Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis.

Severe drought events in recent decades and their catastrophic effects have called for drought prediction and monitoring needed for developing drought readiness plans and mitigation measures. This study used a fusion-based framework for meteorological drought modeling for the historical (1983-2016) and future (2020-2050) periods using remotely sensed datasets versus ground-based observations and climate change scenarios. To this aim, high-resolution remotely sensed precipitation datasets, including PERSIANN-CDR and CHIRPS (multi-source products), ERA5 (reanalysis datasets), and GPCC (gauge-interpolated datasets), were employed to estimate non-parametric SPI (nSPI) as a meteorological drought index against local observations. For more accurate drought evaluation, all stations were classified into different clusters using the K-means clustering algorithm based on ground-based nSPI. Then, four Individual Artificial Intelligence (IAI) models, including Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), Multi-Layer Perceptron (MLP), and General Regression Neural Network (GRNN), were developed for drought modeling within each cluster. Finally, two advanced fusion-based methods, including Multi-Model Super Ensemble (MMSE) as a linear weighted model and a nonlinear model called machine learning Random Forest (RF), combined results by IAI models using different remotely sensed datasets. The proposed framework was implemented to simulate each remotely sensed precipitation data for the future based on CORDEX regional climate models (RCMs) under RCP4.5 and RCP8.5 scenarios for drought projection. The efficiency of IAI and fusion models was evaluated using statistical error metrics, including the coefficient of determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The proposed methodology was employed in the Gavkhooni basin of Iran, and results showed that the RF model with the lowest estimation error (RMSE of 0.391 and R2 of 0.810) had performed well compared to all other models. Finally, the resilience, vulnerability, and frequency of probability metrics indicated that the 12-month time scale of drought affected the basin more severely than other time scales.

Assessment of the management scenarios for groundwater quality remediation of a nitrate-contaminated aquifer.

Nitrate contaminant degrades groundwater quality and threatens the health of the humans, livestock, and the environment. Damaneh-Daran aquifer is located at upstream of the Zayandehrood reservoir in west-central Iran. This aquifer has been highly contaminated by nitrate and is still rapidly being contaminated. Thus, its quality needs to be remediated. This paper is focused on the quantity-quality modeling to predict the average nitrate concentration of the aquifer. Several remediation scenarios are presented in a period beginning from fall 2019, ending in spring 2024. These scenarios address several ways to mitigate the injection of the major sources of contamination in the region, such as equipping the urban regions with wastewater collection and treatment plants and reducing the fertilizers' use. The decreased use of the fertilizers may be achieved through two strategies: directly reducing the amount of the fertilizers by several specific and predefined rates of reduction and indirectly decreasing the amount of the fertilizers used by crop pattern modification. The latter strategy is evaluated to replace all or a part of the areas allocated to the more fertilizer-demanding crops with those of the less fertilizer-demanding crops. Furthermore, some of these scenarios are hybridized to more mitigate groundwater quality degradation. The results of performing the proposed scenarios are once compared together and then compared with the trend scenario letting current case study conditions and facilities be held in the future. The results suggest that the scenario hybridizing the effects of the wastewater treatment plants-equipping scenario with those of the quality-enhancing crop pattern modification scenario is evaluated as the most effective and best-performing scenario, implementation of which offers 20% and 30% reduction of the nitrate concentration for the agricultural and urban areas, respectively.