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In the present study, the spatiotemporal evaluation of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product is performed in capturing meteorological drought over different climatic regions of Iran. The performance of the product as a high spatial resolution dataset in monitoring drought is evaluated against the 68 meteorological stations from short to long scale (i.e., SPI1, SPI3, SPI6, SPI9, and SPI12) in the period of 1987 to 2017. Besides, the capability of the CHIRPS in detecting drought events is assessed in different drought classes. The results suggest that the climate type, the time scale, and the drought class affect the quality of the CHIRPS performance. The CHIRPS offers the best performance in the detection of all drought events with SPI <  - 1 over the SPI1 (0.69 < POD < 0.85). However, the product provides the worst performance for SPI12 (0.50 < POD < 0.70). At the country level, the highest agreement between the CHIRPS- and observation data-based SPI is found over the SPI6 (CC = 0.56), while the lowest is observed over the SPI12 (CC = 0.47). Based on the temporal evaluation, the G6 (0.18 < CC < 0.44, 1.06 < RMSE < 1.28) and G8 (0.17 < CC < 0.43, 1.06 < RMSE < 1.29) regions located in the southern coast of the Caspian Sea have an inadequate performance. However, the southern parts (G4 region) (0.38 < CC < 0.65, 0.83 < RMSE < 1.27) and the northwestern area (G3 region) (0.53 < CC < 0.62, 0.87 < RMSE < 0.97) of the country offer the best performance. The spatial evaluation describes the high accuracy (CC > 0.7, RMSE < 0.5) in some regions, including the western parts of G1, the northern area of G3, and the southern parts of G4. The research findings provided an important opportunity to advance the understanding of drought monitoring over the different climatic regions based on the high-resolution satellite precipitation products., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Drought is one of the natural disasters that causes a great damage to human life and natural ecosystems. The main differences are in the gradual effect of drought over a relatively long period, impossibility of accurately determining time of the beginning and end of drought, and geographical extent of the associated effects. On the other hand, lack of a universally accepted definition of drought has added to the complexity of this phenomenon. In the last decade, due to increasing frequency of drought in Iran and reduction of water resources, its consequences have become apparent and have caused problems for planners and managers. So in this research, regional frequency analysis using L-moments methods was performed to investigate severity and duration of Standardized Precipitation Index (SPI), Standardized Evapotranspiration Index (SEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSI) and to study of meteorological, agricultural, and hydrological droughts in Karkheh River Basin in Iran. Using K-means clustering method, basin was divided into four homogeneous areas. Uncoordinated stations in each cluster were removed. The best regional distribution function was selected for each homogeneous region, and it was found that Pearson type (3) has the highest fit on the data set in the basin. Based on Hosking and Wallis heterogeneity test, Karkheh Basin with H 1  < 1 was identified as acceptable homogeneous in all clusters. The results showed that hydrological drought occurs with a very short time delay in Karkheh River Basin after the meteorological drought, and two indicators show meteorological and hydrological drought conditions well. Agricultural drought occurs after meteorological and hydrological drought, respectively, and its severity and duration are less than the other indicators. Meteorological, hydrological, and agricultural droughts do not occur at the same time in all of the years. In general, the SPI drought index shows the most severe droughts compared with the other three indices. By this way, in 5- to 20-year return period with severity of 3SPI and in 20- to 100-year return period with severity of 7SPI, region IV or the western and northwestern areas of the basin has been affected by severe meteorological drought. By using the regional standardized quantities, it is possible to estimate the probability of drought in any part of the catchment that does not have sufficient data for hydrological studies., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Drought is a gradual phenomenon that occurs slowly and directly impacts human life and agricultural products. Due to its significant damage, comprehensive studies must be conducted on drought events. This research employs precipitation and temperature from a satellite-based gridded dataset (i.e., NASA-POWER) and runoff from an observation-based gridded dataset (i.e., GRUN) to calculate hydrological and meteorological gical droughts in Iran during 1981-2014 based on the Standardised Precipitation-Evapotranspiration Index (SPEI) and Hydrological Drought Index (SSI) indices, respectively. In addition, the relationship between the meteorological and hydrological droughts is assessed over various regions of Iran. Afterward, this study employed the Long Short-Term Memory (LSTM) method to predict the hydrological drought based on the meteorological drought over the northwest region of Iran. Results show that hydrological droughts are less dependent on precipitation in the northern regions and the coastal strip of the Caspian Sea. These regions also have a poor correlation between meteorological and hydrological droughts. The correlation between hydrological and meteorological drought in this region is 0.44, the lowest value among the studied regions. Also, on the margins of the Persian Gulf and southwestern Iran, meteorological droughts affect hydrological droughts for 4 months. Besides, except the central plateau, most regions experienced meteorological and hydrological droughts in the spring. The correlation between droughts in the center of the Iranian plateau, which has a hot climate, is less than 0.2. The correlation between these two droughts in the spring is stronger than in other seasons (CC = 0.6). Also, this season is more prone to drought than other seasons. In general, hydrological droughts occurred one to two months after the meteorological drought in most regions of Iran. LSTM model for northwest Iran showed that the predicted values had a high correlation with the observed values, and their RMSE was less than 1 in this region. CC, RMSE, NSE, and R-square of the LSTM model are 0.7, 0.55, 0.44, and 0.6, respectively. Overall, these results can be used to manage water resources and allocate water downstream to deal with hydrological droughts., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In our original paper (Nat Hazards 76:327-346, . doi:), we analyzed hydrological droughts based on the minimum values of the streamflow drought index at different time scales using L-moments. In this paper, we highlight the importance of the magnitude and type of selected drought index for regional frequency analysis and discuss the validity of results only for station clusters considered in response to the commentary by Bahmani and Khorsandi (Nat Hazards, . doi:). [ABSTRACT FROM AUTHOR]

Drought is one of the main natural hazards affecting large areas' economies and the environment. Therefore, it is necessary to study the different aspects of the drought on the land with several indices like the Standard Precipitation Index (SPI) index which can clarify the existing conditions for decisions and planning. The objective of this study was to analyze the spatial pattern of drought by SPI index in the Fars Province located in the Southern part of Iran. In this paper, according to the data from 42 stations in Fars Province, during 1990-2019, the pattern of drought hazard is evaluated. In the presented model, several drought hazard criteria were used including the maximum severity of drought in the period, the trend of drought, and the maximum number of sequential arid years. The final drought risk map was obtained with an arithmetic mean of 3 criteria: intensity, continuity, and trend. The three criteria maps and final hazard map were interpolated by the Inverse Distance Weightage (IDW) method and classified into five hazard classes none, slight, moderate, severe, and very severe. The obtained maps showed that the intensity, trend, and continuity of drought increases from the southeast to the northwest. The final vulnerability map shows that moderate hazard areas (36% of the region) observed in the Southern parts of the region are less widespread than areas under severe and very severe hazard (64% of the region) observed in the northern and central parts of the region. Preparation of these hazard maps may prove to be helpful for regional managers, and policymakers for environmental and agricultural strategies, not only in Iran but also in other countries facing this hazard. [ABSTRACT FROM AUTHOR]

Drought as a natural phenomenon has always been a serious threat to regions with hot and dry climates. One of the major effects of drought is the drop in groundwater level. This paper focused on the SPI (Standardized Precipitation Index) and SWI (Standardized Water-Level Index) to assess meteorological and hydrological drought, respectively. In the first part, we used different time frames of SPI (3, 6, 12, and 24 months) to investigate drought in Yazd, a dry province in the center of Iran for 29 years (1990-2018). Then, in the second part, the relationship between SPI and SWI was investigated in the three aquifers of Yazd by some rain gauge stations and the closest observation wells to them. In addition to using SPI and SWI, we also used different machine learning (ML) algorithms to predict drought conditions including linear model and six non-linear models of K&#95;Nearest&#95;Neighbors, Gradient&#95;Boosting, Decision&#95;Tree, XGBoost, Random&#95;Forest, and Neural&#95;Net. To evaluate the accuracy of the mentioned models, three statistical indicators including Score, RMSE, and MAE were used. Based on the results of the first part, Yazd province has changed from mild wet to mild drought in terms of meteorological drought (the amount of rainfall according to SPI), and this condition can worsen due to climate change. The models used in ML showed that SPI-6 (score ave = 0.977), SPI-3 (score ave = 0.936), SPI-24 (score ave = 0.571), and SPI-12 (score ave = 0.413) indices had the highest accuracy, respectively. The models of Neural&#95;Net (score ave = 0.964-RMSE ave = 0.020-MAE ave = 0.077) and Gradient&#95;Boosting (score ave = 0.551-RMSE ave = 0.124-MAE ave = 0.248) had the highest and lowest accuracy in prediction of the SPI in all four-time scales. Based on the results of the second part, about the SWI, Random&#95;Forest model (score = 0.929-RMSE = 0.052-MAE = 0.150) and model of Neural&#95;Net (score = 0.755-RMSE = 0.235-MAE = 0.456) had the highest and lowest accuracy, respectively. Also, hydrological drought (reduction of the groundwater level) of the region has been much more severe, and according to the low correlation coefficient of average SPI and SWI (R 2 = 0.14), we found that the uncontrolled pumping wells, as a main factor than a shortage of rainfall, have aggravated the hydrological drought, and this region is at risk of becoming a more arid region in the future., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Although the geographical area historically known as "Persia" has never been properly a "fertile land", water shortage did not represent a problem for the diverse and multiple populations that inhabited the Iranian Plateau throughout millennia. The ancient Persian civilization could flourish thanks to sophisticated water knowledge and water management strategies that allowed it to become the dominating culture of the vast Persian Empire, which by the year 500 BC, extended from the borders of India to the western coasts of Minor Asia and the Caucasus. Even after the fall of the empire, the successive populations could live in arid areas thanks to an ancient system of water provision and management called "Qanat". Qanats not only provided water from an underneath water spring to desert lands and remote areas of the region; they also reflected a specific "water cultural system" based on sharing and managing water as a common good. The paper will discuss how water shortage in present day Iran is, on the one hand, related to a progressive abandonment of the Qanats system, substituted by the use of modern irrigation systems, the privatization of water and the progressive abandonment of the common. On the other hand, this abandonment is related with dramatic cultural change and weakening of community identity, impacting the sustainability of human life in the Iranian Plateau's arid areas. [ABSTRACT FROM AUTHOR]

Agricultural sectors worldwide are under direct threat from climate change conditions. In Iran, agricultural production has decreased due to droughts originating in an increase in annual maximum temperatures—with the corresponding increase in crop respiration and evapotranspiration—and a decrease in accumulated precipitation. Based on a static computable general equilibrium approach, this paper reports implication for the Iranian economy from the effects of climate change on agriculture––as modeled through three scenarios relying on assumptions about the magnitude of continued reduction in total agricultural production. Reductions of 6%, 12%, and 18% in total agricultural production reasonably cover the range of impacts that climate change is expected to impose on the Iranian agricultural sector––under the assumption that no behavioral adaptations or policy interventions are in place. Our simulations suggest that effects on the Iranian economy imply a reduction in GDP ranging between 3.7 and 6.3%. In addition, 5–17% of labor moves away from the agriculture sector––this labor relocation occurs due to declining agriculture incomes. Findings illustrate that climate change will reduce households' consumption and income in all economic sectors, particularly among rural households. We suggest that policies in Iran should focus on improving cultivation methods to save water resources and alleviate the expected effects of climate change. The current study's outcomes are helpful for policymakers, especially in countries with water scarcity. [ABSTRACT FROM AUTHOR]

Climate change has increased the severity and frequency of droughts over the last decades. To alleviate the adverse impacts of droughts, an effective planning and management framework requires high-resolution spatiotemporal data. TRMM multi-satellite precipitation analysis (TMPA) dataset provides sufficient accuracy with fine spatio-temporal resolution. However, it only covers a short temporal span, which limits its applicability for drought studies. This paper presents a methodology for efficient and accurate temporal extension of TMPA using four artificial intelligence (AI)-based models. To improve AI-based model precipitation estimations, fusion techniques including Orness, Orlike, and genetic algorithm (GA)-based weighting methods were employed. Results show that fusion approaches provide more accurate estimates of precipitation. Different timescales of n-SPI time series and drought spatial maps were prepared to visually evaluate the performance of long-term TMPA (LT-TMPA) alongside statistical error indices. The results confirm that this dataset is effective for meteorological drought monitoring over southern Iran. Finally, drought risk assessment was carried out to determine the spatiotemporal characteristics of droughts through severity-duration-frequency (SDF) contour maps. In contrast to the traditional SDF curves, SDF contour maps provide a superior understanding of drought for policymakers since they preserve spatial information., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper proposes a novel approach for drought forecasting based on combining Long Short-Term Memory (LSTM) and Multi-Resolution Analysis Wavelet Transform (MRA-WT), called MRA-WT-LSTM. In fact, Deep Learning (DL) methods provided an outstanding performance in several forecasting fields, especially drought. LSTM has proved a high ability in dealing with time-series drought indices compared to the others existing methods. Therefore, in this study LSTM was used to provide long-term drought forecasts. However, the non-stationarity of the drought indices is a major challenge that needs to be taken into consideration. In this regard, MRA-WT was applied to analyze the non-stationary time-series drought indices. Experiments were carried out in the Sarab region, Iran in the period between 1988–2016 in order to predict the standardized precipitation Evaporation index (SPEI). Drought is the most natural hazards in Sarab region that affect the socioeconomic development. The input data include the station data (i.e. rainfall, temperature (mean, minimum, and maximum), humidity, pressure, and Evaporation) and Normalized Difference Vegetation Index (NDVI) derived from Landsat images. Through the experiment, the proposed methodology is evaluated compared to three ML methods (i.e. Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest Regression (RFR)) and LSTM. The different models were compared in terms of statistical metrics such as the coefficient of determination ( R 2 ), Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results revealed the effectiveness of MRA-WT-LSTM for drought forecasting with R 2 up to 0,93 and RMSE reached 0,02 for the different time lags. [ABSTRACT FROM AUTHOR]

For an effective reservoir operation during drought, the variations of both water supply and water demand which depend on hydrological and meteorological conditions need to be dealt with. This paper aimed to consider these variations in the Aharchay basin (Iran) by coupling a hedging rule (HR)-based reservoir operation model (HRROM) with a climate-based irrigation scheduling model (CBISM) at the farm level. Through the HRROM, optimal long-term decisions for Sattarkhan reservoir were made by considering the probable streamflow scenarios in the system. Given the variable agricultural demands (VAD) in the CBISM, the irrigation water was optimally allocated to the crops using several evapotranspiration (ET) scenarios. The CBISM employs three sub-models including linear programming (LP), nonlinear programming (NLP), and particle swarm optimization (PSO) to maximize the total income of the Aharchay agricultural network as a function of the climate factors and the supplied water. To this end, the daily weather and discharge data from 1990 to 2015 were used in this study. The standardized precipitation-evapotranspiration index (SPEI) and the streamflow drought index (SDI) were used to detect the meteorological and hydrological droughts, respectively. The SPEI was calculated based on the high-resolution-gridded datasets of the Climatic Research Unit (CRU). The findings demonstrated that the HRROM-CBISM generally managed to increase the time-based (αt) and volume-based (αv) reliability indices by 20% and 44%, respectively, compared with the conventional standard operation policy (SOP). For more investigations, the three major droughts of 2000–2002, 2004–2006, and 2008–2014 were separately analyzed. The average values of αt, αv, and vulnerability (V) for SOP were 0.33, 0.51, and 0.48, respectively. With the HRROM-CBISM, these values were about 0.5, 0.55, and 0.45, respectively. Among these indices, αt had the highest variations, while αv had the lowest variations in both the SOP and HRROM-CBISM approaches. The average water shortage for the mentioned droughts was significantly decreased from 89 (SOP) to 75 MCM (HRROM-CBISM). [ABSTRACT FROM AUTHOR]

The present study aims to investigate Iran's agrometeorological drought history and its properties using Multivariate Standardized Drought Index (MSDI). For this purpose, precipitation and soil moisture recorded at 99 synoptic stations in Iran during 1985 to 2018 were selected. Based on MSDI time series, drought properties including duration and severity were calculated and trend analysis was carried on using Trend Free Prewhitening Mann Kendall test. Also, the standard normal homogeneity test was applied to investigate chronological drought change-point detection. From agrometeorological perspective, results showed that longer and more severe droughts are more dominant in southern, southeastern, northeastern, and some western regions of the country. Trend analysis revealed that most study stations have rising frequency and significant drying trend especially in winter at central plateau and western regions. Also, we recognized two main periods 1991–2000 and 2006–2010 that many stations experienced change-point in their MSDI time series, mainly because of climatic and anthropogenic causes. Drought properties assessment shows 6 stations with serious upward trend in drought duration and severity in northeast and western regions. Sens's Slope statistics in these stations was calculated around 1.5 degree per event for drought severity and 1 month per event for drought duration. It is also observed that the 75th quantile of drought severity for most parts of the country is between 13 and 18 and the 75th quantile for drought duration varies between 8 and 24 months in different regions. The probability analysis revealed that most stations have a ratio around 2–5 times increase in drought occurrence probability after change point, leading to longer and more severe droughts in these stations. The paper indicates a serious situation related to agrometeorological drought occurrence, persistence, and severity in Iran because of many climatic and anthropogenic factors over the last 30 years. [ABSTRACT FROM AUTHOR]

Drought is a natural, global and recurring phenomenon caused by climatic anomalies and inevitable meteorological changes. Lake Urmia in northwestern Iran has been drying up in recent years mainly because of its management and operations. On the other hand, with decreasing rainfall, the process of the lake drying up has intensified and affected different aspects of people's lives, especially farmers. In this paper, regional drought was studied in the Urmia Lake Basin from an agricultural perspective in the period of 2001 to 2021 using different vegetation indices such as the Enhanced Vegetation Index (EVI), the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) and the Temperature Vegetation Dryness Index (TVDI) as well as by applying the temperature product and vegetation cover product of the MODIS sensor of the Terra satellite. The efficiency of the methods was evaluated. The results of the linear correlation between drought indices and cumulative precipitation values during 1 month, 2 months, 3 months, 5 months and 7 months before the acquisition of the image showed that the correlation of indices, especially of the TVDINDVI index with the cumulative rainfall of 4 months, showed that the precipitation is more frequent than with other time scales due to the delayed response of vegetation to rainfall. From 2001 to 2020, it was observed that the severity of annual drought decreased according to TVDI values. The area with heavy drought decreased from 4.45 to 1.8% and medium drought decreased from 43.49 to 33%. Also, the correlation coefficient between other vegetation indices and temperature vegetation drought indices (TVDINDVI) showed that TCI and TVDI values were negatively (r = −0.73) correlated. Generally, by observing the calculated TVDI maps, it was seen that this basin has not encountered heavy drought so far. The results showed that the Urmia Lake Basin experienced long-term light and medium drought conditions. On the other hand, by calculating the NDWI index, changes in the area of Lake Urmia were monitored and it was found that the lake surface started to decrease beginning in 2005 and reached its lowest level in 2015, which corresponds to the other droughts that occurred. Finally, by conducting a trend analysis of TVDI, it was observed that there is no specific trend in the drought time series so it can be seen that drought may be one of the causes of a sharp decline in the lake's water level, but not the only factor. [ABSTRACT FROM AUTHOR]

In the last two decades, recurring drought becomes a challenge for Iran's economy, which is located in a drought-prone area, and it has been expected that drought will become more severe and frequent in the future. In this paper, a computable general equilibrium model was applied to give a full scope of drought economic impacts on Iran's economy. In order to model the effects of droughts as a shock to the economy, water entered into the production function as one of the primary factors. Since drought can lead to health problems, the health sector was separated from the service sector to study the effects of drought events on the health sector. The numerical simulations reveal that drought can lead to lower GDP levels up to 7%. Thus, it can have negative impacts on economic growth. Moreover, other factors such as household savings and income can decrease up to 43% and 12% in drought periods, respectively. These adverse effects will lead to a decline in welfare as a socio-economic consequence of drought. Furthermore, demand for health products will rise around 47%; this can refer to some kinds of diseases followed by droughts. Policies and applicable legislation should be introduced to improve water supply usage and develop efficient methods to save water resources, especially in the agriculture sector, which consumes the most significant part of water. [ABSTRACT FROM AUTHOR]

In the original paper, Zamani et al. (Nat Hazards 76:327-346, . doi:) analyzed droughts in arid and semiarid lands based on minimum streamflow values. In that article, drought index values at 3-, 6-, 9-, and 12-month time scales were analyzed by the SDI index and L-moment method. This article points out some problems about the L-moment method, which are discussed and corrected in this discussion. [ABSTRACT FROM AUTHOR]

An extended drought period with low precipitation can result in low water availability and issues for humans, animals, and plants. Drought forecasting is critical for water resource development and management as it helps to reduce negative consequences. In this study, scientometric analysis and manual comprehensive analysis on drought modelling and forecasting are used. A scientometric analysis is used to determine the current research trend using bibliometric data and to identify relevant publication field sources with the most publications, the most frequently used keywords, the most cited articles and authors, and the countries that have made the greatest contributions to the field of water resources. This paper also tries to provide an overview of water issues, such as drought classification, drought indices, historical droughts, and their impact on Asian countries such as China, Pakistan, India, and Iran. There have been many models established for this purpose and choosing the appropriate model for study is a long procedure for researchers. An appropriate, comprehensive, pedagogical study of model ideas and historical implementations would benefit researchers by helping them to avoid overlooking viable model options, thus reducing their time spent on the topic. As a result, the goal of this paper is to review drought-forecasting approaches and recommend the best models for the Asian region. The models are divided into four categories based on their mechanisms: Regression analysis, stochastic modelling, machine learning, and dynamic modelling. The basic concepts of each approach in terms of the model's historical use, benefits, and limitations are explained. Finally, prospects for future drought research in Asia are discussed as well as potential modelling techniques. [ABSTRACT FROM AUTHOR]

Accurate and well-planned forecasts provide critical information for preparedness and mitigation strategies as well as the sustainable practice of water resources conservation. In this paper, an experimental seasonal drought forecasting system has been developed based on meteorological hindcasts, generated by the North American Multi-Model Ensemble (NMME) models. The proposed toolbox comprises (1) NMME data as well as observations, (2) post-processing methods, namely GrandNMME and bias correction methods to statistically post-process precipitation predictions, (3) evaluation metrics of a multi-criteria decision-making method (namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)) to choose the best post-processed improved data, and (4) the Standardized Precipitation Index (SPI) calculator as the central engine, where distribution maps of seasonal drought forecasts are generated. The toolbox has been utilized for the case of Iran. The country is located in semiarid and arid regions of the world, facing considerable water crisis including droughts. Results indicated that the proposed NMME-based drought forecasting toolbox has a significant skill in forecasting droughts over the study area and provides critical information for early warnings, medium-term response planning and taking preventive measures. [ABSTRACT FROM AUTHOR]

Drought is a major global environmental challenge. It affects the livelihoods of many people, mainly in developing countries. Iran is one of the most affected and vulnerable countries in the Middle East to drought. In this paper, we present a microlevel analysis by employing the Tobit multiplicative heteroscedasticity regression to examine the effects of drought on small farm household education expenditures in rural Iran. We collected primary data from a sample of 300 smallholders in Marvdasht County in Fars Province of Iran. The results revealed a negative relationship between the farm income and education expenditures. This means that the farm households in rural Iran that were affected by the drought increased their expenditures on education for their children. The analysis of income elasticity indicated that a one percent decrease in farm income led to a 0.86% increase in education expenditures, which indicated that the education expenditures were necessary. Furthermore, we observed that in drought-affected families, girls were more likely to be pulled out of university education than were boys; however, for school education, there were no significant differences between the boys and girls. Our findings revealed the need to provide improved facilities and further finances for education expenditures, especially for female university students, and to formulate environmental management policies that include the provision of education facilities by the government of Iran in drought-affected villages. Our findings also shed light on the presence of positive externalities and the important role of education in helping rural households better cope with the negative repercussions of drought on their livelihoods., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The transboundary Helmand River basin (HRB) is the main drainage system for large parts of Afghanistan and the Sistan region of Iran. Due to the reliance of this arid region on water from the Helmand River, a better understanding of hydrological-drought pattern and the underlying drivers in the region is critically required for effective management of the available water. The objective of this paper is therefore to analyze and quantify spatiotemporal pattern of drought and the underlying processes in the study region. More specifically we test for the Helmand River basin the following hypotheses for the 1970–2006 period: (1) drought characteristics, including frequency and severity, systematically changed over the study period; (2) the spatial pattern and processes of drought propagation through the Helmand River basin also changed; and (3) the relative roles of climate variability and human influence on changes in hydrological droughts can be quantified. It was found that drought characteristics varied throughout the study period but largely showed no systematic trends. The same was observed for the time series of drought indices SPI (standard precipitation index) and SPEI (standardized precipitation evapotranspiration index), which exhibited considerable spatial coherence and synchronicity throughout the basin, indicating that, overall, droughts similarly affect the entire HRB with few regional or local differences. In contrast, analysis of the SDI (streamflow drought index) exhibited significant negative trends in the lower parts of the basin, indicating an intensification of hydrological droughts. It could be shown that with a mean annual precipitation of ∼ 250 mm yr -1 , streamflow deficits and thus hydrological drought throughout the HRB are largely controlled by precipitation deficits, whose annual anomalies on average account for ±50 mm yr -1 , or ∼ 20 % of the water balance of the HRB, while anomalies of total evaporative fluxes on average only account for ±20 mm yr -1. Assuming no changes in the reservoir management practices over the study period, the results suggest that the two reservoirs in the HRB only played a minor role for the downstream propagation of streamflow deficits, as indicated by the mean difference between inflow and outflow during drought periods, which did not exceed ∼ 0.5 % of the water balance of the HRB. Irrigation water abstraction had a similarly limited effect on the magnitude of streamflow deficits, accounting for ∼ 10 % of the water balance of the HRB. However, the downstream parts of the HRB moderated the further propagation of streamflow deficits and associated droughts because of the minor effects of reservoir operation and very limited agricultural water in the early decades of the study period. This drought moderation function of the lower basin was gradually and systematically inverted by the end of the study period, when the lower basin eventually amplified the downstream propagation of flow deficits and droughts. Our results provide plausible evidence that this shift from drought moderation to drought amplification in the lower basin is likely a consequence of increased agricultural activity and the associated increases in irrigation water demand, from ∼ 13 mm yr -1 at the beginning of the study period to ∼ 23 mm yr -1 at the end, and thus in spite of being only a minor fraction of the water balance. Overall the results of this study illustrate that flow deficits and the associated droughts in the HRB clearly reflect the dynamic interplay between temporally varying regional differences in hydro-meteorological variables together with subtle and temporally varying effects linked to direct human intervention. [ABSTRACT FROM AUTHOR]

Drought is one of the most frequently occurring natural disasters and results in long-term social, economic and environmental impacts that affect large areas and populations. Over the past decade, Iran has experienced a very severe drought that has affected millions of people. In this paper, we employ a standard consumption regression model to perform a microlevel analysis of the effects of drought on small-farm household health expenditures in rural Iran. We surveyed a sample of 300 small-farm households in the Marvdasht County in Fars Province, Iran. The results showed that drought significantly negatively affects smallholder health spending. Farm households that are affected by severe drought decrease their health expenditures more than less-affected households. Further, for drought-affected farmers, health expenditures are a necessity, as the income elasticity of health expenditures is as low as 0.15. Our findings indicate the importance of designing and implementing a health policy that includes the provision of healthcare services in drought-affected villages by the government, which plays an important role in coping with the health effects of drought. [ABSTRACT FROM AUTHOR]

Water stress or more specifically drought assessment plays a key role in water management, especially in extreme climate conditions. Basically, globally gridded satellite-based precipitation products are potential sources of data as alternatives for ground-based measurements. However, for a reliable application, they should be evaluated in different regions. In this paper, two satellite-based rainfall products, namely Modern-Era Retrospective Analysis for Research and Applications (MERRA)-Land and Global Land Data Assimilation System-2 (GLDAS-2), have been evaluated against ground-based observations in terms of precipitation and their application for drought analysis. At first, the coarse-resolution MERRA-Land is downscaled to the finer resolution of interest for better comparison. After comparison of these datasets against ground-based observations in terms of precipitation, it is concluded that MERRA-Land can better estimate precipitation. Then, the nonparametric SPIs at various timescales are derived to analyze how well MERRA-Land performs in drought monitoring. Different categorical and statistical error indices are used to assess the efficiency of MERRA-Land in capturing drought events. The results revealed that the downscaled MERRA-Land data can properly detect short-term and mid-term drought events known as agricultural and meteorological droughts throughout the study area, respectively. In addition, drought maps show that the majority of lands experience mid-term scale drought which are in agreement with ground-based observations. The methodology adopted in this study can be applied in areas lacking in rain-gauge stations which significantly extend current capabilities for drought monitoring and early warning systems. [ABSTRACT FROM AUTHOR]

In this paper, a new approach is proposed based on the Fuzzy-nearest neighbor model to deal with drought monitoring. According to the Standardized Precipitation Index and via Fuzzy-kNN approach, a method has been presented to predict the most likely drought conditions. In order to appraise the precision of results, the model was applied to monitor the drought status in city of Kerman, located in south east of Iran. The results showed that the area has faced drought and also rainfall shortages in recent years. The calculated values of correlation coefficient, RMSE, CRM and MAE coefficients showed the accuracy and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Historically droughts have had a huge impact on conditions of life on earth. With growing population of the Earth the impacts of droughts grow even more severe. Human beings are also affected by drought like other life forms. Studies have taken place to understand and thereby mitigate the effects of these climatological phenomena on humans. The study of drought trends is of great importance since it is directly related to food and water resources availability, that could become scarce in case of droughts. In this paper by using SPI, SSI and TSDI indices we try to understand the drought behaviour in Iran using Mann-Kendall and Sen's slope methods. Canonical correlation analysis between SPI and SSI shows a positive correlation for the most parts of Iran. Also the correlation between TSDI and SPI/SSI is positive for majority of the Iran. Drought trends in Iran are analysed using Mann-Kendall and Sen's slope estimator with 95% confidence level. [ABSTRACT FROM AUTHOR]

The risks of climate change are increasing, and their impacts seem inevitable on the livability of climate-vulnerable urban areas. In this regard, the present study aims to investigate the effects of climate change on the level of livability in Sistan and Baluchistan province, located in the southeast of Iran, which is facing severe climatic tensions. In this paper, daily climate data of temperature, humidity, wind, frost, and precipitation from 2000 to 2020 were analyzed temporally and spatially. From 2010 to 2019, the livability of this province's cities was also evaluated using the multi-criteria decision-making method. Geostatistical analysis methods were also applied to understand the relationship between climate and livability. The results showed that climatic drivers strongly influence the provinces of Sistan and Baluchistan. The northern regions of the province had the highest number of drought, frost, and windy days compared to the southern ones. But, the average humidity and temperature of the central and southern regions were assessed higher than the northern ones. Therefore, the above climatic features exerted different effects on the livability process. The livability situation results indicated that the cities' livability level was different in the studied years. Many cities had a downward trend. Also, based on spatial statistics analysis, the distribution of livability at the province level was uneven and clustered. In addition, the evaluation of the effect of climatic drivers on the livability of cities illustrated that there is a strong correlation between the humidity driver and the trend of livability (with a correlation of 0.35). However, the drivers of temperature, wind, frost, and drought (with correlations of 0.06, 0.02, 0.01, and 0.02) individually did not have a significant relationship with the livability trend. However, a combination of temperature-humidity (with a correlation of 0.3) and drought-humidity (0.31) drives greatly impacted the livability of the province's cities. [Display omitted] [ABSTRACT FROM AUTHOR]

Approximately 97% of the country is experiencing drought conditions. Due to gross water mismanagement and its damaging impact on the country, Iran faces the worst situation in water resources of any industrialized nation. Tens of thousands of villages have been deserted and most of the major urban centers have passed their limits to absorb new rural migrants. Some officials predict that in less than 25 years, 50 million Iranians would be displaced from their current homes because of the pressing ecological conditions. This is happening at the time that the gap between the grassroots and the state has widened and there is increasing dissatisfaction with governance plagued by corruption, nepotism, economic mismanagement, unaccountability and a foreign policy which has produced various regional and trans-regional adversaries. This paper evaluates the pressing ecological challenges in Iran and by conceptualizing political resilience it critically evaluates whether the Islamic Republic is prepared to face the devastating ecological crisis and its socio-economic consequences. [ABSTRACT FROM AUTHOR]

Abstract: In recent decades, extensive land transformations and environmental and climate change events such as floods and droughts, increasing heat waves, and forest fires have been observed in Iran. Monitoring and intensity analysis of the land cover dynamics and land degradation in Iran are lacking due to the lack of fine resolution land cover data. In this paper, we explore the type, extent, and intensity of land transformations and the intensity of transitions among land cover classes from 2000 to 2010 using GlobeLand30 land cover data. Our results indicate that approximately 35% of Iran changed during 2000–2010, due mainly to the active gain of barren land. Furthermore, the increase in barren targeted grass and shrub. Barren expansion in Iran is alarming because most of the country is located in arid and semi‐arid regions. Iran has actively participated in desertification combat plans, and thus, it is critical to explore extra intervals of land dynamics. This will help to evaluate the temporal rate of land degradation at multiple intervals and assess the effectiveness of desertification management strategies. Additionally, investigating the role of climate and human‐made interventions into the type and extent of land transformation is recommended. [ABSTRACT FROM AUTHOR]

Effective monitoring of drought plays an important role in water resources planning and management, especially under global warming effect. The aim of this paper is to study the effect of air temperature on historical long-term droughts in regions with diverse climates in Iran. To this end, monthly air temperature (T) and precipitation (P) data were gathered from 15 longest record meteorological stations in Iran covering the period 1951-2014. Long-term meteorological droughts behavior was quantified using two different drought indices, i.e. the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Linear and non-linear trends in T, P, SPI and SPEI were evaluated using non-parametric and parametric statistical approaches such as non-modified and modified Mann-Kendall Test, Theil-Sen approach, and simple regression. The results indicated that the significant trends for temperature are approximately all increasing (0.2 °C to 0.5 °C per decade), and for precipitation are mostly decreasing (−7.2 mm to −14.8 mm per decade). It was also indicated that long-term drought intensities monitored by the SPI and SPEI have had significant downward trend (drought intensification with time) at most stations of interest. The observed trends in the SPI series can be worsen if air temperature (in addition to precipitation) participates in drought monitoring as SPEI. In arid and extra arid climates, it was observed that temperature has strong effects on historical drought characteristics when comparing the SPI and SPEI series. Due to the determinative role of temperature in mostly dry regions like Iran, the study suggests using the SPEI rather than SPI for more effective monitoring of droughts. [ABSTRACT FROM AUTHOR]

This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P, T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates. [ABSTRACT FROM AUTHOR]

Water scarcity is a critical abiotic stress factor for plants in arid and semi-arid regions, impacting crop development and production yield and quality. Monitoring water stress at finer scales (e.g., farm and plant), requires multispectral imagery with thermal capabilities at centimeter resolution. This study investigates drought stress in pistachio trees in a farm located in Yazd province, Iran, by using Unmanned Aerial Vehicle (UAV) images to quantify evapotranspiration and assess drought stress in individual trees. Images were captured on 10 July 2022, using a Matrix 300 UAV with a MicaSense Altum multispectral sensor. By employing the Surface Energy Balance Algorithm for Land (SEBAL), actual field evapotranspiration was accurately calculated (10 cm spatial resolution). Maps of the optimum crop coefficient (Kc) were developed from the Normalized Difference Vegetation Index (NDVI) based on standard evapotranspiration using the Food and Agriculture Organization (FAO) 56 methodology. The comparison between actual and standard evapotranspiration allowed us to identify drought-stressed trees. Results showed an average and maximum daily evaporation of 4.3 and 8.0 mm/day, respectively, in pistachio trees. The real crop coefficient (Kc) for pistachio was 0.66, contrasting with the FAO 56 standard of 1.17 due to the stress factor (Ks). A significant correlation was found between Kc and NDVI (R2 = 0.67, p < 0.01). The regression model produced a crop coefficient map, valuable to support precise irrigation management and drought prevention, considering the heterogeneity at the farm scale. [ABSTRACT FROM AUTHOR]

In recent years, droughts have become more intense and frequent in arid and semi-arid regions like Iran on the one hand, and water demand has been rising on the other hand and, as a result, their impacts are being aggravated. Therefore, the meteorological and hydrological droughts are receiving much more attention. This paper focused on the meteorological and hydrological drought characteristics for the overlapping periods of 3, 6, 9, and 12 months in northwestern Iran over the period of 1981-1982 to 2010-2011. The results showed that the majority of drought events over the reference periods were in the last 15 years from 1995-1996 to 2010-2011. Furthermore, the driest year based on the meteorological drought index was 2007-2008, while it was detected to be 2010-2011 based on the hydrological drought index. The Spearman's rho and Kendall's tau tests were used for the temporal trends analysis of the meteorological and hydrological droughts. The decreasing time series trends were more evident for the streamflow droughts index than for the standardized precipitation index series. In general, the results of the meteorological and hydrological drought trends showed that the study area suffered from the hydrological drought more than meteorological droughts. Moreover, the results revealed that the study area has become drier during the last three decades. Finally, the Spearman correlation analysis was applied to explore the relationships between meteorological and hydrological droughts which indicated a strong correlation between May-Jul-SPI series and the Jun-Aug-SDI series with a value of 0.65. [ABSTRACT FROM AUTHOR]

Multi-index drought indices are based on a combination of single drought indices and able to monitor the overall status of drought in a certain location. The aim of the present paper is to compare two recently developed multi-index drought indices, i.e., Joint Deficit Index (JDI) and Multivariate Standardized Precipitation Index (MSPI) under different climatic conditions of Iran. Although these drought indices are calculated using different statistical methods, they need twelve modified Standardized Precipitation Index (SPImod) corresponding to 12 time windows from 1 to 12 months as the input variables. In this study, these indices were calculated using the monthly precipitation data of 42 weather stations representing diverse climates of Iran during the period of 1966-2010. JDI and MSPI were compared from different viewpoints including temporal behavior, the relationship between drought severity and duration and frequencies of drought classes. The results showed that both indices have a similar trend of drought severity at all stations. Unlike the MSPI, the JDI faced two substantial problems in all stations of interest: 1) The lowest value of the JDI time series was frequently repeated in different parts of its time series, 2) The JDI was not able to identify the peak of drought severity during the critical drought periods. While the JDI could not identify the extreme (at about all stations) and very severe (at one third of stations) drought classes, the observed frequencies of the MSPI's drought classes successfully matched the corresponding expected frequencies. The obtained results clearly indicate the superiority of the MSPI for monitoring overall status of droughts in different climatic conditions of Iran in comparison to the JDI. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate characteristics of meteorological and agricultural droughts and their trends in Iran, as well as several subregions with different climate conditions from 1980 to 2013. The Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) are used as the primary indicators of meteorological and agricultural droughts, respectively. This study assesses historical droughts using the Multivariate Standardized Drought Index (MSDI), which provides a composite model of meteorological-agricultural drought. Furthermore, this study discusses the behavior of MSDI relative to the other indices (SPI and SSI) over different climatic conditions ranging from humid, semiarid, and hyperarid regions. The Mann-Kendall trend test shows that the northern, northwestern, and central parts of Iran have experienced significant drying trends at a 95 % confidence level. However, no statistically significant drying trend was observed in the eastern part of Iran. The most severe drought across the country occurred between 1998 and 2001, with approximately 80 % of the country experiencing an exceptional drought (<2 % probability of occurrence). This event coincided with a prolonged cold phase El Niño-Southern Oscillation (La Niña) that led to persistently cold sea surface temperatures in the eastern Pacific and warm sea surface temperatures in the Indian and western Pacific. [ABSTRACT FROM AUTHOR]

In this paper, the effects of climate change on nationwide Iranian climate and water resources were investigated by a trend analysis of temperature, precipitation and river flow. The Mann-Kendal trend test was applied to the time series of monthly temperature and precipitation derived from satellite images. The mean monthly stream-flow data associated with representative stream gages of 6 major Iranian catchments was investigated for detecting any possible trend. Furthermore, the standardized precipitation index (SPI) was computed with the observed precipitation relative to 44 selected stations distributed throughout Iran. The results suggested a long-term increasing trend in temperature all over the country, while a statistically significant decreasing trend in precipitation was observed for north western and south eastern Iran. The results also suggested a decreasing trend in the available water resources across the country which is in agreement with the negative trend of satellite derived precipitation detected for some parts of the country. A decreasing trend was also observed in the SPI index indicating that the occurrence of severe/extreme long lasting droughts in some parts of the country might be attributed to the effects of climate change and global warming. [ABSTRACT FROM AUTHOR]

To assess the response of promising safflower genotypes to late-season drought stress in delayed planting conditions, an experiment was conducted in two years (2016-2017 and 2017-2018) in Iran. The irrigation regime was specified in two levels, including normal irrigation and irrigation cut off in the seed filling stage in main plots, and five safflower genotypes, including Soffe, Goldasht, Golmehr, Padideh, and Parnian were categorized in subplots. Applied drought stress significantly reduced the seed yield and yield components of all genotypes, which accompanied with a substantial decrease in oil content and oil yield of all genotypes. However, the highest seed and oil yield in drought stress conditions obtained in the Parnian genotype by value 2338 and 561 kg/ha, respectively. Moreover, a significant drought-induced increase in palmitic, stearic, and oleic acids, as well as a decrease in linoleic acid content, was observed in all genotypes. Parnian genotype with high unsaturated fatty acids content (90.9%) and the minimum amount of saturated fatty acids (8.7%) might be a promising genotype to starting a formal crop improvement program to achieve more drought-tolerant safflower genotype. [ABSTRACT FROM AUTHOR]

Drought is one of the most common natural phenomena. Many indices using multiple data types have been created, and their success at recognizing periods of extreme wetness and dryness has been well documented. The merit of the method is the utilization of terrestrial water storage (TWS) variations from Gravity Recovery and Climate Experiment (GRACE) quantification of drought intensity. Alongside with these observations, we add precipitation data to equations. In this study, we analyze Merged-dataset Drought index (MDI) using GRACE-derived TWSA and precipitation in Iran, where most of the area is desert and mountain in the middle and South of the country. Our sample period is from January 2003 to December 2014. MDI shows a strong correlation with existing drought indices, especially with the Palmer Drought Severity Index (PDSI). Based on the obtained results, MDI indicates a moderate Drought event in 2008 and 2012–2015, which is compatible with the recorded result of PDSI. The longest drought took 22 months (from January 2008 to October 2009). Interestingly, the coefficient of correlation between MDI and PDSI is 0.67. [ABSTRACT FROM AUTHOR]

The main problem using meteorological drought indices include inappropriate distribution of meteorological stations. Satellite data have reliable spatial and temporal resolution and provide valuable information used in many different applications. The Standardized precipitation index has several advantages. The SPI is based on rainfall data alone and has a variable time scale and is thus conducive to describing drought conditions for different application.This study aims to calculate SPI using satellite precipitation data and compare the results with traditional methods. To do this, satellite-based precipitation data were assessed against station data and then the standardized precipitation index was calculated. The results have indicated that satellite-based SPI could illustrate drought spatial characteristic more accurate than station-based index. Also, the standardized property of the SPI index allows comparisons between different locations, which is one of the remote sensing drought indices limitations. [ABSTRACT FROM AUTHOR]

To assess the response of winter oilseed rape promising line genotypes to late-season drought stress in delayed cultivation conditions and select the superior genotypes, an experiment was conducted for two years (2015-16 and 2016-17) in Iran (Karaj). In this experiment, the sowing date was specified in two levels including timely cultivation (October 7) and delayed cultivation (October 27) and irrigation factor including normal irrigation and irrigation interruption from podding stage as factorial in main plots and four winter oilseed rape genotypes (L1030, L1204, L1110, and L1114) and a commercial cultivar (Okapi) were categorized in subplots. Applying drought stress after the podding stage declined the seed yield and seed oil yield, and the highest and lowest mean of these traits were observed in the L1204 and L1114 genotypes, respectively. The interaction effect of the sowing date × genotype on all the studied traits was significant. With the standard erucic acid, the genotype L1204 in both normal and delayed sowing dates had the highest seed yield of 5118 and 3015 kg/ha. Besides, with high oleic acid with a mean of 63.65% and the minimum amount of glucosinolate of seed with a mean of 21.55 µmol/g, this genotype is recommended in delayed cultivation conditions. [ABSTRACT FROM AUTHOR]

This study aims to employ joint modeling for drought and dust hazards. Long-term monthly precipitation and temperature were used to calculate drought characteristics based on the standardized precipitation- evapotranspiration index. Three-hourly horizontal visibility and wind erosion event codes were also obtained to detect dust events. The results of drought hazard assessment indicated that about 52% of the total area of Iran was affected by moderate hazard. Other classes of drought hazard i.e., severe, low, and very severe, covered approximately 24, 18, and 6%, respectively. The assessment of dust hazard was conducted by weighting the frequency of dust events based on the horizontal visibility. The findings on dust hazard showed that approximately 18% of the total area of Iran was affected by the more intense hazard, which covered the southeastern sectors. Bivariate copulas were considered for joint modeling of drought and dust hazards. The copulas of Student's t, Gaussian, Gumbel, and others were applied to construct a joint model. Bivariate assessment of drought and dust hazard illustrated that the eastern part of Iran, which covers around 47% of the country, exposed at greater hazard than other parts. The copula-based conditional probability was also obtained to quantify the probability of dust occurrence under different drought hazard levels. The conditional probability values showed that by intensification of drought conditions the probability of dust hazard increased. In addition the frequency of dust occurrences increased when the region faced very severe droughts. Generally, the prolonged and severe droughts were identified as the most important reasons for the increased hazard of dust and drought in the study region. This study results indicated that the joint risk of drought and dust hazard along with vulnerability factors, can be used for possible mitigation of the risks of these natural hazards. [ABSTRACT FROM AUTHOR]

The water level of the Urmia Lake Basin (ULB), located in the northwest of Iran, started to decline dramatically about two decades ago. As a result, the area has become the focus of increasing scientific research. In order to improve understanding of the connections between declining lake level and changing local drought conditions, three common drought indices are employed to analyze the period 1981–2018: The Standard Precipitation Index (SPI), the Standard Precipitation-Evaporation Index (SPEI), and the Standardized Snow Melt and Rain Index (SMRI). Although rainfall is a significant indicator of water availability, temperature is also a key factor since it determines rates of evapotranspiration and snowmelt. These different processes are captured by the three drought indices mentioned above to describe drought in the catchment. Therefore, the main objective of this paper is to provide a comparative analysis of drought over the ULB by incorporating different drought indices. Since there is not enough long-term observational data of sufficiently high density for the ULB region, ECMWF Reanalysis data version 5(ERA5) has been used to estimate SPI, SPEI, and SMRI drought indicators. These are shown to work well, with AUC-ROC > 0.9, in capturing different classes of basin drought characteristics. The results show a downward trend for SPEI and SMRI (but not for SPI), suggesting that both evaporation and lack of snowmelt exacerbate droughts. Owing to the increasing temperatures in the basin and the decrease in snowfall, drought events have become particularly pronounced in the SPEI and SMRI time series since 1995. No significant SMRI drought was detected prior to 1995, thus indicating that sufficient snowfall was available at the beginning of the study period. The study results also reveal that the decrease in lake water level from 2010 to 2018 was not only caused by changes in the water balance components, but also by unsustainable water management. [ABSTRACT FROM AUTHOR]

Agricultural drought refers to soil moisture deficit. It can directly impact crops and adversely affect various economic sectors. Hence, its accurate and precise prediction can help develop plans for agricultural drought and reduce crop damage. This research used a long short-term memory (LSTM) model for estimating drought over Iran during spring and summer. The predictions were made based on the large-scale indicator named El Nino-Southern Oscillation (ENSO), normalized difference vegetation index (NDVI), and meteorological parameters (i.e., precipitation and temperature). For this purpose, the agreement between NDVI, meteorological parameters, and ENSO was studied at time lags of 1–4 months to understand the agricultural drought predictors. The calculated RMSE and NSE of the LSTM model revealed that the model showed a better performance in predicting NDVI values in semi-arid and arid areas such that the RMSE and NSE of the LSTM model are less than 0.3 and higher than 0.8, respectively. Furthermore, the prediction performance in humid and semi-humid areas was better in spring than in summer. The average RMSE across Iran was 0.4 in the spring, whereas the RMSE increased to 0.78 in the summer. In addition, precipitation deficit exhibited its effects on agricultural drought at a time lag of 1 month, whereas ENSO caused agricultural drought at longer time lags. Moreover, In April, agricultural droughts in the north, northwest, and southwest of Iran occurred under the ENSO effects. In northern regions (Caspian Sea coasts), the correlation between agricultural drought and ENSO is close to 0.1. The correlation equals 0.8 in the west and northwest of Iran. ENSO had the greatest impact on NDVI in these regions in April, with a 1 month delay. These findings can be exploited for issuing agricultural drought warnings in Iran. [ABSTRACT FROM AUTHOR]

Drought is a significant aspect of Iran's climate, affecting both dry and wet regions. This study examines the river flow data from four hydrometric stations in the Zarrinehrood Basin (Dareh-Panbedan (DP), Pol-Anian (PA), Sonateh and Nezamabad) to analyze and forecast hydrological drought in the present and future periods, considering the impact of climate change. Additionally, the study aims to account for and mitigate the dam's effect on one of the studied stations. The analysis of river flow changes at the studied stations indicated a decreasing trend over the study period. To predict the hydrological drought index for the study area, the SDI index was utilized for reference periods of 3, 6, 9, and 12 months, along with climate change predictors derived from the CanESM2 climate model as outlined in the fifth IPCC report. The CARMA model and climate predictors were employed to simulate and forecast river flow for future periods. The results of the CARMA model investigation for simulating river flow in the test phase indicate that the error value at DP station is 4.07, at PA station is 6.46, at Sonateh station is 4.07, and at Nezamabad station is 70.14 cubic meters per second. These findings suggest that the hydrological drought in the studied basin is expected to worsen in the coming years. [ABSTRACT FROM AUTHOR]

Water has been the source of life since it was found. In recent years, the false assurance of the permanence of water services in Iran has led to the creation of institutional structures related to water in an inappropriate and incompatible manner with environmental changes, and when water crises occur, such as droughts, floods, and, on a larger scale, climate changes, they do not have the flexibility and resilience to these changes. To this end, recognizing the current water governance system in the country is considered an essential need. The present study examines the regime of water governance and its structural characteristics in the Kor River basin, which is one of the areas facing water shortage challenges. In order to analyze these characteristics and determine the regime of the water governance system, the method of social network analysis (SNA) was used in the network of formal and informal institutions of Kherameh county downstream of the Kor River basin. This research has been done in a period of 9 months and includes designing questionnaires, field visits, completing questionnaires and analyzing the results. The results showed that the water governance network in this region has an unstable and weak structure and as a result, the level of cooperation and coordination between the institutions is low and the level of power centralization is high. As a result, the governing regime is centralized, and therefore, the establishment of a polycentric governance system requires the improvement of horizontal relations between formal and informal institutions and protection and development institutions. [ABSTRACT FROM AUTHOR]

Barley is an important feed crop in Iran and is threatened by an increased frequency of drought. Increasing diversity in the form of evolutionary populations (EPs) and mixtures is one strategy to increase the resilience of crops. Four barley EPs, which have evolved in different locations over 7 to 10 years from the same original population, were evaluated for agronomic trait and stability together with two landraces, and one improved variety for three cropping seasons in four locations. Modest but significant differences were found only for plant height with a range of less than 4 cm. Stability, measured with cultivar superiority, as well as environmental variance and reliability measures generally indicated a superior stability of EPs—with two of them ranking first and second for grain yield reliability—but also differences between the EPs. The effect of recurrent droughts on the diversity within EPs is discussed as a possible explanation for the lack of divergent evolution. The seed management of Eps, including seed exchange between farmers, is suggested as a possible strategy to avoid the reduction in diversity within populations. Future research will address the nutritional value of the EPs, which is often quoted by sheep owners as superior to commonly grown varieties. [ABSTRACT FROM AUTHOR]

Water is the most essential resource for the production of agricultural goods and services. However, high levels of water stress and increased frequency and intensity of droughts, which are mainly driven by climate change dynamics, have reduced the stock of freshwater resources in arid and semi-arid regions, such as Iran. Despite the major reduction of freshwater resources, the efficiency of irrigation water use has remained very low in the country and performance of water management schemes is far from satisfactory. Using the strengths, weaknesses, opportunities and threats/political, economic, social, technological, legal and environmental (SWOT/PESTLE) analysis, this paper explores 40 internal and external factors that influence irrigation water management in Iran and recognizes legal, social, technological and political dynamics as the major reasons for failure of irrigation water management in the country. A combination of the threats, opportunities, weaknesses and strengths (TOWS) matrix and bottleneck analysis was used to suggest irrigation water management strategies that rehabilitate the failed schemes and enhance water security in the agriculture sector. Rethinking the role of intensified agriculture in development along with raising the awareness and attitude of decision makers towards the risk of shortsighted water resource development plans, in addition to promoting agro-based industries and developing integrated plans to improve water efficiency are the strategies that can contribute to a more effective management of irrigation water in Iran. [ABSTRACT FROM AUTHOR]

Due to the adverse impacts of severe droughts on various aspects of human life and ecosystem services (ESs), the spatio-temporal assessment of the resilience of ESs under droughts is essential. In this paper, we propose a new methodology for assessing ESs-based resilience, taking into account the seven resilience principles: a) redundancy and diversity, b) managing connectivity, c) managing slow variables and their feedback, d) complex adaptive system (CAS) thinking, e) experimentation and learning, f) broadening participation, and g) polycentric governance. These principles enable us to consider the main social, political, hydrological, economic, and environmental aspects concerning resilience which have been overlooked in previous studies. The methodology is evaluated by applying it to Zarrinehroud River Basin (ZRB) in north-western Iran. A set of qualitative and quantitative criteria and their sub-criteria are proposed for quantifying the ES-based resilience and generating time series of resilience against severe droughts in several sub-basins in the study area. To evaluate the criteria and sub-criteria, the required data are derived from calibrated SWAT and MODSIM models as well as experts' judjments. The time series of ES-based resilience under 128 Water and Environmental Resources Management (WERM) scenarios (for enhancing agricultural practices, altering and modernizing irrigation methods, improving irrigation network and drainage facilities) and Climate Change (CC) scenarios (RCP 4.5, RCP6.0, and RCP 8.5) are derived for each sub-basin based on short-term (2020–2049) and long-term (2020–2098) periods. The low resilience values (0.53–0.6) of all sub-basins under the base management scenario (the status quo scenario (SC 0)) illustrate the need for implementing some projects to enhance the ESs in the study area. The results show that the WERM scenario SC 12346 can improve the values of the resilience criterion in sub-basins up to 0.85 and reduce the vulnerability of the study area to droughts. By evaluating all management scenarios, scenario SC 12346 is reported as the best scenario, since it can significantly increase the resilience of all sub-basins against extreme droughts with an acceptable cost of 636 million US dollars compared to other scenarios. Under this WERM scenario, the resilience values of sub-basins increase up to 40%. This scenario suggests implementing a set of projects such as improving irrigation networks and drainage facilities. [Display omitted] • A new methodology is proposed to assess basin-wide spatio-temporal resilience. • 128 scenarios are evaluated using a novel two-point evidential reasoning approach. • The best management scenario enhances the resilience value from 0.53 up to 0.85. • The selected scenario suggests improving irrigation networks and drainage facilities. • The seven resilience principles decrease the width of uncertainty band by 0.44%. [ABSTRACT FROM AUTHOR]

Iran experienced increasing droughts in recent years. Since droughts have devastating effects on the economy and irreparable impacts on the environment, knowing the factors affecting droughts can effectively help their early warning and loss reduction. The present study used Palmer Drought Severity Index (sc-PDSI) and large-scale atmospheric indices (ENSO, IOD) to examine how various factors affected drought (precipitation, temperature, evapotranspiration, VPD, NDVI) in eight regions of Iran from 1981 to 2020. Results revealed that the northwest and southeast of Iran experienced longer duration and more severe droughts in the last 40 years. There is an increase in annual total precipitation and average daily temperature in all regions of Iran. The vegetation cover has changed slightly; however, its density has increased on the Caspian Sea shores due to increased rainfall. Correlation analysis revealed ENSO as Iran's primary driver of droughts, while the IOD affects sc-PDSI only in the central plateau. Besides, the ENSO significantly affects the vegetation changes in Iran. General trends in precipitation, temperature, evapotranspiration, VPD, NDVI, and sc-PDSI indicate northwest Iran as most prone to drought. [ABSTRACT FROM AUTHOR]

The aim of this study was investigating the effect of drought on groundwater storage and measuring the impact of future drought events under climate change conditions on Najafabad aquifer in Iran. A Support Vector Machine (SVM) model was developed to simulate groundwater storage by using the Particle Swarm Optimization (PSO) to optimize SVM parameters. Various meteorological and hydrological drought indices were considered as model inputs and the best combination of drought indices was selected by the Gamma Test (GT) method. The drought analysis revealed that the Groundwater Resource Index (GRI) and the Reconnaissance Drought Index (RDI) were the most effective indices in modelling the aquifer. To examine future drought events under climate change, the outputs of five ISI-MIP5 (Inter-Sectoral Impact Model Inter-comparison Project) General Circulation Models (GCMs) were derived under two Representative Concentration Pathways (RCPs). The results indicated that the most severe droughts will occur during the years 2035–2039 and 2033–2041 under RCP4.5 and RCP8.5, respectively. Continuing the current aquifer withdrawal policy, the groundwater storage will be affected by future drought events and decrease by 8.8% and 9.5% compared to the base period (1990–2016) under RCP4.5 and RCP8.5, respectively. [ABSTRACT FROM AUTHOR]

This study spatially monitored drought in Iran using drought indicators. Four drought indicators measured from 2016 to 2020 were used: temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), and precipitation condition index (PCI). Moreover, a standardized precipitation index (SPI) was prepared using rainfall measurements from 1989 to 2019. The TCI revealed that most of Iran was classified as having "severe drought" in 2020. The highest value of VCI showed for northern Iran, which belongs to the class without drought. The VHI indicated that vegetation stress increased over the study period throughout the region, and areas of severe and moderate drought reached their greatest extents in the aforementioned years. Significant droughts occurred in central, eastern, and southeastern Iran, and mild droughts occurred in northern Iran. The PCI indicated that rainfall amounts have diminished in most of the country over the period of study. The 30-year SPI showed that northern Iran received fine rain and the region has parts that can be classified as either extremely wet or very wet. However, most of the country was extremely dry and severely dry. The analysis of the VHI index for agricultural plants showed that 27.71% of Iran's agricultural regions, including the provinces of Razavi Khorasan, Hamadan, and Khozestan, experienced "critical drought" conditions. The study provides guidance for the selection of the most useful drought-monitoring indicators and can enable a more thorough understanding of drought in arid and semiarid regions. [ABSTRACT FROM AUTHOR]

Drought is a major problem in the world and has become more severe in recent decades, especially in arid and semi-arid regions. In this study, a Google Earth Engine (GEE) app has been implemented to monitor spatiotemporal drought conditions over different climatic regions. The app allows every user to perform analysis over a region and for a period of their choice, benefiting from the huge GEE dataset of free and open data as well as from its fast cloud-based computation. The app implements the scaled drought condition index (SDCI), which is a combination of three indices: the vegetation condition index (VCI), temperature condition index (TCI), and precipitation condition index (PCI), derived or calculated from satellite imagery data through the Google Earth Engine platform. The De Martonne climate classification index has been used to derive the climate region; within each region the indices have been computed separately. The test case area is over Iran, which shows a territory with high climate variability, where drought has been explored for a period of 11 years (from 2010 to 2021) allowing us to cover a reasonable time series with the data available in the Google Earth Engine. The developed tool allowed the singling-out of drought events over each climate, offering both the spatial and temporal representation of the phenomenon and confirming results found in local and global reports. [ABSTRACT FROM AUTHOR]