Mahnoosh Moghaddasi

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.

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.

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.

This study aims to investigate the effects of climate change on return level of extreme maximum temperature (EMT) events in Iran. To this end, the CRU 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 performed best under the latest Shared Socioeconomic Pathways-Representative concentration pathways (SSPs-RCPs) emission scenarios for the 2015–2100 period. The non-stationary state of the distribution was considered under three models GEV0 (location and scale parameters are constant), GEV1 (nonstationary of location), and GEV2 (nonstationary of 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°C compared with 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°C). MCMC and DE-MC showed no significant differences and demonstrated that all stations are non-stationary in terms of the location parameter (GEV1).

Groundwater level forecasting is an essential priority for planning and managing groundwater resources. This study aims to investigate the effect of climate change on the monthly groundwater level in the Golpayegan aquifer in the future (2017–2032). After a spatio-temporal analysis, the Least Squares Support Vector Regression (LSSVR) model was used to simulate the monthly groundwater level in the historical period (2002–2017). The input data included precipitation, temperature, pan evaporation, soil moisture (from the ESA CCA SM product), and groundwater level in observation wells on a monthly time-scale. Future climatic data were downloaded from the CanEsm5 model of CMIP6 for the SSP1-2.6 and SSP5-8.5 climate scenarios and then downscaled using the Change Factor Approach (CFA). The spatial analysis of groundwater levels indicated four different behaviors in the observation wells in the Golpayegan aquifer, resulting in four different clusters using the AGNES clustering method. Historical and future period modeling were performed separately for each of the four observation wells from each cluster. The modeling in the historical period demonstrated an average of NRMSE (0.09), MBE (0.030), and R2 (0.94) for the four clusters. The groundwater level in all clusters showed a decreasing trend in the future period, with SSP5-8.5 (average: 3.9 cm/month) showing a greater decrease than the SSP1-2.6 (average: 0.5 cm/month) scenario. The decline in groundwater level under SSP5-8.5 compared with SSP1-2.6 was more, respectively, 4.8, 5.8, 9.9 and 3.7 metres for clusters 1–4. The results indicate the acceptable efficiency and accuracy of the LSSVR model results in evaluating the effects of climate change on groundwater levels.

This study aims to investigate the effect of climate change on the probability of drought occurrence in central Iran. To this end, a new drought index called Multivariate Standardized Drought Index (MSDI) was developed, which is composed of the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSI). The required data included precipitation, temperature (from CRU TS), and soil moisture (from the ESA CCA SM product) on a monthly time scale for the 1980–2016 period. Moreover, future climate data were downloaded from CMIP6 models under the latest SSPs-RCPs emission scenarios (SSP1-2.6 and SSP5-8.5) for the 2020–2056 period. Based on the NRMSE, S_n, and NS evaluation criteria, the Galambos and Clayton functions were selected to derive copula-based joint distribution functions in both periods. The results showed that more severe droughts and longer will occur in the future compared to the historical period and in particular under the SSP5-8.5 scenario. From the derived joint return period, a drought event with defined severity or duration will happen in a shorter return period as compared with the historical period. In other words, joint return period indicated a higher probability of drought occurrence in the future period. Moreover, the joint return period analysis revealed that the return period of mild droughts will remain the same, while it decresed over extreme droughts in the future.