DROUGHT is a continuous period of lack of rainfall that leads to damage to a variety of water consumers, especially in the agricultural sector and reduces their yield. DROUGHT is considered one of the unpredictable disasters. DROUGHT is different from other natural disasters such as floods, earthquakes, storms, etc. Based on the type of meteorological, hydrological, or agricultural DROUGHTs, various indices are designed to assess DROUGHTs such as SPI, PDSI, and SWSI. The objective of this study is to evaluate an INTEGRATED INDEX that includes the main causes of DROUGHT. The INTEGRATED INDEX includes various DROUGHT factors such as meteorological, hydrological, agricultural, socio-economic, and environmental. Isfahan province has been selected as a case study due to successive DROUGHTs in recent decades. A combination of static and dynamic layers has been used for designing the INTEGRATED INDEX. Static layers include land use, slope, and soil type of the basin. Dynamic layers include precipitation, average temperature, available surface water, available groundwater, groundwater quality, and cultivated area. The results showed that the highest water stress occurred in the 1386 and 1391 years in the province and the lowest water stress and wet season in different parts of the province in 1387 and 1390 years.

DROUGHT is a widespread phenomenon that its intensity varies in time and place. DROUGHT monitoring using meteorological data alone, is not accurate due to small number and inadequate distribution of meteorological stations. Therefore, the use of remote sensing technique and vegetation indices is important as a useful INTEGRATED solution for the study of DROUGHT. The present study was conducted for DROUGHT zoning in Yazd-Ardakan plain by combining meteorological and remote sensing data. In this research, data of meteorological stations of Yazd, Meybod and Mehriz between 2002 and 2015 have been used. First, the Standard Precipitation INDEX (SPI) was calculated in monthly scale for all three stations. The mean values of monthly SPI computed and mapped. Using Landsat images, the SAVI INDEX was calculated and its map was prepared in 16 different times. Due to the role of the temperature in the occurrence of DROUGHT, the (IDI) (INTEGRATED DROUGHT INDEX) was calculated using temperature and precipitation and SAVI INDEX. Zoning of mean SPI values showed that the lowest SPI values are in the middle, north and southeast and the highest values are related to the western heights of the plain. SAVI zoning showed that the rangelands of Meybod and Yazd regions have the lowest density. The correlation coefficient of SPI and SAVI values varies between 0.5 to 0.65, and larger values belong to Mehriz region. (IDI) values are lower in higher rangelands, meaning that these areas have been less affected by DROUGHT. The mean values of (IDI) in Mehriz, Yazd and Meybod regions reach a maximum of 5, 100 and 160. Temporal changes in DROUGHT indices showed that when rainfall increased, the (IDI) INDEX decreased. The possibility of lag time between precipitation and changes in (IDI) was also observed. Comparison of monthly (IDI) values and rainfall showed that the time effect of rainfall on (IDI) INDEX is about three months.

In the recent decades, continuous DROUGHTs in Iran caused a growing withdrawal trend from groundwater resources and reduce in surface water resources, especially storage volume of dams. Therefore, operation of water resources requires reviewing based on hydrologic conditions and especially, DROUGHT. On the other side, for the definition and characterization of DROUGHT by indices, a comprehensive survey of data existence, INDEX applicability and goals in water resources operation is needed. In this article and towards an applicable standardized INDEX in the conjunctive use of water resources, nonparametric functions used to achieve integration and applicability. The status of water resources is calculated via integration of climatic water balance and available hydrologic water by multivariate nonparametric function to obtain INTEGRATED DROUGHT INDEX ((IDI)). In this study, the Zayandehrud River Basin is selected as the case study due to conjunctive use of surface and groundwater and occurrence of recent DROUGHTs in it. Zayandehrud Dam has a key role in streamflow regulation for downstream demands in the basin. With the INDEX tool to determine the condition of the subbasins, dam resource reoperation is followed by the purpose of the minimum DROUGHT intensity. This way of operation resulted in demands supply reliability of more than 90%, Lenjanat aquifer balance, storage increase of Isfahan-Borkhar and Kuhpaye-Segzi aquifers by 400 million cubic meters, supply of Gavkhooni wetland water right with reliability of 96% and mean increase of 2.7 million cubic meters per month in streamflow entering the wetland in comparison with the past. The results underline the importance of the DROUGHT conditions consideration in the conjunctive use of water resources at the basin.

In this study, the efficiency of two fuzzy methods and the INTEGRATED desertification INDEX in Torbat-e-Heydariyeh, Khorasan Razavi province, have been compared using enhanced vegetation indices, vegetation condition INDEX, salinity INDEX, synthetized DROUGHT INDEX, and temperature INDEX for 2000 and 2020. The indices were normalized using maximum-minimum and fuzzy methods and weighted by analytical hierarchical method (AHP). Next, by weighted overlay combination and (IDI) method, the intensity of desertification was determined. The results showed that in the fuzzy method, 45% (1676 Km2) of the region suffers from severe and very severe desertification, and a major section (ie 55%, 2048 sq. Km) has mild and moderate intensity. In comparison, in the (IDI) method, no area fell into the very severe class, but at the same time, 67% (2496 Km2) of the total area fell into the extreme class. Accordingly, although the two methods have classified the area at risk of desertification, but this classification in the fuzzy method has been much stricter than the (IDI) method. The (IDI) method tends to overestimate desertification conditions. The comparison between the measured field data and similar values ​​in the obtained maps showed that the (IDI) method (kappa INDEX of 0. 73) was more compatible with the ground truth than the fuzzy method (kappa INDEX of 0. 54). Therefore, it can be concluded that the (IDI) method, although more efficient, has also overestimated the desertification in the region. Finally, this method is proposed to evaluate desertification in the region against the fuzzy method.

Introduction: DROUGHT as one of the most important environmental stresses and limiting production of crops such as wheat, often leads to a significant decline in production. Identify and use specific physiological traits in breeding programs and also producing cultivars with characteristics consistent with DROUGHT conditions is one of the most important steps in DROUGHT stress studies. The aim of this study was to evaluate the diversity of bread wheat landraces in response to DROUGHT stress based on some agro-physiological traits (grain yield, gas exchange parameters, relative water content, chlorophyll and carotenoid contents) and also to exploring the possibility of selecting superior agro-physiological traits in identify DROUGHT tolerant genotypes using INTEGRATED selection INDEX. Materials and methods: This experiment was conducted on 19 landraces of bread wheat in a randomized complete block design with three replications under normal and rainfed conditions in the Campus of Agriculture and Natural Resources, Razi University, Kermanshah, Iran during 2011-2012 cropping season. Sowing was done by hand at five row plots, 2 m length, and 0. 20 m row spacing as 400 seeds per square meter density. Chemical fertilizers were not used during the research. The weeds control wad done by hand and using 2, 4-D herbicide before stem elongation stage of wheat. Non-stressed plots were irrigated according to regional needs at heading, flowering and kernel filling stages. At full physiological maturity, two middle rows of each plot at both conditions were harvested to determine kernel yield per unit area. Results: The mean of grain yield in irrigated and rainfed conditions were 1651 and 1273 Kg/ha, respectively, means that DROUGHT stress reduced grain yield as much as 22. 86% but it increased proline by 253%. The considerable variation was not observed among genotypes in both conditions for gas exchange traits, except stomatal conductance. All the gas exchange-related traits were reduced due to DROUGHT stress so that the stomatal conductance had the highest reduction by 45%. Genotypes number 4, 9, 18 and 11 were known as tolerant genotypes based on all studied traits using INTEGRATED selection INDEX. In ranking method which calculated based on all studied traits under rainfed conditions, mean rank, standard deviation of ranks and rank sum parameters, genotypes number 5, 2, 15 and 18 with the lowest rank sum were identified as the most DROUGHT tolerant genotypes by consideration the rank of each genotypes for kernel yield under both conditions, the value of studied traits and the INTEGRATED selection INDEX, while genotypes number 3, 9, 13 and 10 as the most sensitive. In examining the relationship between various physiological indicators of DROUGHT tolerance, grain yield under both conditions and INTEGRATED selection INDEX, using Biplot, the PCA1 and PCA2 axes justifed 51. 46 % of total variation. Conclusions: INTEGRATED selection INDEX introduced genotypes 4 (WC-4530), 9 (WC-47381), 18 (WC-47636) and 11 (WC-4566) as the most DROUGHT tolerant. Principal component analysis showed that INTEGRATED selection INDEX (ISI) had close relationship with chlorophyll a, chlorophyll b, carotenoid, leaf temperature, proline concentration, photosynthesis rate, stomatal conductance and grain yield under both conditions, therefore these traits were identified as superior traits for selecting DROUGHT tolerant genotypes using biplot graph.

DROUGHT monitoring is difficult, though it is essential for risk management of this destructive natural disaster. A number of indices which play a major role in detection of DROUGHT severity, its onset and end for monitoring purposes, have been introduced and applied in this regard. This paper compares the capabilities of three DROUGHT indices for the 1997-2000DROUGHTs in Tehran province, Iran. A series of maps of the province are used to illustrate how the indices would have been able to detect the onset of the DROUGHT, as well as monitor its progress on a monthly timescale. The results show that the DJ (Deciles INDEX) is very responsive to the rainfall events of this year, but with inconsistent spatial and temporal variations. The SPI (Standard Precipitation INDEX) shows a slow response to the DROUGHT according to the applied timescale. Finally, the EDI (Effective DROUGHT INDEX), a relatively new DROUGHT INDEX, is able 10 detect the onset of DROUGHT and its spatial as well as temporal variations very consistently, and seems the most promising as an operational INDEX for local and regional DROUGHT watch system in the province. Also, EDI the ability of EDI to detect DROUGHT status on a daily basis is another advantage of this INDEX for the management of DROUGHT risk.

DROUGHT is one of the most important problems which affects agriculture section and water resources. Nowadays, the use of the remote sensing technique has been considered as useful tool for DROUGHT monitoring. This study aimed to predict the temporal DROUGHT using ground station and TRMM3B43 satellite data between the years of 1998-2017. Therefore, precipitation data were first converted to the SPI INDEX, and then, using the intelligent Gaussian Process Regression (GPR) method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD), Tabriz DROUGHT was investigated. Different models were defined and the impact of different input parameters were assessed. It was observed that the rainfall amounts from the TRMM satellite in the monthly scale had a good correlation with the Tabriz station precipitation and the results of DROUGHT analysis using satellite data were almost similar with ground station data. The obtained results proved the high capability and efficiency of the applied method in predicting the SPI DROUGHT INDEX and it was observed that time series decomposition based on the complementary ensemble empirical mode decomposition led to more accurate outcomes. The input data decomposition increased the predictive accuracy by approximately 30 to 40 percent. It was observed that in prediction of DROUGHT the climatic elements including mean monthly temperature and relative hum(IDI)ty, as well as SPI INDEXes related to the previous months, were effective and by climatic parameters eliminating, the modeling error increased up to 15-20%. Also, the results of sensitivity analysis showed that SPIt-1 is the most effective parameter in modeling.

Background and Objective: One of the most important consequences of DROUGHT is reducing the amount of vegetation. Reducing vegetation and environmental conditions lead various problems such as soil erosion, increased runoff levels and flood risk. Accordingly, evaluation of the effects of DROUGHT on vegetation has a great importance. The purpose of this study is to use the DROUGHT Effect INDEX ((IDI)), remotely sensed data and terrestrial data in the study area. Method: The (IDI) INDEX indicates the long-term effects of climate conditions in the study area on the vegetation cover in area. In this study, the (IDI) INDEX combines data of the meteorological stations of Malayer Plain to prepare rainfall and temperature maps (information from 5 synoptic stations inside and outside of study area with a 19-years scale) and a series of Landsat TM satellites and ETM + were calculated for the NDVI vegetation mapping (including 6 images in May and 2000, 2002, 2007, 2009, 2013 and 2015). The zonation map was prepared based on this information. This investigation has been done in May 2019. Findings: The results showed that there is a meaningful correlation between aggregate data ((IDI)) and SPI INDEX, indicates the efficiency of combined data. Results of Pearson correlation showed that there is a significant correlation between the mean SPI and (IDI) of 0. 963 at a significant level of P<0. 01. Discussion and Conclusions: So far, land and satellite data have been widely used for the study of DROUGHTs, but the INDEX derived from the integration of these data has received little attention from researchers, so the aim and innovation of this research is to make it possible to use DROUGHT impact INDEX ((IDI)). Combine terrestrial and satellite data in the study area.