Regional Datasets Research Articles

Deep learning-enhanced remote sensing-integrated crop modeling for rice yield prediction

This study introduces a novel crop modeling approach based on cutting-edge computational tools to advance crop production monitoring methodologies, and, thereby, tackle global food security issues. Our approach pioneers integrating deep learning and remote sensing with process-based crop models to enhance rice yield predictions while leveraging the strengths and weaknesses of each model. We developed and evaluated four models based on distinct deep neural network architectures: feed-forward neural network, long short-term memory (LSTM), gated recurrent units, and bidirectional LSTM. All the models demonstrated high predictive accuracies, with percent biases of 0.74–2.62 and Nash–Sutcliffe model efficiencies of 0.954–0.996; however, the LSTM performed best among the four models. Notably, the models' performances varied when applied to regional datasets that were not included in the training phase; this highlighted the critical need for diverse training data to enhance model robustness. This research marks a significant advancement in agricultural modeling by combining state-of-the-art computational techniques with established methodologies, setting a new standard for crop yield prediction.

Pathways to reduce global plastic waste mismanagement and greenhouse gas emissions by 2050.

Plastic production and plastic pollution negatively affect our environment, environmental justice, and climate change. Using detailed global and regional plastics datasets coupled with socio-economic data, we employ machine learning to predict that, without intervention, annual mismanaged plastic waste will nearly double to 121 Mt (100 - 139 Mt 95% CI) by 2050. Annual greenhouse gas emissions from the plastic system are projected to grow by 37% to 3.35 Gt CO2 equivalent (3.09 - 3.54 CO2e) over the same period. The United Nations plastic pollution treaty presents a unique opportunity to reshape these outcomes. We simulate eight candidate treaty policies and find that just four could together reduce mismanaged plastic waste by 91% (86% - 98%) and gross plastic-related greenhouse gas emissions by one third.

Evaluation of meteorological drought and its impact on crop yield over Afar region, northeast Ethiopia

Drought is a natural hazard caused by a prolonged precipitation deficit that cannot meet human, livestock, and environmental demands. In Ethiopia, frequent and severe droughts increasingly affect the socio-economic and environmental sectors. This study evaluates the current and future projected meteorological drought and its impact on crop yield over the Afar region in northeast Ethiopia. We used surface stations, satellite climate estimates, downscaled atmospheric reanalysis, and regional climate model datasets. We evaluated the occurrence of drought using the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) calculated at 3-month and 12-month time scales. The drought vs. regional cereal yield is correlated to explain yield variability in the region. Results showed that more intense droughts were analyzed in 1984, 1985, 2002, 2008, 2009, 2010, 2015, and 2016. Among these years, 1984, 2002, 2008, 2009, and 2015 were the driest years across all locations in the study area. The regression of SPI and SPEI with yield showed that the indices significantly explained (r2 = 0.56 for SPI and 0.18 for SPEI) the observed yield variation. Spatially, more intense drought prevails over the northern, northwestern, and southwestern parts of Afar, where these parts are more prone to severe drought. The projected drought pattern showed increases in the intensity and frequency of drought in the middle and end of the century. The findings of this study are helpful for stakeholders working on drought mitigation in the region. Keywords: Climate dataset, meteorological drought indices, drought projection, pastoral community.

An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity

Abstract. The atmospheric composition forecasting system used to produce the Copernicus Atmosphere Monitoring Service (CAMS) forecasts of global aerosol and trace gas distributions, the Integrated Forecasting System (IFS-COMPO), undergoes periodic upgrades. In this study we describe the development of the future operational cycle 49R1 and focus on the implementation of the thermodynamical model EQSAM4Clim version 12, which represents gas–aerosol partitioning processes for the nitric acid–nitrate and ammonia–ammonium couples and computes diagnostic aerosol, cloud, and precipitation pH values at the global scale. This information on aerosol acidity influences the simulated tropospheric chemistry processes associated with aqueous-phase chemistry and wet deposition. The other updates of cycle 49R1 concern wet deposition, sea-salt aerosol emissions, dust optics, and size distribution used for the calculation of sulfate aerosol optics. The implementation of EQSAM4Clim significantly improves the partitioning of reactive nitrogen compounds, decreasing surface concentrations of both nitrate and ammonium in the particulate phase, which reduces PM2.5 biases for Europe, the US, and China, especially during summertime. For aerosol optical depth there is generally a decrease in the simulated wintertime biases and for some regions an increase in the summertime bias. Improvements in the simulated Ångström exponent are noted for almost all regions, resulting in generally good agreement with observations. The diagnostic aerosol and precipitation pH calculated by EQSAM4Clim have been compared to ground observations and published simulation results. For precipitation pH, the annual mean values show relatively good agreement with the regional observational datasets, while for aerosol pH the simulated values over continents are quite close to those simulated by ISORROPIA II. The use of aerosol acidity has a relatively smaller impact on the aqueous-phase production of sulfate compared to the changes in gas-to-particle partitioning induced by the use of EQSAM4Clim.

Enhancing Climate Resiliency Through Improving Ecosystem Services in Shoreline Municipalities – Lessons from Canada

The accelerated impacts of climate change in waterfront areas and the proven inefficacy of the aging hardened shoreline infrastructure have driven shoreline management practices to evolve toward the enhancement of ecosystem services at the land-water interface. Gaining momentum as an adaptive approach in regenerative projects, living shorelines are comprised of natural ecosystem components used in combination or in place of traditional hard engineering methods to provide coastal protective services and erosion mitigation. The success of living shorelines in protecting shoreline property and ecosystem integrity varies based on the biogeomorphology and hydrology of the region and is also heavily reliant on social acceptance of the chosen approach and best practice for implementation. The relatively lower lifecycle cost and associated co-benefits of living shorelines have well positioned them as a promising alternative approach in theory. There are, however, gaps in regional long-term datasets and evidence-based guidelines. This research provides an overview of the underlying geopolitical readiness for integrating nature-based solutions in climate adaptation strategies within shoreline municipalities based on a comprehensive literature review complimented by expert interviews. The synthesized data can inform decisions for minimizing the destructive effects of traditional shoreline erosion prevention approaches and encourage successful implementation of solutions that offer ecological, health, social, and economic benefits.

Evaluation of gridded precipitation datasets in mountainous terrains of Northwestern Mexico

Study regionThe complex mountainous terrains of the Sierra Madre Occidental in northwestern Mexico. Study focusAcquiring high-resolution precipitation data in regions with limited conventional rain gauge coverage poses significant challenges. Gridded precipitation (GP) datasets, including gauge-based, satellite, and reanalysis products, provide a potential solution, but their reliability in areas with complex terrain and intricate precipitation patterns remains uncertain. This study comprehensively evaluates the performance of four GP datasets—AORC, CHIRPS, Daymet, and ERA5—in estimating precipitation. The evaluation was conducted at a daily, monthly, and seasonal timescale, further analyzing extreme precipitation, the influence of elevation, and spatial averaging across hydrologic basins, using as reference the NERN rain gauge data from 2002 to 2004. New hydrological insights for the regionResults indicate that Daymet and AORC are the most accurate GP datasets for daily and monthly timescales, respectively. All datasets improve in accuracy over longer timescales but face challenges during the wet summer monsoon months and extreme events, with Daymet performing relatively better. Terrain elevation had a minimal impact on overall dataset accuracy, though a slight improvement in precipitation detection was noted as elevation increased. This work provides valuable insights into the strengths and limitations of GP datasets in regions with complex terrain and orographically-forced convective precipitation, offering practical outcomes for climate and hydrologic studies in similar regions.

Seeing the Big‐ to Fine‐Grained Picture: Exploring the Baseline Status of Mammal Occupancy Across Myanmar Using Scale‐Optimised Modelling

ABSTRACTAimMyanmar, an Indo‐Burmese biodiversity hotspot, lacks baseline data on species occurrence and distribution. This hinders biodiversity monitoring and optimisation of conservation and development plans. We aim to document baseline mammal occupancy, interactions with environmental factors and scale‐dependent responses.LocationHkakaborazi National Park, Htamanthi Wildlife Sanctuary, Alaungdaw Kathapa National Park, Rakhine Yoma Elephant Range, Say Taung and Myinmoletkhat Key Biodiversity Areas distributed across Myanmar.MethodsCamera trap data throughout Myanmar were used to analyse species occupancy. We conducted a multiscale hierarchical spatial modelling process, using local and pooled data across Myanmar. We also optimised spatial scale across five scales and six predictors, using univariate occupancy models. We then selected scale‐optimised variables for multivariate modelling, repeating this process for each species across local, regional and national datasets.ResultsThe study identified 47 terrestrial species and observed strong scale‐dependent nonstationarity in occupancy estimates. Relationships with environmental variables differed among species and were highly scale dependent. Importantly, occupancy estimates produced by pooling data across sites were greatly different from any of the estimates for the individual sites, suggesting that high heterogeneity in occurrence and abundance across sites among species requires local or nested occupancy estimates to account for spatial heterogeneity and variation.Main ConclusionsFuture conservation efforts should focus on Northern Myanmar if range‐restricted and rare species are to be protected, while focus should still be given to common species which serve as potential indicators of overall community structure. The nonstationarity of occupancy results from different datasets underscores the potential for misleading interpretations from aggregated data in nonstationary ecological systems. Metareplicated analyses of local, geographically and ecologically proximal regional datasets provide an important view of spatial variation in occupancy patterns guiding conservation design and improving understanding of the drivers of biodiversity patterns and change across large regions, such as Southeast Asia.

A practitioner-oriented regional hydrology data product for use in site-specific hydraulic applications

In the 465,000 km2 Canadian Prairies ecozone, robust hydrological input data for hydraulic model applications are uncommon because of the sparse monitoring network and the intermittently connected stream network. New hydrological datasets can offer a valuable advancement for making water management decisions and designing infrastructure in this water stressed region. The Prairie Hydrology Design and Analysis Product (PHyDAP) was created to address existing limitations, and provides a comprehensive regional dataset for use in hydraulic modelling applications. PHyDAP is a collection of outputs from a physically based hydrological modelling framework, run for periods ranging from 38 to 150 years, according to three climate forcing datasets. The dataset includes vertical and lateral fluxes (rainfall, snowmelt, upland runoff, and open water evaporation) and basin streamflow, at hourly or 3-hourly intervals for the >4000 small basins of approximately 100 km2 that span the region. This contribution describes the motivation for this work and methodology used to derive the data product, summarizes the data and its accessibility, and provides an overview of potential use cases.

Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.

Single-cell transcriptomic atlas of enteroendocrine cells along the murine gastrointestinal tract.

Enteroendocrine cells (EECs) produce over 20 gut hormones which contribute to intestinal physiology, nutrient metabolism and the regulation of food intake. The objective of this study was to generate a comprehensive transcriptomic map of mouse EECs from the stomach to the rectum. EECs were purified by flow-cytometry from the stomach, upper small intestine, lower small intestine, caecum and large intestine of NeuroD1-Cre mice, and analysed by single cell RNA sequencing. Regional datasets were analysed bioinformatically and combined into a large cluster map. Findings were validated by L-cell calcium imaging and measurements of CCK secretion in vitro. 20,006 EECs across the full gastrointestinal tract could be subdivided based on their full transcriptome into 10 major clusters, each exhibiting a different pattern of gut hormone expression. EECs from the stomach were largely distinct from those found more distally, even when expressing the same hormone. Cell clustering was also observed when performed only using genes related to GPCR cell signalling, revealing GPCRs predominating in different EEC populations. Mc4r was expressed in 55% of Cck-expressing cells in the upper small intestine, where MC4R agonism was found to stimulate CCK release in primary cultures. Many individual EECs expressed more than one hormone as well as machinery for activation by multiple nutrients, which was supported by the finding that the majority of L-cells exhibited calcium responses to multiple stimuli. This comprehensive transcriptomic map of mouse EECs reveals patterns of GPCR and hormone co-expression that should be helpful in predicting the effects of nutritional and pharmacological stimuli on EECs from different regions of the gut. The finding that MC4R agonism stimulates CCK secretion adds to our understanding of the melanocortin system.

The effects of land use on soil carbon stocks in the UK

Abstract. Greenhouse gas stabilisation in the atmosphere is one of the most pressing challenges of this century. Sequestering carbon in the soil by changing land use and management is increasingly proposed as part of climate mitigation strategies, but our understanding of this is limited in quantitative terms. Here we collate a substantial national and regional data set (15 790 soil cores) and analyse it in an advanced statistical modelling framework. This produced new estimates of the effects of land use on soil carbon stock (Sc) in the UK, different in magnitude and ranking order from the previous best estimates. Soil carbon stocks were highest in woodlands, followed by rough grazing, semi-natural grasslands, and improved grasslands, and they were lowest in croplands. Estimates were smaller than the previous estimates, partly because of new data, but mainly because the effect is more reliably characterised using a logarithmic transformation of the data. With the very large data set analysed here, the uncertainty in the differences among land uses was small enough to identify consistent mean effects. However, the variability in these effects was large, and this variability was similar across all surveys. This has important implications for agri-environment schemes seeking to sequester carbon in the soil by altering land use, because the effect of a given intervention is very hard to verify. We examined the validity of the “space-for-time” substitution, and, although the results were not unequivocal, we estimated that the effects are likely to be overestimated by 5 %–33 %, depending upon land use.

The effects of an abundance of natural resources on the healthcare industry: The global evidence

AbstractThe current study investigates the effects of an abundance of natural resources on the healthcare industry worldwide. Various natural resources proxies such as rents for coal, mineral, natural gas, and oil are used and analyzed by using a quarterly datasets for the period 2000:Q1–2020:Q4. Results from regional and income group datasets show that various forms of natural resources abundance around the globe exert statistically significant, but a mix of positive and adverse effects on the healthcare sector. Results also reveal that changes in and shocks given to natural resources precede significant changes in the healthcare sector in general. This study advises that countries need to achieve careful resource management, open governance, and economic diversification top priority in order to lessen reliance on a single resource and mitigate any negative effects. Further implications are provided at the end of this study.

ALBATROSS: Advancing Southern Ocean tide modelling with high resolution and enhanced bathymetry

The knowledge of bathymetry and ocean tides plays a pivotal role at the crossroads of various scientific fields, especially in the Polar regions. Its significance extends to ocean circulation modeling and understanding the coupled dynamical response of the ocean, sea-ice and ice-sheet systems. In the Southern Ocean, conventional satellite altimetry measurements are rare below the 66° parallel. Hydrodynamic models are thus useful tools to provide spatially continuous information about ocean tides. However, the accuracy of ocean tide models around the Antarctic continent is currently limited by the quality of bathymetry. Recent reprocessing of decade-long CryoSat-2 data has facilitated a new computation of bathymetry around Antarctica, bringing innovative information on bathymetry gradients. This, combined with new compilations of bathymetry, ice draft, coastline, and grounding line datasets in ice-shelf regions, allows improving models and knowledge of ocean tides in the Southern Ocean. We developed a new high-resolution tidal model that implements the improved bathymetry data and includes data assimilation of satellite-altimetry tidal retrievals computed from CryoSat-2, filling the gap between the 66°S-limited coverage of the TOPEX-Jason suite missions and the Antarctic coast. Comparisons with tidal estimates derived from tide gauge measurements showed very good consistencies with an RMSE of 3 cm.

Temperature Sensitivity of Peatland Soils Respiration Across Different Terrestrial Ecosystems

Sequential (S) and equal-time (ET) methods were applied to assess the temperature sensitivity of respiration of peat soils in different terrestrial ecosystems: southern tundra, northern taiga, and mixed coniferous–broadleaved forests. The Q10 values varied widely (1.3–4.8) and in case of the ET method decreased from northern to temperate latitudes. In the cold range (5–15°С), Q10 increased from the southern tundra (3.5) to the northern taiga (4.8) and then sharply decreased in the zone of mixed forests (2.5). Meanwhile, warm range (15–25°С) showed a clear decline of Q10 from northern to temperate latitudes: southern tundra (2.6) > northern taiga (1.6) > coniferous–broadleaved forests (1.3). Application of the S method resulted in low variability of Q10 values. Our results demonstrate a higher temperature sensitivity of the respiration of peat soils in northern latitudes as compared to that in the temperate zone. The Q10 values obtained in this study can be useful for calibration of regional carbon cycle datasets that consider the contribution of peat soils.

Incidence and Dynamics of CRC Stage Migration: A Regional vs. a National Analysis.

Due to an increased rate of surveillance colonoscopy, we aim to determine the impact of stage migration on the incidence and overall survival (OS) of patients who underwent pathological staging of colorectal cancer (CRC) at our Health Network System. Two datasets were included: subjects from the tumor registry at a regional Comprehensive Cancer Center (n = 1385) and subjects from the Surveillance, Epidemiology, and End Results (SEER) national database (n = 202,391). A significant increase in the diagnosis of CRC Stage 1 and 4 was observed, with a decrease in stage 2, and no change in Stage 3 in the National datasets (p < 0.01). There was an increase in Stage 4 CRC diagnosis, with a concurrent decrease in stage 2, and no changes in stages 1 and 3 in the regional dataset (p < 0.05). OS followed the expected and progressive decrease in OS by stage (from 1 to 4, p < 0.01). The present findings confirmed CRC stage migration in our Health Network System, along with a national trend conducive to an increased OS for early CRC stages.

VMD-ATT-LSTM electricity price prediction based on grey wolf optimization algorithm in electricity markets considering renewable energy

Electricity price prediction is essential for the optimal dispatch in power markets, with accurate prediction models being critical for efficient power system operations and market trading decisions. Deep learning networks, with their powerful nonlinear modeling capabilities, have shown promising results in electricity price forecasting. However, their design techniques, especially the selection of network parameters, remain challenging. This indicates that the optimization and exploration of deep learning networks in electricity price forecasting models require further investigation. This paper innovatively proposes a forecasting model that uniquely integrates Variational Mode Decomposition (VMD), Grey Wolf Optimization (GWO), Attention Mechanism (ATT), and Long Short-Term Memory Network (LSTM), optimizing the model from three different perspectives. First, during the data preprocessing phase, the training set is subjected to VMD to reduce noise, thereby enhancing the capture of multi-scale characteristics inherent in electricity price time series. The ATT layer is integrated to adaptively allocate weights, enhancing the model's focus on significant features. The GWO is applied to optimize hyperparameters of the LSTM, accelerating convergence and improving iteration accuracy, thereby reducing model error. A series of experiments were conducted using multiple regional electricity price datasets, evaluated with several metrics including RMSE. The results validated the effectiveness of the proposed three modules in improving the performance of the time series network, with VMD making the most significant contribution. Among all models, VMD-GWO-ATT-LSTM consistently outperformed others, demonstrating its effectiveness and robustness in electricity price forecasting.

Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models

The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years, it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging 2 years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring the original CERRA. Validation with in-situ observations further confirms the model’s accuracy in approximating ground measurements.

Regional significant wave height forecast in the East China Sea based on the Self-Attention ConvLSTM with SWAN model

Accurate prediction of regional significant wave heights is crucial for marine resource development, especially in areas with limited wave observations. This paper proposes a method for predicting regional significant wave heights by integrating the numerical wave model Simulating Wave Nearshore (SWAN) with the spatio-temporal prediction network Self-Attention ConvLSTM. The SWAN model is used to simulate regional significant wave heights in the East China Sea. To improve simulation accuracy, calibration of wind input, whitecapping dissipation, quadruplet wave–wave interactions, and bottom friction parameterization schemes in the model source terms were conducted, resulting in a simulated correlation coefficient of 0.97 with measured significant wave heights. The regional significant wave height training dataset constructed using the SWAN model preserves the intrinsic relationships between waves, wind speed, and wind direction over a recent period. Subsequently, a regional prediction system is built based on the output of the SWAN model, integrating the self-attention mechanism into the spatio-temporal prediction model ConvLSTM to construct SA-ConvLSTM. This model extracts correlated features among regional waves from the training dataset to predict future significant wave heights at different time intervals. The prediction performance of four spatio-temporal forecasting networks was compared to verify the accuracy of the SA-ConvLSTM model. The average errors in predicting regional significant wave heights at 3, 6, and 12 h were 0.07 m, 0.09 m, and 0.09 m, respectively. The R-Square metrics for these three prediction intervals were 0.85, 0.78, and 0.7, respectively.

Whole rock lithogeochemical analysis of the Mount Read Volcanics: a new tool for geochemical exploration

The Mount Read Volcanics (MRV) of western Tasmania is a belt of Middle to Late Cambrian submarine volcanics and volcaniclastics and one of the major base and precious metal provinces within Australia. Exploration success in the MRV has been in decline in recent years due to a variety of challenges including intensely altered rocks with complex stratigraphy and lithofacies variation, and a lack of accessible outcrop. As a result, local and regional mapping of the MRV is often restricted to Group-level stratigraphy, rendering geological mapping ineffective for identifying decametre-scale mine stratigraphy. Surface rock chip and drill hole samples were collected and analysed using commercially available geochemical assays to create a regional geochemical dataset of the MRV, covering the entire province and all major stratigraphic units. From these data, a lithogeochemical methodology was developed, utilizing Ti/Nb and V/Sc systematics that effectively characterizes the samples according to composition and magmatic processes. This method is shown to be robust to biases due to alteration and lithofacies, eliminating the need for highly selective sampling or extensive data filtering. To demonstrate the utility of this method in an exploration context, data from the K-lens of the Rosebery polymetallic volcanic-hosted massive sulfide (VHMS) deposit was used as a case study. Using Ti/Nb–V/Sc systematics, a rock composition classification was developed that allowed for discrimination of the footwall, hanging wall and host stratigraphy of the Rosebery mine sequence. A geochemical fingerprint for the footwall stratigraphy to mineralization was developed, and by correlating this signature to the regional unclassified samples, geochemical analogues for the Rosebery footwall were readily identified. This approach rapidly reduces the exploration search space and provides valuable information for future mineral exploration. The methodology is applicable in other terrains; however, appropriate orientation studies are required to investigate potential biases due to lithofacies, alteration and mineralogical variability.

A comprehensive taxonomy for forest fire risk assessment: bridging methodological gaps and proposing future directions.

Forest fire risk assessment plays a crucial role in the environmental management of natural hazards, serving as a key tool in the prevention of forest fires and the protection of various species. As these risks continue to evolve with environmental changes, the pertinence of contemporary research in this field remains undiminished. This review constructs a comprehensive taxonomic framework for classifying the existing body of literature on forest fire risk assessment within forestry studies. The developed taxonomy categorizes existing studies into 8 primary categories and 23 subcategories, offering a structured perspective on the methodologies and focus areas prevalent in the domain. We categorize a sample of 170 articles to present recent trends and identify research gaps in forest fire risk assessment literature. The classification facilitates a critical evaluation of the current research landscape, identifying areas in need of further exploration. Particularly, our review identifies underrepresented methodologies such as optimization modeling and some advanced machine learning techniques, which present routes for future inquiry. Moreover, the review underscores the necessity for model development that is tailored to specific regional data sets but also adaptable to global data resources, striking a balance between local specificity and broad applicability. Emphasizing the dynamic nature of forest fire behavior, we advocate for models that integrate the burgeoning field of machine learning and multi-criteria decision analysis to refine predictive accuracy and operational effectiveness in fire risk assessment. This study highlights the great potential for new ideas in modeling techniques and emphasizes the need for increased collaboration among research communities to improve the effectiveness of assessing forest fire risks.