Predicting runoff in the Iranian Dez River Basin under climate change is the primary objective of this study. Since Tireh, Marberah, Sezar, and Bakhtiari are the four subbasins included in this case study, four synoptic and hydrometric stations were chosen (for each subbasin). For the years 1980–2012, time series data per month on precipitation, temperature, and runoff were required for the selected stations. Furthermore, for the 2020–2052 timeframe, upcoming climate data were extracted from three CMIP6 models including CANESM5, BCC-CSM2-MR and IPSL-CM6A-LR using the most recent SSPs–RCPs emission scenarios (SSP1-2.6 and SSP5-8.5) for selected station. Using observational data, the historical period’s extracted monthly temperature and precipitation variations by CMIP6 models were examined. These models were evaluated using a variety of metric indices, such as the correlation coefficient (R), mean absolute error (MAE), mean bias error (MBE), and root mean square error (RMSE), Willmott index calculation (WI), and modified index of agreement (md). Among them, the CANESM5 model was selected to extract future data at four subbasin. Conversely, three Decision trees (DT), ensemble, and Gaussian Process Regression (GPR) are examples of single artificial intelligence (SAI) models—were developed for each Dez basin subbasin in order to predict runoff. Training (calibration) and testing (validation) phases were applied to the input (temperature and precipitation) and output (runoff) datasets in a ratio of 80%:20% for each subbasin. The SAI models were investigated and Ensemble model was selected to predict runoff. Then, using training Ensemble model, runoff of each subbasin under two scenarios was predicated. The results showed that while precipitation isn’t trending much, temperature is trending upward, especially at SSP5-8.5 scenario. Additionally, runoff is trending upward in the Tireh Basin whereas it is trending downward in the other sub-basins. Consequently, water harvesting is only possible at Tireh Basin.

This study aims to predict a new composite drought-hot extreme index (CDHEI) that combines the standardized maximum temperature index (SMTI) and standardized precipitation index (SPI) in different climates across Iran. To this end, daily climatic data were sourced from 40 synoptic stations for the 1987–2019 period. Then, to monitor the mentioned indices simultaneously, a coupled function was employed to develop CDHEI for each climate. Three machine learning (ML) models—namely decision tree (DT), ensemble, and multilayer perceptron (MLP)—were developed to model CDHEI under three scenarios. Since machine learning models are inherently characterized by a “black box” nature, this study employed Ceteris paribus and partial dependence (CP-PD) profiles. The assessment of concurrent historical droughts and hot extremes was conducted by considering the CDHEI values and relevant categories in different climatic regions of Iran from 1998 to 2000. The results illustrated the effectiveness of the suggested index in monitoring the simultaneous occurrence of droughts and hot extreme events across different time frames and geographical areas. The most accurate ensemble model, with average values ranging from 0.1247 to 0.2047 and 0.9282 to 0.9674 (normalized root mean square error (NRMSE) and correlation coefficient (R), respectively), was the one that performed the best at the climatic zones. The CP-PD profile values demonstrated that maximum temperature had a significant effect on the model’s results across all climates in scenario 2. In scenario 3, however, SPI and SMTI proved to be the most influential features.

This study investigated the optimal management and allocation of irrigation water under different flow scenarios focusing on economic water productivity (EWP) index. In this study, the aim was to allocate and distribute water between networks and lower crops of Maroon reservoir dam The multi-stage stochastic programming method was used to develop the optimization model under three scenarios of arid, normal and wet years in two management modes and the results were compared with the traditional management figures. For this purpose, hydrometric data was sourced from the Marun network station for the 2006–2016 periods. Finally, the results of the second run provided a better irrigation program with a 19% increase in the total area under cultivation and a 7% increase in objective function profit. Moreover, the highest mean EWP in the three scenarios was obtained in the second run for the North network at 9% more than the first run.

This study focuses on enhancing long-term, multi-step forecasting of dissolved oxygen (DO), a key indicator of river water quality. We introduce a novel hybrid method, Hidden Pattern Feature Extraction-Statistical Mode Decomposition (HPFE-SMD), integrated with explainable ensemble learning models, namely Random Forest (RF) and Extra Trees Regressor (ETR), both in standalone and hybrid configurations (HPFE-RF and HPFE-ETR). The models were trained and evaluated using monthly DO data spanning 1974-2023 from two sites within the Mississippi River Basin, across forecasting horizons of 1, 3, 9, and 15 months. The hybrid models consistently outperformed their standalone counterparts. For instance, at a 15-month horizon for Site 1, the HPFE-ETR model reduced the Mean Absolute Error (MAE) by 98.1 % compared to standalone ETR. In comparison with TVF-EMDbased models, HPFE-SMD achieved a 10.8 % and 4.3 % reduction in Mean Absolute Percentage Error (MAPE) for RF and ETR, respectively, at the 9-month horizon. Overall, HPFE-RF and HPFE-ETR achieved high predictive performance with RMSE values below 0.25 mg/L and R2 values exceeding 0.99, even for long-term forecasts. SHAP (SHapley Additive exPlanations) analysis revealed that key statistical features, such as vibration amplitude (RMS), energy, skewness, kurtosis, and crest factor, played a dominant role in model predictions. Additionally, the proposed method demonstrated strong generalizability by accurately forecasting other water quality parameters, including total nitrogen, pH, total dissolved solids, and sodium adsorption ratio. These results highlight the added value of the HPFE-SMD approach over traditional decomposition or standalone ML models, showcasing its potential for integration into advanced water quality monitoring and management systems.

Study region: The Bam and Babolar stations are located in hyperarid and humid climates in Iran, respectively. Study focus: The primary objective of this study is to implement Artificial Intelligence (AI) to enhance multistep forecasting of the Standardized Precipitation Evapotranspiration Index (SPEI) for time horizons of one, six, and twelve months. The research employs an innovative hybrid approach that integrates a novel decomposition technique known as Hidden Pattern Feature Extraction Statistical Mode Decomposition (HPFE-SMD), along with Recursive Feature Elimination (RFE) for feature selection, and the Extra Tree Regressor (ETR) model. Additionally, the effectiveness of the suggested model (HPFE-ETR) was assessed and contrasted with two common methods, Time-Varying Filter-based Empirical Mode Decomposition (TVF-EMD) and Variational Mode Decomposition (VMD), both of which were combined with ETR. New hydrological insights for the region: The results showed that the HPFE-ETR model consistently outperformed comparative models, and it significantly improved drought forecasting accuracy, with the largest improvements for SPEI 12 (t + 12) and more moderate gains for SPEI 12 (t + 1). In particular, the model reduced forecasting errors for SPEI 12 by about 70 % in humid climates and 43 % in hyperarid climates, demonstrating its adaptability across different climatic conditions at both study stations. Explainability results revealed that mean features had the strongest positive influence on SPEI 12 forecasts, underscoring the model’s robustness in capturing key drought drivers. These findings highlight the HPFE-ETR model’s potential to revolutionize drought early warning systems and water resource management strategies.

This study investigates the nonstationary behavior of extreme temperatures in Pakistan across six diverse climatic zones over two distinct periods (1951–1985 and 1986–2020). The nonstationarity assessment is done by incorporating time and antecedent soil moisture (AMS) levels as nonstationarity covariates in Generalized Extreme Value (GEV) distribution. Results show significant intra-zonal, intra-seasonal, and intra-periodic variabilities in nonstationarity impacts on extreme temperatures. With few exceptions, maximum temperatures exhibited an increase in annual return levels particularly for second period while annual minimum temperature extreme gave mixed pattern during both periods. Seasonal analysis reveals notable intra-period variabilities, with most zones shows higher increase in maximum temperature return levels during second period up to + 2.3 °C and + 0.91 °C for spring and summer respectively. Conversely, minimum temperature analysis shows higher increase in return levels during spring (up to + 2.47 °C) and summer (up to + 2.02 °C) across most of the zones with decreased (up to -1.16 °C) return levels in central and eastern-western regions during winter. Except for two coastal zones, increased soil moisture levels were associated with significant reductions in maximum temperatures across most zones, with zonal average decrease in maximum temperature ranging from -0.01 °C to -0.05 °C per 1

Study region: The Yazd and Ramsar stations are located in hybrid arid and humid climates in Iran, respectively. Study focus: This research study develops a complementary expert system for accurately forecasting reference evapotranspiration (ET0) over one, three, and seven-day horizons by integrating Machine Learning (ML) models with a novel multivariate decomposition technique. Initially, significant input predictor lags were established through cross-correlation, and the War Strategy Optimization (WSO) algorithm was used for optimal Feature Selection (FS) and determining Multivariate Variational Mode Decomposition (OMVMD) parameters. Each predictor was decomposed into Intrinsic Mode Functions (IMFs) to enhance the temporal characteristics of the data. The study introduced the FS-OMVMD-RF, FS-OMVMD-KNNR, and FS-OMVMD-ETE models, utilizing Random Forest (RF), K-Nearest Neighbors Regressor (KNNR), and Extra-Trees Ensemble (ETE) techniques for daily ET0 estimation. These hybrid models were benchmarked against alternatives combining Time-Varying Filter-based Empirical Mode Decomposition (TVF-EMD) with individual models. New hydrological insights for the region: Results demonstrated that the developed models significantly enhance ET0 forecasting capabilities across multiple time scales. Notably, the FS-OMVMD-ETE model achieved the highest accuracy for ET0 (t + 7) at Yazd and Ramsar stations. The analysis indicated that in a hyper-arid climate, the U2 feature has the greatest impact on forecasting, while in a humid climate, Tmean is the most influential factor.

ABSTRACT Reservoir rule curves (RCs) are crucial for guiding operators on the optimal water release based on the available water at the start of each month. In the absence of RCs, simulation and optimization techniques can be effectively employed to develop these curves. This study evaluates the performance of various optimization techniques for deriving optimal reservoir RCs for the Zarrineh Rud reservoir using soft computing (SC) algorithms. The algorithms investigated include the genetic algorithm (GA), particle swarm optimization (PSO), and gravitational search algorithm (GSA). To this end, monthly demand and discharge data from 1987 to 2018 were collected. Historical RCs were first simulated using the sequent peak algorithm (SPA), and optimal RCs were subsequently derived through the GA–SPA, PSO–SPA, and GSA–SPA algorithms to minimize water shortages. The results indicated that the GSA–SPA generally improved the time-based (αt) and volume-based (αv) reliability indices by 3 and 2%, respectively, compared to the historical SPA (SPA-Hist). Additionally, simulations with the GSA–SPA significantly reduced the mean annual shortage and total shortage by approximately 8% compared to SPA-Hist. The PSO–SPA ranked second, with a 7.4 and 6.8% reduction in mean annual shortage and total shortage, respectively.

Due to a changing climate, the impacts of nonstationarity on drought assessment are becoming critical, yet have not been studied in Pakistan. To address this critical research gap, the study quantifies the effects of nonstationarity on drought assessment in Pakistan. It evaluates stationary and time-nonstationary reconnaissance drought index (RDI) for two distinct periods (1951–1986 and 1986–2021) using gamma distribution, which was a better fit than the log-normal distribution. The drought and wet classifications analysis reveals a significant reversal effect of nonstationarity between period-1 and period-2. Under nonstationarity, drought conditions were increased in the northern high mountains, sub-mountainous, and agricultural plain areas of Pakistan, with the impact reaching up to + 4.05

Long-term drought forecasting plays a crucial role in mitigating drought risks by providing early warnings. Researchers have long been interested in achieving accurate long-term drought forecasting, which is challenging since accuracy generally decreases by increasing the forecasting period. The primary aim of this research is to propose a new method for high-accuracy long lead time drought forecasting by combining various Feature Extraction (FE) and selection techniques. In this study, monthly time-series datasets encompassing precipitation, potential evapotranspiration, actual evapotranspiration, runoff, surface and root-zone soil moisture-were utilized to forecast SPEI-6 over various lead times including 1-, 3-, 6-, 9-, 12-, 18-, and 24-months using global gridded products with a 0.5O x 0.5O spatial resolution spanning the years January 1980 to December 2022. The method was evaluated using two different approaches, namely Gaussian Process Regression (GPR) as a simple machine learning technique and Long Short-Term Memory (LSTM) as a deep learning method. The findings provided improved accuracy, particularly for long-term forecasting when employing the proposed methodology. When utilizing LSTM with FE instead of the original datasets as inputs, the error reduced from RMSE = 0.16 to RMSE = 0.07 (a 56 % decrease), while the correlation increased from R = 0.65 to R = 0.90 (a 38 % increase) when forecasting SPEI-6 12 months ahead. The results showed that the GPR with FE and selection model outperformed the LSTM with original datasets model for SPEI-6 (t + 24) with a correlation coefficient (R) of 0.9811 and a Normalized Root Mean Square Error (NRMSE) of 0.1380, compared to R = 0.6517 and NRMSE = 0.4307 for the LSTM with original datasets. These findings can offer valuable insights for early agricultural drought warning in arid areas.

Drought assessment is inherently complex, particularly under the influences of climate change, which complicates long-term forecasting. This study introduces a novel hybrid deep learning model, Deep Feedforward Natural Networks (DFFNN), enhanced by War Strategy Optimization (WSO), aimed at forecasting the Standardized Precipitation Evapotranspiration Index (SPEI) for lead times of one, three, six, nine, and twelve months. Key parameters of the DFFNN, including the number of neurons and layers, learning rate, training function, and weight initialization, were optimized using the WSO algorithm. The model’s performance was validated against two established optimizers: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Evaluations were conducted at two synoptic stations with distinct climatic conditions in Iran. Results demonstrated that the WSO-DFFNN model achieved superior performance for SPEI 12 (t + 1) with a correlation coefficient (r) of 0.9961 and Normalized Root Mean Square Error (NRMSE) of 0.1028; for SPEI 12 (t + 3) with r = 0.8856 and NRMSE = 0.1833; for SPEI 12 (t + 6) with r = 0.8573 and NRMSE = 0.2203; for SPEI 12 (t + 9) with r = 0.7951 and NRMSE = 0.2479; and for SPEI 12 (t + 12) with r = 0.7840 and NRMSE = 0.3279 at the Chabahar station. Additionally, the WSO-DFFNN model outperformed for SPEI 12 (t + 1) with r = 0.9118 and NRMSE = 0.1704; for SPEI 12 (t + 3) with r = 0.8386 and NRMSE = 0.2048; for SPEI 12 (t + 6) with r = 0.7602 and NRMSE = 0.2919; for SPEI 12 (t + 9) with r = 0.6379 and NRMSE = 0.2843; and for SPEI 12 (t + 12) with r = 0.6044 and NRMSE = 0.3463 at the Anzali station. The results obtained from this study have the potential to improve drought management strategies.

The objective of this study was to model a new drought index called the Fusion-based Hydrological Meteorological Drought Index (FHMDI) to simultaneously monitor hydrological and meteorological drought. Aiming to estimate drought more accurately, local measurements were classified into various clusters using the AGNES clustering algorithm. Four single artificial intelligence (SAI) models-namely, Gaussian Process Regression (GPR), Ensemble, Feedforward Neural Networks (FNN), and Support Vector Regression (SVR)-were developed for each cluster. To promote the results of single of products and models, four fusion-based approaches, namely, Wavelet-Based (WB), Weighted Majority Voting (WMV), Extended Kalman Filter (EKF), and Entropy Weight (EW) methods, were used to estimate FHMDI in different time scales, precipitation, and runoff. The performance of single and combined products and models was assessed through statistical error metrics, such as Kling-Gupta efficiency (KGE), Mean Bias Error (MBE), and Normalized Root Mean Square Error (NRMSE). The performance of the proposed methodology was tested over 24 main river basins in Iran. The validation results of the FHMDI (the compliance of the index with the pre-existing drought index) revealed that it accurately identified drought conditions. The results indicated that individual products performed well in some river basins, while fusion-based models improved dataset accuracy more compared to local measurements. The WMV with the highest accuracy (lowest NRMSE) had a good performance in 60% of the cases compared to all other products and fusion-based models. WMV also showed higher efficiency in 100% of the cases than all other fusion-based and SAI models for simultaneous hydrological and meteorological drought estimation. In light of these findings, we recommend the use of fusion-based approach to improve drought modeling.

Accurate forecasting of dissolved oxygen (DO) levels is vital for river ecosystem health. A novel methodology, MVMD-TSA-GPR, combines Multivariate Variational Mode Decomposition (MVMD), the Tunicate Swarm Algorithm (TSA), and Gaussian Process Regression (GPR) to improve DO level predictions. This study also incorporated Generalized Additive Model and Regression Bagged Ensemble (RBE) for 1- and 3-month forecasts using monthly data (1974–2023) from 16 water quality parameters across five Mississippi River basin sites. Key predictors identified through cross-correlation include lagged values and parameters like water temperature, discharge, pH, total phosphorus, potassium, and sulfate, which significantly influence DO levels. The MVMD-TSA-GPR model outperformed others, especially at site 5, showing substantial improvements in accuracy with decreased RMSE values across various scenarios. Model ranking via the Taylor Diagram indicated MVMD-TSA-GPR had the highest performance, followed by MVMD-TSA-RBE and others. Notably, the GPR model’s RMSE at site 3 decreased from 2.11 to 1.01 (109

AbstractThis 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.

This study aims to investigate the effects of climate change on the return level of extreme maximum temperature (EMT) events in Iran. To this end, the Climate Research Unit-gridded dataset was used to collect EMT for the 1901-2014 period, and future data were projected from four available CMIP6 models, where the BCC-CSM2-MR model performed best under the latest SSP-RCP emission scenarios for the 2015-2100 period. The non-stationary state of the distribution was considered under three generalized extreme value (GEV) models: GEV0 (location and scale parameters are constant), GEV1 (non-stationary in location), and GEV2 (non-stationary in both location and scale) based on the evaluation criteria. The findings indicate that when using a non-stationary approach and considering the SSP5-8.5 scenario for a 2-year return period, the return level of extreme temperature increased by up to +4 degrees C compared to the stationary approach, while considering a non-stationary approach without climate change, the increase in the return level of extreme temperature was much smaller (up to +0.7 degrees C). MCMC and DEMC showed no significant differences and demonstrated that all stations are non-stationary in terms of the location parameter (GEV1). Also, the joint set of the total period of historical data and future data was the best dataset based on convergence with GEV1. HIGHLIGHTS Temperature frequency was analyzed based on the Bayesian method (MCMC and DEMC) under three generalized extreme value models. The impact of the non-stationary approach was investigated on the return level of maximum temperatures during historical and future periods. Intra-period trends were assessed in the extreme temperature due to climate change (CMIP6).

Univariate drought indicators are insufficient for characterizing the complicated effects and conditions of droughts. Accordingly, this study aimed to introduce and assess a composite drought index called the Integrated Drought Index (IDI), composed of the most important water balance variables including, temperature, precipitation, streamflow, and soil moisture to simultaneously monitor hydrological, agricultural, and meteorological drought. To this end, four widely used linear and non-linear combination approaches—namely the kernel mean component analysis (KMCA), copula function (CF), entropy weighting (EW), and the principal component analysis (PCA)—were used here, whose products are called IDI-KMCA, IDI-CF, IDI-EW, and IDI-PCA, respectively. The research data were extracted from ERA5 (ECMWF Reanalysis v5) datasets on a monthly scale for the 1979–2020 period. According to the findings, all proposed composite indices exhibited a mostly similar variation pattern as the individual indices and performed well in monitoring drought conditions—except for IDI-CF, which slightly deviated from the pattern during the 1989–1990 period. High values of the index of agreement (with the average values ranging between 0.5 and 0.9) and correlation coefficient (with the average values ranging between 0.7 and 0.9) also suggested a good agreement among the proposed composite indices. Since climate and hydrologic conditions in the region were not complex, they evaluated the same drought conditions through linear and non-linear approaches. In addition, Frank functions were selected to derive the joint distribution functions of drought characteristics for bi-variate and tri-variate functions. Finally, considering the spatial distribution of the drought return period, the probability of mild droughts remained the same under bi-variate and tri-variate conditions, whereas the occurrence probability of extreme drought changed (increasing and decreasing in the case of "and" and "or").

This study assesses climate change's impact on drought in Iran's Dez Basin. It introduces the Hydro-Meteorological Drought Index (HMDI), integrating the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Runoff Index (SRI). Using Climatic Research Unit Time Series (CRU TS) data (1980-2012) and downscaling forecasted data from three CMIP6 models (2020-2052) for SSP1-2.6 and SSP5-8.5 scenarios, we employ the rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning Hydrological Bureau's Water Balance Model (HBV)-Light model to predict future streamflow. Drought characteristics are analyzed. Under SSP5-8.5, CanEsm5 shows substantial temperature and runoff increases, notably in Bakhtiari and Borujerd sub-basins (63% and 56%). Future droughts are expected to intensify, particularly under SSP5-8.5. The most severe HMDI-derived drought (HMDI 12) in Borujerd station is projected to increase from -43.44 to -44.05. SSP5-8.5 is likelier to cause severe and prolonged HMDI-derived droughts than SSP1-2.6 or the historical period. The analysis suggests that normal drought levels will persist, while mild and severe drought levels will rise in the future. HIGHLIGHTS Using CMIP6 to assess the impact of climate change.; Using compound drought index to monitor hydrological and meteorological drought.;

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 (alpha(t)) and volume-based (alpha(v)) 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 alpha(t), alpha(v), 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, alpha(t) had the highest variations, while alpha(v) 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).

The analysis of hydroclimate extremes is gaining more attention due to the devastating effects of intense floods, droughts, etc. This study aims to analyze the stationary (S) and non-stationary (NS) behavior of the annual maximum temperatures (AMT) for two different climatic zones of Iran including the arid and excessively humid provinces of Kerman and West Azerbaijan, respectively. The research datasets included maximum temperature (from CRU TS) and soil moisture (from ERA5) on a monthly time scale (spanning 1901–2019 and 1979–2019). Trend, homogeneity, and stationarity tests were applied to define the basic characterization of the AMTs. The frequency analyses of the AMTs were carried out using generalized extreme value (GEV) under two assumptions of S-GEV and NS-GEV. Moreover, the fitted distribution parameters were estimated using a maximum likelihood estimator. In addition to time-varying NS-GEV investigations, the soil moisture during summer (SM-June, July, and August) was also employed as the covariate to quantify the relationship between drought and AMTs in these climatic zones. The research findings revealed that the Akaike information criterion in S-GEV and NS-GEV estimations decreased from 309 to 223 and 329 to 254 for arid and excessively humid climatic zones, respectively. Therefore, the NS-GEV frequency analyses has increasing effects on return levels of the AMTs than the S-GEV. In the following, the spatial NS-GEV investigations in all 12 and 15 stations of both provinces, showed that NS-GEV with SM as a covariate has better performance in excessively humid climatic zones.

Drought is one of the most complex natural disasters due to its slow onset and long-term impact. Today, the use of remote sensing techniques and satellite imagery has been considered a useful tool for monitoring agricultural drought. The objective of the present study was to evaluate spatial and temporal monitoring of agricultural drought in the lake Urmia catchment area with the ETDI drought index which is calculated from Nova satellite images based on actual evapotranspiration from the SEBS algorithm and compared with the ground index SPI. For this purpose, 248 AVHRR sensor images and NOAA satellites during the statistical period of 1998-2000 and 17 meteorological stations with a statistical period of 30 years were used to calculate the indicators. To determine agricultural lands, six thousand points were marked for different uses and their actual evapotranspiration was calculated using the SEBS algorithm. The results showed that with the onset of the drought period in 1998, the ETDI index indicated 9.4% in weak drought conditions in May and 90.6% in normal conditions. Over time, in June of 1998, the situation was different with 95% in a weak drought situation and 5% in a normal situation for the city of Tabriz. In July, the entire catchment area experiences a slight drought. Then, in August, 84% of the basin is in normal condition and 16% in Tabriz and Urmia are declared weak drought. It was also founded that the ETDI drought index due to the combination of visible and infrared bands and its combination with terrestrial data has a physical meaning and has high certainty and predicts drought faster and more accurately.