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

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.

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.

Abstract Despite the need to determine the threshold of sustainable use of rangeland ecosystems, especially in drought conditions, no significant progress has been made in recent years, and still time-consuming and costly sampling of species production in a period of 5 to 10 years is used. In this study, to determine and monitor the threshold of sustainable use of rangeland ecosystem, the satellite index and meteorological drought index has been used, while the satellite index was determined by field measurement of canopy cover in a one-year period 2018-2019 in part of the basin of the Karaj river watershed. In this regard, In 3, 6, 9 and 12 month time steps, And the maximum satellite index (NDVI) during the growth period in the target years was determined over a period of 20 years. The results showed that there is a correlation coefficient of about 60% between satellite index and plant density, and in the normal years the satellite index has a correlation coefficient of is about 70% with the index of meteorological drought and 9-month cumulative rainfall leading to the period of maximum growth. Using the permitted harvest coefficients, the operating thresholds for the allowable range of the satellite index were determined and eventually the value of satellite index (NDVI) for the sustainable exploitation of the rangeland was proposed as (0.07459 ± 0.00908) and (0.1799±0.0194) for dry and wet years respectively, is recommended.

In this study, the effects of climate change on runoff and SPI drought index in the Neka River basin in Mazandaran province were investigated. For this purpose, the data of the weather stations of Barikola, Pajim, Sefidchah, Glevard and Tirtash were used. Historical data of the Neka river discharge was obtained from Glevard hydrometeor Station. For this purpose, during the upcoming period (2018-2081), temperature and precipitation were simulated under three RCP2.6, RCP4.5 and RCP8.5 climate scenarios for the Neka river basin. To determine the mean value of climatic parameters of rainfall and temperature, the Thiessen interpolation method was used between meteorological stations. Then drought and runoff values were calculated. The IHACRES model was used to simulate runoff. The results showed that in all three climate scenarios, drought events will increase during the upcoming periods and wet and normal periods will decrease compared to the base period. Based on the findings, during the upcoming periods, the average annual flow of the Neka River under RCP2.6, RCP4.5, and RCP8.5 scenarios decrease between 17 and 44 percent. Thus, the average annual runoff volume of the Naka River at the Glevard dam will be reduced from 95 MCM in 2014 to 53 to 78 MCM. Therefore, in order to reducing the negative effects of the drought period and also reducing the flow of the Neka River, it is necessary to planning more accurate to avoid social, environmental, economic and cultural tensions.

Analysis and comparison of drought indices are several of the studies needed to monitor and evaluate drought by multiple indicators. Drought evaluation is typically performed using climatic and hydrological parameters. So far, several indices have been introduced to the analysis of this phenomenon. This paper in order to monitor drought in Chaharmahal and Bakhtiari province is compared to the Performance of three drought indicators, SPEI, SPI and SPTI. The basis of the comparison of indices is the use of the SOFM neural network, which by use of the results of the topology of this network, is resulted that the drought indices are in one category or not? Subsequently, the frequency analysis of drought classes and all types of drought analysis have been done. The results show that both numerical and SOFM methods can analyze and evaluate the outputs of drought indices, accurately. Also, according to the obtained results, the most severe period of drought had been occurred during the period between 1988 and 1990 in all three drought indices. On the other hand, the most severe drought was happened in this study in September 2003 based on the SPEI index at Behesht Abad Station with a value of -5.9. From other results of this paper can also be referred to the high sensitivity of the SPEI than two other drought indices in calculations.

Drought is one of the main factors of ecological potential stress due to the wide extension of the time scale and impact space. The performance of regional drought monitoring using the meteorological indicators is a challenge due to spatial distribution constraints. In present study, 168 time series images including NDVI and LST indices were used. Integrating these two indices, VSWI was used to assess near-real-time and spatial-temporal spatial drought monitoring in Gavkhoni basin during 2003 to 2014. The SPI was assigned as the basis and the feedback of satellite and meteorological indices was assessed using Pearson correlation test. The results of the study of the relationships between the mean annual values of the VSWI and SPI indices during the same period showed a value of 0.578. In order to study the significance of the indices correlation, a nonparametric Spearman correlation test was added to the study. In addition, the results of the spatio-temporal pattern of the annual VSWI, indicating a severe long-term drought in the southeastern parts of the Zayandehrud dam and the entire Gavkhoni wetland area. The adaptation of this map to the meteorological drought map indicates the efficiency of integrating NDVI and LST satellite indices in drought monitoring.

Drought can be caused by reducing rainfall and/or increasing temperature. Drought has negative impact on water resources and vegetation, accelerates the desertification. In order to investigate the relationship between annual droughts and vegetation changes in southern part of the Yazd province, meteorological drought indices and remote sensing technology were employed. Firstly, annual drought intensities were determined using SPI and RDI indices. Five interpolation methods have been investigated and compared for drought zoning. In the next step, mean annual and seasonal NDVI were calculated using time series of MODIS images of 2000 to 2014 years. Then, relationship between drought indices and NDVI in 16 vegetation types were determined. According to the results, the drought intensity of the study area during time span of 1999-2000 and 2007-2008 were moderate and very high, respectively. Analyzing of correlation between NDVI and drought indices in different vegetation types indicates a significant correlation between annual, spring, and summer NDVI in most of the vegetation types (P

Due to high spatiotemporal resolution of gridded data, reanalysis precipitation databases, have many applications in climate prediction, climate change modeling, water resources management and hydrological modeling, especially in areas without observational data. Therefore, in the present study, hydrological simulation was evaluated by SWAT model and spatial data mining for drought monitoring with SPI and SDI indexes over Maharlu Lake and assessing the temporal sense of Asfazari national database by observational stations as a reference on the spatial extent. The results showed high accuracy of Asfazari database in simulating of runoff in comparison with simulated runoff with observation database. The coefficient of determination and Nash efficiency presented the average accuracy of 0.6 in the simulation. During the cold and rainy seasons, the performance of this database is higher than is the warm season. During rainy months of year, the correlation coefficient between observation and Asfazari is about 0.85, and the POD index is more than 0.9. Furthermore, subsequently the accuracy of monitoring drought by Asfazari is too high, so it can be said that the Asfazari database can be used as a reliable database in runoff simulating and drought monitoring, especially in areas with poor or no sufficient precipitation data.

To predict climate change and its effect on drought future situation in Tamar Basin, first daily output data of CanESM2 model downscaled and predicted by SDSM model and also RCP 2.6 and RCP 8.5 scenarios in the 2020-2049 period. Then, drought conditions evaluated by predicted data and SPI and SPEI indices in the future. Trend analysis of temperature and precipitation variables also carried out by Mann-Kendall non-parametric test. The results of trend analysis showed that precipitation changes is negligible and increase of temperature in most time series is significant. The performance of SDSM model to predict temperature and precipitation data is also very suitable and its outputs showed that temperature and precipitation have increased rather than that in the baseline period. The results of SPI index indicated that in both the periods the most droughts and wets have occurred at the late and first half of the two periods respectively. Evaluation of drought by SPEI index showed more severe drought rather than SPI index and according to increase of temperature trend in the baseline period and also temperature increase in the future can say the results of SPEI index are more actual and logical than the results of SPI index.

In recent years, Urmia Lake faced with declining water level and shrinkig area of Lake due to reduced water inflow from sub basins. The Simeneh Rud and Zarrineh Rud Rivers comprise 51.6 % of the long term total surface water inflow into the Lake. The main aim of this research is investigation climate behavior of basins in view of metrological drought during recent four decates. In this case drought condition was monitored by using Standardized Precipitation Index (SPI) in deferent time scaled in 31 meteorological stations in Southern basins of Urmia Lake. Spatial distribution of drought indices using by geostatics methods including ordinary kriging, Co-Kriging and inverse distance method (IDW) were calculated, and regional drought map zoning were provided for wet and drough years.The results show that studid area experienced two wet period and one drouth period during recent four decates. After domination of 3 years drough and again govering normal condition over basins, drying Urmia Lake was continued because more factors, mainly human activities, beside of climate effects. In many years, the ordinary kriging model was more appropriate than the Co-Kriging and IDW for mapping regional drought.

In this study the climate data (mean annual rainfall and temperature) were examined to investigate possible causes of rain-fed fig orchards drying of Estehban. Precipitation and temperature data for 32-years period 1980-2012 were obtained. Trend test and Mann-Kendall method were used to study changes in precipitation and temperature time series during the statistical period. Drought severity and duration were obtained as well. Results of this study represent no significant trend in the study area average annual precipitation and temperature parameters. However for precipitation of May and temperatures in the months of March, June and July, a significant trend was seen. The results represent no significant trend for precipitation and temperature average in series, annual and monthly in most cases in the study area. Precipitation Changes in stations (Abadeh tashk, Fasa, Aliabad Khafr and Gozon) show decreasing trend in temperature changes at March in stations (Jahan Abade Bakhtegan, Roniz Olia and Do Boneh). Increasing trend and also of drought in the first six months years of With persistence 5 to 7 year was associated Considering to the impact of precipitation distribution on fig Orchards drying. The results of this study show that 36% of trees are affected by the drought. Although in recent years the frequency of mild drought has been more and persistence of drought, with severe and moderate drought years 2007-2008 and 2008-2009 and also an increase in temperature some months leads to drying are rain-fed fig trees.

Today, Climate change issue is one of the main challenges for scientists and due to the critical role of water in human life, the study of climate change impacts on severity and frequency of drought in each region seems to be indispensable (Hulme et al., 1999). Drought is usually occurred over a period of water shortage owing to less rainfall, high evapotranspiration and pumping a huge amount of water from water tables. This issue could have extensive consequences on agriculture, ecosystems and communities. The objectives of this study were to predict meteorological parameters, calculation of drought index and its zoning under the changing climate in Fars province.
In order to predict the future climate in nine districts of Fars province (including Shiraz, Eghlid, Fasa, Lar, Lamerd, Darab, Zarghan, Neiriz and Abadeh), two climate models (HadCM3 and IPCM4) was applied under three scenarios (B1, A1B and A2). LARS-WG software was applied to downscale climate parameters (Semenov and Barrow, 2002). To predict incident probability of drought in the all study locations, a drought index (Standardize Precipitation Index, SPI) was calculated at a time scale of 12 months. SPI is the most commonly used drought index. SPI is calculated based upon the differences between monthly rainfall and average rainfall for a certain period of time according to the time scale (Mckee et al., 1995). In this study the SPI time series have been estimated for the historical base period 1980-1990 and for three future periods (2011-2030, 2046-2065, 2080-2099). Finally, drought maps and zoning were conducted in the whole province using GIS and based on IDW interpolation method.
Results of climate models evaluation indicated that LARS-GW well predicted radiation, and maximum and minimum temperatures (RMSE of 0.51, 0.46 and 1.02%, respectively). However, the accuracy in prediction of rainfall was not as good as the other climatic variables (RMSE of 11.48%). This is mainly due to the fact that there is a high variability in rainfall under arid and semi-arid conditions. Other studies also showed that LARS-WG often over- or underestimate rainfall compared with other climatic variables. According to the simulated aridity index in the baseline period, Abadeh and Lar classified into extreme drought class (-2.48 and -2.09) while under future climate change Lamerd categorized in the extreme drought class. The most severe drought occurred in Neyriz (1.33) using HadCM3 model under A2 scenario in 2080-2099. While, the lowest drought severity obtained in Lamerd (-2.58) using IPCM4 model under A1B scenario in 2046-2065. According to the zoning maps, a vast majority of Fars province had normal climate in the baseline which, are mainly located in southern part of Fars including Neyriz, Darab, Fasa, Lamerd and Eghlid. In contrast, only a limited part of the study locations classified as drought included Abadeh, Zarghan and Lar. Results of t-test also showed that there is no difference between HadCM3 and IPCM4 climate models in terms of future climate prediction (p≥0.05). Results also revealed that for most of study locations, SPI would be in normal class for the all three periods compared with the baseline.
Drought zoning in the baseline in 12 month time scale indicated that the lowest drought was occurred in southern part of Fars while the most severe was observed in both northern areas and some limited part of the south. It was generally concluded that the major part of the Fars province was in normal (the southern half of the province) and moderate class (the northern half of the province) for baseline period according to SPI. However, for projected period, major part of regions would be in normal class. As the Fars province is one of the major producers of cereals in the country, it is estimated the area will benefit from climate change in the future particularly under rainfed conditions.
The results of the current study showed that drought would be intensified under climate change in Fars province and most of the area will benefit from changing climate in the future. However, it is necessary for the authorities to take the results into account, and have applicable water resources management strategies to be able to deal with possible problems in the future decades. Decision makings also should be accomplished with especial considerations to the uncertainties that almost appear in the results.
Acknowledgements: The authors acknowledge the financial support of the project (No. 600/4330 on March 2015) by Vice President for Research and Technology, Shahid Beheshti University, G.C., Iran.

Drought forecasting is an important tool for managers for exploitation of the limited resources of soil and water. Recently, Southern Khorasan has become one of the main centers in the country which suffers from severe drought. This study was aimed to assess the capability of CANFIS for drought forecasting of Birjand area through the combination of global climatic signals with rainfall and previous values of Standardized Precipitation Index (SPI). SPI was used to define and monitor the drought event in monthly time scale. In this study, nine global climatic indices were selected for drought simulation. Using stepwise regression and correlation analyses, the signals NINO 1, NINO 3, MEI, TSA, AMO and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. In this work, for modeling, 41 years of climatic data records (1970-2010) were collected in which 60%, 15%, and 25% of them were extracted to be used in training, cross validation and testing processes, respectively. The momentum algorithm with Gaussian fuzzy membership function was utilized in network training process. Based on the results from stepwise regression analysis, 12 models were extracted for further processing by CANFIS. However, due to the limitation of CANFIS in the execution of training process with the inputs higher than 5, only 8 models were analyzed using CANFIS. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model No. 4, as the model with the lowest error during training and testing processes, NINO 1(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. After that, the variables rainfall, NINO 1(t) and NINO 3(t-6) with the average sensitivity of 0.59, 0.28 and 0.28, respectively could have the highest effect on network performance. According to network performance metrics, it was established that the global indices with a time lag represented a better correlation with ENSO. Finally, the fourth model with a combination of the input variables NINO 1 (with 5 months of lag and without any lag), monthly rainfall and NINO 3 (with 6 months of lag) showed a correlation coefficient of 0.903 (between observed and simulated SPI) and was selected as the most accurate model for drought forecasting using CANFIS in the climatic region of Birjand.

Iran is an arid country with water crisis. Regarding the role of surface water in the region's ecosystems and the impact of drought on water quantity and quality, the present study was conducted to investigate the frequency of droughts in Khuzestan province. For this purpose, the annual rain fall of statistics synoptic stations were used for Dezful, Shoushtar, Behbahan, Abadan, Masjed Soleiman, Ahvaz and Izeh for a period of 20 years. Data correctness and uniformity test were done on the method of test runs and doubly mass. Then, the occurrence frequency and droughts severity was studied based on five indexes namely, analysis of rainfall data (SPI), normal threshold climate Index (RI), reliable rainfall index (DR) and the methods of Niche and PNPI. Results for SPI index showed that in the stations of Ahvaz, Dezful, Abadan, Masjed Soleiman, Behbahan, Izeh and Shoushtar were 7, 6, 6, 6, 7, 9 and 8 cases of droughts, respectively. On the basis of RI index at selected stations were 10, 3, 4, no drought, 4, 5 and 4 cases of droughts.On basis of DR index at the stations of Ahvaz, Dezful, Masjed Soleiman, Behbahan and Izeh 6 cases and at the stations of Abadan and Shoushtar 5 cases of droughts, based on Niche at the stations of Ahvaz and Dezful 5 cases, at the stations of Abadan and Behbahan 4 cases, at the stations of Masjed Soleiman 3 cases, at the stations of Shoushtar 2 cases and at the station of Izeh one case of drought were observed. However, the results of research based on PNPI index showed that at the stations of Bahaman and Izeh 9 cases of droughts, at thestationsof Ahvaz,Masjed Soleiman and Shoushtar 7 cases and at the stations of Dezful and Abadan 6 cases of droughts were occurred. The results showed that in all the studied stations during each year at least 30% drought has occurred.