دکتر محمدعلی علیجانیان

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.

Equipping stochastic dynamic programming with copula function to determine reservoir operation rule curves and risk assessment for current and future conditions

( Article . )

Klaho, M.H., Moeini, R., Alijanian, M.

Environment, Development and Sustainability (1387585X)

Spatiotemporal drought propagation over the Eastern Middle East using the complex network

( Article . )

Abedian, S., Alijanian, M., Yazdian, H.

Natural Hazards (15730840)

2023

A spatially promoted SVM model for GRACE downscaling: Using ground and satellite-based datasets

( Article . )

Yazdian, H., Salmani-dehaghi, N., Alijanian, M.

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

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