Journal of Drought and Climate Change Research
Year:2024 | Volume:2 | Issue:4|Papers Count:8

Groundwater, particularly from aquifers in Afghanistan's arid and semi-arid eastern basin, has long served as a primary water source for industry, agriculture, and domestic use. However, recent decades have witnessed a troubling trend of over-extraction driven by population growth and recurrent droughts, compounded by insufficient planning. Effective groundwater management, focusing on con-trolled extraction that aligns with aquifer capacity, is essential for long-term sustainability. This study employs Arc GIS software to assess quantitative and qualitative shifts in groundwater dynamics within the Kabul Basin. Data from 54 wells, monitored at various intervals from 2005 to 2020, were meticu-lously analyzed, incorporating geological, climatic, and hydrological parameters. The findings reveal significant fluctuations in groundwater levels, with an average decline of 16.5 meters over the 13-year study period. The groundwater level decreased by 12 meters in some parts of the Kabul aquifers, at a rate of 80 centimeters per year. Alterations in flow distribution patterns were observed, particularly in the Paghman-Darulaman and central Kabul aquifers. Water quality parameters also changed, with 82% of samples collected in November 2020 showing electrical conductivity values greater than 1,000 μS/cm, compared to 73% in 2004, indicating increasing salinity. The total groundwater storage loss in the Kabul aquifer during the study period was estimated at 358 million cubic meters. Groundwater consumption in 2020 was approximately 277 million cubic meters, twice the natural recharge rate. Fu-ture projections indicate an accelerated depletion of groundwater reserves, especially in densely popu-lated urban regions like Kabul, necessitating immediate intervention to avert impending water scarcity crises.

This research examines sodium removability from agricultural wastewater using sugarcane bagasse sorbents, which helps ease the pressure on water resources during droughts. The biochar was produced in an electric furnace, activated with KOH, microwave-heated, and magnetized using a 2:1 ratio of Iron (III) chloride hexahydrate to Iron (II) sulfate heptahydrate. Three KOH-to-biochar ratios, microwave powers, and activation times were used, while sodium concentrations in wastewater samples were adjusted to 2, 4, and 8 g/l with sodium nitrate. Results indicated that higher initial sodium concentrations improved removability. Activated nano biochar achieved 74.4% more sodium removal than non-nano biochar on average. Magnetization reduced sodium removal by an average of 18.8%, with reductions ranging from 10.9% to 31.6%. The activated nano biochar's removability was 1.6 times greater than that of the non-activated version, and magnetization decreased efficiency by 20%. The highest sodium removal occurred at a 3:1 activator-to-biochar ratio during the 200 and 400 W treatments, achieving maximum removability of 61.4% for activated nano biochar and 58.3% for the magnetized version.

This study proposed an integrated decision-making framework that systematically incorporated specific industrial characteristics with fundamental sustainability considerations. The framework introduced a structured, analytical approach based on a dual methodology, combining SWARA (Step-wise Weight Assessment Ratio Analysis) and VIKOR (Višekriterijumsko kompromisno rangiranje) within a fuzzy logic framework. This integrated approach leveraged the strengths of each technique, offering a robust, multi-dimensional model to support precise and reliable decision-making in complex, sustainability-oriented contexts. The fuzzy SWARA method was used to determine the criteria and sub-criteria weights, followed by fuzzy VIKOR to rank decision alternatives. Five wastewater treatment technologies for the steel industry were identified and prioritized based on sustainability principles. These included CASPF (Conventional Activated Sludge with Mold Flow), MBR (Membrane Bio-Reactor), SBR (Sequencing Batch Reactors), AS (Activated Sludge), and UASB (Up-flow Anaerobic Sludge Blanket). The study demonstrated that this integrated approach yields more reliable and informed decisions in complex evaluations. Findings revealed that experts largely favour SBR technology as the most sustainable option.

Climate change, as one of the global challenges of the present century, has profound impacts on water resources and agriculture. The increase in temperature and decrease in rainfall in arid and semi-arid regions have made optimal water resource management a top priority.In countries facing climate change and drought, accurate estimation of evapotranspiration plays a vital role in water resource management and ensuring food security.One of the key factors affecting evapotranspiration is the vapor pressure deficit (VPD), which significantly impacts the accuracy of related calculations. This study focuses on predicting the vapor pressure deficit using advanced machine learning techniques. The methods employed include linear regression (LR), generalized additive model (GAM), random subspace (RSS), random forest (RF), and M5 Purned model(M5P). In this study, monthly average data, including temperature, humidity, precipitation, and vapor pressure deficit, were extracted from the JRA-55 database for the period from 1958 to 2023. The analysis of vapor pressure deficit data in the study areas of Birjand, Sarayan, Qaen, and Tabas showed that the annual average vapor pressure deficit increased by 6 Pascals, 10 Pascals, 4 Pascals, and 5 Pascals, respectively.In the next step, the extracted data for temperature, precipitation, and humidity were used as input variables, and vapor pressure deficit was used as the target variable in machine learning algorithms. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), Pearson correlation (CC), and Kling-Gupta efficiency (KGE) as evaluation indices.The results showed that the GAM model outperformed other models in all study areas. The evaluation values for each region were as follows: Birjand [ RMSE=0.308, MAE=0.247, KGE=0.914, CC=0.920], SAarayan [RMSE=0.401, MAE=0.303, KGE=0.937, CC=0.951], Qaen [RMSE=0.072, MAE=0.055, KGE=0.987, CC=0.997] and Tabas[RMSE=0.230, MAE=0.184, KGE=0.920, CC=0.942] The predictions made by the model indicated that, over the next 10 years, the annual average vapor pressure deficit in the studied regions will significantly increase: Birjand: 9 Pascals, Sarayan: 10 Pascals, Qaen: 7 Pascals and Tabas: 5 PascalsThis increase signifies serious challenges for water resources and an increase in water consumption in the region’s hot and dry climatic conditions. Finally, this study recommends the GAM model as an effective tool for future research, especially for use in the development of smart irrigation systems, which play a crucial role in sustainable water resource management.

In order to compare new wheat cultivars and lines, an experiment was implemented in the form of split plots (randomized complete blocks) with three replications for two years at the Chahartakhte research station, Shahrekord, located in the agricultural and natural resources research center of Chaharmahal and Bakhtiari Province. The main treatments included three levels of irrigation, corresponding to (FI) 100%, (I80) 80%, and (I65) 65% soil moisture deficiency, while the subplots featured six wheat cultivars (Mihan, Heydari, line CD-94-9, line CD-94-8, Oroom, and Pishgam). The results of analysis of variance showed that the effect of irrigation levels on grain yield, total yield, plant height, harvest index, 1000 grams grain weight, spike length, water use efficiency and water productivity were significant at 1% level. The highest amount of grain yield was obtained from the Mihan cultivar with FI treatment with 7.85 tons/ha, which were placed in the same statistical group with the yield of two cultivars, Heydari and Pishgam. The lowest amount of grain yield was obtained from the Heidari cultivar treatment with the I65 irrigation level with 2.81 tons per hectare. The highest water use efficiency and water productivity were 2.06 and 1.83 kg/m3, respectively, obtained from Mihan cultivar treatment in FI irrigation management. The lowest water use efficiency and water productivity were 1.13 and 1.01 kg/m3, respectively, obtained from Heydari cultivar and I65 treatments. .

IDF (Intensity-Duration-Frequency) curves play a crucial role in hydrological modeling, infrastructure design, and flood risk management. Traditional methods, relying on ground-based observations, face challenges such as limited spatial coverage, short temporal records, and the stationary assumption, particularly under climate change. This study addresses these issues by utilizing ERA5 reanalysis data to develop basin-scale IDF curves for the Karkheh River Basin (KRB) in Iran. Annual Maximum Precipitation (AMP) series for 6-, 12-, 18-, and 24-hour durations were extracted from ERA5 data and corrected for bias using observations from seven synoptic stations. Bias correction significantly improved ERA5 estimates, particularly in high-altitude regions prone to systematic errors. An elevation-bias relationship was established to extend corrections basin-wide. The corrected AMP data were modeled with the Generalized Extreme Value (GEV) distribution under stationary and non-stationary conditions to construct spatially distributed IDF curves. Based on 82 grid points, these curves provide detailed rainfall intensity estimates, overcoming limitations of station-based methods. The findings underscore ERA5 data's potential, combined with bias correction, to enhance hydrological analyses in data-scarce regions by better capturing spatial variability and extreme precipitation. This work supports improved flood management and infrastructure planning. However, future research must address uncertainties in bias correction and parameter estimation while extending data records. High-resolution reanalysis datasets are pivotal for adapting to evolving climatic conditions, extreme weather, and prolonged droughts.

This study investigates the impact of climate change on annual rainfall and runoff of Kasilian catchment through two distinct approaches. Firstly, it utilizes hybrid models by integrating the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) with Artificial Neural Networks (ANN), Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP) separately, as well as employing the Long Ashton Research Station Weather Generator (LARS-WG). Secondly, it employs Google Earth Engine (GEE) to analyze changes in annual rainfall and runoff for the observed period, compensating for incomplete data from hydrometric and climatological stations. The results demonstrate that under the SSP585 scenario, from various climate models in LARS WG and when employing hybrid models, the median annual rainfall is projected to increase in the future compared to the base period, while the median annual runoff is expected to decrease due to rising temperatures and increased evapotranspiration. Consistent with these projections, GEE data from 1981 to 2023 also indicates an increase in annual rainfall and a decrease in annual runoff. Additionally, there is a reduction in annual erosion and sedimentation rates, attributed to the reduced capacity of runoff to transport sediment. These findings highlight the potential for more extreme rainfall events, increased annual precipitation, and a subsequent decrease in annual runoff and sediment yield in the Kasilian catchment, providing valuable insights for future water resource management and climate adaptation strategies in the region.

Given the escalating challenges posed by climate change and the increasing frequency of droughts, participatory management has emerged as a critical strategy for enhancing resilience and mitigating vulnerability within the governance frameworks of water resources. This includes influencing decision-making processes, policies, and institutional arrangements related to water management. This study critically examines the role of participatory management in mitigating vulnerability and enhancing resilience to climate change and drought, utilizing bibliometric analysis of articles published between 2007 and 2024, sourced from the Web of Science. Analysis of the data using advanced tools such as VOSviewer and Biblioshiny revealed a marked increase in participatory management research within the context of climate and drought resilience, particularly since 2015. Key themes emerging from the literature include resilience, vulnerability, and the integration of public participation in water resource management decision-making processes. The transition from conceptual to applied research has been accompanied by the growing incorporation of cutting-edge technologies, including remote sensing, Geographic Information Systems (GIS), and machine learning models. These technologies have been proven instrumental in facilitating data sharing, modeling climate change impacts, and enhancing participatory decision-making frameworks. Geographically, there is a global trend toward strengthening local community engagement in water resource governance, with such participatory efforts playing a pivotal role in building resilience and developing sustainable water crisis management strategies. This study identifies key research gaps including the development of predictive resilience models, enhancement of local participation, and effective use of real-time data and advanced technologies improving water resource management under changing climate conditions