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Journal of Hydrology: Regional Studies (22145818)58
Study Region: The Urmia Lake Basin (ULB) in northwestern Iran, a region highly susceptible to drought. Study Focus: Effective drought risk reduction necessitates comprehensive spatiotemporal assessment. The ULB has experienced a severe, near two-decade-long drought resulting in a 90 % reduction in lake area. This situation has fueled debate between attributing the crisis to governance failures and emphasizing the role of drought. Hence, this study performs a comprehensive drought assessment, considering temporal, spatial, and risk reduction aspects. Using a 50-year (1971–2020) time series of multivariate Standardized Precipitation-Evapotranspiration Index (SPEI) data (3, 6, 9, and 12-month timescales), the study employs Mann-Kendall, modified Mann-Kendall, and Sen's slope analyses to determine drought trends, examines spatial patterns using Severity Area Frequency (SAF) curves, and finally, evaluates drought risk reduction using resilience, vulnerability, and exposure factors. New Hydrological Insights for the Region: the study reveals a significant decreasing trend in SPEI values across the basin, particularly pronounced in the southern region, indicating worsening drought conditions. Regional drought assessment identified 1998 as the most severe drought event, exhibiting a 50-year return period basin-wide. Finally, risk assessment based on drought mitigation indicators shows the highest short-term and mid-term (SPEI3 and SPEI9) drought risk in the southern region. However, for long-term droughts (SPEI12), the eastern region displays the highest risk, while the southern region shows at the lower rank. © 2025 The Authors
Stochastic Environmental Research And Risk Assessment (14363259)39(4)pp. 1605-1621
One major characteristic of the Standardized Drought Indices is their dependence on one variable. However, different variables influence drought simultaneously. To address this issue, this study modifies and develops the Surface Water Supply Index (SWSI) to involve multiple aspects of droughts. Data on precipitation, runoff, reservoir volume, and discharge (instead of snow) over 21 years are used to modify the SWSI in Marun, Khuzestan Province, Iran. Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), Standardized Reservoir Storage Index (SRSI), and Standardized Discharge Index (SDI) are used as standard univariate indices to evaluate a new comprehensive index. The Shannon Entropy (SE) method is utilized innovatively to determine the weights of the comprehensive index, instead of deriving them depending on the weather experts. Additionally, a Modified-Surface Water Supply Index (M-SWSI) is also proposed to align it with the existing standard indices. For comparison, the weights are also calculated using the Proportioning Objective Procedure (POP) method, and presented in the POP-SWSI index. The results indicate that utilizing the SE method to determine weights provides an exceptional perspective on the meteorological and hydrological conditions of the drought-affected upstream region; while determining the weights using the POP method provides an insightful socio-economic and hydrological view on the drought-affected downstream region. In 2017, one of the most severe years, the M-SWSI index detected a drought event one month earlier than the POP-SWSI, with a drought duration value of 6 months close to the duration value of 5 months identified by POP-SWSI. Furthermore, the severity values were similar between the two indices, although the M-SWSI indicated lower drought severity than the univariate indices. Copulas are also employed for drought events analysis and to build the joint distribution function of drought severity (S) and duration (D) for the M-SWSI. Bivariate cumulative probability distribution functions are created and analyzed to determine the periodic “and” and “or” bivariate drought return periods. Additionally, Severity-Duration-Frequency (SDF) curves are established to evaluate the M-SWSI index. The Marun dam has been chosen as a case study to analyze the surface water supply under drought conditions, aiming to develop management policies for downstream decision-making. Consequently, the M-SWSI can be applied to any dam reservoir for similar analyses. This index involves various aspects of drought and can be utilized for reservoir management, assessing risks, and addressing flood and flood risk management challenges. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Environment, Development and Sustainability (1387585X)
In this study, a novel approach is proposed in which the transition probability matrix (TPM) of stochastic dynamic programming (SDP) methods is determined based on the copula function (TPM-C) by calculating the conditional probability. The obtained results are compared with the TPM based on the Markov Chain method (TPM-M). Here, the Marun dam reservoir, in southwestern Iran, is selected as a case study. In addition, for the historical period, the best values of reliability, resilience, vulnerability, sustainability, a minimum of water deficit as an objective function are obtained, and the average reservoir storage value of TPM-C is better than TPM-M. Finally, to update the reservoir operation rule curves, three different scenarios are proposed depending on the historical and future conditions. For this purpose, the water inflow values into the dam reservoir are predicted using a Multi-Layer Perceptron (MLP) model depending on the predicted precipitation of the climate change model. Therefore, the inflows, reservoir storage volumes, and water demand values are used to simulate and forecast the water release values into the reservoir. The results show the superior performance of the proposed TPM-C compared to the TPM-M approach to determine the reservoir operation rule curves at uncertain future conditions. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.
Natural Hazards (15730840)
Drought disasters can affect socio-economic systems and ecosystems. Furthermore, based on the observations, the Eastern Middle East (EME) region has experienced significantly faster warming compared to other populated areas. Wherein, Iran has also suffered from several long droughts. Predictions regarding the spatial and temporal distribution of drought events over Iran and its neighboring countries improve our understanding of this process and help reduce exposure to water scarcity and its destructive effects by preparing timely measures. In this study, complex networks are applied to analyze the paths and propagation distances of droughts, identifying the sources and vulnerable regions in EME. Additionally, event synchronization techniques to detect drought events that simultaneously impact multiple regions are utilized. For this purpose, important events are determined according to the SPEI index during 1970–2020. Based on the developed network over the country, the inward and outward strength criteria of drought events across different time scales and eight geographical directions are calculated. The findings reveal a significant vulnerability in the western and northwestern regions of Iran, highlighting their susceptibility to drought events originating in its southwestern and northwestern neighboring countries. This pattern suggests a strong influence of regional meteorological conditions and hydrologic al connectivity on drought propagation within these regions. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.
Journal of Hydrology (00221694)626
Satellite-based terrestrial water storage changes have been recorded using the Gravity Recovery and Climate Experiment (GRACE) satellite which causing it an important dataset in hydrology and other related fields. GRACE dataset is widely utilized in many studies, but its coarse spatial resolution is a limiting drawback. Machine-learning approaches (e.g., ANN and SVM) are commonly applied in spatially downscaling. However, their input formation, which is in vector form, is a limitation of considering neighbor relations between the gridded-based inputs, specifically in spatial downscaling. Thus, developing an appropriate, simple, fast, and novel model to spatially downscale GRACE resolution is initially necessary for its utilizations. In this study, a Spatially Promoted Support Vector Machine (SP-SVM) model is innovatively proposed for GRACE downscaling from 0.5° to 0.25°. This promotion is investigated utilizing the distances between the unknown target points (with 0.25°) and their surrounding GRACE-valued points (0.5°), called their Distance Effect Coefficient (DEC), as the SP-SVM model input. In addition, the efficiencies of different in-situ and satellite-based datasets (fifteen variables from May 2005 to August 2020) are evaluated as the inputs of the GRACE downscaling models. After finding the most influential datasets, showing the best correlation with the GRACE, their best combinations in GRACE downscaling are identified. Based on the results, the set of PERSIANN-CDR without delay, the in-situ evaporation with a 1-month delay, and the soil moisture in 0–10 cm depth with a 1-month delay show the best performance in GRACE downscaling. The results of GRACE downscaling by the SP-SVM approach are also compared with the ones based on a usual statistical SVM (S-SVM) model, consisting of an intermediate bias interpolation to improve the estimations through a bias correction step. The results show that the SP-SVM model outperforms the common statistical SVM-based. Thus, compared with the usual S-SVM approach, the proposed SP-SVM (linear) model could be used as a simpler and more accurate model for downscaling any variable in a hierarchical process. © 2023
Hydrological Sciences Journal (02626667)68(14)pp. 2075-2088
In situ rainfall data play a significant role in drought assessment studies. However, they are not available with reliable spatiotemporal coverage. With the advancements in satellite rainfall estimates (SREs), monitoring hydrological events in ungauged basins is possible. Additionally, the evaluation of newly released SREs such as CHIRPS, with a long-term record and comparably high resolution (0.05°), in the assessment of extreme hydrological events (dry/wet spells) has scarcely been carried out, which is the most novel motivation of this study. Moreover, evaluation of CHIRPS in developing copula-based multivariate severity–duration–frequency curves based on the severity and duration of the occurred events in 1988–2019 over the Zayandehroud basin (a critical central basin of Iran), is innovatively appraised. An evaluation of CHIRPS in drought assessment shows its acceptable performance, with slight underestimation, in assessing the severity and duration of dry spells. In contrast, an overestimation is identified for wet spells. © 2023 IAHS.
PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science (25122819)91(5)pp. 391-404
Snow cover is an informative indicator of climate change and surface hydrological cycles. Despite its essential accurate dynamic measurement (i.e., accumulation, erosion, and runoff), it is poorly known, particularly in mountainous regions. Since passive microwave sensors can contribute to obtaining information about snowpack volume, microwave brightness temperatures (BT) have long been used to assess spatiotemporal variations in snow water equivalent (SWE). However, SWE is greatly influenced by geographic location, terrain parameters/covers, and BT differences, and thus, the low spatial resolution of existing SWE products (i.e., the coarse resolution of AMSR-based products) leads to less satisfactory results, especially in regions with complex terrain conditions, strong seasonal transitions and, great spatiotemporal heterogeneity. A novel multifactor SWE downscaling algorithm based on the support vector regression (SVR) technique has been developed in this study for the Zayandehroud River basin. Thereby, passive microwave BT, location (latitude and longitude), terrain parameters (i.e., elevation, slope, and aspect), and vegetation cover serve as model input data. Evaluation of downscaled SWE estimates against ground-based observations demonstrated that when moving into higher spatial resolution, not only was there no significant decrease in accuracy, but a 4% increase was observed. In addition, this study suggests that integrating passive microwave remote sensing data with other auxiliary data can lead to a more efficient and effective algorithm for retrieving SWE with appropriate spatial resolution over various scales. © 2023, Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V.
International Journal of Climatology (10970088)42(12)pp. 6441-6458
In situ rainfall data plays a vital role in drought assessment. However, adequate in situ data are not available in many parts of the world, and they do not provide the proper spatial coverage for drought assessment. With the advacements in satellite rainfall estimates (SREs), it is possible to monitor droughts in ungauged basins. However, the applications of SREs for drought forecasting are not widely explored due to the inherent uncertainties associated with these products.In this study, we evaluated two long-term SREs for drought forecasting in the Zayandehrood basin, a critical region in the central plateau of Iran. The performance of two SREs, including Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) are compared with observations during 1983–2015. The overall results indicate that utilizing MSWEP data in the forecasting model can slightly overestimate the probability of spring drought based on winter drought (with the highest error of 8.5%). In comparison, the PERSIANN-CDR underestimated the probabilities (with the lowest error being −44%). The performance of copula models and SREs can vary based on the thresholds for drought severity. For example, the performance of MSWEP datasets for predicting moderate to severe droughts during the Spring season is closer to the predicted values by gauge datasets. It is concluded that the MSWEP may be considered more reliable in drought forecasting than the PERSIANN-CDR. Our results highlight the potential application of copula-based forecasting models for seasonal drought forecasting using SREs datasets. Such models can be implemented for global-scale drought predictions, especially in ungagged basins. © 2022 Royal Meteorological Society.
Natural Hazards (15730840)102(3)pp. 1249-1267
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. © 2020, Springer Nature B.V.
Journal of Hydrology (00221694)579
Satellite Rainfall Estimates (SREs) can provide rainfall information at finer spatial and temporal resolutions, however their performance varies with respect to gauged precipitation data in different climatic regions. A limited number of studies investigated the performance of SREs for spatio-temporal (regional) drought analysis, which is a key component for developing tools for regional drought planning and management. In this study, the performance of two recent SREs (data length > 30 years), which includes Artificial Neural Networks Climate Data Record (PERSIANN-CDR) and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) are selected for spatio-temporal drought assessment over different climatic regions located in Iran. Firstly, the accuracy of SREs was evaluated for deriving standardized precipitation index (SPI) at different time scales (1, 3, 6, 9 and 12 months) for four climatic regions during the period of 1983–2012. Secondly, the performance of SREs was evaluated for regional drought assessment based on the concept of the Severity-Areal-Frequency (SAF) curves. It was observed that the performance of SREs can be different with respect to gauge data in terms of quantifying drought characteristics (e.g., severity, duration, and frequency), identification of major historical droughts, and a significant difference can be observed based on the SAF analysis. For example, the number of drought events based on shorter time scales (SPI-1 and 3) found to be greater for SREs in comparison to gauge information for all climatic regions. While investigating the major historical droughts, discrepancies can be observed between these two types of data sets. For example, gauge data suggests wetness (i.e., SPI-3 > 0.5) near southern Iran, whereas, SREs show droughts (SPI < -1.0) in the same spatial domain. The performance of SREs with respect to gauge data varies largely in terms of quantifying the frequency component embedded in the SAF curves for selected climatic regions located in Iran. Our research findings can be useful for drought assessment in ungagged basins, as well as to develop regional drought management plans to improve water security by integrating multivariate nature of drought events. © 2019 Elsevier B.V.
International Journal of Climatology (10970088)37(14)pp. 4896-4914
In situ rainfall data observed by gauges is the most important data in water resources management. However, these data have some limitations both spatially and temporally. With the advancements in satellite rainfall products, it is now possible to evaluate whether these products can capture the climatology of known rainfall characteristics. In this study, five satellite rainfall estimates (SREs) were evaluated against gauge data based on different rainfall regimes over Iran. The evaluated SREs are Climate Prediction Center Morphing Technique, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM), PERSIANN Climate Data Record (PERSIANN-CDR) and the most recently available Multi-Source Weighted-Ensemble Precipitation (MSWEP) data. The performance of these five SREs is evaluated with respect to gauge data (total: 958 stations) in eight different climatic zones at daily, monthly, and wet/dry spells during a ten-year period (2003–2012). Performance of SREs was evaluated using metrics of comparison based on correlation coefficient (CC), root mean square error, and relative error. The study shows that MSWEP has the highest CC (0.72) followed by TRMM (0.46) and PERSIANN-CDR (0.43) at daily time scale. The performance of SREs varies with respect to climatic regimes, for example, the best correlation was observed in the south, the shore of Persian Gulf with ‘very hot and humid’ climate with CC values of 0.72, 0.70, and 0.82 for MSWEP, TRMM and PERSIANN-CDR, respectively. Further, the performance of SREs was evaluated using the categorical statistics to capture the rainfall pattern based on different groups (e.g. light, moderate and heavy rainfall events). Results show that MSWEP, PERSIANN-CDR, and TRMM performed well to distinguish rain from no-rain condition, whereas for higher rainfall rates, PERSIANN-CDR outperforms the other SREs. © 2017 Royal Meteorological Society
Journal of Irrigation and Drainage Engineering - ASCE (07339437)137(6)pp. 383-397
Optimal crop planning and the conjunctive use of surface water and groundwater resources are imperative for the sustainable management of water resources, especially in semiarid regions. In recent years, considerable attention has been given to crop planning and water resources management under uncertainties caused by climate changes that affect irrigation planning in terms of decisions to determine the amounts of water that can/must be allocated. In this paper, optimal crop planning and conjunctive use of surface water and groundwater are developed for the Najafabad Plain, a part of the Zayandehrood River basin in west-central Iran. The fuzzy inference system (FIS) is used to account for the experience and expert judgments of decision makers and farmers to obtain optimal crop planning and cultivation with a reliable water demand based on climate conditions. In the present work, fuzzy regression is used for considering uncertainty and ambiguity in the data used in the simulation model as well as the uncertainties in interactions between surface water and groundwater. The objective function of the optimization model is to minimize shortages in supplying irrigation demands. The results are applicable to a wide range of climate conditions. © 2011 American Society of Civil Engineers.
