Rainfall Patterns Research Articles

Coral Sr/Ca-SST reconstruction from Fiji extending to ~1370 CE reveals insights into the Interdecadal Pacific Oscillation.

The southwestern tropical Pacific is a key center for the Interdecadal Pacific Oscillation (IPO), which regulates global climate. This study introduces a groundbreaking 627-year coral Sr/Ca sea surface temperature reconstruction from Fiji, representing the IPO's southwestern pole. Merging this record with other Fiji and central tropical Pacific records, we reconstruct the SST gradient between the southwestern and central Pacific (SWCP), providing a reliable proxy for IPO variability from 1370 to 1997. This reconstruction reveals distinct centennial-scale temperature trends and insights into Pacific-wide climate impacts and teleconnections. Notably, the 20th century conditions, marked by simultaneous basin-scale warming and weak tropical Pacific zonal-meridional gradients, deviate from trends observed during the past six centuries. Combined with model simulations, our findings reveal that a weak SWCP gradient most markedly affects IPO-related rainfall patterns in the equatorial Pacific. Persistent synchronous western and central Pacific warming rates could lead to further drying climate across the Coral Sea region, adversely affecting Pacific Island nations.

Annual and Interannual Rainfall Variability in Indonesia Using Empirical Orthogonal Function (EOF) Analysis and Its Response to Ocean-Atmosphere Dynamics

We investigate rainfall variability in Indonesia using the Empirical Orthogonal Function (EOF) method. The analysis starts by taking three main modes of EOF results, namely EOF1, EOF2, and EOF3. The EOF1 region is southern Indonesia, from southern Sumatra to Timor Island, parts of Kalimantan, parts of Sulawesi, and parts of Irian Jaya. The EOF2 region is located in northwestern Indonesia and includes the northern part of Sumatra and the northwestern part of Kalimantan. The EOF3 region covers Maluku. This study aims to analyze the annual and inter-annual variability of rainfall in anticipation of the threat of hydrometeorological disasters. Based on the correlation value of the principal component (PC) with the dipole mode index (DMI) and Niño3.4 index, it has a period similar to El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Rainfall in Indonesia is very sensitive to sea surface temperature (SST) in the southeastern Indian Ocean and the central Pacific Ocean, which means that rainfall patterns in Indonesia can change significantly if SST in the region changes.

Increasing WNP tropical cyclone-related extreme precipitation over East Asia during boreal summer associated with PDO shift

Over the past two decades, there has been a significant shift in tropical cyclone (TC) activity in the western North Pacific (WNP) basin during the boreal summer. Our analysis of data spanning from 1979 to 2021 reveals significant shifts in the WNP TC characteristics and rainfall pattern variation. To deepen our understanding of TC-related precipitation dynamics, we expressly address the difference between TC-related core precipitation (TCP) and remote precipitation (TRP). The results show that TRP significantly impacts the East Asian (EA) continent, especially on the Korean Peninsula. Notably, TCP exhibits decadal variability, with a pronounced negative correlation identified between it and the Pacific decadal oscillation (PDO) following a strong climate shift. This pivotal shift was marked by the PDO first transitioning to its negative phase in 1997, a notable change since 1979, resulting in a marked increase in TC-related extreme rainfall over the EA area. Concurrently, the rising sea surface temperatures (SSTs) over the WNP have intensified the western Pacific subtropical high (WPSH) circulation. The easterly steering flow associated with the WPSH then strengthened, leading to the continental migration of TC trajectories, thereby intensifying TC-related extreme precipitation.

The Impact of Climate Change on Road Traffic Crashes in Ghana.

Despite the substantial injuries and fatalities from Road Traffic Crashes (RTCs), evidence of climate change's impact on RTCs in Ghana is lacking. This study assessed the impact of climate change on RTCs in Ghana by combining quantitative (Mann-Kendall trend tests, Continuous Wavelet Transform analysis, causal inference analysis) and qualitative (15 key stakeholder interviews) methods. The quantitative analysis employed monthly rainfall and temperature data (1991-2021) alongside RTC data (1998-2021) across 10 regions. While rainfall trends varied regionally, the wet season (April through mid-October) showed a strong link to crash severity for all regions across Ghana. Wavelet analysis showed higher crash severity in the wet season within every 2-8 months period in a particular annual year during the study period. Causal inference analysis revealed rainfall's stronger influence (3.59%) on fatal crashes during the wet season compared to temperature (0.04%). Key stakeholder interviews highlighted perceived changes in temperature and intense rainfall patterns affecting RTCs, especially during rainy seasons suggesting an association between increased rainfall and crash severity. These findings emphasize the multifaceted role of climate change on road safety and the need to address weather-specific risks.

Climate Change Causes Food Insecurity for Developing Countries

At the moment, climate change is one of the most urgent issue worldwide. This is due to long-term climate trends and changes in social lifestyle. Another reason is the greenhouse gases produced by excessive fuel use. The intensity of weather, the decline in human health, and the devastation of natural ecosystems are all contributing factors. Intergovernmental Panel on Climate Change (IPCC) suggests controlling the avaeage temperate rise at 1.5 degrees Celsius to reduce global warming's intensity. The three major green house gasses responsible for global warming are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emitted from burning fossil fuels. Climate change is affecting the entire world, but poor and developing countries are suffering the most. The effects of climate change are all-encompassing. It affects agriculture, human health and crop productivity. A decrease have been observed in maize and wheat production in worst cases of climate change. This deficiency can lead to a food crisis. Poor and developing countries do not have the necessary resources to cope with the situation. They can't mould themselves quickly enough. Due to the lack of resources, they cannot discover new seeds to increase food production and are unable to improve their irrigation system. Climate change and rising temperatures contribute to the rapid growth of harmful bacteria and pathogens. These microbes have become a source of food contamination. Changes in rainfall patterns is also an important issue that affects the agricultural sector and the quality of irrigation water used. The risk of water pollution also increases. The gases produced from food waste, like greenhouse gases, are also harmful. Food waste in neighbourhoods releases methane gas, harming the environment. Commodity shortage and rising prices are major problems for people in poor and developing countries. The best strategy to deal with all these problems is to find new methods for agriculture that cope with climate change. To solve all these problems, government and international organizations should come forward and work together to help poor nations.

Climate change adaptation: Challenges for agricultural sustainability.

Climate change poses a substantial threat to agricultural sustainability globally. Agriculture is a vital component of the gross domestic production of developing countries. The multifaceted impacts of climate change on agriculture, highlighting how extreme weather events such as water stress, heatwaves, erratic rainfall, storms, floods, and emerging pest infestations are disrupting agricultural productivity. The socioeconomic status of farmers is particularly vulnerable to climatic extremes with future projections indicating significant increment in ambient air temperatures and unpredictable, intense rainfall patterns. Agriculture has historically relied on the extensive use of synthetic fertilizers, herbicides, and insecticides, combined with advancements in irrigation and biotechnological approaches to boost productivity. It encompasses a range of practices designed to enhance the resilience of agricultural systems, improve productivity, and reduce greenhouse gas emissions. By adopting climate-smart practices, farmers can better adapt to changing climatic conditions, thereby ensuring more sustainable and secure food production. Furthermore, it identifies key areas for future research, focusing on the development of innovative adaptation and mitigation strategies. These strategies are essential for minimizing the detrimental impacts of climate change on agriculture and for promoting the long-term sustainability of food systems. This article underscores the importance of interdisciplinary approaches and the integration of advanced technologies to address the challenges posed by climate change. By fostering a deeper understanding of these issues to inform policymakers, researchers, and practitioners about effective strategies to safeguard agricultural productivity and food security in the face of changing climate.

Innovative trend analysis technique with fuzzy logic and K-means clustering approach for identification of homogenous rainfall region: A long-term rainfall data analysis over Bangladesh

Understanding regional climatic trends is crucial for taking appropriate actions to mitigate the impacts of climate change and managing water resources effectively. This study aims to investigate the dissimilarities and similarities among various climate stations in Bangladesh from 1981 to 2021. Fuzzy C-means (FCM) and K-means clustering techniques were employed to identify regions with comparable rainfall patterns. Moreover, the innovative trend analysis (ITA) and the Mann-Kendall (MK) test family were utilized to analyze rainfall trends. The results indicate that both K-means and FCM methods successfully detected two rainfall regions in Bangladesh with distinct patterns. The ITA curve analysis revealed that out of the 29 stations, 13 had a non-monotonic increasing trend having no monotonic increasing trend, 8 had a non-monotonic decreasing trend, and 8 exhibited a monotonic decreasing trend. Additionally, the MK tests employed in the study showed predominantly negative trends across Bangladesh. The majority of stations (65.51%) fell into Cluster 1, while the remaining 34.48% were in Cluster 2. In terms of ITA analysis, 17.24% of stations exhibited a monotonic decrease, while there were no stations with a monotonic increase. However, 37.93% of stations showed a non-monotonic increase, and 44.83% displayed a non-monotonic decrease. These identified regions can provide valuable insights for water resource management, disaster risk reduction, and agricultural planning. Moreover, detailed rainfall analysis can help policymakers and scientists develop sustainable and effective regional-scale policies for managing the country's flood and drought situations, ultimately supporting agricultural development and environmental planning.

An Expanded Spatial Durbin Model with Ordinary Kriging of Unobserved Big Climate Data

Spatial models are essential in the prediction of climate phenomena because they can model the complex relationships between different locations. In this study, we discuss an expanded spatial Durbin model with ordinary kriging on unobserved locations (ESDMOK) to predict rainfall patterns in Java Island. The classical spatial Durbin model needed to be expanded to obtain a parameter estimation for each location. We combined this with ordinary kriging because the data were not available in some locations. The data were taken from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) website. Since climate data are big data, we implement a big data analytics approach, namely the data analytics life cycle method. As the exogenous variables, we used air temperature, humidity, solar irradiation, wind speed, and surface pressure. The authors developed an R-Shiny web applications to implement our proposed technique. Using our proposed technique, we obtained more accurate and reliable climate data prediction, indicated by the mean absolute percentage error (MAPE), which was equal to 1.956%. The greatest effect on rainfall was given by the surface pressure variable, and the smallest was wind speed.

Can blue–green infrastructure counteract the effects of climate change on combined sewer overflows? Study of a swiss catchment

Abstract Combined sewer overflows (CSOs), the discharge of untreated sewage mixed with stormwater into surface waters, are expected to increase under climate change as a result of more extreme rainfall. Blue–green infrastructure (BGI), such as bioretention cells and porous pavements, can help to reduce the amount of stormwater entering combined sewer systems, thus reducing CSO discharge. However, our understanding of the potential for BGI to mitigate CSOs in a future climate is still lacking, as performance is typically evaluated for individual BGI elements with fixed implementation areas under historical climate conditions or limited future scenarios. In response, this study investigates the performance of 30 combinations of BGI elements and implementation rates to prevent increases in CSOs under a range of future climate scenarios in an urban catchment near Zurich, Switzerland. Median total annual rainfall, projected to increase by as much as 46%, could double the median annual CSO volume and increase median annual CSO frequency by up to 52%. Four BGI combinations that include bioretention cells show the most promise to prevent increases in CSO volume and frequency in a future climate; and given the diverse responses of BGI elements to distinct rainfall patterns, their combinations can enhance CSO discharge reduction across varying climate patterns. BGI is also likely to become more cost-effective under future climatic conditions as projected increases in total rainfall led to larger CSO volume reductions obtained through BGI. However, there is a trade-off between robustness to climate change and cost-effectiveness, since CSO volume reduction capacity scales with BGI implementation rate but cost-effectiveness declines. Our study illustrates the effectiveness of various BGI combinations to prevent increases in CSOs in a future climate, calling for a range of BGI elements and implementation areas to be considered for urban drainage adaptation.

A Machine Learning Approach to Monitor the Physiological and Water Status of an Irrigated Peach Orchard under Semi-Arid Conditions by Using Multispectral Satellite Data

Climate change is making water management increasingly difficult due to rising temperatures and unpredictable rainfall patterns, impacting crop water availability and irrigation needs. This study investigated the ability of machine learning and satellite remote sensing to monitor water status and physiology. The research focused on predicting different eco-physiological parameters in an irrigated peach orchard under Mediterranean conditions, utilizing multispectral reflectance data and machine learning algorithms (extreme gradient boosting, random forest, support vector regressor); ground data were acquired from 2021 to 2023 in the south of Italy. The random forest model outperformed in predicting net assimilation (R2 = 0.61), while the support vector machine performed best in predicting electron transport rate (R2 = 0.57), Fv/Fm ratio (R2 = 0.66) and stomatal conductance (R2 = 0.56). Random forest also proved to be the most effective in predicting stem water potential (R2 = 0.62). These findings highlighted the potential of integrating machine learning techniques with high-resolution satellite imagery to assist farmers in monitoring crop health and optimizing irrigation practices, thereby addressing the challenges determined by climate change.

Natural Capping Enhancing the Resilience of Earthen Heritage Under Rainfall Impacts

ABSTRACT Soft capping has proven effective in enhancing masonry ruins in humid regions. To-date, there are limited studies on the use of soft capping for earthen heritage in arid and semi-arid regions. Considering the diverse climatic conditions and the presence of vegetation on the Northern China earthen sites, there is an urgent need to study the effect of these vegetations on earthen heritage sites. As the plant species used in the previous studies are mainly based on artificial selection, we propose to use native plant species that are typically found in the study site and define the application of native plant species as ‘natural capping’. Taking the Ming dynasty Shanxi Great Wall sections as case study, four scale-down rammed earth test walls were subjected to rainfall tests under four patterns of rainfall once. Results showed that native plants excel in Northern climates. Under moderate rainfall, the moisture at the wall head of natural capping walls is nearly constant. However, when subjected to heavy and extreme rainfall, natural capping walls reduced inner wall moisture infiltration by 5.56%–7.52% and material loss by 58.24%–75.04%. Post-rainfall monitoring demonstrated that natural capping has sustainable benefits for temperature and moisture reduction in summer.

High-Resolution Rainfall Estimation Using Ensemble Learning Techniques and Multisensor Data Integration.

In Indonesia, the monitoring of rainfall requires an estimation system with a high resolution and wide spatial coverage because of the complexities of the rainfall patterns. This study built a rainfall estimation model for Indonesia through the integration of data from various instruments, namely, rain gauges, weather radars, and weather satellites. An ensemble learning technique, specifically, extreme gradient boosting (XGBoost), was applied to overcome the sparse data due to the limited number of rain gauge points, limited weather radar coverage, and imbalanced rain data. The model includes bias correction of the satellite data to increase the estimation accuracy. In addition, the data from several weather radars installed in Indonesia were also combined. This research handled rainfall estimates in various rain patterns in Indonesia, such as seasonal, equatorial, and local patterns, with a high temporal resolution, close to real time. The validation was carried out at six points, namely, Bandar Lampung, Banjarmasin, Pontianak, Deli Serdang, Gorontalo, and Biak. The research results show good estimation accuracy, with respective values of 0.89, 0.91, 0.89, 0.9, 0.92, and 0.9, and root mean square error (RMSE) values of 2.75 mm/h, 2.57 mm/h, 3.08 mm/h, 2.64 mm/h, 1.85 mm/h, and 2.48 mm/h. Our research highlights the potential of this model to accurately capture diverse rainfall patterns in Indonesia at high spatial and temporal scales.

Investigation on failure of deep excavations in erodible sandy strata triggered by heavy rainfall

To date, few researches have been contributed to the excavation failures frequently caused by rainstorm events featuring different rainfall patterns, especially those in erodible sandy strata. To explore the characteristics and failure mechanisms of both sloped shallow and multi-propped deep excavations in sandy strata associated with intense rainfall, this study employed a computational fluid-dynamic (CFD) and discrete-element-method (DEM) coupling method (CFD-DEM), which was validated by a typical failure of a sloped excavation triggered by heavy rainfall in China. Subsequently, extensive parametric studies were carried out to investigate the effects of rainfall amounts and patterns on the excavation responses. It was disclosed that for a sloped shallow excavation retained by soil nails, rainfall infiltration caused initial instability at the slope toe and then incurred sliding failure along a linear slip surface; regarding a multi-propped deep excavation, sand flowing failure occurred and its basal strata underwent evident upward movements, accompanied by kicking out of the embedded retaining wall. With increasing of precipitation and variation coefficient of rainfall patterns, excavation deformations increased and the rainfall-induced primary displacement zone (PDZ) expanded as well. Finally, simplified solutions were proposed to estimate the distribution of PDZ.

Comparison of Two-Moment and Three-Moment Bulk Microphysics Schemes in Thunderstorm Simulations over Indian Subcontinent

We simulated three different thunderstorm events over India using a regional model with two different multi-moments cloud microphysical schemes: (i) Morrison double moment (Morrison-2 M) and (ii) National Taiwan University (NTU) triple moment (NTU-3 M). The results show that the choice of microphysical scheme significantly impacts storm structures, cloud formations, lightning intensity, and rainfall patterns. The lightning flash counts from the in-situ lightning detection network (LDN) are also used to compare the simulated storms. The Lightning Potential Index (LPI) was computed for both Morrison-2 M and NTU-3 M microphysics schemes and compared it with the LDN observation. Differences in simulations are attributed to the simulation of ice crystals, snow, and graupel by the microphysical schemes. The effects on the size distributions of cloud hydrometeors between the two microphysical schemes are also found to be important. The inclusions of ice crystal shapes likely accounted for the key differences between the two microphysics schemes. The results demonstrate that the simulation of lightning event characteristics is sensitive to the choice of microphysical parameterization schemes in numerical weather prediction models. In comparison to both the microphysical schemes, some aspects are better in the 2 M microphysics scheme, and some are better in the 3 M microphysics scheme. Morrison-2 M simulates more mixing ratio for the majority of cloud hydrometeors across all cases than the NTU-3 M whereas the Morrison-2 M has less rainfall bias over the ocean for the majority of the cases. Overall, the present study suggests that the choice of microphysical scheme can greatly influence the accuracy of thunderstorm and lightning event predictions in operational weather forecasting models.

Historic Flood Events and Current Flood Hazard in Ulaanbaatar City, Central Mongolia

Climate change will increase the frequency of extreme weather events, alter rainfall patterns, and exacerbate flood disasters in Ulaanbaatar City. Here we combine aerial and satellite imagery with cadastral data, to scrutinize the historical trajectory of rainfall patterns and flood disasters in Ulaanbaatar over the past six decades. The study focusses on the causative factors behind historical floods, current flood conditions, the geographical distribution of floods, land ownership in floodprone areas, and the spatial allocation of fences and buildings based on social conditions. Over the last 60 years, Ulaanbaatar received a total of 16,780 mm of precipitation, with a staggering 80.5% of this total occurring during the summer season. Over this period, the city has endured about ten significant flood disasters. The most severe and destructive events occurred in 1966, 1982, 1994, 2003, 2009, and 2023 as river basins and mountain flash floods. These flood events claimed at least 220 lives, affected around 46,000 households, and caused economic losses of ca. 3.3 million U.S. dollars. Our study identifies several flood hazard areas along the Tolgoit, Selbe, Uliastai, and Tuul River valleys, which define a flood buffer zone extending 200 m from their banks, encompassing 59 khoroos of 7 districts in Ulaanbaatar. There are 27,970 fences and 12,887 buildings in the 200 m buffer zone, which is 66.5% of all fence unit area, and 46.3% of the total building, situated within the identified flood risk areas. In response to these findings, we emphasize the urgent need for comprehensive long-term strategy for sustainable flood management based on disaster resilence.

Seasonal variation in the ranging behavior of elephants in the Laikipia-Samburu ecosystem.

African savanna elephants are a highly mobile species that ranges widely across the diversity of ecosystems they inhabit. In xeric environments, elephant movement patterns are largely dictated by the availability of water and suitable forage resources, which can drive strong seasonal changes in their movement behavior. In this study, we analyzed a unique movement dataset from 43 collared elephants, collected over a period of 10 years, to assess the degree to which seasonal changes influences home range size of elephants in the semi-arid, Laikipia-Samburu ecosystem of northern Kenya. Auto-correlated Kernel Density Estimation (AKDE) was used to estimate elephants' seasonal home range size. For each individual elephant, we also calculated seasonal home range shifts, as the distance between wet season home range centroids and dry season home range centroids. Core areas (50% AKDE isopleths) of all individual elephants ranged from 3 to 1743 km2 whereas total home range sizes (the 95% AKDE isopleths) ranged between 15 and 10,677 km2. Core areas and home range sizes were 67% and 61% larger, respectively, during the wet season than during the dry season. On average, the core area centroids for all elephants were 17 km away from the nearest river (range 0.2-150.3 km). Females had their core areas closer to the river than males (13.5 vs. 27.5 km). Females differed from males in their response to seasonal variation. Specifically, females tended to occupy areas farther from the river during the wet season, while males occupied areas further from the river during the dry season. Our study highlights how elephants adjust their space use seasonally, which can be incorporated into conservation area planning in the face of increased uncertainty in rainfall patterns due to climate change.

Studies on Variation of Temperature and Rainfall using Climatological data for the Plain and Hill zones of Uttarakhand

The present study examines long-term annual, seasonal and monthly changes and short-term fluctuations in monsoonal rainfall and temperature over plain and hill zones of Uttarakhand. Both rainfall and temperature data for period of 1981-2020 were analyzed in this study. Statistical trend analysis techniques namely Mann–Kendall test and Sen’s slope estimator were used to examine and analyze the problems. The detailed analysis of the data for 40 years indicates that the annual range of temperature is increasing in the hill zone. In the context of agriculture, the observed trends in temperature and rainfall patterns pose significant implications for farming practices and crop yields in Uttarakhand. The increasing annual range of temperature, particularly in the hill zone, suggests a heightened risk of temperature extremes such as heat waves and cold spells. During the monsoon season, while rainfall levels may remain relatively stable or exhibit minor fluctuations, the variability highlighted by the box and whisker plots underscores the importance of preparedness for both excess and deficit rainfall scenarios.

Hybrid Model for Multistep-Ahead Rainfall Forecast in Northeast India: A Comparative Study

Abstract Among all hydrometeorological parameters, rainfall strongly correlates with hydrometeorological disasters. The rainfall forecast process remains challenging due to the nonlinear, nonstationary nature and multiscale variability of rainfall. Moreover, the unique microclimate in different regions further complicates the forecasting process. This study proposes a hybrid model employing multivariate singular spectrum analysis (MSSA) and long short-term memory (LSTM) for multistep-ahead hourly rainfall forecasting in urban areas of northeast India. The model is trained and evaluated using high-resolution (12 km) hourly meteorological data from the Indian Monsoon Data Assimilation and Analysis (IMDAA) dataset for Guwahati (plain) and Aizawl (hilly) regions from 2015 to 2019. The hybrid model outperforms the single LSTM model in both plain and hilly regions, with an average percentage gain of 47.99% and 43.88% for symmetric mean absolute percentage error (SMAPE) and root-mean-square error (RMSE) in the case of the Guwahati dataset and 84.59% and 82.27% in the case of the Aizawl dataset, respectively. The performance of the LSTM model significantly improves as the zero values in the observed data are eliminated after reconstruction by MSSA. This enables the model to discern essential patterns and relationships in the data, which leads to more accurate forecasts. However, the hybrid model underestimates the rainfall, which can be tackled by hypertuning the parameters. The study highlights the importance of considering the interplay between rainfall and meteorological parameters for accurate rainfall forecasting in urban areas. The proposed MSSA–LSTM model can be used as a decision support tool for urban planning and disaster management. Significance Statement This study addresses a critical need in multistep-ahead hourly rainfall forecasting in urban areas, with a focus on the unique conditions of northeast India. Our research has identified the presence of red noise in the rainfall data, shedding light on the complexities of the underlying rainfall patterns. Furthermore, we delve into the intricate interplay between rainfall and meteorological parameters, providing valuable insights into the factors influencing rainfall dynamics. Notably, our study underscores the region-specific challenges in rainfall forecasting. While the hybrid model demonstrates reasonable accuracy in the plains, its performance in the hilly region falls short of expectations. This highlights the nuanced nature of rainfall prediction in areas with varying topography and emphasizes the need for tailored forecasting approaches.

Impact of forest fires in bromo national park: Analysis of the environment and recovery efforts

Background: Forest fires represent natural disasters that often stem from environmental conditions, including events like El Niño or extreme climatic situations. Rainfall, a crucial factor, plays a role in influencing extreme climate occurrences. In Indonesia, human activities predominantly contribute to forest fires, exacerbated by extreme climatic conditions. Areas with high potential for forest fires encompass conservation zones closely linked to human activities. An illustrative example is the Bromo Tengger Semeru National Park (TNBTS), which serves both as a conservation and tourist area. The presence of human tourists or visitors in these conservation zones creates conditions conducive to forest fires, particularly when supported by extreme climatic conditions. Method: This study used field observation methods, satellite data analysis, and interviews with authorities and environmental experts. The data collected included the area affected, the plant types, and the environmental conditions after the fire. Findings: TNBTS experiences a specific rainfall pattern, with the lowest precipitation observed in May-Jun-Jul-Aug-Sept and the highest in Nov-Dec-Jan-Feb-Mar. During periods of low rainfall, TNBTS becomes susceptible to fires, necessitating preventative measures in the month leading up to the dry season. Initiating preventative actions at the onset of the dry season, notably in May, is crucial for controlling forest fires. Analyzing hotspot data of fire incidents from 2001 to 2020 reveals peak occurrences among other nationalities in 2001, 2014, and 2019. Identification based on data indicates that not all hotspots signify fire incidents; some result from heat emanating from Mount Semeru's crater at the TNBTS location. Conclusion: This article concludes the importance of cooperation between authorities, environmental organizations, and communities in forest fire prevention and recovery efforts in Bromo National Park. It emphasizes the need for increased preventive and responsive measures to protect similar conservation areas in the future. Novelty/Originality in this article: This study also offers a new perspective in the interpretation of hotspot data by distinguishing between forest fires and volcanic activity and identifying May as a critical period for initiating preventive action analysis based on long-term rainfall patterns.

Indonesia's economic and environmental resilience in the face of climate change: Analysis and implementation strategies

Backgorund: Climate change is a significant threat to Indonesia, an archipelago that is vulnerable to the negative impacts of this phenomenon. This study aims to identify the effects of climate change in Indonesia and evaluate mitigation and adaptation strategies needed to improve economic and environmental resilience. Based on climate change projections, increases in surface temperature, changes in rainfall patterns, sea level rise, and changes in water salinity have negative impacts on various sectors such as agriculture, fisheries, infrastructure, and public health. Method: The research method includes secondary data analysis from various official sources and a study of relevant literature, with an analytical descriptive approach to identify critical challenges and necessary strategies. Findings: The results show that limited fiscal space and the need for budgetary reform are significant challenges that need to be addressed through the introduction of a carbon tax, budgetary incentives for green technologies, and the removal of fossil fuel subsidies. In addition, mobilization of non-budget funding sources is needed to support green projects, with strategies such as the development of public-private partnerships, access to international funds, and technical capacity building. Conclusion: In conclusion, implementation of the recommended mitigation and adaptation strategies can increase Indonesia's resilience to climate change, reduce its risks and negative impacts, and ensure the sustainability of economic and social development. Novelty/Originality of this study: This research provides practical guidance for policymakers to develop effective strategies to deal with climate change