFIS Simulation. Simulation performance is estimated using the Mean Squared Error, Mean Absolute Error and Coefficient of Determination.

Validation of the simulation in the three month profile shows that the Absolute Error Value and the Root Mean Square Error in the Zabol Station are lower by Anfis Simulation. This article examines the Compatible Adaptive Neuro Fuzzy Inference System (ANFIS) and ANFIS models with Ant Colony Optimization Algorithm (ANFIS-ACOR) for drought forecasting. Drought forecasting was done using monthly precipitation data from synoptic stations Zabol (1983-2020) and Zahak (1994-2020). The results showed (ANFIS-ACOR) improved the performance of the Adaptive Neuro Fuzzy Inference System. The model predictive correlation test (ANFIS-ACOR) values in the 3, 6, 9 and 12-month intervals are equal to 0.738, 0.854, 0.801 and 0.898 at the Zabol Station. As well 0.792, 0.804, 0.759 and 0.887 at Zahak Station, respectively. In addition, Anfis Simulation-ant colony has a higher coefficient of determination. Validation of the simulation related to the three-month profile of Anfis-ant Colony Simulation showed that the absolute error and the Root Mean Square Error are lower and the coefficient of determination is higher. In the Zahak Station, the Anfis Simulation is more accurate in the 3-month formation, but in the validation section, the Anfis-ant Colony Simulation is superior. Overall, in the three-month SPI profile, Aanfis Simulation training is more accurate, but Anfis Colony Simulation is superior in validation.

Conclusion and Suggestions :

Ant colony optimization as a function of the data population progressively brings the proposed solutions closer to the overall optimal solution. This problem increased the efficiency of the numerical calculation of the ant colony compared with the Anfis in predicting drought. Anfis Simulation in combination with ant colony provides efficiency for large-scale problems in a short time by auto-tuning. These features reduce the cost of calculating natural data with irregular geometric pattern or data recorded in high volume. Simulations which optimize will fail at the local optimum level. Anfis-Ant Colony Simulation evaluates simulation space for predicting droughts on a global scale. This research proposes to compare the Anfis Simulation with other numerical calculations and to choose the best simulation for the Zabol Watershed.

Drought can be considered as a dry period that lasts long enough to cause an imbalance in the hydrological situation. In the calculation of drought parameters, four characteristics of drought intensity, duration, frequency and extent are studied. In general, drought is divided into 4 main categories. The most basic type of drought is defined as meteorological drought, which is caused by a lack of precipitation, an increase in air temperature, and evaporation. In the long run, this phenomenon will lead to hydrological drought and lack of surface and underground water resources, and as a result, agricultural drought and decrease in soil moisture and loss of vegetation. Agricultural drought begins when the amount of moisture in the root of the plant decreases to such an extent that it causes wilting and ultimately the reduction of agricultural products. the severity, volume of damages, the boosting trend of drought and its negative economic, social and environmental effects can be predicted and controlled. So, damages can be Minimize the consequences. Also, remote sensing technology has made it possible to evaluate variable surface phenomena namely, drought. In recent decades, due to the nonlinear nature of the phenomena, artificial neural networks have shown the best ability in modeling and forecasting time series in hydrology and water resources engineering. On the other hand, artificial neural networks are able to identify the nonlinear relationship between input and output variables from the data structure. Drought monitoring in Iran, done through methods based on weather stations, is not accurate due to the lack of a scattered network and lack of access to timely data. Remote sensing technology, along with geographic information system, by creating appropriate spatial and temporal capabilities, has made it possible to evaluate and monitor variable surface phenomena such as drought, so that in the last two decades, the use of methods based on satellite data for Drought monitoring has become one of the first priorities of research and specialized organizations. Drought prediction in water resources systems plays an important role in reducing drought damages. In the last few decades, mathematical models have been widely used to predict drought. These models take time series into account and model processes linearly. In recent decades, due to the nonlinear nature of the phenomena, artificial neural networks have shown the most ability in modeling and forecasting time series in hydrology and water resources engineering.

In this research, a standardized precipitation index (SPI) and the normalized difference vegetation index were used to analyze the correlation in the mountainous climate of Iran. Firstly, monthly rainfall data of 88 meteorological stations from 2000 to 2018 were gathered. After performing the necessary statistical tests, the SPI values were calculated in time scales of 1-, 3-, 6-, and 12-month). Then, OLI sensor images of Landsat 8 satellite with a resolution of 30 m were used to extract the NDVI. These images were obtained from USGS on a monthly basis between 2013 and 2018. In total, 72 months were studied in the entire statistical period. After performing radiometric and atmospheric corrections, an average image was prepared every month for NDVI calculation. Then, a multi-layer perceptron (MLP) neural network was used to predict NDVI data for the next month. Last month's NDVI data and one-month SPI were used as input data to predict the next month's NDVI data during the growing season.

According to NDVI, between 2013 and 2018, May is responsible for the highest amount of vegetation density. In addition, SPI-1 shows the amounts of droughts with more intensity and accuracy than other time steps. Hence, in the mountain region of Iran SPI of the dry season takes a larger amount during the first & last months of the year while during summer, especially in October, drought is much more visible. According to SPI, the return period of droughts is 5-6 years. There is a significant correlation between monthly SPI data and NDVI in the growing season. The highest Pearson correlation coefficient between SPI & NDVI is related to SPI with a 1-month time series and the value of this correlation is much higher in April and May. So, the lack of rain in these months will cause a reduction in growing agricultural products in the spring. 

Artificial neural networks are able to identify the nonlinear relationship between input and output. In this type of simulation, even when the set has disturbance and measurement error, the neural network will be able to provide good results. If there is a change in environmental conditions over time, the neural network will be able to provide new results by adjusting new parameters. NDVI has the highest sensitivity to changes in vegetation and is more useful against atmospheric and soil effects, except in cases where there is little vegetation. In conclusion, for predicting vegetation changes during growing seasons in the pastures of the mountainous climate of Iran, using NDVI data and the monthly SPI data is an efficient process. Therefore, it can be concluded that the neural network is a capable model in relation to agricultural drought prediction.

Affecting the hydrologic cycle, climate change may increase the chances of natural hazards occurrence, including drought, which is considered as one of the most destructive types of such hazards. On the other hand, considering the increasing trend of global temperature and its impact on local climates, climate change is predicted to alter the frequency and intensity of extreme events such as drought. Meanwhile, General Circulation Models (GCMs) have been used in recent decades to predict future climate changes under different emission scenarios, and various drought monitoring indicators have been developed to assess drought. Zarrineh River basin is regarded as one of the main sub-basins supplying the inflow of water to Lake Urmia, which is threatened by numerous long-term droughts. Therefore, as the drying of the lake may bring about a wide range of economic, social, and environmental consequences for the region, monitoring drought and implementing water resources management programs play pivotal roles in preventing the lake to get dried.

Located northwest of Iran, Zarrineh River basin covers an area of 12512 km2, being bounded by Iranian West and East Azerbaijan provinces and the Kurdistan province. This study used the meteorological data, including precipitation rate, and minimum and maximum daily temperature collected from four synoptic stations during the 1990-2018 period to simulate runoff. Also, the monthly runoff rate was collected from five hydrometric stations during the 1996-2017 period to calibrate and validate the SWAT model. Moreover, the data obtained from the general circulation model (HADGEM2-ES) and statistical downscaling methods (CCT) were used to simulate precipitation and temperature under RCP2.6 and RCP8.5 emission scenarios for the future period (2025-2049). Finally, the SPI was applied to evaluate the meteorological drought in the base and the future periods, and the SRI obtained from the outputs of the SWAT model was used to evaluate the hydrological drought.

The data collected from five hydrometric stations were parameterized and calibrated on discontinuous stream networks. Accordingly, it was found that the R2 values varied from 0.52 and 0.70 for calibration and from 0.44 to 0.73 for validation. However, the NSE values varied from 0.52 to 0.64 for calibration and from 0.42 to 0.64 for the validation stage. Moreover, the model’s outputs were found to be satisfactory for most hydrometric stations, indicating the applicability of the SWAT model to the ZRB. On the other hand, based on the CCT model under the RCP8.5, the results of temperature and precipitation variations throughout the 2025-2049 period indicated that compared to the observation period, annual precipitation would decrease by 2.9% in the future period, and the annual minimum and maximum temperature rates would increase by 2.4°C and 3.6°C, respectively. Furthermore, the analysis of the annual temperature and precipitation changes under the RCP2.6 revealed that compared to the observation period, the precipitation rate would increase by 3.6%, and the annual minimum and maximum temperature would increase by 1.8°C and 3°C, respectively. Moreover, the results of the SPI analysis for the future period under the RCP8.5 indicated the occurrence of the extreme drought event. However, while the frequency of severe drought did not change significantly for the future period under both scenarios, the frequency of moderate drought decreased for the future period compared to the base period. On the other hand, the most extreme hydrological drought in terms of the SRI was observed in basin 9 during the base period (equal to -3.02). It was also found that the most hydrological drought occurred in basins 10 and 2 throughout the base period. Furthermore, the most extreme hydrological drought for the future period was found as -4.13 in sub-basin 8 under the RCP8.5, which is greater than that of the base period. Discussion and

The results suggested the satisfactory applicability of the SWAT model for simulating runoff in Zarrineh River basin, as the model considers almost all the physical conditions of the basin for the simulation process, possessing a wide variety of inputs to do so. The results of the analysis of temperature changes for the future period showed that the average minimum and maximum annual temperature would increase in the basin. Moreover, the results of the analysis of annual temperature and precipitation changes under the RCP2.6 revealed that compared to the observation period, the precipitation rate would increase by 3.6%, and the annual minimum and maximum temperature rate would increase by 1.8°C and 3°C in future, respectively. On the other hand, according to the results of SPI and SRI analysis for the future period, it was found that the intensity of meteorological and hydrological drought would increase on average in the basin under both scenarios (RCP2.6 and RCP8.5). Also, the results of the RCP8.5 suggested the possibility of a more severe drought compared to the RCP8.5. Considering an increase in minimum and maximum temperature found for the future period, we can expect an increase in the evaporation rate, probably leading to an increase in the severity of drought and a decrease in water resources of the Zarrineh River basin, which, in turn, will reduce the discharge of the basin’s water flow to the Urmia Lake.

Drought monitoring is an essential component of drought risk management. This paper presents characteristics of droughts simulated by 13 global climate models (GCMs) under two greenhouse gases emission (RCP4.5 and RCP8.5) at three levels of risk (0.25, 0.50, and 0.75) in the Semnan region. For this purpose, the Reconnaissance Drought Index (RDI) and standardized precipitation index (SPI) were calculated for three future periods 2010-2039, 2040-2069, and 2070-2099 at different time scales. The calculated RDI and SPI for different climate change scenarios were compared with those of the past period (1965-2009). The results revealed that as compared to the RCP4.5 scenario, in the RCP8.5 scenario, there will be more dry and wet periods. Drought duration predicted by RDI index compared to SPI index (at 1-year and 3-mon time scales), under both emission scenarios in the future period (2010-2099) has decreased compared to the past period (1965-2009), but the number of droughts has increased. Additionally, increases and decreases in the drought magnitude and severity were found at time scales 3-month and annually, respectively. These results provide observational evidence for the increasing risk of droughts as anthropogenic global warming progresses.

Drought is one of the natural hazards that have random and nonlinear behavior due to its various climatic parameters. SPI index is the most common index extracted from rainfall that has been used in modeling drought by various researchers.

The use of computational intelligence methods to model drought in recent years has been much considered by researchers in the field of water resources. In this research, SVR and GPR algorithms individually and also the combination of these algorithms with wavelet algorithms have been modeled and predicted by SPI index, and the purpose was to evaluate the improvement of computational intelligence algorithms in combination with wavelet. In this research, the time series data of 10 synoptic stations in Iran in the period 1961 to 2017 have been used on a monthly basis for modeling the drought as the input of the studied algorithms.

The results of this study showed that the use of the wavelet method in combination with SVR and GPR computational intelligence algorithms improved the results in all time scales. Also, the modeling improvement is due to the use of wavelet in combination with the SVR model with an average RMSE difference of -0.1540 and R2 difference of 0.1491 and the GPR model with an average RMSE difference of -0.1554 and R2 difference of 0.1530 Compared to the single SVR and GPR models showed that the GPR model in general (all time scales and all stations) had a better improvement in the hybrid model than the single model.

Drought is an unpleasant climatic phenomenon that directly affects different dimensions of human societies. In order to know and choose the right management decision, it is necessary to design and develop an integrated approach to more effectively control this phenomenon and provide early warnings.In this study, twelve various remotely sensed indices of the Moderate Resolution Imaging Spectroradiometer (MODIS) and digital elevation model (DEM) were used to monitor drought during 2000–2018 growing season. Standardized Precipitation Index (SPI) with time scales of 1 to 12 months was used as reference data. The relations between thirteen indices and SPI with different time scales were modulated using machine learning approach. The random forest technique was used to construct a comprehensive drought monitoring model in Ilam Province. Validation data were provided based on relative soil moisture, Standardized Precipitation Evapotranspiration Index (SPEI), and crop yield data. It was observed that random forest produced good applicability (R2 = 0.88) for SPI prediction. In the next step, the Drought Hazard Index (DHI) was generated based on the probability occurrences of drought using the comprehensive drought model which was made in the previous step. The Drought Vulnerability Index (DVI) was calculated by using 7 socioeconomic indices. Finally, the Drought Risk Index (DRI) was obtained by multiplying DHI and DVI for Ilam province. The result of the DRI map showed that 2 Counties are at very high risk of drought, 4 Counties are at high risk and 4 Counties are at moderate and low risk of drought. Overall, the result of our study provides a comprehensive method for assessment of regional drought. Also based on this model, Counties with high vulnerability can be identified to provide timely management programs to help improve the situation.

Due to the lack of facilities and commitment in climate stations establishment in large scale interpolation has become an important technique in spatial mapping studies. In this study, the Kriging interpolation technique as a prevalent technique has been explained and 17 Kriging models were evaluated, in order to select the most suitable one, using a case study. Because of increasing trend of drought in Iran and management necessity of water resources, meteorological drought of Fars province has been investigated in this study. For this purpose the calculated SPI-3 months of the Fars province synoptic stations were calculated. SPI-3 months is widely applied in agricultural drought studies. The empirical semi-variogram of the SPI-3 of the stations was calculated and graphed, fitting 17 Kriging models to select the best one using the cross-validation technique. Results showed that the Linear, Power, Square root, Logarithmic and Gaussian models were the best interpolation techniques for SPI-3 spatial mapping. Geostatistic methods could be used to determine the adequacy of the rainfall gauge stations, especially in the western mountainous region of the province. A complete-length of data could modify the results. The Gaussian model as one of the best-fitted models defines good spatial continuous of precipitation in the region. The semivariograms were fitted to data with the hypothesis of no precipitation trend. Considering the linear and non-linear trends of data, and analyzing error maps may result in better models.

Groundwater is the most valuable water resources in any region and in many arid and semi-arid regions of the world, such as Iran, is the main source for drinking and agricultural needs. In recent years, with the increase in population and as a result of increasing withdrawals from aquifers and climate change, many of aquifers are in poor condition, and these conditions continue or are deteriorating. In this study, in order to determine the effect of climate change on groundwater drought on the aquifer of Shahrekord plain, the output of CMIP6 models and SPI and GRI drought indices have been used. Simulations of GFDL-ESM4 model show that the average rainfall by 2050 in Shahrekord plain, under scenario SSP1-2.6 will increase by 4.85 mm and under scenario SSP5-8.5 will decrease by 21.34 mm. In order to determine the effect of climate change on the aquifer of Shahrekord plain, a regression relationship between the two indices of SPI and GRI in six selected piezometers has been used. The results show that droughts with higher intensity and duration will occur in the region and more than 60 percent of the future period of Shahrekord plain aquifer will be in drought conditions and the most severe drought under SSP1-2.6 scenario will last 52 months and its severity will be 59.32 and under the SSP5-8.5 scenario the most severe drought will last 70 months and its severity will be 86.59.

Drought as a natural hazard significantly impacts various sectors such as agriculture and water resources and causes considerable damage to these sectors worldwide. Therefore, adaptation strategies should be taken to reduce drought damage, and in the meantime, planning and adaptation to drought conditions using drought forecasting is one of the most effective strategies. Due to the need for drought forecasting and the limited studies that evaluated drought indicators obtained from the rainfall forecast output from the North American Multi-Model Ensemble (NMME) in Iran. This study evaluated these models in four catchments of Karkheh, Karun, Heleh, and Hindijan-Jarahi for1982-2018.

In this study, the monthly output of different NMME ensembles were evaluated in the forecast leads of 0 to 9 months from 1982 to 2018, the SPI drought index was calculated. Comparison of these data with GPCC data was used for evaluation. Three quantitative criteria, including correlation coefficient, RMSE, and BIAS, were used for evaluation. Also, to integrate the existing models, two

a: Arithmetic mean between the existing models and B: Weighted average between the models have been evaluated by considering the correlation coefficient (CC) results. Also, two criteria (i.e., POD and FAR) and the quantitative statistical criterion (i.e., correlation coefficient) were used to evaluate the SPI drought index.

The results of the precipitation evaluation of the models showed that the integrated models have better performance than the individual models. In this integrated model, the weighted model also had better performance. Evaluation of spatial distribution of precipitation models also showed the excellent performance of NMME models in Karun and Hindijan-Jarahi catchments in the zero-month forecast lead and Hindijan-Jarahi catchments in the one-month forecast lead. The results of drought index evaluation showed that integrated models, although having better performance in precipitation forecasting, but in drought forecasting, the best performance belongs to NASA-GMAO-062012 and CFSv2 models. The results also showed that drought index forecasts in three and six-month periods have better performance than one month. Spatial distribution evaluation also showed that the models perform better in the southern basins. In general, it can be concluded that NMME models have good performance in predicting drought in some places and specific forecast leads, so they should be evaluated at each point before use.

The results of precipitation evaluation showed that, in general, integrating the output of dynamic models increases its proficiency, and integration in weighted mode (WeightedNMME) performs better than the non-weighted model (NMME). According to the zero-month forecast among individual models, the NASA-GMAO-062012 model has the most skills in terms of the correlation coefficient. However, in the one-month forecast lead in terms of the correlation coefficient, RMSE and BIAS, the best performance belongs to the CFSv2 model. Evaluation of drought indices showed that the model's performance could be different from their performance in predicting rainfall. WeightedNMME, for example, performed well in NASA-GMAO-062012 and CFSv2 months, although they performed well in predicting drought. The spatial evaluation also showed that the southern catchments perform better than other basins.