High-risk Zone Research Articles

Machine Learning–Based Decision Support Framework for Construction Injury Severity Prediction and Risk Mitigation

Construction is a key pillar in the global economy, but it is also an industry that has one of the highest fatality rates. The goal of the current study is to employ machine learning in order to develop a framework based on which better-informed and interpretable injury-risk mitigation decisions can be made for construction sites. Central to the framework, generalizable glass-box and black-box models are developed and validated to predict injury severity levels based on the interdependent effects of identified key injury factors. To demonstrate the framework utility, a data set pertaining to construction site injury cases is utilized. By employing the developed models, safety managers can evaluate different construction site safety risk levels, and the potential high-risk zones can be flagged for devising targeted (i.e., site-specific) proactive risk mitigation strategies. Managers can also use the framework to explore complex relationships between interdependent factors and corresponding cause-and-effect of injury severity, which can further enhance their understanding of the underlying mechanisms that shape construction safety risks. Overall, the current study offers transparent, interpretable and generalizable decision-making insights for safety managers and workplace risk practitioners to better identify, understand, predict, and control the factors influencing construction site injuries and ultimately improve the safety level of their working environments by mitigating the risks of associated project disruptions.

Impact of Urbanization on Land use and Land Cover Changes in Growing Cities of Rwanda

Objectives : This research examined the effect of urbanization on environmental justice in Rwanda from 1999 to 2019. Specifically, the research sought to document changes in land use and land cover in the study region in order to analyze the environmental consequences of urban growth in Rwanda.Methods : Land use/land cover patterns were identified and modelled using remotely sensed data.Results and Discussion : The findings revealed that in the majority of cases, economic development influenced land use change in various aspects, both in terms of area and intensity, which replaced agricultural land with human settlements. For example Nyagatare city, built-up areas have expanded by 15 to 20%, while forests, vegetation, and waterbodies have decreased by 49%. It has been noted that urbanization is out of step with the natural urban environment, resulting in soil erosion, violent winds, flooding, and landslides, all of which are linked to environmental injustice. Also, settlement destruction, flooding, infrastructure damage, loss of natural habitat, and agricultural loss were all linked to urban expansion, particularly uncontrolled settlements in metropolitan contexts, with 64 people killed and 178 injured in just five years.Conclusion : Therefore, this shows that there are consequences of biodiversity losses which affect land use and land cover. To overcome, the research suggests current households located in high-risk zones be moved to the convenient zones and the government policy to focus on family planning, encouraging the flexibility of land use, crop production, and farming systems based on agroecosystem models, and strengthening the greening city strategy by raising public understanding on environmental issues.

Ultrasonographic Identification of the High-Risk Zone for Medial Antebrachial Cutaneous Nerve Injury in the Elbow.

ObjectiveTo demonstrate the sonoanatomy of the medial antebrachial cutaneous nerve (MACN) in the elbow region using high-resolution ultrasonography (HRUS) to identify areas at a high risk of MACN injury.MethodsA total of 44 arms were included in the study. In the supine position, the participants’ arms were abducted 45° with the elbow fully extended. The MACN was visualized in the transverse view. The anterior branch of the MACN (ABMACN), posterior branch of the MACN (PBMACN), and location of the branching sites were determined. The distance between the ABMACN and superficial veins, including the basilic vein (BV) and median cubital veins (MCV) was measured. For the PBMACN, the distance to the ulnar nerve (UN) and to BV were measured.ResultsThe MACN was subdivided into 2.18±1.00 branches, including ABMACN and PBMACN. The ABMACN and PBMACN were subdivided into 1.60±0.78 and 1.07±0.25 branches, respectively. The branching point of the MACN was 8.40±2.42 cm proximal to the interepicondylar line (IEL). We demonstrated that the ABMACN is located close to the BV and MCV in the elbow region, and the PBMACN was located approximately 1 cm and 0.8 cm anterior to the UN and posterior to the BV at the IEL level, respectively.ConclusionConsidering the location of the MACN, including ABMACN and PBMACN, clinicians can perform invasive procedures around the elbow region more carefully to lower the risk of MACN injury.

Mapping the forest fire risk zones using artificial intelligence with risk factors data.

Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires, and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of 11 human-induced factors, a natural factor, and temperature, which is one of the risk factors that determine the conditions for the occurrence of forest fires. Moreover, k-means clustering algorithm, which is an artificial intelligence method, was employed in preparation of the fire risk zone map. Turkey was selected as the study area because there are social and economic variations among its regions. Thus, the regional forest directorates in Turkey were separated into four clusters as extreme-risk zone, high-risk zone, moderate-risk zone, and low-risk zone. Also, a map presenting these risk zones were provided. The map reveals that, in general, the western and southwestern coastal areas of Turkey are at high risk of forest fires. On the other hand, the fire risk is relatively low in the northern, central, and eastern areas.

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

This study assessed landslide susceptibility in Shahpur valley, situated in the eastern Hindu Kush. Here, landslides are recurrent phenomena that disrupt the natural environment, and almost every year, they cause huge property damages and human losses. These damages are expected to escalate in the study area due to the high rate of deforestation in the region, population growth, agricultural expansion, and infrastructural development on the slopes. Landslide susceptibility was assessed by applying “weight of evidence” (WoE) and “information value” (IV) models. For this, the past landslide areas were identified and mapped on the SPOT5 satellite image and were verified from frequent field visits to remove the ambiguities from the initial inventory. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, and proximity to roads and streams were identified based on indigenous knowledge and studied scientific literature. The relationship of landslide occurrence with contributing factors was calculated using WoE and IV models. The susceptibility maps were generated based on both the WoE and IV models. The results showed that the very high susceptible zone covered an area of 14.49% and 12.84% according to the WoE and IV models, respectively. Finally, the resultant maps were validated using the success and prediction rate curves, seed cell area index (SCAI), and R-index approaches. The success rate curve validated the results at 80.34% for WoE and 80.13% for the IV model. The calculated prediction rate for both WoE and IV was 83.34 and 85.13%, respectively. The SCAI results showed similar performance of both models in landslide susceptibility mapping. The result shows that the R-index value for the very high LS zone was 29.64% in the WoE model, and it was 31.21% for the IV model. Based on the elements at risk, a landslide vulnerability map was prepared that showed high vulnerability to landslide hazards in the lower parts of the valley. Similarly, the hazard and vulnerability maps were combined, and the risk map of the study area was generated. According to the landslide risk map, 5.5% of the study area was under high risk, while 2% of the area was in a very high-risk zone. It was found from the analysis that for assessing landslide susceptibility, both the models are suitable and applicable in the Hindu Kush region.

Supervised models to predict the Stunting in East Aceh

Nowadays, Undernutrition is the main cause of child death in developing countries. There are many people and organizations try to mitigate or minimize case of child death. Thus, this paper aimed to has excellent method to handle undernutrition case by exploring the efficacy of machine learning (ML) approaches to predict Stunting in East Aceh administrative zones of Indonesia and to identify the most important predictors. The study employed ML techniques using retrospective cross-sectional survey data from East Aceh, a national-representative data is collected from government by using 2019 about stunting data. We explored Random forest commonly used ML algorithms. Random Forest (RF) as an extension of bagging that in addition for taking random sample of data and also uses random subset of features which mitigates over fitting. Our results showed that the considered machine learning classification algorithms by random forest can effectively predict the stunting status in East Aceh administrative zones. Persistent stunting status was found in the east part of Aceh. The identification of high-risk zones can provide more useful information and data to decision-makers for trying to reduce child undernutrition.

Modeling wildfire risk in western Iran based on the integration of AHP and GIS.

This study aimed at delineating the wildfire risk zones in a fire-prone region located in a rarely addressed area of western Iran (Paveh city)by assessing the potential of factors such as NDVI, topographic factors (elevation, slope, and aspect), land cover, and evaporation in explaining the fire occurrence probability. Analytic hierarchy process (AHP) and geographical information system (GIS) methods were used synergistically to integrate the mentioned factors into analysis, following an informed categorization of each factor based on the information on previous fire occurrence. In the AHP process, elevation and evaporation data were considered to be the most critical factors. It was found that the predicted wildfire risk areas were in agreement with past fire events by the use of the methodology proposed by this study. Accordingly, the study's final wildfire risk map indicated that approximately 64.7% of the study area is located in the high- and very high-risk zones. Land-use planners and decision-makers may use the developed map to setup and implement fire prevention strategies and enhance or develop the fire-surveillance logistics and infrastructure, including but not limited to the positions of fire watchtowers, fire lines, and fire sensors, with the aim to minimize potential fire impacts.

Managed retreat from high-risk flood areas: exploring public attitudes and expectations about property buyouts

ABSTRACT Increasing flood risk requires governments to develop innovative solutions for flood risk management. The effectiveness of these solutions depends, in part, on their social acceptability. This paper presents the findings of a national survey to explore the social acceptability of property buyouts as a form of managed retreat from flood risk in Canada. It discusses public attitudes and expectations towards property buyout programmes in high-risk flood zones, including their salience, essential design elements, and factors that would influence household acceptance of a property buyout offer. The results show there is an appetite for property buyout programmes to reduce flood risk in high-risk zones. Moreover, the social acceptability of such programmes is highest when participation is voluntary, flexible pricing options are combined with financial incentives, and programme design and implementation are transparent. Participants indicated costs for these programmes should be borne primarily by governments and shared between governments at different levels. The findings suggest that although property buyouts—and managed retreat more generally—are considered a socially acceptable approach to flood risk management, their efficacy will depend on programme design, stakeholder collaboration, and effective communication of risk to vulnerable populations. Policy recommendations are discussed in response to these findings.

Optical coherence tomography versus punch biopsy for diagnosis of basal cell carcinoma: a multicentre, randomised, non-inferiority trial.

Punch biopsy is the gold standard for diagnosis and subtyping of basal cell carcinoma. The aim of this study was to assess whether use of optical coherence tomography (OCT), a non-invasive imaging tool, might avoid the need for biopsy. In a multicentre, randomised, non-inferiority trial, patients (aged ≥18 years) with an indication for biopsy of a suspected basal cell carcinoma outside the H-zone (high-risk zone) of the face were randomly assigned (1:1) to receive either OCT or punch biopsy (regular care) via a web-based randomisation system. Patients were enrolled from three participating centres in the Netherlands: Maastricht University Medical Centre+, Catharina Hospital Eindhoven, and Zuyderland Medical Centre Heerlen. Stratification factors for randomisation were participating centre and the grade of clinical basal cell carcinoma suspicion (high vs low). The primary endpoint was the proportion of patients free from a recurrent or residual lesion (malignant or premalignant) 12 months after treatment. Modified intention-to-treat and per-protocol analyses were conducted, with a predefined non-inferiority margin of -10%. This trial is registered with ClinicalTrials.gov number, NCT03848078, and is complete. Between Feb 25, 2019, and Sept 2, 2020, 598 patients were enrolled and randomly assigned to either the regular care group (n=299) or the OCT group (n=299). Data on the primary endpoint were available in 553 patients (n=268 in the regular care group, n=285 in the OCT group). After median follow-up of 12·7 months (IQR 11·2-14·1) in the OCT group and 12·6 months (10·8-14·3) in the regular care group, 253 (94%) of 268 patients in the OCT group and 266 (93%) of 285 patients in the regular care group were free from recurrent or residual lesions (malignant or pre-malignant) 12 months after treatment. According to our modified intention-to-treat analysis, the absolute difference (OCT vs regular care) was 1·07% (95% CI -2·93 to 5·06; one-sided p=0·30), with the lower limit of the 95% CI not exceeding the predefined non-inferiority margin of -10%. Per-protocol analyses led to proportions free from a residual or recurrent lesion (premalignant or malignant) of 95% (250 of 263) in the OCT group and 94% (262 of 278) in the regular care group, and an absolute difference of 0·81% (95% CI -2·98 to 4·60; one-sided p=0·34). OCT-guided diagnosis and treatment of basal cell carcinoma is non-inferior to regular care punch biopsy. Implementation of OCT for diagnosis of basal cell carcinoma could reduce the number of consultations and invasive procedures. The Netherlands Organization for Health Research and Development and Maurits en Anna de Kock Stichting.

Insured Corn Losses in the United States from Weather and Climate Perils

Abstract This study used corn insurance data as a proxy for agricultural loss to better inform producers and decision-makers about resilience and mitigation. Building on previous research examining crop losses based on weather and climate perils, updates to the peril climatology, identification of peril hotspots, and the quantification of annual trends using inflation-adjusted indemnities for corn were performed over the period 1989–2020. Normalization techniques in loss cost and acreage loss at county-level spatial resolution were also calculated. Indemnity data showed drought and excess moisture as the two costliest and most frequent perils for corn in the United States, although changes in the socioeconomic landscape and frequency of extreme weather events in the recent decade have led to significant increases in corn indemnities for drought, heat, excess moisture, flood, hail, excess wind, and cold wet weather. Normalized losses also displayed significant trends but were dependent on the cause of loss and amount of spatial aggregation. Perhaps most notable were the documented robust increases in corn losses associated with excess moisture, especially considering future projections for increased mid and end-of-century extreme precipitation. Subtle decreasing trends in drought, hail, freeze/frost, and flood loss cost over the study period indicates hedging taking place to protect against these perils, especially in corn acreage outside the Corn Belt in high-risk production zones. The use of crop insurance as a proxy for agricultural loss highlights the importance for quantifying spatiotemporal trends by informing targeted adaption to certain hazards and operational management decisions. Significance Statement This study quantified the climatology and trends of weather and climate perils affecting corn in the United States. Robust increases in losses were noted with perils causing excess moisture, which is cause for further concern given projected increases in extreme rainfall under a warming climate.

Adaptation and Adverse Selection in Markets for Natural Disaster Insurance

This paper quantifies frictions in uptake, tests for adverse selection, and analyzes welfare effects of proposed reforms in natural disaster insurance markets. I find that willingness to pay is remarkably low. In high-risk flood zones, fewer than 60 percent of homeowners purchase flood insurance even though premiums are only two-thirds of own costs. Estimating flood insurance demand and cost elasticities using house-level variation in premiums from recent US congressional reforms reveals that these homeowners select into insurance based on observable differences in adaptation but not private information about risk. These findings change the sign of predicted welfare effects of proposed policies. (JEL D82, G22, Q54)

Effects of Tsunami Shelters in Pandeglang, Banten, Indonesia, Based on Agent-Based Modelling: A Case Study of the 2018 Anak Krakatoa Volcanic Tsunami

On 22 December 2018, the volcanic eruption of Anak Krakatoa in the Sunda Strait, Indonesia, triggered a tsunami causing 437 deaths. The highest death toll and the second highest number of damaged houses were recorded in Panimbang. This study proposes optimum evacuation shelters to reduce the mortality rate. A digital elevation model (DEM) and information dataset are used. The suggested horizontal evacuation shelters (HESs) are places of worship, schools, and government offices. Multimodal agent-based modelling (ABM), to analyse the sensitivity of parameters and the effect of vertical evacuation shelters (VESs) under multiple scenarios, is presented for the volcanic tsunami in December 2018. A tsunami hazard map is created by combining relative weights and parameter scores for topography, slope, and the distance from the shoreline and rivers. In the ABM results, the transportation mode choice depicts a significant decrease in the number of casualties. The mortality rate is sensitive to the milling time caused by delay time τ and agent decision-making time σ. VESs are proposed at the hot spots based on the location of deaths in the sensitivity tests and the high and very-high risk zones in the hazard map. As a result, combinations of VESs and HESs show a decrement in the number of deaths by 1.2–2 times compared to those with HESs only. The proposed VESs in the study area have a significant positive impact on decreasing the mortality rate.

Analyzing flash flood risk in a section of ntawogba creek, Port Harcourt city, Rivers State

Flood is a natural disaster and occurred in different forms. It is also a factor of climate change. Flash flood is a type of flood that occurs within few hours of intensive precipitation. It poses serious threat to urban areas, particularly in less developed countries. Geospatial technology has been very useful in mapping spatial extent and quantified damages associated with flash flood events. Proximity analysis and AHP has been widely utilized in modeling flash flood vulnerable zones. Little was known about MCA ranking method which is considered appropriate for few parameters in making complex decisions. This study utilized MCA ranking method to analyzed flash flood risk within Ntawogba Creek, Port Harcourt, Rivers State. Rainfall, slope model, impervious surface and distance to river were used in the decision making. The datasets were processed into common spatial referenced system. Each criterion was scaled from 5 to 1, with 5 being very high risk and 1 very low risk area. Weights were assigned to the criteria based on the importance, with higher weight to criterion that is more important in the decision making process. The weights are; distance to water channel 48, impervious surface 25, slope 22 and rainfall 5. The results obtained showed area of 93.0ha as very high flash flood risk representing 6% of the area. This zone was located along Ntawogba Creek and on major roads. Total area of high risk zone was 864.1ha representing 56%, moderate risk and low risk zone was 452.2ha and 134.7ha with 29.3 5 and 8.7% respectively. For further study, drainage density and soil infiltration should be included in the flash flood risk analysis.

Understanding Flood Risk and Vulnerability of a Place: Estimating Prospective Loss and Damage Using the HAZUS Model

In the field of flood management, risk and loss estimation is a prerequisite to undertake precautionary measures. Among several available tools, the HAZUS model is one of the most effective ones that can assist in the analysis of different dimensions of natural hazards, such as earthquakes, hurricanes, floods, and tsunamis. The flood hazard analysis portion of the model characterizes the spatial variation of flood regimes for a given study area. This research attempts to illustrate how the geoinformatics tool HAZUS can help in estimating overall risk and potential loss and damage due to floods and how this knowledge can guide the decision-making process and enhance community resilience. Examining a case study in the Rural Municipality of St. Andrews in Manitoba, Canada, this study found that both the ‘Quick Look’ and ‘Enhanced Quick Look’ analyses provided robust results. However, for the RM of St. Andrews, which is characterized by differing levels of exposure on the floodplain, and where many new housing starts occur in high-risk flood zones, ‘Enhanced Quick Look’ with spatially explicit building stock is recommended. The case study of the RM of St. Andrews demonstrates that the HAZUS model can predict loss and damage with increasing magnitude of flooding depth. It is thus recognized that the risk and loss estimation tools can be effective means for future flood loss and damage reduction.

Wildfire Smoke, Air Quality, and Renewable Energy—Examining the Impacts of the 2020 Wildfire Season in Washington State

The 2020 wildfire season was devastating, setting negative records in many states and regions around the world, especially in North America. Five of the six largest fires in California’s recorded history burned in 2020. In the Pacific Northwest region of the United States, Oregon and eastern Washington almost doubled their 10-year average of burnt acres recently. Depending on wind speed and direction conditions, the smoke from wildfires may significantly impact the air quality and reduce solar photovoltaic (PV) generation even in regions located hundreds of kilometers away from high-risk zones. Thus, during those periods, power system operators must ensure reliability and resilience across power generation, transmission, and distribution, while minimizing carbon emissions that can harm the air quality of the affected communities during wildfire events even more. This paper analyzes the impact of the 2020 wildfire season in the state of Washington, verifying the wind speed and solar irradiance data, and correlating these with the particulate matter 2.5 (PM 2.5) concentration and aerosol optical thickness (AOT) through a multi-variable regression model. The results show that PV production may be significantly reduced during the periods of high concentration of wildfire smoke and reduced wind speeds, thus highlighting the need for efficient and sustainable power system operations during wildfire events.

GIS-based landslide susceptibility zonation and risk assessment in complex landscape: A case of Beshilo watershed, northern Ethiopia

Landslides necessitate comprehensive prediction of susceptible zones for timely intervention to mitigate any disaster. Present study at Beshilo-watershed in Amhara Regional State of Ethiopia was attempted to prepare landslide susceptibility zonation (LSZ) map and to carry out risk assessment. GIS-based multi-criteria decision analysis was adopted by considering factors; land-use/land-cover, slope, elevation, aspect, drainage-density, rainfall, lithology, lineament/fault density, soil depth, and texture. Factor weights were determined through Analytical Hierarchy Process (AHP) and the factor classes rating were assigned through logical judgment. Landslide susceptibility indices were determined based on a continuous numerical scale developed for this purpose. Very high and high susceptibility zones were found to spread in 1215.28 km2 and 2279.87 km2, respectively in the eastern and north-eastern parts that are characterized by high lineament/fault density, barren lands, shrubs, bushes, grassland, or croplands mostly having unconsolidated soils with shallow depth. The Receiver Operating Characteristics (ROC) curve showed acceptable results. Further, risk assessment indicated that populations in high to very high-risk zones need timely attention of the local authorities to avert any unwanted happenings.

Performance evaluation of eco-friendly scrap tyre base isolation technology in distinct construction quality RC framed buildings located in seismic risk zone

The present investigation interprets the seismic and cost performance of scrap tyre rubber pad (STRP) bearings obtained from used automobile tyres to be installed in a developing country of a high seismic risk zone. As a consequence, Meghalaya, a state from India is selected for the research. The buildings vary in its construction quality such as poor, moderate and good construction and varying in storey heights such as three and five stories. Nonlinear analysis and cost-analysis of the buildings with and without STRP bearings are performed. The nonlinear modal time history analysis/fast nonlinear analysis is performed using SAP 2000 software and SeismoMatch software to check for convergence of the selected accelerograms with target spectrum. The dynamic response such as base shear, acceleration, storey drift and displacement are discussed and compared. The study exemplifies that the base shear reduction is superior in good construction type buildings compared to poor construction type. Moderate construction type buildings report good acceleration response, storey drift and displacement response compared to poor and good construction type. Substantially, it is reported that there is a significant reduction in cost with the implementation of STRP bearings of about 12% to 25% which is a promising one.

Heavy metal pollution in groundwater of urban Delhi environs: Pollution indices and health risk assessment

The excess presence of heavy metals in water resources deteriorates the quality and has a high potential for bioaccumulation and environmental contamination. The study of heavy metals in water is essential because of their integration in the food chain and the potential for sublethal effects on aquatic and human life. To understand the extent of heavy metal pollution, a total of 64 groundwater samples (32 in each pre-and post-monsoon season) were collected around the Yamuna River's flood plains in the Delhi region. In this study, pollution indices and health risk assessment methodologies were used to estimate the significant threat to humans. In examined seasons, the sequence of heavy metal content in groundwater is Fe > Mn > Zn > B > As>Ni > Pb. The heavy metal pollution index (HPI) revealed that in the pre-and post-monsoon seasons, 53% and 44% (HPI >100), of groundwater samples are at high-risk zone respectively. 53% of pre-monsoon and 56% of post-monsoon samples were found highly polluted, according to the degree of contamination (Cd). Moreover, health risk assessment shows that hazard index (HI) values for heavy metals were found significantly high (HI >1) in groundwater samples inferring increased non-cancerous risk to the local community. The results imply that continuous exposure can lead to chronic diseases in the population residing in the study region. In both carcinogenic and non-carcinogenic assessments, children's hazard index and carcinogenic risk assessment (CR) scores were found higher. As a result, compared to adults in the study region, children are more vulnerable to potential health threats. The principal component analysis (PCA) method was used to figure out the origin of heavy metals, and it was found that As, Fe, Mn, and Zn come from non-anthropogenic sources, whereas mixed sources (natural and anthropogenic) may be responsible for B, Ni, and Pb presence. The results of the study will help to develop an effective strategy for environmental assessment and monitoring to control groundwater pollution of the Delhi urban environs.

Changing Demographics and the Environmental Equity of Coastal Floodplain in Tampa, Florida

A considerable amount of research in the United States (US) utilizes sociodemographic determinants of individuals or communities and investigates disparities associated with flood hazards from the environmental equity (EE) perspective. However, little is known about how sociodemographic changes influence the vulnerability of individuals living in the Federal Emergency Management Agency (FEMA) designated flood zones to flood hazards and their impact on the improvement or decline in the EE situation. Our study addresses this literature gap by investigating the topic between 1996 and 2018 in the City of Tampa, a coastal city in Florida, using Geographical Information System (GIS) and statistical analyses. Findings suggest that the relationship between demographic characteristics and flood risk in Tampa is not time-invariant. Furthermore, the changes partially confirm our hypothesis of disproportionate distribution of individuals of certain social groups concerning age, ethnicity, and income in the high-risk flood zone and are more vulnerable to flooding. While the proportion of young children continues to be a significant predictor of flood risk in either period, the vulnerable relationships have shifted from lower per capita income groups to Hispanic groups over time. These changing relationships have important implications for EE research on flood hazards and related policy formulation and mitigation efforts.

Risk Assessment of Snow Disasters for Animal Husbandry on the Qinghai–Tibetan Plateau and Influences of Snow Disasters on the Well-Being of Farmers and Pastoralists

In the context of global warming, meteorological disasters occur more frequently in various regions which exert increasing influences on human life. Snow disasters are some of the natural disasters that most seriously affect the development of husbandry on the Qinghai–Tibetan Plateau (QTP), so it is necessary to explore their spatio-temporal variations and perform comprehensive risk assessment. Based on the daily snow depth data set in China, obtained by inversion of satellite remote sensing data, the spatio-temporal variation characteristics of snow disasters on the QTP from 1980 to 2019 were studied. The regional difference in the comprehensive risks of snow disasters for the husbandry on the QTP was evaluated from four perspectives, i.e., the risk of hazard factors, sensitivity of hazard-inducing environments, vulnerability of hazard-affected bodies, and disaster prevention and mitigation capacity. The farmer and pastoralist well-being (FPWB) index in five typical regions was constructed to discuss the possible influences of snow disasters on the FPWB since the 21st century. Results show that, in the last 40 years, the frequency, duration, average snow depth, and grade of snow disasters on the QTP all exhibited significant interannual and interdecadal variabilities, and they also displayed a declining long-term trend. The comprehensive risk of snow disasters for the husbandry on the QTP is low in the north while high in the south. The high-risk zone accounts for 1.54% of the total and is mainly located in Kashgar City in the north-western end of the QTP; the sub-high-risk and medium-risk zones are mainly found in the south of the plateau and are distributed in a tripole pattern, separately covering 15.96% and 16.32% of the total area of the plateau; the north of the plateau mainly belongs to low-risk and sub-low-risk zones, which separately account for 43.06% and 23.12% of the total area of the plateau. Since the beginning of the 21st century, the FPWB in five typical regions, namely, Kashgar (I), Shigatse (II), Nagqu (III), Qamdo (IV), and Yushu (V), has been increasing, while the risk of snow disasters has gradually decreased. Every 1% decrease in the risk of snow disasters corresponded to 0.186%, 0.768%, 0.378%, 0.109%, and 0.03% increases in the FPWB index in the five regions. Snow disasters affect FPWB mainly by directly or indirectly damaging material resources (livestock inventories and meat production) and social and financial resources.