Arc-shaped bank erosion induced by scouring is a prevalent type of bank degradation observed in the middle and lower reaches of the Yangtze River. It is characterized by the continuous collapse of bank soil under the strong scouring of the flow near the bank. An indoor experiment was conducted to assess the flow field and topography of bank erosion at various developmental stages by solidifying the terrain. This experiment elucidated the changing characteristics of flow structure within the collapse area. Additionally, a generalized development model of arc-shaped bank erosion due to scouring was proposed to explain why the horizontal expansion rate of the collapse area exceeds that of the longitudinal expansion. The findings revealed: (i) The development process of arc-shaped bank erosion due to scouring could be categorized into two stages: “Parallel retreat” and “ Upper expansion “. The ratio of width to length of the collapse area was less than 0.17 in the first stage and exceeds 0.17 in the second stage. (ii) During the second stage, the horizontal and vertical erosion rates of the collapse area were primarily influenced by flow velocity and turbulence, respectively. (iii) The stratified backflow model was employed to compute the flow velocity during different periods, yielding results that closely matched the measured flow velocity distribution. This model could clarify the distribution of flow velocities in the arc-shaped collapse area and be used to explain why the boundaries of the collapse area eventually form major arcs.
Eco-friendly coastal forests effectively mitigate tsunami forces on structures. However, traditional models using rigid, uniform cylinders fail to capture the complex vertical structure of real coastal trees, which significantly influences wave attenuation. This study incorporates a three-layer vegetation model—aerial roots, trunk, and crown—to better simulate tsunami impact reduction, with layer-specific cylinder counts based on drag coefficient variations along the vertical stand. A three-dimensional numerical simulation was conducted utilizing OpenFOAM, implementing the Volume of Fluid method and the Realizable k-ε turbulence model. A nested hexahedral mesh optimized computational efficiency, while adaptive time-stepping maintained numerical stability (Courant number < 0.5). Model validation accurately predicted bore height, velocity fields, and force distributions. The study evaluates single-layer (VM1: trunk), double-layer (VM2: trunk and roots), and three-layer (VM3: trunk, roots, and crown) vegetation in tsunami force reduction. Results show that VM1, VM2, and VM3 reduce forces by 45.4%–52.8%, 51.4%–76.1%, and 57.7%–76.3%, respectively, with VM3 providing the highest attenuation. These findings highlight the significance of modeling coastal vegetation as a multi-layered structure incorporating trunk, roots, and crown in mitigating tsunami-induced forces and reinforcing nature-based solutions as effective coastal defenses.
Understanding rainfall variability and drought dynamics is essential for water-resource planning in arid regions such as Saudi Arabia. This study provides a comprehensive spatiotemporal assessment of rainfall characteristics and meteorological drought conditions across 25 meteorological stations in Saudi Arabia during the period 2000-2024. Rainfall variability was analysed using rainfall-intensity classification and Rescaled Adjusted Partial Sums (RAPS), while drought conditions were assessed using the Standardised Precipitation Index (SPI-12). The Mann-Kendall test and Sen's slope were used to assess the long-term trend behaviour and magnitude. The results reveal marked spatial heterogeneity in rainfall trends, with southwestern stations such as Abha, Al-Baha, and Gizan exhibiting statistically significant increases in annual rainfall of approximately 5-23 mm yr-1, while northern and interior stations including Al-Jouf, Hail, and Al-Qaysumah display long-term declines of 5-7 mm yr-1. Rainfall intensity analysis indicates a significant increase in light and very heavy rainfall events (40%-80%) at several southwestern stations after 2010, indicating heightened hydroclimatic variability and increased probability of extreme precipitation. SPI-12 analysis identifies two major multi-year drought episodes (2006-2009 and 2013-2016), during which up to 30% of stations experienced severe drought (SPI <= -1.50), with several stations reaching extreme drought conditions (SPI <= -2.00). These drought phases are consistent with cumulative rainfall deficits highlighted by RAPS trajectories. In contrast, recent years (2023-2024) exhibit widespread wet anomalies, with more than 20% of stations reaching moderately wet conditions (SPI >= 1.00) and several attaining very wet to extremely wet classifications (SPI >= 1.50). Despite this recent recovery, persistent rainfall deficits remain evident across central and northern regions. These findings provide observational evidence of emerging regional divergence in rainfall and drought behaviour across Saudi Arabia. These results offer a data-driven foundation for adaptive water-management strategies under Saudi Vision 2030 by identifying region-specific planning, enhanced drought monitoring, and integrated rainwater-harvesting and flood-mitigation strategies to strengthen long-term national water security.
The present study aimed to find out multi-dimensional (areal, hydrological, morphological and ecological) wetland transformation and explored a relationship among them taking Telkar wetland, a fast transforming marshy wetland in Rarh tract of Eastern India. Spectral water indices were used for wetland mapping and monitoring. Hydrological strength in form of Water richness (WR) was modeled using Analytic Hierarchical Process (AHP) based weighted compositing approach and validated using field reference data. Ecological transformation was assessed using Trophic state index (TSI). Spatial relation among the indicators was executed following ordinary least square (OLS) regression. From the study it is revealed that in last 30 years, post-monsoon wetland area was reduced from 28.18 km2 to 14.43 km2. In 1991, 60.90% area under very high water richness was reduced to 6.38% showing very rapid hydrological transformation. In reference to landscape morphology 73.65% large core area (20.75 km2) was reduced to 36.05% (5.20 km2) in between 1991 and 2021highlighting significant increase of patch and edge area. Ecologically, Telkar wetland witnessed oligotrophic state to eutrophic state in last 30 years. All the transformation components are strongly and statistically associated. Agriculture extension was found as a major factor behind wetland loss and hydrological transformation was identified as a pivotal to lead other forms of transformation. So, hydrological restoration through reviving connecting tie channel, re-vitalizing spill water flow from nearby river Ganga may help to save its identity.
Groundwater is a critical irrigation source in arid and semi-arid regions. This study evaluated groundwater quality trends and irrigation suitability using multiple standard water quality indices and a GIS-based irrigation water quality index (IWQI) in various groundwater sources in Isfahan, Iran, from 1995 to 2024. A total of 412 wells, 306 Qanats, and 108 springs used for agricultural irrigation were analyzed. Additionally, the IWQI was applied at ten-year intervals using interpolation and overlay techniques in GIS. The results indicated that wells had the highest mean values for electrical conductivity (EC), 6033.3 mu S/cm, and total hardness (TH), 1153.3 mg/ L, but the lowest value for residual sodium bicarbonate (RSBC),-13.8. In contrast, springs recorded the highest soluble sodium percentage (SSP), 44.6. Findings indicated fluctuations in suitability levels over the study period, with a general decline in groundwater quality. According to the IWQI, groundwater sources were classified under severe and high restriction categories for irrigation use across all studied years. The IWQI classification provided a clear spatial visualization of groundwater quality, serving as a valuable decision-support tool for sustainable groundwater management and crop selection.
The hydrodynamics of fish breeding tanks directly affect fish behaviour and the tanks’ self-cleaning properties. This study presents a novel configuration of the flow inlet system to enhance the performance of octagonal aquaculture tanks. The flow hydrodynamics and self-cleaning of octagonal aquaculture tanks are investigated through an experimental study (a tank with dimensions of 100 cm × 100 cm × 50 cm) and numerical simulation. The Navier-Stokes three-dimensional equations are discretized and solved by the finite volume method, coupled with the realizable k-ε turbulence model. For this purpose, the effect of different configurations of submerged flow inlet systems, including uniform and non-uniform discharge of flow into the tank at three different height levels in the water column of the tank, is studied on the parameters of size distribution (leftover food and solid fish waste), velocity uniformity, and self-cleaning. According to the outcomes, the non-uniform distribution of the inlet flow velocity in the tank’s water column has a positive influence on the distribution of its velocity and uniformity, as well as the removal of solid residues. It was concluded that the non-uniform distribution of the inlet flow velocity in the tank’s water column reduces the value of the uniformity index.
Extreme temperature events in Asia are intensifying, necessitating advanced predictive models that account for spatial heterogeneity and complex dependencies among climate variables. This study integrates Multiscale Geographically Weighted Regression (MGWR) and Copula Regression to enhance the accuracy of future extreme temperature projections. By incorporating General Circulation Models (GCMs), this study assesses the evolution of maximum temperatures under changing dependency structures among key climate variables, particularly geopotential height, precipitation, humidity, and wind speed. Our results demonstrate that the hybrid MGWR-Copula model significantly outperforms conventional machine learning approaches, such as Random Forest and Support Vector Machines, in capturing non-linear dependencies and spatial variations. Compared to global regression models, our approach provides higher predictive accuracy, particularly in regions with complex terrain like South Korea and Japan. Furthermore, projections under different Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) indicate notable increases in extreme temperatures, with high-emission scenarios leading to greater variability and forecast uncertainty. This study presents a robust framework for climate modeling, improving our ability to predict extreme temperatures and informing climate adaptation strategies. By integrating spatial regression, dependence modeling, and machine learning, this offers critical insights for climate risk assessment and policy development in vulnerable regions.
Large-scale floating solar farms (FSFs) have gained prominence in major coastal cities as a sustainable solution for renewable energy generation. This study investigates the effects of large-scale FSFs on local hydrodynamics and suspended sediment transport in coastal waters, induced by the turbulence modulation of surface boundary layer under tidal currents with the alternating open and covered water surface within the farm. Laboratory experiments were first conducted on a scaled FSF to measure the turbulence modulation of the surface boundary layer. Subsequently, a two-phase numerical model based on the Reynolds-Averaged-Navier-Stokes approach was established and validated by the experimental measurements. The model was then used to investigate the influence of different FSF configurations, water depths, and current magnitudes comprehensively. The results show that the FSF increases the thickness of the surface boundary layer significantly and amplifies its turbulent intensity due to flow detachment, while inducing spanwise secondary currents. The shear stress under the FSF coverage follows a linear distribution that supports the use of a modified logarithmic velocity profile near the boundary. The magnitudes of surface friction coefficient are found to be similar to those of smooth walls, which can act as a reference for the design and environmental assessments of future FSFs. Furthermore, the capacity for suspended sediment transport in the water body is found to decrease significantly due to the presence of large-scale FSFs based on empirical formulas, especially in shallow coastal waters. This reduction can lead to potential long-term sediment accumulation and deposition on the seabed beneath the FSF.
Standardized Precipitation Index (SPI) is widely used for monitoring drought due to its simplicity and effectiveness. However, various uncertainties arise from multiple factors in SPI calculation including the length of precipitation data, accumulation periods, probability distributions, and parameter estimation methods. This study aims to quantify the relative contribution of these factors to SPI uncertainty using a linear mixed model (LMM). In this study, various SPI calculation scenarios were considered by combining three data lengths (20, 30, and 50 years), four accumulation periods (1, 3, 6, and 12 months), five probability distributions (gamma, normal, log-normal, logistic, and generalized extreme value), and two parameter estimation methods (maximum likelihood estimation and L-moment). In our study, reference precipitation was defined as the amount of precipitation corresponding to a target SPI value (e.g., -1.0 or-2.0), determined by inverting the standard SPI calculation process. The uncertainty was quantified by calculating the root mean square error (RMSE) between the reference SPI and calculated SPI from various SPI calculation scenarios. The results showed that uncertainty decreased with longer accumulation periods and data lengths, while the RMSE was substantially higher and more variable under SPI = -2.0 than SPI = -1.0. The LMM was then used to assess the contribution of each uncertainty factor. The results revealed that for moderate drought conditions (SPI = -1.0), the primary contributors to uncertainty were sample size and accumulation period. However, under extreme drought conditions (SPI = -2.0), probability distribution accounted for over 50% of the total variance, reaching up to 84% in some cases. The impact of parameter estimation methods was relatively nonsignificant under all conditions, consistently accounting for less than 3% of the total variance. These findings suggest that selecting an appropriate distribution and using long-term precipitation data are critical for improving the reliability of SPI-based drought assessments. This study highlights the critical need for long-term precipitation records (at least 50 years), appropriate accumulation periods, and rigorous selection of probability distributions, particularly under extreme drought conditions.
Reservoir systems serve as a prevalent mechanism for the control and management of water resources. Given the constraints of limited resources and the escalating demands for water, it is imperative that these systems are operated optimally to enhance the efficiency of water utilization. Despite advancements in addressing real-world challenges, classical optimization methods frequently fall short of delivering optimal solutions due to the structural complexity and the multitude of variables involved. As a result, there exists an urgent need for more effective and robust methodologies to address these challenges. Meta-heuristic algorithms, particularly those inspired by biological evolution and referred to as evolutionary computation, represent reliable and straightforward approaches for tackling complex optimization problems, positioning themselves as viable alternatives to traditional optimization techniques. Evolutionary computation can be classified into two primary categories: evolution strategies and swarm intelligence. While meta-heuristic algorithms based on swarm intelligence are characterized as multi-agent systems that emulate individual behaviors, those grounded in evolution strategies employ adaptive search mechanisms derived from evolutionary processes. This research aims to quantify the uncertainty associated with meta-heuristic algorithms and to evaluate their efficacy in the planning and management of water resources, specifically for the optimal operation of a single reservoir. The study assesses 101 evolutionary algorithms, categorized into eight groups, with a focus on their application in optimizing reservoir system operations to enhance efficiency. The case study centers on the Gheshlagh Reservoir located in Kurdistan, Iran. A comparative analysis of the performance of these algorithms revealed that the SHADE algorithm outperformed its counterparts, achieving a minimum objective function value of 9.59 x 10_ 10 and demonstrating superior computational speed. Notably, SHADE attained a demand deficit of zero million cubic meters for the reservoir, whereas the FOA algorithm recorded the highest deficit of 10.74 million cubic meters. Furthermore, DE class algorithms exhibited the highest overall performance in the operation of the Gheshlagh Reservoir, showcasing reduced computation times, enhanced robustness, and improved decision-making capabilities. The study underscores the significance of algorithmic structure and problem type in determining performance outcomes, recommending the adoption of SHADE or DE class algorithms for the formulation of operational policies in complex reservoir systems. These findings provide valuable insights for researchers seeking to introduce new or modified algorithms and offer guidance to administrators in selecting the most appropriate algorithm based on specific operational requirements.
The morphodynamic behaviour of river mouth sandspits, sustained by wave-driven longshore sediment transport, is governed by complex interactions between prevailing hydrodynamics and anthropogenic forcing. At many coastal environments, the lack of field-measured datasets, more often than not, hampers long-term morphodynamic investigations on river mouth sandspits, thus making them data-scarce locations. This study investigates the wave-dominated and micro-tidal Volta River mouth in Ghana using process-based Delft3D model simulations. The simulations of the river mouth's unrestricted (natural) updrift spit state were undertaken using hydrodynamic data schematisation approaches and satellite-derived and global bathymetries. Model results showed that, among varying wave conditions, relatively higher wave heights (similar to 1.8-1.9 m) facilitate a narrowwidth spit growth at a faster rate. Conversely, an elongating spit with a slower growth rate and a larger width was observed under relatively moderate wave heights (similar to 1.2-1.3 m). The results indicate that the growth rate of an unrestricted spit decreases with increasing width. These findings are important for understanding how unrestricted spit's formation and morphodynamic evolution affect river mouths. Most importantly, the results can be related to morphodynamic feedback during spit breaching events, formation of intruded spits, narrowing or closure of river mouths, and inland flooding of surrounding estuarine and coastal communities.
Accurate forecasting of flood runoff peaks during rainstorms remains challenging because prediction errors usually increase near flood thresholds and peak discharge. Most deep learning models learn patterns from data only and do not explicitly emphasize peak-critical errors during training. Therefore, we propose a data and knowledge-driven (DK-TCIT) model that integrates Time-Distributed Convolutional Neural Networks (TD-CNN) for local feature extraction, Informer with ProbSparse attention for global temporal dependencies, and Temporal Convolutional Networks (TCN) for local-global sequence modeling. A key innovation is a knowledge-guided loss function that embeds expert knowledge of flood dynamics, assigning higher learning priority to the critical peakflow region detected from observed flood thresholds. DK-TCIT was evaluated on two basins in China (ChangHua and TunXi) using a 12-hour input window to predict the next 6 h of runoff. Results show that DK-TCIT consistently outperformed ConvLSTM, CNN, SLSTM, TD-CNN-LSTM, STALSTM, Informer, and TCN across all metrics. In TunXi, it achieved RMSE reductions of 31-42% and NSE improvements of 26-41% compared with the best baseline model, while similar gains were obtained in the ChangHua basin. The proposed loss function also surpassed Mean Squared Error (MSE), Mean Absolute Error (MAE), and standard Huber loss, with the largest gains observed around peak runoff conditions. These findings indicate that combining hybrid spatiotemporal learning with explicit peak-focused supervision improves short-term flood peak forecasting and provides a practical solution for flood hazard management applications.
Hydro-abrasion is a process of wear resulting from the mechanical stress exerted by impacting particles in the flow on a riverbed or banks or on the invert of hydraulic structures. Hydro-abrasion models represent the mechanics of invert abrasion by bed load particles and allow to predict hydro-abrasion rates. The present study deals with the enhancement of the existing mechanistic saltation hydro-abrasion model by incorporating new equations for particle velocity, hop length, an exponential cover effect term, and two additional important terms accounting for particle hardness and saltation probability, respectively. We particularly focus on the effects of particle and bed lining material hardness, bed cover, and low aspect ratio on hydro-abrasion, which were not holistically investigated in previous studies. The non-dimensional hydro-abrasion coefficient kv (also known as the rock resistance coefficient) in the enhanced model was calibrated using both experimental laboratory data and field measurements obtained from three Swiss Sediment Bypass Tunnels as part of our research project. A constant value of kv = 4.8 +/- 2.2 x 104 was obtained for a range of different materials with less scattering compared to the coefficients reported in previous studies. The enhanced model demonstrated a good performance when validated with independent data from laboratory and field studies, indicating that the laboratory results can be upscaled to prototype conditions.
Hydro-abrasion refers to the gradual loss of material on the surface of a solid body, caused by mechanical stress, mainly from the impacts of sediment saltation in flowing water. Hydraulic structures like weirs, spillways, diversion tunnels, and especially sediment bypass tunnels (SBTs) experience significant hydro-abrasion due to high flow velocities and elevated sediment transport rates. The hydro-abrasion process is critical in hydraulic engineering, where material loss can lead to structural damage and costly repairs, and in geomorphology, where it drives bedrock incision and shapes landscape evolution over time. This study aims to advance the knowledge of hydro-abrasion mechanics (part I, present paper) and to enhance a mechanistic saltation hydro-abrasion model (part 2) for predicting river and landscape evolution and hydro-abrasion at hydraulic structures. To this end, hydro-abrasion tests of polyurethane foams and weak mortar mixtures as bed materials were systematically conducted in a 0.20 m wide, 0.7 m deep and 13.5 m long laboratory flume at VAW at ETH Zurich, under supercritical flow conditions. The study investigates the effect of flume width-to-flow depth aspect ratios, approach flow Froude numbers, particle diameter and hardness and sediment supply rate on hydro-abrasion rate and pattern. The focus is on the latter two parameters, which were not previously and systematically investigated in flume studies at low aspect ratios. The abrasion depths were measured using a 3D high precision laser scanner. Results revealed that abrasion rate increases with sediment supply rate with maximum abrasion occurring when sediment transport capacity is reached. Harder sediment leads to higher abrasion rates. Abrasion patterns depend on the aspect ratio, causing the formation of one or two incision channels. Three cover effect functions, namely, linear, exponential, and probabilistic were compared to the data. The exponential cover function provides the best representation of the present data. These findings provide new insights into the physical mechanisms of hydro-abrasion under varying hydraulic, sediment, and bed material conditions. This research contributes to the enhancement of a well-known mechanistic saltation abrasion predictive model by incorporating the proposed hardness and cover equations, which is detailed separately in the accompanying paper as Part 2.
The interaction of sedimentation and backwater in the reservoir area after the operation of the reservoir causes the backwater and sedimentation to continuously extend upstream. Studying the variation pattern of backwater length after reservoir sedimentation is of significant importance for assessing the reservoir inundation range. Based on the calculation of backwater after sedimentation at the BDa Reservoir, the variations in the backwater under the delta deposition at the reservoir were revealed. During the flood season, there are two inflection points in the backwater surface profile, occurring respectively near the pivot point and starting point of the delta. The impact of deposition thickness on the rise in backwater elevation is mainly reflected at the topset reach of the delta. Furthermore, the depth calculation formulas of foreset reach, topset reach, and sedimentation-affected reach of the delta deposition were established. Based on these, factors influencing the backwater length under delta deposition were identified as the depth at the dam, the distance from the pivot point to the dam, and the inlet discharge. Then a rapid estimation method for backwater length under delta deposition was proposed and validated. Results provide a rapid estimation of reservoir backwater length, which can prevent the protected projects from being inundated by the reservoir backwater.
The stability of ecosystems fundamentally depends on dynamic and mutualistic relationships among various ecological processes. However, comprehensive insights into the spatiotemporal dynamics of these processes, particularly their cumulative effects in space, remain insufficiently developed. This study systematically analyzed key ecological indicators in the arid Tarim River Basin (TRB) of northwestern China from 2000 to 2020. Key ecological indicators analyzed included Leaf Area Index (LAI), Gross Primary Productivity (GPP), Evapotranspiration (ET), and Water Use Efficiency (WUE), collected through remote sensing, field observations, and model prediction. The results revealed the dynamic interactions between surface ecological factors and soil indicators, emphasizing the influence of ecological-hydrological relationships on water resource management and overall ecosystem health. The influences of tributaries on the mainstream occurred within a 20 to 150-day time lag, presenting both positive and negative feedback effects. Furthermore, when the average tributary WUE surpassed 1.06, it was sustained for 1 to 2 months, accompanied by a marked increase in the mainstream’s WUE. Significant positive indirect cumulative effects were observed for LAI (0.27), total vegetation GPP (0.1875), ET (0.345), and soil moisture content (0.419). The results emphasize the effectiveness of multiple linear regression models in simulating ecological parameters within the mainstream of the TRB. This study advances the knowledge of ecosystem dynamics in arid environments and offers critical guidance for sustainable water resource management in the TRB and similar regions globally.
Environmental free-surface flows encompass a wide range of applications in civil and environmental engineering. Hydraulic models, physical and numerical, are developed based upon the fundamental principles of similitude and dimensional analysis, as well as conservation of mass, momentum and energy, to ensure a reliable prediction of full-scale performances. Free-surface flows are modelled using a Froude similitude because gravity effects are important. Practically, the vast majority of free-surface flow models use water and air as in prototype. This constraint implies an invariant Morton number. With a combined Froude and Morton similarity, the Reynolds number is proportional to the mass flux. and it is typically much smaller in the hydraulic model. The difference in Reynolds numbers between model and prototype accounts for potential scale effects in terms of both viscous and capillary processes. It is demonstrated that the Weber number is irrelevant when the Reynolds number is retained. A few hydraulic models used different fluids between models and full-scale applications, and their application is discussed.
A 2D hydrodynamic and mass transport model was developed in this study, focusing on inorganic nutrients in water and utilizing GPU acceleration to improve simulation efficiency. Compared to traditional water environment models, this model is not only capable of simulating the transport processes of key water quality factors, including the nitrogen cycle, phosphorus cycle, dissolved oxygen balance, and chlorophyll alpha, but also significantly enhances computational efficiency. It was applied to Yanming Lake No.5 under various water flow conditions, using measured data to ensure accuracy. The results indicated that reliable simulations were provided by the model, accurately reflecting changes in water dynamics and quality. Meanwhile, under the same simulation conditions, its computational efficiency was approximately seven times greater than that of CPU devices. As throughput increased, overall water depth and velocity were found to remain stable, while concentrations of water quality factors gradually decreased, primarily affecting the lake's entrance. Over time, signs of poor nutrient conditions due to eutrophication were noted in the lake. This model enables detailed simulations of the transport of environmental variables and their interactions, serving as a valuable tool for predicting and preventing water pollution.
Meteorological drought is characterized by prolonged periods of below-average precipitation and is a major environmental hazard that significantly affects agriculture, water resources and ecosystems. Drought assessment and understanding its patterns are important for effective water management and risk mitigation. This study aims to assess the spatiotemporal variability and characteristics of meteorological drought in Northern Thailand from 1980 to 2016, using precipitation and temperature data from 22 meteorological stations provided by the Thai Meteorological Department (TMD). We used the Standardized Precipitation Evapotranspiration Index (SPEI) to identify drought events and analyze their trends using Spearman's Rho test. Additionally, we applied Run theory to quantify drought characteristics, including duration, severity and intensity. The novelty of this study lies in its comprehensive approach, integrating long-term climate data with advanced statistical methods to assess the impact of rising temperatures on drought frequency. The results revealed significant increasing trend in mean, minimum, and maximum temperatures across most meteorological stations, contributing to frequent drought events. Notably, severe droughts were observed during 1982-1983, 1986-1987, 1991-1993, 1997-1998, 2004-2005, 2009, and 2014-2016. Thus, these SPEI analysis highlights the growing influence of temperature-driven evapotranspiration which lead to soil moisture loss and crop failure. The insights from this study emphasizes on the need of proactive drought risk management and adaptation strategies particularly for agriculture sector. Future research should focus on assessing the socio-economic impacts of drought and developing predictive models for improved mitigation planning.
