Keetch-Byram Drought Index Research Articles

Drought Analysis in Ketapang District using the Keetch-Byram Drought Index Method

Ketapang Regency is one of the areas in West Kalimantan that is prone to drought. Drought can trigger forest and land fires. In this research, the Keetch-Byram Drought Index (KBDI) method was used to determine the level of drought in Ketapang Regency. The KBDI method relies on annual rainfall accumulation, daily rainfall, and maximum air temperature. The KBDI values obtained were correlated with the number of hotspots using Pearson correlation. This research was conducted throughout 2018-2022. Based on the monthly average KBDI value, the highest drought in Ketapang Regency occurred in August and September, while the lowest drought occurred in December and January. In terms of the annual average, the highest drought occurred in 2019. During the ENSO phenomenon in 2019, the El Niño phase experienced higher drought than the La Niña phase and normal years. In the El Niño phase, drought levels reach high to extreme categories. The correlation value between annual KBDI and the number of hotspots is 0.88, indicating a solid relationship. An increase in the KBDI value will be followed by an increasing number of hotspots.

DFEAT: A multifaceted yearly Drought FEature Assessment Tool from daily soil water content

The characterization of yearly drought events represents a key information for conducting impact assessments and forecasting future threats, and usually relies on a single index of duration or severity. Mostly based on daily soil or atmospheric water balances, the derivation of key drought facets is not yet standardized or embedded in a single tool, thus limiting intercomparisons between studies. We developed DFEAT as a fully-automated tool designed to characterize yearly drought features, based on any simulated/observed/remotely sensed soil water content time series. We provide here an application assessment performed with the Keetch-Byram Drought Index (KBDI) as a standard daily soil water model over a 60-year period and covering the Mediterranean aridity gradient in Lebanon (Middle-East) experiencing humid to semi-arid climate conditions. We computed and tested 19 drought features related to duration, severity, onset, offset, drying and wetting rates, driest peak day, and rainfall pulses across three soil water desiccation thresholds. For our study area, we revealed the uncorrelated specificities of 6 features, allowing to regionally discriminate between mountainous Mediterranean climate experiencing shorter drought duration (45 days), later onset (Day of the Year = 198), less rainfall pulses intensities (3.35 mm), and slower drying (1.68 mm/day) and wetting (−2.72 mm/day) rates, but similar offset date (Day of the Year = 356) compared to the coast. DFEAT also captured regions with prolonged Multi-Year Droughts not fully refilling field capacity under arid bioclimate reaching up to 22 years. We demonstrate here the applicability of DFEAT across water-limited bioclimates and the non-correlation between drought features with contrasted agro-ecological impacts.

Predicting drought stress under climate change in the Southern Central Highlands of Vietnam.

In the Southern Central Highlands of Vietnam, droughts occur more frequently, causing significant damage and impacting the region's socio-economic development. During the dry season, rivers, streams, and reservoirs often face limited water availability, exacerbated in recent years by increasing drought severity. Recognizing the escalating severity of droughts, the study offers a novel contribution by conducting a comprehensive analysis of surface water resource distribution in Lam Dong province, focusing on assessing water demand for agricultural production, a crucial factor in ensuring sustainable crop growth. Two scenarios, Current-2020 (SC1) and Climate Change-2025 (SC2), are simulated, with SC2 based on climate change and sea level rise scenarios provided by the Ministry of Natural Resources and Environment (MONRE). These scenarios are integrated into the MIKE-NAM and MIKE-HYDRO basin models, allowing for a thorough assessment of the water balance of Lam Dong province. Furthermore, the study utilizes the Keetch-Byram Drought Index (KBDI) to measure drought severity, revealing prevalent dry and moderately droughty conditions in highland districts with rainfall frequency ranging from 50 to 85%. Severe drought conditions occur with a rainfall frequency of 95%, indicating an increased frequency and geographic scope of severe droughts. Additionally, the study highlights that under abnormally dry conditions, water demand for the winter-spring crop is consistently met at 100%, decreasing to 85%, 80%, and less than 75% for moderate, severe, and extreme droughts, respectively. These findings offer insights into future drought conditions in the Lam Dong province and their potential impact on irrigation capacity, crucial for adaptation strategies.

Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis

This study developed a comprehensive framework using Google Earth Engine to efficiently generate a forest fire inventory dataset, which enhanced data accessibility without specialized knowledge or access to private datasets. The framework is applicable globally, and the datasets generated are freely accessible and shareable. By implementing the framework in Peninsular Malaysia, significant forest fire factors were successfully extracted, including the Keetch–Byram Drought Index (KBDI), soil moisture, temperature, windspeed, land surface temperature (LST), Palmer Drought Severity Index (PDSI), Normalized Vegetation Index (NDVI), landcover, and precipitation, among others. Additionally, this study also adopted large language models, specifically GPT-4 with the Noteable plugin, for preliminary data analysis to assess the dataset’s validity. Although the plugin effectively performed basic statistical analyses and visualizations, it demonstrated limitations, such as selectively dropping or choosing only relevant columns for tests and automatically modifying scales. These behaviors underscore the need for users to perform additional checks on the codes generated to ensure that they accurately reflect the intended analyses. The initial findings indicate that factors such as KBDI, LST, climate water deficit, and precipitation significantly impact forest fire occurrences in Peninsular Malaysia. Future research should explore extending the framework’s application to various regions and further refine it to accommodate a broader range of factors. Embracing and rigorously validating large language model technologies, alongside developing new tools and plugins, are essential for advancing the field of data analysis.

Spatiotemporal Drought Assessment in Ningxia Autonomous Region: A Machine Learning and Remote Sensing Approach

Drought represents a significant disaster that directly impacts the economic and ecological welfare of any nation it afflicts. This study focused on the climatic anomalies of drought over the Ningxia Hui autonomous region in northwest China over the last two decades. The study employed an in-depth machine learning model, which incorporated drought indices, thus leading to a data-informed analysis of Ningxia drought patterns. The study accomplished this by using MODIS satellite data products available for vegetation and moisture monitoring. The MOD09GA, MOD11A2, and MCD43A4 data streams were loaded into Google Earth Engine as factors to develop a time-series dataset of vegetation indices. Indices are Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Temperature (LST) measurements are taken into account. Data on temperature, precipitation, and evapotranspiration was compiled for the period from 2003 to 2023 and calculated standardized indices on pixel level for the whole Ningxia region to develop the Standardized Precipitation Index (SPI), Keetch-Byram Drought Index (KBDI), and Standardized Precipitation-Evapotranspiration Index ( The study results indicated that SPI fell significantly from the year 2003 to 2023, from 0.7 to -0.3. The SPEI plummeted from 0.5 to -0.2 during the observed time frame. KBDI also went up, through 581.33 in 2003 and 681.091 in 2023, showing deterioration of aridity and drying of the soil. The conclusion of this study focuses on the deterioration of drought conditions in the Ningxia region in the last 20 years.

Analisis Spasial Temporal Indeks Kekeringan dan Sebaran Hotspot dengan Pemanfaatan Data Satelit pada Kabupaten Ogan Komering Ilir dengan Metode Keetch Byram Drought Index (KBDI)

Analisis Spasial Temporal Indeks Kekeringan dan Sebaran Hotspot dengan Pemanfaatan Data Satelit pada Kabupaten Ogan Komering Ilir dengan Metode Keetch Byram Drought Index (KBDI)

Adapting to Climate-Change-Induced Drought Stress to Improve Water Management in Southeast Vietnam

In Southeast Vietnam, droughts have become more frequent, causing significant damage and impacting the region’s socio-economic development. Water shortages frequently affect the industrial and agricultural sectors in the area. This study aims to calculate the water balance and the resilience of existing water resource allocations in the La Nga-Luy River basin based on two scenarios: (1) business-as-usual and (2) following a sustainable development approach. The MIKE NAM and MIKE HYDRO BASIN models were used for rainfall–runoff (R-R) and water balance modeling, respectively, and the Keetch–Byram Drought Index (KBDI) was used to estimate the magnitude of the droughts. The results identified areas within the Nga-Luy River basin where abnormally dry and moderate drought conditions are common, as well as subbasins, i.e., in the southeast and northeast, where severe and extreme droughts often prevail. It was also shown that the water demand for the irrigation of the winter–spring and summer–autumn crop life cycles could be fully met under abnormally dry conditions. This possibility decreases to 85–100% during moderate droughts, however. In contrast, 65% and 45–50% of the water demand for irrigation is met for the winter–spring and summer–autumn crop life cycles, respectively, during severe and extreme droughts. Furthermore, this study demonstrates that the water demand for irrigation could still be met 100% and 75–80% of the time during moderate, and extreme or severe droughts, respectively, through increased water use efficiency. This study could help managers to rationally regulate water in order to meet the agricultural sector’s needs in the region and reduce the damage and costs caused by droughts.

Regional climate modeling in the Amazon basin to evaluate fire risk

ABSTRACT Studies regarding deforestation, the hydrological cycle, climate change and fire weather can benefit from the detailed simulations provided by regional climate models (RCM). While much attention has been given to fire activity in the Amazon, few studies have used RCM runs to assess fire risk and variables associated to fire occurrence. We evaluated precipitation, temperature and a fire risk index from the ensemble of Eta model simulations coupled with three different global climate models for the Amazon basin. The RCM runs were compared to reanalysis data for the dry season from 1979 to 2005. The maximum and 2-m temperature fields were underestimated over the entire region, but showed a statistically significant spatial correlation with the reference data. Precipitation was overestimated over the Amazon, in accordance with the major sources of moisture analyzed. The Keetch-Byram drought index (KBDI) was not significantly affected by the bias found in temperature and precipitation, and the ensemble improved relative to the individual member simulations. KBDI estimations performed better with the ensemble of the three evaluated members, however the Eta model showed some limitations. The validation of modeled fire risk could benefit from the use of satellite hotspot data. Furthermore, the KBDI can also be used in the assessment of how climate change interacts with fire activity in the Amazon region.

Multi-Indices Diagnosis of the Conditions That Led to the Two 2017 Major Wildfires in Portugal

Forest fires, though part of a natural forest renewal process, when frequent and on a large -scale, have detrimental impacts on biodiversity, agroforestry systems, soil erosion, air, and water quality, infrastructures, and the economy. Portugal endures extreme forest fires, with a record extent of burned areas in 2017. These complexes of extreme wildfire events (CEWEs) concentrated in a few days but with highly burned areas are, among other factors, linked to severe fire weather conditions. In this study, a comparison between several fire danger indices (named ‘multi-indices diagnosis’) is performed for the control period 2001–2021, 2007 and 2017 (May–October) for the Fire Weather Index (FWI), Burning Index (BI), Forest Fire Danger Index (FFDI), Continuous Haines Index (CHI), and the Keetch–Byram Drought Index (KBDI). Daily analysis for the so-called Pedrógão Grande wildfire (17 June) and the October major fires (15 October) included the Spread Component (SC), Ignition Component (IC), Initial Spread Index (ISI), Buildup Index (BUI), and the Energy Release Component (ERC). Results revealed statistically significant high above-average values for most of the indices for 2017 in comparison with 2001–2021, particularly for October. The spatial distribution of BI, IC, ERC, and SC had the best performance in capturing the locations of the two CEWEs that were driven by atmospheric instability along with a dry environment aloft. These results were confirmed by the hotspot analysis that showed statistically significant intense spatial clustering between these indices and the burned areas. The spatial patterns for SC and ISI showed high values associated with high velocities in the spread of these fires. The outcomes allowed us to conclude that since fire danger depends on several factors, a multi-indices diagnosis can be highly relevant. The implementation of a Multi-index Prediction Methodology should be able to further enhance the ability to track and forecast unique CEWEs since the shortcomings of some indices are compensated by the information retrieved by others, as shown in this study. Overall, a new forecast method can help ensure the development of appropriate spatial preparedness plans, proactive responses by civil protection regarding firefighter management, and suppression efforts to minimize the detrimental impacts of wildfires in Portugal.

Exploring the influence of the Keetch–Byram Drought Index and McArthur’s Drought Factor on wildfire incidence in Victoria, Australia

Background Wildfires are thought to become more prevalent during periods of extended dry weather. This issue is examined using two dryness metrics commonly applied in Australian fire management agencies. Aims This paper investigated links between wildfire incidence and the Keetch–Byram Drought Index (KBDI) and McArthur’s Drought Factor (DF) across the state of Victoria, Australia. Methods Weather records and data from 41 418 wildfires that occurred across the State over a 17-year period were compiled to examine the distributions of KBDI and DF on days with fires smaller and larger than 5 ha in area and all days, using kernel density plots. Key results Days with fires, particularly days with fires that escaped initial attack, have higher DFs and KBDIs compared with all days. These differences vary between regions and are greatest in areas with moist climates. Conclusions and implications An appreciation of dryness conditions using tools such as KBDI and DF is useful for understanding fire potential, particularly in areas that experience higher and more regular rainfall.

Reliability of cross-regional applications of global fire danger models: a Peruvian case study

BackgroundFire danger indexes (FDIs) are used as proxies for fire potential and are often developed for specific locations. For practical purposes, the extrapolation of the underlying calculations into novel locations is common, but it is generally uncertain if the relationships between FDIs and fire potential observed in the environment in which the index was developed are equally relevant in others. For example, although a topographically, ecologically, and climatologically complex country, f ire danger forecasts in Peru use a standard set of nationwide thresholds applied to the Fire Weather Index. In this study, we validate the underlying assumption that weather-fire relationships are spatially uniform within Peru by (1) making cross-regional comparisons of the statistical distributions of four FDIs—Burning Index, Energy Release Component, Fire Weather Index, and Keetch-Byram Drought Index, and (2) making cross-regional comparisons of the expected daily MODIS hotspot count percentiles conditioned on FDI values.ResultsSignificant regional differences in the distributions of daily FDI values were observed in every pair of regions within Peru, and with the exception of a pair of regions within the Amazon, little data (<90 days) were necessary to detect these differences. After controlling for FDI values and seasonal and annual effects with regressions, differences in predicted hotspot percentiles were common, differing by as much as 47 percentage points. Across the pairs of regions, the magnitude of these differences tended to decrease as climatic similarity increased, but some counterexamples were also apparent.ConclusionsThe noticeable differences in the distributions of daily FDI values suggest that a standard set of breakpoints may produce unreliable inferences regarding fire potential. We also find that even if the climatic conditions were similar across Peru, the same FDI values in two locations can produce substantially differing predictions of wildfire activity. This suggests that other factors besides FDI values can strongly mediate wildfire activity and that better fire potential predictions could be produced if these factors are accounted for.

The Impact of Climate Change on Gender Inequality in the Labour Market: A Case Study of South Africa

South Africa has been exposed to climate change and has been experiencing associated extreme climatic events such as droughts, floods, and heat waves. These have impacted water and fuel sources, habitats, human health, and economic productivity. Poorer populations and particularly females are more affected. The main objective of this study is therefore to assess gender inequalities in employment resultant from the effects of climate change and extreme climatic events. The study employs binary, ordered, and multinomial logistic models to analyse the effects on employment, intensity of employment and the effects in economic sectors, respectively. The study computes temperature deviations from its long-run mean as climate change variable and uses the Keetch–Byram Drought Index and number of heatwave days per year as proxies for extreme climatic events. Data for the work are from the South African Weather Services database and the National Income Dynamic Survey. The findings suggest that climate change reduces the probability of being employed more for males than females, but extreme events have more negative effects on female employment than males. We suggest that while climate change mitigations and adaptation measures geared towards the labour market should take priority in general, when extreme climate events occur, labour market support measures should weigh more towards females.

Temporal and Spatial Variability of Dryness Conditions in Kazakhstan during 1979–2021 Based on Reanalysis Data

The spatial and temporal variability of dryness conditions in the territory of Kazakhstan during the period 1979–2021 was investigated using monthly and hourly ERA5 reanalysis data on air temperature and precipitation as well as various aridity indices. A large part of the territory is characterized by the air temperature increase in summer and spring, as well as precipitation reduction, especially during the summer months. It was shown that the end of the 20th century (1979–2000) and the beginning of the 21st century (2001–2021) are characterized by different trends in air temperature and precipitation. All applied indices, i.e., the Palmer Drought Severity Index (PDSI), the Keetch–Byram Drought Index (KBDI), Standardized Precipitation (SPI) and Standardized Precipitation Evapotranspiration (SPEI), showed increased dryness in most parts of the territory of Kazakhstan. KBDI indicated an increased risk of wildfires, especially in the southwestern and northwestern regions. The hottest and driest areas are situated in the regions that are simultaneously affected by rising temperatures and reduced precipitation in spring and summer. The strongest increase in aridity and fire risk in the southwest and northwest is mainly due to reduced precipitation in the summer. Minimal risks of droughts occur in the northern and central regions, where conditions in the early 21st century became even less favorable for drought formation compared to the late 20th century (increased precipitation in both spring and summer and lower summer temperatures).

Evaluating Skill of the Keetch–Byram Drought Index, Vapour Pressure Deficit and Water Potential for Determining Bushfire Potential in Jamaica

Bushfire management which incorporates fire potential indices is still in its infancy in Jamaica and the Caribbean. In this study three bushfire potential indices—Keetch–Byram Drought Index (KBDI), Vapour Pressure Deficit (VPD) and Water Potential (Ψw)—are calculated for south-central Jamaica where bushfire frequencies are highest. The skills of the indices are evaluated using their representation of the normalised bushfire climatology, monthly and seasonal (December–March/DJFM; April–June/AMJ; July–August/JA and September–November/SON) fire variability for the periods 2013–2017, 2010–2019 and 2001–2019. Fire data are obtained from the MODIS C6 Archive and Jamaica Fire Brigade (JFB). The relationship between the fire indices and large-scale oceanic and atmospheric features are also examined. The results suggest that Ψw exhibits strong correlations with the MODIS and JFB climatologies and represents well the maxima in March and July and the local minima in May–June and October. Ψw and VPDI also show good hit rates for moderate and high-risk categories in south-central Jamaica (though with relatively high false alarm rates). Regression models premised on Ψw and VPD respectively show good skill in representing AMJ (R2 = 57–58%), SON (R2 = 57–58%) and JA (R2 = 57–60%) fire variability. Variability during DJFM is poorly captured by any fire index. Although the KBDI represents the normalised climatology reasonably well its peaks occur one month later, that is, in April and August. KBDI exhibits strong and statistically significant correlations with JFB and MODIS climatologies, but seasonal models premised on KBDI do not perform as well as for the other two indices except in JA. All indices had a statistically significant relationship on both monthly and 1 month lag time scales for NINO3 and TNA-NINO3 large-scale climate indices. The indices, and in particular Ψw, show good prospects for producing seasonal bushfire outlooks for south-central Jamaica and Jamaica in general. These results also suggest the usefulness of monitoring large-scale oceanic patterns as part of the monitoring framework for bushfires in the island.

Development of Spatial Peatland Fire Danger Index Using Coupled SWAT-MODFLOW Model

The Keetch–Byram Drought Index (KBDI) is a numerical value reflecting the dryness of the top layer of soils, deep forest litter, logs, and living vegetation. The KBDI is expressed as a scale from 0 to 200, where the number represents the amounts of rainfall (in millimeters) to return the soil to saturation. We proposed a method to integrate peatland groundwater as a key variable for the peatland forest fire hazard, and we called it mKBDI. The groundwater table was obtained from the SWAT-MODFLOW model simulation. The MODFLOW model uses a 200 m × 200 m grid. The SWAT-MODFLOW model was calibrated and validated using daily water level measurements in the river. The model failed to represent peak flow, but the model produced the average water level. Output from the simulation was read using the FloPy module, and then mKBDI was calculated. The daily calculations of the mKBDI for each grid for the catchment were saved in the NetCDF format using the x-array module. We applied this model in the Peatland Hydrological Unit (PHU) Merang-Kepahyang, South Sumatera, Indonesia, in 2015 (El-Niño year) and 2016 (La-Nina Year). The daily mKBDI index from all the grids in the catchment was classified into three classes: low (mKBDI &lt; 100), moderate (mKBDI = 101–150), or high (mKBDI &gt; 150). Then, the whole catchment was classified according to these classes. Therefore, every day the percentage of the area with low, moderate, or high class in the catchment dynamically changed. When these classes were verified with hotspot data, all hotspots only coincided with the high hazard classes, where more than 60% was area of the catchment. No hotspot data were reported on low/moderate levels throughout 2015/2016. In the larger area with high mKBDI classes, the frequency of hotspots substantially rose. Sixty-three hotspots occurred during August–October of 2015 when the area of high hazard classes was above 70%. Through this finding, we proposed to use a 60% area of the catchment with high mKBDI classes as a threshold value indicating that the area is prone to peatland fire. Therefore, the peatland restoration project in preventing the fire could be evaluated using this indicator. If the restoration projects could reduce the area with high mKBDI classes to less than 60% for the whole year, we could accept it as a successful project.

Assessment of drought condition in Guilan Province using the Keetch–Byram Drought Index (KBDI) in accordance with the Percent of Normal Precipitation Index (PNPI)

خشکسالی از جمله بلایای طبیعی است که نسبت به سایر پدیده‌های طبیعی در یک بازۀ زمانی طولانی اتفاق می‌افتد که به‌صورت متناوب جوامع بشری را از طریق اثرات منفی بر روی منابع آب و کشاورزی و به‌دنبال آن اقتصاد را دچار مشکل می‌سازد. پدیدۀ آتش‌سوزی از جمله عوارض خشکسالی است که باعث صدمات جبران‌ناپذیری به محیط زیست و اکوسیستم‌ها می‌شود. برای بیان کمی خشکسالی شاخص‌های گوناگونی وجود دارد. در این پژوهش با استفاده از شاخص‌های درصد از نرمال بارندگی (PNPI) و شاخص خشکسالی کچ-بایرام (KBDI) به بررسی ویژگی‌های زمانی و مکانی خشکسالی در چهار ایستگاه رشت، بندر انزلی، آستارا و منجیل استان گیلان، طی یک دورۀ آماری 30 ساله (1396-1366) پرداخته شده است. بر اساس مطالعات انجام شده، طبق شاخص خشکسالی KBDI طبقه خشکسالی نرمال 3/96 درصد طبقۀ خشکسالی متوسط، 6/3 درصد و طبقۀ خشکسالی شدید 5/0 درصد از سری‌های زمانی ماهانۀ منطقه را به‌طور میانگین به‌خود اختصاص داده است. از سوی دیگر نیز با تحلیل شاخص PNPI نیز درصد فراوانی طبقات خشکسالی نرمال، متوسط، شدید و خیلی شدید به‌ترتیب 9/68، 1/8، 5/7 و 4/15 درصد بوده است که به‌طور تقریبا مشابه تحلیل شاخص KBDI، با در نظر گرفتن درصد تجمعی طبقات خشکسالی منطقه، می‌توان خشک بودن عادی منطقه را استنباط کرد. از سوی دیگر شدیدترین سال خشک برحسب شاخص KBDI، سال 1367 و برحسب شاخص PNPI، سال 1389 بوده است. هم‌چنین طولانی‌ترین دورۀ‌ خشکی به‌طور تقریبا مشترک در هر دو شاخص بین سال‌های 1383 تا 1386 رخ داده شده است. در نهایت می‌توان اشاره داشت احتمال وقوع خشکسالی شدید بر اساس تحلیل سری زمانی گذشته به‌طور گسترده در ماه‌های تیر و مرداد بیش‌تر خواهد بود. نتایج مبین این واقعیت است که خشکسالی در این استان رو به افزایش است که به‌تدریج بر شدت آن نیز افزوده شده است. هم‌چنین با توجه به روند کاهشی بارش منطقه می‌توان انتظار رخداد خشکسالی بیش‌تری را داشت. بنابراین بهتر است، مدیران و کارشناسان در طی فعالیت‌های اجرایی خود از حیث پیش‌گیری و مدیریت منابع آبی و پوشش گیاهی در دو ماه تیر و مرداد توجه ویژه‌ای داشته باشند.

Assessment of fire hazard weather indices in the eastern Amazon: a case study for different land uses

ABSTRACT The fire frequency in the Amazon increased rapidly after the 1990s due to deforestation and forest degradation, and it is expected to increase in response to climate change. We analyzed the fire occurrence and assessed seven fire hazard indices in the municipality of Canaã dos Carajás, in the eastern Amazon, for different land use and land cover (LULC) types. We used data from three weather stations located at different heights to compare the performance of the indices using skill scores and success percentages for each LULC. Overall most hotspots occurred in deforested areas and native forests, which were the main LULC types, while few were observed in rupestrian fields, urban areas, and mining areas. However, forests presented the lowest number of hotspots per unit area, especially inside protected areas, and all hotspots in forest areas were observed after a severe drought in 2015. The performance of the fire indices varied as a function of the LULC class and the weather station considered, which indicates the importance of choosing the most appropriate location of the station according to the purpose of the monitoring. The Keetch-Byram Drought Index showed the best performance for predicting fire occurrence for all LULC classes, and forests and deforested areas individually. Despite its simplicity, the Angstrom index stood out due to its good performance in the prediction of days with more than six hotspots.

IDENTIFIKASI WILAYAH DENGAN DAMPAK KARHUTLA TERTINGGI DI PROVINSI SUMATERA SELATAN MENGGUNAKAN SATELIT LANDSAT-8 (Identification Area With The Highest Forest Fire Impact In South Sumatra By Using LANDSAT-8 Satellite)

Forest and land fires in Sumatra, which were preceded by a long drought, have become more frequent. 2015 is considered to be the year with the largest incidence of forest and land fires. This incident had a significant impact on changes in land cover. This study aims to identify areas in South Sumatra that have the highest impact caused by forest and land fires in 2015. The analysis used is satellite imagery, obtained from the Landsat-8 satellite to determine conditions before and after forest and land fires. The changes in land cover indicate the impacts caused by forest and land fires. The areas with the highest potential impact were verified based on the degree of drought. The identification of drought levels is calculated using the Keetch-Byram Drought Index method and hotspot data, so that it can be used as an indicator of the danger of forest and land fires. Hot spot data is obtained from NASA’s TERRA and AQUA satellites using the Moderate Resolution Imaging Spectroradiometer sensor. The results showed that the area with the highest impact on the 2015 forest and land fires was the Ogan Komering Ilir Regency area. Knowing the areas with the highest impact can be used as a reference in the future to prevent and manage forest and land fires.

US wildfire potential: a historical view and future projection using high-resolution climate data

Recent wildfires in the western United States have led to substantial economic losses and social stresses. There is a great concern that the new climatic state may further increase the intensity, duration, and frequency of wildfires. To examine temporal and spatial features of historical wildfire trends and future changes, a common fire index, the Keetch–Byram Drought Index (KBDI), is calculated over the contiguous United States (CONUS) and Alaska. We introduce an efficient initialization method and calculate 36 years (1982–2017) of historical KBDI at 4 km using a high-quality observational dataset. KBDIs are also calculated at 12 km using regional climate models and extended into the mid- and late-21st century. Based on the observational data, annual mean (and 95th percentile) KBDI over forested regions in the southwestern and northwestern CONUS has risen since 1982 at a rate of 5.2 (4.0) and 2.9 (3.3) per year, respectively, indicating a persistent drying trend fostering fire activities; the number of days exceeding the top 5% historical KBDI has increased by 16 and 25 d in the 36 years. Multiple regional climate model simulations project increasing wildfire potential and longer fire seasons over broader areas based on the estimated KBDI for the mid- and late-21st century. By the end of the century, most of the CONUS would experience about 90–189 more days per year exceeding the historical local maximum KBDI; areas with high KBDI (>600), whose occurrence correlates with large burned area, are projected to broaden by nearly 60 times in the southern CONUS. While both temperature and precipitation contribute to future KBDI changes, warming is the main driver of more frequent, intense and wide-spread extreme wildfires indicated by high KBDIs in future projections.

A global assessment of wildfire potential under climate change utilizing Keetch-Byram drought index and land cover classifications

In recent years, climate change has caused wildfires across the globe to generally become larger and more severe, creating new challenges for public health, housing and infrastructure, and natural resource management. As the characteristics of wildfires change, it is critical to understand where emerging risks for human and natural systems are most profound. We assess how future wildfire potential, defined here as the meteorological conditions and the availability of burnable vegetation types conducive to wildfire occurrence, are projected to change in the future. To achieve this, we combine modelled temperature and precipitation to calculate the Keetch-Byram Drought Index (KBDI) as a proxy for soil moisture deficit and overlay a weighting factor representing burnable vegetation derived from land cover classifications. Through our analysis of daily data at both mid- and end-of-century, we find that climate-related changes, such as increasing temperatures and drying patterns, will elevate wildfire potential globally, both in terms of severity of maximum daily KBDI and frequency of high KBDI days. We find that regions which have recently endured major fire events, including the western United States, Australia, and the Amazon, could experience increases in maximum KBDI of up to 100 in places, with more than 60 additional days of high KBDI by mid-century, compared to the historical baseline. Additionally, at the end-of-the-century, regions across much of Africa, Central America, and Southern Asia are projected to emerge as wildfire hotspots. While the occurrence of wildfires may still be rare today in many regions, we find that climatological trends are projected to increase wildfire potential for much of the globe, creating new risks for some, and raising the challenge for already wildfire-prone communities to effectively manage forests and protect people and critical resources.