Mitigation Plans Research Articles

Urban flash floods modeling in Mzuzu City, Malawi based on Sentinel and MODIS data

Floods are major hazard in Mzuzu City, Malawi. This study applied geospatial and hydrological modeling techniques to map flood incidences and hazard in the city. Multi-sensor [Sentinel 1, Sentinel 2, and Moderate Resolution Imaging Spectroradiometer (MODIS)] Normalized Difference Vegetation Index (NDVI) datasets were used to determine the spatio-temporal variation of flood inundation. Ground control points collected using a participatory GIS mapping approach were used to validate the identified flood hazard areas. A Binary Logistic Regression (BLR) model was used to determine and predict the spatial variation of flood hazard as a function of selected environmental factors. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was used to quantify the peak flow and runoff contribution needed for flood in the city. The runoff and peak flow from the HEC-HMS model were subjected to extreme value frequency analysis using the Gumbel Distribution approach before input into the Hydrologic Engineering Center River Analysis System (RAS) (HEC-RAS). The HEC-RAS model was then applied to map flood inundated areas producing flood extents maps for 100, 50, 20, and 10-year return periods, with rain-gauge and Climate Prediction Center MORPHed precipitation (CMORPH) satellite-based rainfall inputs. Results revealed that selected MODIS and Sentinel datasets were effective in delineating the spatial distribution of flood events. Distance from the river network and urban drainage are the most significant factors (p < 0.05) influencing flooding. Consequently, a relatively higher flood hazard probability and/susceptibility was noted in the south-eastern and western-most regions of the study area. The HEC-HMS model calibration (validation) showed satisfactory performance metrics of 0.7 (0.6) and similarly, the HEC-RAS model significantly performed satisfactorily as well (p < 0.05). We conclude that bias corrected satellite rainfall estimates and hydrological modeling tools can be used for flood inundation simulation especially in areas with scarce or poorly designed rain gauges such as Mzuzu City as well as those affected by climate change. These findings have important implications in informing and/updating designs of flood early warning systems and impacts mitigation plans and strategies in developing cities such as Mzuzu.

Evaluating the effectiveness of mitigation measures in environmental impact assessments: A comprehensive review of development projects in Korea

Rapid urbanization and development projects in Korea have posed significant threats to biodiversity; thus, effective mitigation measures are required to preserve natural habitats. Nevertheless, the factors underlying variations in mitigation measure effectiveness according to the disturbance level and surrounding environmental conditions have not been clarified. This study evaluated the effectiveness of mitigation measures implemented in environmental impact assessments (EIAs) of development projects in Korea, with a focus on their effectiveness with respect to the disturbance level and surrounding environmental conditions. A review of 288 EIA reports from selected projects that implemented all 10 mitigation measures classified according to the Wildlife Conservation Comprehensive Plan was conducted. Using the biodiversity tipping point framework, the effects of mitigation measures on biodiversity were categorized into four levels and analyzed. Analysis of variance and redundancy analysis were then performed to discern the variance in mitigation measure effectiveness in terms of the disturbance level, surrounding environment, and species. The results revealed significant variations in the effectiveness of mitigation measures depending on the surrounding environment and disturbance level. Linear projects exhibited a clear impact on various species as the disturbance level increased, whereas area-based projects did not exhibit such pronounced effects. All species demonstrated a negative relationship with development duration, development area, and distance from urban centers. Notably, avian and amphibian species showed a strong negative correlation with the digital elevation model while reptiles and mammals exhibited a strong positive relationship with pre-development biodiversity and distance from protected areas, respectively. Mitigation measures play a key role in alleviating the adverse effects of development projects; therefore, our findings indicate the need for spatially tailored mitigation plans to augment their effectiveness.

Climate variability impacts on runoff projection in the 21st century based on the applicability assessment of multiple GCMs: A case study of the Lushi Basin, China

In this study, we initiatively employed the improved Secure Hash Algorithm (SHA) to evaluate and compare the competencies of multiple Coupled Model Intercomparison Project Phase Six (CMIP6) General Circulation Models (GCMs). The selected GCM outputs were bias-corrected by the non-parametric quantile mapping (QM) method. The calibrated and validated data were then used to run the XAJ (Xin’anjiang) model to assess long-term climate variation impacts on runoff under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), where the results were compared with the baseline period (1976–2000). Overall, the CNRM-CM6-1-HR, FIO-ESM-2–0, INM-CM5-0 and NorESM2-MM models reconstructed better spatio-temporal evolutionary trends related to historical precipitation and evaporation, and were applied as selected GCMs for reliable runoff projection applications. The intensification amplitudes of precipitation and evaporation produced by selected GCMs gradually spread from SSP1-2.6 to SSP5-8.5 throughout the 21st century. Compared to the baseline period (257 m3/s), the extreme streamflow during the wet periods is likely to increase for SSP5-8.5 (288 ∼ 298 m3/s), SSP3-7.0 (276 ∼ 294 m3/s), and SSP2-4.5 (222 ∼ 280 m3/s), whereas SSP1-2.6 decreases to varying degrees (178 ∼ 255 m3/s). Meanwhile, during the dry periods, the extreme streamflow is projected to decrease by 28 ∼ 73 m3/s (SSP5-8.5), 32 ∼ 38 m3/s (SSP3-7.0), 8 ∼ 31 m3/s (SSP2-4.5), and 5 ∼ 33 m3/s (SSP1-2.6) from historical levels (62 m3/s). The overwhelming majority of increasing trends in the wet periods and vastly decreasing trends in the dry seasons indicate that the basin will face some floods and water shortage risks in the mid to late 21st century. The findings broaden the efficacious applications of SHA in the performance selection of GCMs and QM method in bias correction. The conclusions provide a reference for flood and drought mitigation planning and water resources policymaking in the Yellow River Basin.

Enhancing ecosystem productivity and stability with increasing canopy structural complexity in global forests.

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.

Building Urban Climate Resilience: Assessing Awareness, Perception, and Willingness regarding Nature-Based Solutions and Climate Change among Stakeholders in Iligan City, Philippines

Iligan City has been the heart of major industries in steel, cement, and hydropower and is one of the highly urbanized coastal cities in Mindanao, Philippines. Due to its geological state, environmental challenges like flooding, natural disasters, and environmental degradation have been experienced by the city. With the climate crisis, these risks are being magnified. Recently, nature-based solutions (NBS) have gained significant attention worldwide, viewed as a key solution to the consequences of climate change. Yet implementation of strategies and policies regarding NBS, especially in developing countries, has received poor attention. Hence, this study aims to assess the awareness and perceptions of stakeholders in Iligan City regarding climate change, NBS, and willingness to engage in actions involving NBS. Stakeholder mapping and surveys were conducted through purposive sampling. A total of 187 respondents were interviewed from different sectors (LGUs, NGOs, the private sector, and academia) in Iligan City. Results revealed significant differences in awareness and perceptions across the distinct socio-economic backgrounds of stakeholders, where respondents with higher education and income have greater levels of awareness, perception, and willingness. The study recommends future targeted approaches and increased campaigns for climate change and sustainable solutions such as integrating NBS in adaptation strategies and mitigation plans to foster multi-level stakeholder collaboration.

The Maharashtra River Basin's Flood-Drought-Water Scarcity Nexus

<p>India's droughts and floods, which have affected 8 million hectares yearly since the 1950s, make future adaptation and mitigation plans necessary, as well as an awareness of potential changes. In order to address the growing issues of flood and drought events in urban settings, the study emphasizes the importance of comprehensive water management strategies that ensure both routine water needs and severe event readiness. This study article recommends including drought and flood considerations in building codes and urban planning regulations to boost urban water resilience. The paper provides workable solutions for developing water-sensitive urban design, rainwater collecting, greywater reuse programs, and decentralized water management systems through a collection of case studies and strategy models. It also emphasizes the importance of teaching the population about the dangers of droughts and floods, water conservation methods, and emergency response procedures. The recommendations place a lot of emphasis on the need for a variety of policies that encourage cooperation between stakeholders, including local and state governments, business owners, community leaders, and experts. The policy recommendations include requests for green infrastructure, building standards for flood-resistant homes, water conservation requirements, and climate-responsive legislation, among other subjects. By encouraging sustainable water use behaviors, these concepts aim to increase urban resilience and make cities better prepared to mitigate the consequences of drought and flooding. These recommendations can aid local and state governments in creating a culture of preparedness for catastrophes, protecting communities, and fostering urban water resilience. This case study looks at the intricate relationships between a specific Maharashtrian river's floods, droughts, and water scarcity.</p>

Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble

AbstractBase metals are key materials for various industrial sectors such as electronics, construction, manufacturing, etc. Their selling price is important both for the profitability of the mining and metallurgical companies that produce and trade them, as well as for the countries whose economies rely on their exports or tax revenues as a means for national income. Prices are also critical for companies that use base metals as inputs to fabricate end products. The prediction of prices’ future movements can serve as a tool for risk mitigation and better budget planning. In this study, the logarithmic returns of base metals are forecasted using an autoregressive Light Gradient Boosting Machine (LightGBM) as well as an ensemble comprising the aforementioned algorithm and a classical time series forecasting model (i.e., ARIMA). The two models are then compared to three simpler benchmark models, namely a global mean model, an exponential smoothing model and an ARIMA model. When comparing using RMSE, the autoregressive LightGBM model outperformed the three univariate benchmark models (and the ensemble) for forecasting 6 months ahead for aluminum and nickel returns, while copper and zinc returns were forecasted better by the ensemble. Neither of the proposed models performed better than an ARIMA model when it comes to forecasting lead and tin returns.

The Critical Role of Energy Intensity in Decarbonizing ASEAN: Integrating Growth and Emissions Reductions

This research analyzes drivers of CO2 emissions across ASEAN countries from 1971-2017 to inform effective policies for sustainable decarbonized development. The goal is to identify critical factors influencing emissions growth and reductions to guide strategic climate mitigation planning. Data includes 3128 emissions, GDP, population, and energy consumption observations from 9 ASEAN nations. Results using the Kaya Index and Logarithmic Mean Divisia Index decomposition reveal GDP growth and population as primary drivers of increasing emissions, while energy efficiency dampens growth. Indonesia saw the highest emissions growth, driven by population and economic expansion. Thailand's phenomenal GDP growth of 3003.7% led to its emission increases. Singapore and the Philippines achieved notable reductions. Findings suggest integrated policies like clean energy, efficiency gains, infrastructure planning, and low-carbon economic reform are needed. Transitioning towards affordable clean energy systems with proactive leadership can enable ASEAN to sustain growth while mitigating climate risks. Further research should identify optimal policy mixes to maximize socio-economic progress and welfare while meeting urgent emission targets tailored to each nation. This study provides vital insights into key factors influencing ASEAN emissions and highlights pathways towards sustainable decarbonized development. The analysis of historical trends aims to inform strategic policymaking for decarbonization.

Resilient Supply Chain Management in Manufacturing Sector - A Qualitative Study

This study acknowledges that today’s business environment is dynamic and prone to unanticipated changes. The study aims to understand the implications of such disruptions in the supply chain especially in manufacturing sector. The main goal is to understand the challenges faced and the versatile practices followed to stay resistant to such dynamic environment. The study also focuses on the new practices businesses adopt to form resilient supply chain. Qualitative approach was done to capture the rich essence of the subject matter. The study used focus groups and in-depth interviews as techniques and interviewed members from different nodal points of a supply chain including manufacturers, logistic service providers and mid players in logistics. As a result, it was found out that Multinational companies are well equipped, and they have much better mitigation plans while small scale and mid-scale players need to work more on resiliency instead of just surviving the situations.

Drought Monitoring Using Moderate Resolution Imaging Spectroradiometer-Derived NDVI Anomalies in Northern Algeria from 2011 to 2022

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans.

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

PurposeThis research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.Design/methodology/approachUsing the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).FindingsThe validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.Research limitations/implicationsLand cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.Practical implicationsIt helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.Social implicationsThe social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.Originality/valueLandslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

IMPLEMENTATION OF THE PRISM SURVEY FORM TO CREATE SEISMIC RISK MAPS

This study details a seismic risk assessment of stone and adobe masonry structures in the Agios Dometios Municipality, Nicosia, Cyprus. The study integrates field inspections using the PRISM survey form, designed exclusively for masonry buildings, with advanced analytical procedures. The initial phase involves comprehensive field inspections to evaluate the structural condition and vulnerabilities of these historic masonry buildings. The data gathered from these inspections provide essential insights into potential seismic risks. This data, alongside fragility curves generated from 3D finite element (FE) models using nonlinear time-history analysis, informs the creation of seismic risk maps for Agios Dometios. These maps offer a comprehensive depiction of the area's seismic vulnerability, particularly concerning the masonry structures, serving as invaluable resources for local authorities and conservationists in strategizing preservation and reinforcement measures, and in formulating mitigation plans. This study's approach, blending field inspections with advanced computational analysis, provides a robust framework for assessing the seismic risks of masonry structures. It holds significant implications for preserving architectural heritage in seismically active regions, offering a model that can be replicated in similar urban contexts.

Formulating flood mitigation through public participation, remote sensing analysis, and flood genetic understanding: study case Samarinda City flood January 2020

Samarinda, a city characterized by its unique topography and proximity to major rivers, has been grappling with recurring and escalating flood events. The frequency and severity of these floods have been well-documented in numerous past academic studies, underscoring the pressing need for innovative flood mitigation strategies. In response to this challenge, this study employs a multi-faceted approach by incorporating public participation, remote sensing analysis, and flood genetic understanding. To facilitate a comprehensive flood mitigation strategy, we initiated the collection of real-time reports from the public during flood events. The participation of the local community in this data collection process has provided critical insights into the dynamics of flood occurrence and its impacts. The integration of crowdsourced data has been instrumental in augmenting our understanding of the local flood patterns. In parallel, we harnessed the capabilities of the Sentinel-1 satellite to conduct remote sensing analysis, enabling us to identify and map inundated areas during specific flood events. This approach, utilizing Sentinel-1, has been validated in prior flood studies. Our research further combines these datasets, merging live reports with remote sensing flood mapping to detect and assess affected areas. We introduce a novel categorization system based on flood genetics, dividing the floodprone region into three distinct zones according to hydrogeological condition of each water catchment area. We divided the area into three zones based on its genetic. This model guides us to the innovative approach enriching our flood mitigation strategy based on those valuable insights. The culmination of our study is the formulation of tailored mitigation plans for each of the three identified flood genetic zones. Incorporating public participation, remote sensing, and flood genetic understanding, our study provides a holistic framework for flood mitigation in the flood-prone city of Samarinda. By harnessing the collective intelligence of the community, leveraging cutting-edge technology, and integrating advanced scientific concepts, we present a comprehensive approach to address and mitigate the ongoing flood challenges in the region.

IMPLEMENTATION OF THE PRISM SURVEY FORM TO CREATE SEISMIC RISK MAPS

This study details a seismic risk assessment of stone and adobe masonry structures in the Agios Dometios Municipality, Nicosia, Cyprus. The study integrates field inspections using the PRISM survey form, designed exclusively for masonry buildings, with advanced analytical procedures. The initial phase involves comprehensive field inspections to evaluate the structural condition and vulnerabilities of these historic masonry buildings. The data gathered from these inspections provide essential insights into potential seismic risks. This data, alongside fragility curves generated from 3D finite element (FE) models using nonlinear time-history analysis, informs the creation of seismic risk maps for Agios Dometios. These maps offer a comprehensive depiction of the area's seismic vulnerability, particularly concerning the masonry structures, serving as invaluable resources for local authorities and conservationists in strategizing preservation and reinforcement measures, and in formulating mitigation plans. This study's approach, blending field inspections with advanced computational analysis, provides a robust framework for assessing the seismic risks of masonry structures. It holds significant implications for preserving architectural heritage in seismically active regions, offering a model that can be replicated in similar urban contexts.

Tourism Destination Development in Kapur Ciampea Mountain, Bogor

The research was conducted at Kapur Ciampea Mountain, Bogor on January 1, 2024. The purpose of this research is to find out the application of 3A at Kapur Ciampea Mountain and the development of the tourist destination. The method of analysis in this study is qualitative, with a qualitative descriptive analysis approach and SWOT analysis. The results of the study, the tourist attractions provided are very diverse, the amenity/facilities provided are quite good but there are still some facilities that need to be improved such as parking areas and public toilets, access to transportation is the advantage of this destination, the condition of the road to the destination is quite good, and access to information still needs to be improved, especially in access to digital information. To overcome these problems, the right strategy to develop Kapur Ciampea Mountain is to take advantage of the potential of website development and personal social media, improve inadequate facilities, training to staff and collaboration with influencers or community leaders, build a comprehensive disaster mitigation plan, carry out environmental restoration activities, and focus on improving the quality of services and nature education programs to overcome threats and risks that may occur.

Landslide Hazard Assessment for Wanzhou Considering the Correlation of Rainfall and Surface Deformation

The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in the uncertainty of InSAR results, where some deformations cannot be calculated, and some are not true deformations. This uncertainty of InSAR results will lead to errors in landslide hazard assessment. Here, we attempt to evaluate landslide hazards by considering combined rainfall and surface deformation. The main objective of this research was to mitigate the impact of bias and explore the accurate landslide hazard assessment method. A total of 201 landslides and 11 geo-environment factors were utilized for landslide susceptibility assessment by support vector machine (SVM) model in Wanzhou District, Three Gorges Reservoir Area (TGRA). The preliminary hazard is obtained by analyzing the statistical data of landslides and rainfall. Based on the SAR image data of Sentinel-1A satellites from September 2019 to October 2021, the SBAS-InSAR method was used to analyze surface deformation. The correlation between surface deformation and rainfall was analyzed, and the deformation factor variables were applied to landslide hazard assessment. The research results demonstrate that the error caused by the uncertainty of InSAR results can be effectively avoided by analyzing the relationship between rainfall and surface deformation. Our results can effectively adjust and correct the hazard results and eliminate the errors in the general hazard assessment. Our proposed method can be used to assess the landslide hazard in more detail and provide a reference for fine risk management and control.

Lionfish (Pterois miles) in the Mediterranean Sea: a review of the available knowledge with an update on the invasion front

Invasive species often severely impact ecosystems and human activities in the areas that they invade. The lionfishes Pterois miles and P. volitans are regarded as the most successful invasive fishes in marine ecosystems. In the last 40 years, these Indo-Pacific predators have established in the tropical western Atlantic Ocean, with well-documented detrimental effects on the local fish communities. Around 10 years ago, a second invasion began in the Mediterranean Sea, which is being colonised by P. miles. Given the invasive potential of P. miles and the fact that the ecology and biodiversity of the temperate/sub-tropical Mediterranean Sea offer a different setting from the tropical western Atlantic, specific knowledge on this second invasion is needed. Here, we: (i) review the scientific knowledge available on the ecology of invasive lionfishes, (ii) discuss such knowledge in the context of invasion ecology and (iii) suggest future research avenues on the P. miles invasion in the Mediterranean Sea. In addition, we offer an update on the spread of P. miles in the Mediterranean Sea. While the history and development of the Mediterranean invasion are resolved and some mitigation plans have been implemented locally, the study of the interactions of P. miles with Mediterranean species and their impact on the local biodiversity is in its infancy. Closing this gap will lead to important fundamental insights in invasion ecology and will result in predictions on the impact of P. miles on the ecology and ecosystem services of the Mediterranean Sea. Such information will have practical implications for policy-makers aiming to devise sound and efficient mitigation plans.

Quantifying CH4 emissions from coal mine aggregation areas in Shanxi, China, using TROPOMI observations and the wind-assigned anomaly method

Abstract. China stands out as a major contributor to anthropogenic methane (CH4) emissions, with coal mine methane (CMM) playing a crucial role. To control and reduce CH4 emissions, China has made a dedicated commitment and formulated an ambitious mitigation plan. To verify the progress made, the consistent acquisition of independent CH4 emission data is required. This paper aims to implement a wind-assigned anomaly method for the precise determination of regional-scale CMM emissions within the coal-rich Shanxi province. We use the TROPOspheric Monitoring Instrument (TROPOMI) CH4 observations from May 2018 to May 2023, coupled with ERA5 wind and a bottom-up inventory dataset based on the IPCC (Intergovernmental Panel on Climate Change) Tier 2 approach covering the Changzhi, Jincheng, and Yangquan regions of the Shanxi province. The derived emission strengths are 8.4 × 1026 molec. s−1 (0.706 Tg yr−1, ±25 %), 1.4 × 1027 molec. s−1 (1.176 Tg yr−1, ±20 %), and 4.9 × 1026 molec. s−1 (0.412 Tg yr−1, ±21 %), respectively. Our results exhibit biases of −18 %, 8 %, and 14 %, respectively, when compared to the IPCC Tier 2 bottom-up inventory. Larger discrepancies are found when comparing the estimates to the Copernicus Atmosphere Monitoring Service global anthropogenic emissions (CAMS-GLOB-ANT) and Emissions Database for Global Atmospheric Research (EDGARv7.0) inventories (64 %–176 %), suggesting that the two inventories may be overestimating CH4 emissions from the studied coal mining regions. Our estimates provide a comprehensive characterization of the regions within the Shanxi province, contribute to the validation of emission inventories, and provide additional insights into CMM emission mitigation.

UAV based comprehensive modelling approach for flood hazard assessment and mitigation planning

The assessment of flood hazard in semi-arid region under the scare data situation is a challenging task under the changing climatic situation. Therefore, the present research is carry out for Rel river catchment, Gujrat, India as case study and presented the comprehensive approach to identify the possible flood extend and flood mitigation measures for better flood assessment. The UAV based high resolution (3.6 cm × 3.6 cm) DEM is generated for most flood vulnerable area (i.e. Dhanera city), whereas scare precipitation data under ungauged condition is generated using GEE and National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) system. The HEC-HMS based hydrologic model is utilized for simulation of stream flow under the observed data, however, the 2D HEC-RAS based open-source model is used for flood assessment in 2D under extreme flood, dam break and levees condition. The flood decision making maps i.e water surface elevation, flood inundation, flood arrival time, and flood velocity are prepared for flood event 2017. The six flood hazard classes were selected from H1 (Low) to H6 (High) categories. The hazard maps of flood for different scenario i.e., 2D hydrodynamic analysis, 2D dam breach, 2D analysis with extended levees, and Dam break analysis with extended levees are derived. The model was validated with observed river gauge (water level) and discharge data at Dhanera City of flood 2017. The developed UAV based comprehensive modelling approach for flood hazard and mitigation planning provides the systematic approach for flood hazard assessment and would be adopted as a system framework for flood risk assessment for any similar case worldwide.

Observation and study of consecutive dust storms in the Taklimakan desert from March 16 to 27, 2022, using reanalysis models and lidar

A series of severe dust storms hit the Taklimakan desert between March 16 and 27, 2022, significantly deteriorating air quality throughout China. This study presents a comprehensive analysis of the vertical structure of aerosols during these dust storms, as well as their causes and impacts on China's regional and city-scale air quality, utilizing data from reanalysis models, coherent Doppler wind lidar, and air quality monitoring stations. During dust storms, intense wind layers increase dust emissions, resulting in reduced lidar detection ranges and signal strengths. In addition, lower temperatures prevail due to sunlight absorption and scattering. Moreover, high-speed winds at high altitudes increase dust particles that serve as cloud condensation nuclei, leading to increased humidity, decreased temperature, and precipitation. The Taklimakan desert's trough serves as a wind convergence zone, which promotes favorable conditions for the initiation of dust storms. Due to the steep pressure gradients, strong winds enter the Taklimakan desert through the gap between its mountains in the east, coming from northern Xinjiang and Inner Mongolia via the Hexi corridor, which facilitates the lifting and transport of dust aerosols. Dust is transported long distances from the Taklimakan desert to the eastern coasts, impacting numerous cities along the way. The emissions from these dust storms swept across most Chinese provinces. Even though each dust storm was over in the Taklimakan desert, its effects on China's air quality continued for several days. The coarse PM concentrations (PM10–2.5) in Hotan, Kashgar, Aksu, Korla, Hami, Xining, Yinchuan, Taiyuan, and Beijing spiked to levels around 14, 36, 11, 6, 7, 12, 11, 12, and 6 times higher than their 2022 averages, respectively. This study provides valuable insights into the causes and effects of the Taklimakan desert dust storms, helping authorities develop effective mitigation plans.