Introduction
One of the major natural disasters that remains a significant challenge in Indonesia is drought. According to a report by the National Disaster Management Coordinating Board [1], historical data analysis indicates that drought occurrences in Indonesia are significantly linked to the El Niño-Southern Oscillation (ENSO) phenomenon. Since 1844, there have been 43 recorded drought events in Indonesia, with only six of them unrelated to El Niño. The strong impact of El Niño contributes to an extended dry season compared to the rainy season, ultimately increasing the likelihood of prolonged drought disasters [2].
Aceh Besar Regency is one of the regions in Indonesia that frequently experiences drought. Research indicates that drought in this area is influenced by the El Niño phenomenon, leading to extended dry periods and reduced availability of clean water. Additionally, drought in Aceh Besar occurs annually, particularly during the transition from the rainy season to the dry season (inter-monsoon period), and affects almost all areas within the regency. To address this issue, the local government manually collects data to identify drought-affected areas. Mitigation efforts are then carried out by distributing clean water to areas experiencing drought and water shortages.
Several researchers have highlighted that drought is one of the most complex and recurrent natural hazards, affecting ecological, economic, social, agricultural, cultural, and political aspects in different ways [3, 4]. In the digital economy era, cross-border e-commerce plays a crucial role in enhancing regional competitiveness and driving the transformation of international trade. Various policies, such as cross-border e-commerce pilot zones and urban civilization initiatives, have been implemented in China to optimize economic growth and urban sustainability through industrial innovation, institutional reform, and the strengthening of digital infrastructure. This study explores the impact of these strategies, highlighting how sustainable development in cross-border e-commerce and urban policies can contribute to stable and sustainable economic growth [5-7]. Furthermore, drought is a recurring and long-lasting phenomenon, ranking among the most economically damaging natural disasters globally [8]. The frequency and intensity of drought events are projected to increase due to rising water demand driven by population growth, constrained and unpredictable water supplies, and climate variability. Drought is typically classified into three categories: meteorological, agricultural, and hydrological [9, 10]. However, prior studies have primarily categorized drought based on characteristics of water deficits [11] or its impacts [12] into four types: meteorological drought, hydrological drought, agricultural drought, and socio-economic drought.
The study on earthquake disaster risk in Bener Meriah, Aceh, Indonesia, using a GIS approach. They analyzed the 2013 earthquake event (6.2 on the Richter scale) by overlaying hazard, vulnerability, and capacity factors based on the National Agency for Disaster Countermeasure (known as BNPB) Regulation 2/2012 standard [13]. They investigated geothermal potential at Mount Seulawah, Aceh, by estimating land surface temperature (LST) through remote sensing, utilizing the split-window algorithm (SWA) and lineament density analysis to evaluate geothermal manifestations [14]. They monitored agricultural drought in rice fields in Aceh Besar Regency using Sentinel-2A satellite data, applying the Vegetation Condition Index (VCI) and Normalized Difference Vegetation Index (NDVI) to assess drought severity over five planting seasons [15]. They analyzed the distribution of Green Open Space (GOS) in Banda Aceh using high-resolution Planetscope-3A satellite imagery, employing supervised classification and NDVI transformation to categorize land cover into eight categories, achieving an accuracy rate of 76.036% and a kappa value of 0.726 [16].
Fundamentally, every disaster impacts different regions in unique ways. Therefore, disaster risk assessment is essential to determine potential losses, impacts, and mitigation efforts, including drought risk in Aceh Besar Regency. The risks associated with drought in this region include reduced water supply for daily needs, crop failures, environmental degradation, and even social conflicts. The BNPB has mapped the drought risk index for Aceh Province at a scale of 1:1,300,000, categorizing Aceh Besar Regency as a low-risk area [17]. However, this mapping covers a broad scale and is not detailed enough for effective disaster management at the local level. Additionally, given that drought is a recurring event with uncertain onset and duration, continuous monitoring and identification of drought risk factors are necessary to develop a drought risk model. Furthermore, drought hazard, vulnerability, and risk maps are needed to support drought disaster mitigation planning [2].
To date, disaster risk models have evolved alongside research advancements in disaster risk assessment. Some models integrate hazard with vulnerability [18, 19], while others incorporate probability with potential losses [20]. Additionally, some researchers argue that disaster risk is influenced by susceptibility, exposure, and vulnerability factors [21]. Another approach calculates disaster risk as susceptibility multiplied by vulnerability and divided by capacity [14, 17].
This study has broad implications for scientific development, researchers, government agencies, and the general public. From a scientific perspective, the research contributes to enhancing knowledge related to detection methods and approaches for drought disaster management. Additionally, this study is useful in assessing the hazard of regions to drought based on social, economic, and environmental factors, allowing for a comprehensive drought hazard analysis in the study area. For researchers, this study expands knowledge on technological applications and approaches in drought disaster mitigation. For government agencies, it provides valuable information on drought hazard levels in Aceh Besar Regency, which can serve as a guideline for formulating more effective drought mitigation policies. Meanwhile, for the general public, this research enhances awareness of drought-prone areas and serves as an early warning system, particularly for residents in high-hazard drought zones.
Based on this background, the objective of this study is to develop a drought hazard mapping model using GIS to identify the spatial distribution of drought hazard levels in Aceh Besar Regency, considering both drought and regional hazard. By the end of this research, recommendations and guidelines will be formulated as a reference for planning and implementing drought disaster mitigation strategies in Aceh Besar Regency. This study differs from previous research not only in terms of the study location but also in its objectives, variables used, analytical methods, and the comprehensiveness of the discussion. This study incorporates a comprehensive set of variables, including average annual rainfall, slope gradient, soil type, geology, distance from water sources, and land cover, to develop a drought hazard mapping model. This approach enhances spatial accuracy compared to traditional single-parameter or meteorological-based drought assessments. The study provides a validated mapping model that can serve as a scientific basis for policymakers and stakeholders in Aceh Besar Regency to implement more effective drought response and resilience strategies, bridging the gap between hazard assessment and practical application.
Materials and Methods
The data required for this study consists of two types: spatial data and non-spatial data. Spatial data includes raster data in the form of a Digital Elevation Model (DEM) and vector data, which consists of maps related to variables associated with drought susceptibility. Meanwhile, non-spatial data includes tabular data, such as demographic data and supporting information providing a general overview of the study area (Fig. 1). For more details, refer to Table 1.
A spatial approach using a quantitative method that employs statistical indicators to measure and compare various variables has been utilized in this study. The spatial approach aims to analyze drought disasters from a spatial perspective. Data collection was conducted through institutional surveys and literature reviews, utilizing secondary data. The data analysis techniques applied include scoring analysis, weighting, and map overlay.
Scoring is the process of converting responses into numerical values, representing quantitative measures [22]. The scoring process is based on theories related to drought hazard factors, references from previous research, and empirical field experiences regarding the extent to which each class influences drought. A higher influence on drought results in a higher score. The scoring for these parameters is conducted linearly within the classes of a particular drought parameter, with scores ranging from 1 to 5. A score of 1 is assigned to classes with minimal influence on drought, while a score of 5 is given to classes with a significant impact on drought.
This study employs the ranking method for weighting, which is among the simplest techniques for assigning weight values [24]. Each parameter is ranked based on importance. The ranking determination is subjective; for instance, the most important parameter is assigned a value of 1, an important parameter is given a value of 2, and a less important parameter is assigned a value of 3. Alternatively, a reverse approach can be applied, where a less important parameter is given a value of 1, an important one a value of 2, and the most important one a value of 3. The weighting is calculated using Eq. (1) [2].
Table 1.
The data supports the drought analysis.
where Wj is the normalized weight for the j (j = 1, 2, 3,… n), n is the total number of parameters, rj is the ranking position or importance level of a given parameter, and p represents the parameters (p = 1, 2, 3,... n).
Results and Discussion
The variables used to develop the drought hazard mapping model include annual average rainfall, slope gradient, soil type, geology/rock composition, distance from water sources, and land cover. These variables were analyzed using a scoring approach and then assigned weights accordingly.
Annual rainfall data is obtained from the latest datasets provided by relevant government agencies, such as BMKG. If such data is unavailable, it can be retrieved from CHIRPS (Climate Hazards Group InfraRed Precipitation), as shown in Fig. 2a). The classification and scoring for annual rainfall data are presented in Table 2.
Fig. 2.
a) annual average rainfall map; b) DEM map; c) slope map; d) soil map; e) geology map; f) land cover map.
Table 2.
Rainfall, soil, and land cover classification.
| Parameter | Score | ||
|---|---|---|---|
| Low = 0.333 | Medium = 0.666 | High = 1 | |
| Rainfall | > 3,000 mm | 1,500 - 3,300 mm | < 1,500 |
| Soil Type | Organic/Peat | - | Non-Organic/Mineral |
| Land cover | Forest | Plantation/Garden | Dryland Farming, Shrubland, Grassland |
In Fig. 2b), the DEM map represents the terrain elevation of the study area, providing a raster-based representation of surface topography. It is used to analyze variations in elevation, which play a crucial role in assessing drought susceptibility, as elevation influences factors such as water flow, soil moisture retention, and vegetation distribution. In Fig. 2c), the Slope map is derived from the DEM and illustrates the degree of inclination across the terrain. Slope influences water runoff, infiltration rates, and soil erosion potential, making it a critical parameter in drought hazard assessment. Steeper slopes tend to experience higher runoff and lower water retention, whereas flatter areas retain more moisture, affecting the overall drought hazard classification. The next step is to create a soil type classification map and geology map (Fig. 2d) and 2e)). Peat soil is categorized as a low-hazard class, while all other soil types fall into the high-hazard class (Table 2). The land cover data layer was analyzed to generate a land cover classification map (Fig. 2f)). The classification of land cover related to drought hazard was divided into three land types, each assigned specific scores and weights, as shown in Table 2.
The weighting concept for drought hazard component variables is based on the extent to which each variable contributes to drought occurrence, as explained: the drought process begins with a decrease in rainfall intensity, serving as the initial indicator of drought occurrence. Rainwater flows from higher to lower areas, making the slope gradient of a region a significant factor. Furthermore, the availability and retention of water in the soil are influenced by the soil type and the geological composition of the area, assessed based on their permeability. These four conditions are indirectly related to the availability of surface water sources. In terms of drought hazard, the distance from a water source also plays a crucial role. Additionally, land cover depends on soil type, geological structure, and rock composition. Based on this analysis, the parameter variables are ranked according to their influence on drought hazard (Fig. 3). The most influential variable is assigned the highest weight, followed by the others in decreasing order. The weights are then normalized using the ranking method, following Eq. (1). After assigning scores and weights, all drought hazard variables are overlaid. The drought hazard map is generated by combining or summing the total scores from the thematic maps representing the contributing factors. The final map is then reclassified into five hazard classes using the Natural Breaks method, ensuring that the classification retains the essential characteristics of each category.
Based on the analysis conducted, three drought hazard classes have been identified in Aceh Besar Regency: Not Vulnerable, Medium, and Highly Vulnerable. The detailed classification is presented in the following table. Based on Table 3 and Fig. 4, the drought hazard level in the study area can be analyzed based on two main categories: medium and high. The variation in land area within these two categories reflects different levels of drought hazard across sub-districts.
The largest area in the medium hazard category is Kota Jantho (198.67 km²), followed by Lhoong (100.39 km²) and Kuta Cot Glie (85.03 km²). Other sub-districts with significant areas in this category include Lembah Seulawah (82.36 km²) and Seulimeum (71.12 km²). These areas have considerable drought potential in the medium category, which could impact water availability for agriculture, consumption, and local ecosystems. On the other hand, the smallest areas in the medium category are Krueng Barona Jaya (0.11 km²), Simpang Tiga (0.89 km²), and Darul Kamal (1.00 km²). The limited extent of these areas suggests a relatively lower hazard of medium-level drought compared to other sub-districts. In the high hazard category, the largest area is Kota Jantho (376.18 km²), significantly higher than other sub-districts. Seulimeum (317.98 km²) and Kuta Cot Glie (232.83 km²) also have substantial land areas classified under high hazard. This indicates that these three sub-districts are highly susceptible to severe drought, which can threaten water resources and environmental sustainability. Conversely, the smallest areas in the high hazard category are Peukan Bada (13.13 km²), Krueng Barona Jaya (6.12 km²), and Darul Imarah (19.59 km²). Although these areas have a relatively smaller extent in the high hazard category, the impacts of drought must still be considered, particularly in terms of water resilience and community well-being.
Sub-districts with large high-hazard areas, such as Kota Jantho, Seulimeum, and Kuta Cot Glie, require special attention in water resource management. Mitigation efforts such as reservoir construction, groundwater management, and water-efficient irrigation technologies could help reduce drought impacts. Additionally, sub-districts with large medium-hazardareas, such as Lhoong, Lembah Seulawah, and Seulimeum, need strong adaptation strategies to prevent worsening drought conditions. Reforestation programs and soil conservation efforts can help maintain water availability in these regions.
Table 3.
Drought-prone area coverage in Aceh Besar Regency.
Table 4.
A comprehensive review of agricultural drought monitoring, water scarcity analysis, and drought hazard assessment across global regions.
Overall, this data provides crucial insights into drought hazard levels across different sub-districts. Proper mitigation and adaptation measures are essential to minimize drought impacts and ensure water security in high-hazard areas.
Table 4 provides a brief review of studies related to agricultural drought monitoring, drought and water availability analysis, and drought hazard assessment, highlighting various methodologies and applications. It presents research efforts that utilize remote sensing, drought indices, and geospatial techniques to assess drought severity, water scarcity, and their impacts on agriculture and ecosystems. The reviewed studies also emphasize the development of new drought indicators and hazard assessment models to enhance drought preparedness and mitigation strategies across different geographical regions.
Based on the analysis of drought hazard levels in Aceh Besar Regency, the active involvement of various stakeholders is essential to develop effective mitigation strategies [58, 59]. The local government should design GIS-based policies to prioritize areas with the most severe impacts, such as Kota Jantho, Seulimeum, and Kuta Cot Glie, in water infrastructure planning and environmental conservation efforts [60, 61].
Relevant agencies, such as the Public Works Department and Agriculture Department, should focus on strengthening irrigation systems and improving water use efficiency in the agricultural sector to mitigate the effects of drought. Additionally, academics and researchers can contribute by developing data-driven drought prediction models to provide more accurate recommendations for mitigation planning [62, 63].
Similarly, water-related challenges extend beyond shipyards [64]. In Hudaira Drain, arsenic contamination poses severe health risks. GIS analysis reveals high arsenic concentrations in industrial zones, with strong correlations between drain water, groundwater, and soil. Alarmingly, 75% of children and 50% of adults are at risk of arsenic exposure through groundwater consumption. The potential for excess lifetime cancer risk is significant, necessitating urgent mitigation efforts [65]. Elsewhere, environmental hazards continue to impact communities. The Kostanay region faces heightened wildfire risks due to dry conditions and a shifting climate. Remote sensing data identifies key factors: fuel availability, geomorphology, and meteorology, driving fire outbreaks. High-risk zones dominate the landscape, emphasizing the need for improved fire prevention strategies [66]. Geological disasters further compound environmental threats. In Heitai, landslides endanger infrastructure and human lives. A novel risk assessment method maps disaster-bearing zones, quantifying economic and life risks. GIS-based modeling highlights areas of extreme hazard, reinforcing the need for proactive mitigation measures [67].
The community should also be actively involved through educational programs and awareness campaigns on water resource management and the implementation of soil and forest conservation practices to reduce future drought risks. Through synergistic collaboration between the government, academia, private sector, and local communities, a sustainable mitigation system can be established to address the annual drought threats in this region.
Despite the valuable insights provided by this study, several limitations should be acknowledged. First, the drought hazard mapping model relies on the availability and accuracy of spatial data, including annual average rainfall, slope gradient, soil type, geology, distance from water sources, and land cover. Any inconsistencies or gaps in these datasets may affect the precision of the analysis. Second, this study focuses solely on hazard mapping without integrating socio-economic factors, such as population density, water demand, and agricultural dependency, which are critical in assessing the overall drought risk. Incorporating these factors in future research could provide a more comprehensive risk assessment framework. Third, the study employs a static GIS-based approach to drought hazard mapping, which does not account for temporal variations in drought conditions. Seasonal changes and climate variability may significantly influence drought intensity and distribution. A dynamic modeling approach, incorporating real-time climate data and predictive models, would enhance the accuracy and applicability of the findings. Finally, while the study highlights the spatial distribution of drought hazard levels, it does not evaluate the effectiveness of existing mitigation strategies. Future research should include an assessment of current drought management policies and their impact on reducing drought vulnerability in Aceh Besar Regency. Addressing these limitations in future studies will contribute to a more robust understanding of drought hazards and improve the development of targeted and sustainable mitigation strategies.
Conclusions
Drought in Aceh Besar Regency is a recurring phenomenon that occurs annually, and until now, its management has remained short-term in nature. Therefore, continuous monitoring and analysis of drought hazard factors are necessary to develop a more comprehensive drought hazard mapping model. This model can serve as a basis for more effective and sustainable drought mitigation planning.
Based on the analysis of land area within the medium and high hazard categories, several sub-districts exhibit higher drought hazard levels than others. The Kota Jantho sub-district has the largest affected area in the medium category (198.67 km²), while Krueng Barona Jaya has the smallest affected area (0.11 km²). In the high hazard category, Kota Jantho also recorded the largest affected area (376.18 km²), whereas Peukan Bada had the smallest affected area (13.13 km²). These variations in affected areas indicate differences in drought hazard levels across regions, which should be carefully considered in mitigation strategies.
The drought hazard mapping model using GIS to identify the spatial distribution of drought hazard levels in Aceh Besar Regency based on key variables such as: annual average rainfall; slope gradient; soil type; geology/rock type; distance from water sources; land cover, with this mapping model, mitigation strategies can be focused on high-hazard areas, allowing for more precise and effective prevention and response measures. The drought poses a significant threat in several sub-districts of Aceh Besar Regency. Therefore, hazard-based mitigation and adaptation efforts are needed to support optimal water resource management, reduce drought impacts, and enhance regional resilience against future drought disasters.
