Low Terrain Research Articles

Spatial Coupling Relationship Between Water Area and Water Level of Dongting Lake Based on Multiple Temporal Remote Sensing Images Data at Its Several Hydrological Stations

It is very well-known that the reliable coupling relationship between water area and water level is very important in analyzing the risks of floods and droughts for big lakes, such as Dongting Lake, especially when remote sensing images are absent and in situ measurements cannot be carried out. To obtain this relationship, two types of mathematical models—polynomial regression (PR) based on the least square algorithm and machine learning regression (MLR) based on the BP (Backpropagation) neural network algorithm—are constructed using the water area data extracted from multiple temporal remote sensing images and water levels recorded at several representative hydrological stations for nearly 30 years. In this study, Dongting Lake is divided into three parts: East Dongting Lake (EDL), South Dongting Lake (SDL), and West Dongting Lake (WDL). This is because water slope exists on its surface, which is formed by several inflow rivers and the high and low terrain. To calculate the total water area of this lake, two ways are put forward by choosing the water levels: from EDL, SDL, and WDL in their turn; or from all three simultaneously. In other words, three univariate and one multivariate regression. For PR, there are perfect coefficients of determination (most nearly 0.95, the smallest being 0.76), which is in line with regression test relative errors (between 0.27% and 6.7%). For MLR, which was initially applied to this problem, the best node number (10 for the first way, 8 for the second way) in the hidden layer of the neural network is adaptively chosen, with coefficients of determination (similar to PR), together with training and testing error performances (between 1% and 10%). These results confirm the validity and reliability of them. The regression and prediction results on the two models are better than the documented way (only focus on the water level of EDL). These results can provide some references for researchers and decision makers in studying similar big Lakes.

Resposta das alterações da paisagem rural às atividades humanas em áreas montanhosas cársticas

ABSTRACT: Landscape patterns in mountainous areas with rapid economic development are strongly affected by multiple human activities. However, the changes in landscape disturbances across karst mountainous areas attributed to complex human activities remain unclear. Research on the degree of landscape disturbance in mountainous areas under the influence of human activities is of great significance to the conservation and scientific management of mountainous landscapes. We selected rural zones with four typical small-scale landform types in karst mountainous areas as the research objects and analyzed the response of changes in rural landscape disturbance to human activity in these areas. Our results revealed that the landscape disturbance degree and human activity influence index increase in the majority of karst mountainous areas. The changes in human activity and landscape disturbances are generally observed in the low terrain relief region. High-intensity land use associated with rapid economic development has a serious negative impact on the degree of landscape disturbance, while ecological restoration projects exert a positive impact. Accelerating the implementation of the grain for green project and regulating the unregulated expansion of production and living land are crucial measures for the ecological management in karst mountainous areas. Future ecological management strategies should focus on relatively flat terrain areas. Our findings contributed to the landscape management and human activity regulation of different small-scale landform types in karst mountainous areas.

An Assessment of the Tipping Point Behavior for Shoreline Retreat: A PCR Model Application at Vung Tau Beach, Vietnam

Storm waves and rising sea levels pose significant threats to low-lying coastal areas, particularly sandy beaches, which are especially vulnerable. The research on the long-time-scale changes in sandy coasts, especially the identification of tipping points in the shoreline-retreat rate, is limited. Vung Tau beach, characterized by its low terrain and rapid tourism-driven economic growth, was selected as a typical study area to quantify the shoreline retreat throughout the 21st century under various sea-level rise (SLR) scenarios, and to identify the existence of tipping points by investigating the projected annual change in shoreline retreat (m/yr). This study employs the Probabilistic Coastline Recession (PCR) model, a physics-based tool specifically designed for long-term coastline change assessments. The results indicate that shoreline retreat accelerates over time, particularly after a tipping point is reached around 2050 in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Under the SSP5-8.5 scenario, the median retreat distance is projected to increase from 19 m in 2050 to 89 m by 2100, nearly a fourfold rise. In comparison, the retreat distances are smaller under the SSP1-2.6 and SSP2-4.5 scenarios, but the same accelerating trend is observed beyond 2050. These findings highlight the growing risks associated with sea-level rise, especially the rapid increase in exceedance probabilities for retreat distances by the end of the century. By 2100, the probability of losing the entire beach at Vung Tau is projected to be 22% under SSP5-8.5. The approach of identifying tipping points based on the PCR model presented here can be applied to other sandy coastal regions, providing critical references for timely planning and the implementation of adaptation measures.

A Study of the Drainage Capacity, Flood Risks and Mitigation Measures for Selected Areas in Guyana’s Coastal and Riverine Regions: Williamsburg- Hampshire, Kilcoy-Chesney and Mabaruma-Kumaka-Barabina

This research investigates the drainage challenges and flood risks in three areas of Guyana: Williamsburg-Hampshire, Kilcoy-Chesney, and Mabaruma-Kumaka-Barabina. These drainage areas are characterised by low-lying terrain, proximity to waterways, and inadequate drainage infrastructure. The study identifies how these factors lead to frequent and sometimes costly flooding, especially during periods of peak rainfall and high tides that prevent the opening of drainage sluices. Using rainfall and runoff data, with the analysis of drainage coefficients and discharge capacities, the study examines the existing drainage systems in these regions. It highlights the inefficiencies of undersized drainage structures, inadequate maintenance, and the growing pressure from urbanisation, which has led to the expansion of impervious surfaces, thus increasing runoff. In the case of Mabaruma-Kumaka-Barabina, under- maintained sluices, narrow bridge culverts, and low earthen dams exacerbate the area’s flood vulnerabilities. To address these issues, the study recommends several key interventions. These include upgrading drainage infrastructure by widening internal drains, replacing narrow culverts, installing additional sluices, and using modern technologies like automated pumps and self-actuated sluices. The construction of higher, vegetated embankments and raising the earthen river dams are also recommended to prevent erosion and overtopping during high tides. Monitoring water levels and rainfall data regularly is proposed to inform drainage maintenance and flood prevention efforts. The findings underscore the need for public-private partnerships to maintain drainage systems while engaging the community in flood risk mitigation. By implementing these recommendations, these regions can better manage water flow, reduce the impact of flooding, and improve the quality of life for residents, particularly as climate change continues to pose greater challenges for water management in Guyana.

The Bend on the Haiyuan Strike‐Slip Fault Leads to Segmented Activity of the Minle‐Damaying Thrust Fault in the Qilian Shan, the Northeastern Tibetan Plateau

AbstractThe present tectonic regime of the Qilian Shan is dominated by large northeast and northwest striking strike‐slip faults and northwest striking thrust faults. Deformation distribution between the subparallel Haiyuan Strike‐slip Fault and the Minle‐Damaying Thrust Fault (MDF) is crucial for understanding the orogenic mechanism of the northeastern Tibetan Plateau. However, the uncertain kinematics of the MDF and the stress variation along the strike‐varying Haiyuan Fault inhibit further discussion of their relationship. Five key sites along the MDF were selected for analysis of terrace abandonment ages and vertical offsets to determine the slip rates. Two finite element models were constructed to calculate the stress‐strain relationship between the Haiyuan Fault and MDF. We find that the activity of the MDF can be divided into two segments by a stepover with less activity and lower terrain at the Xida River site. Shortening rates of the MDF vary between 0.2 and 2.4 mm/a since the late Pleistocene with trapezoidal trends on both fault segments. The two finite element models and GPS data reveal that the strain rates are lower at the Xida River site but higher at the Menyuan Bend on the Haiyuan Fault. We infer that long‐term strain accumulation at the Menyuan Bend may have mitigated the tectonic activity northeast to the bend under the northeastward stress field, including the activity of the MDF at the Xida River site, and resulted in the segmentation of the MDF.

Autobiography: A 50-Year Quest for Understanding in Geoscience

Readers will be led down a random path from continental dynamics to paleoclimate. A key to understanding continental dynamics is recognizing that differences in gravitational potential energy per unit area between high and low terrain govern much of large-scale continental deformation. Removal of mantle lithosphere, not just crustal thickening, plays a crucial, but difficult-to-test, role in changes in surface elevation. Although measuring past surface heights remains a challenge, indications of such processes suggest that surface uplift associated with such removal can affect relative plate motion. Climate change, from a warmer to cooler climate, and associated changes in erosion and sedimentation introduce further complications to determining past elevations. The phenomena that led to such cooling include a number of possibilities, but I favor the emergence of islands in the Maritime continent, which transformed the Pacific Ocean from one with a warm eastern tropical Pacific, as during El Niño events, to the present-day La Niña–like background state. Teleconnections from the eastern tropical Pacific to Canada affect the duration of summers and the potential of high-latitude ice to accumulate. ▪Lateral gradients in gravitational potential energy per unit area (GPE), a force per unit length, govern large-scale continental dynamics.▪Removal of mantle lithosphere and thickening of crust raise GPE; knowledge of mean surface elevations provides a test of these processes.▪Climate change from a warmer to cooler climate and from one with less to more erosion can give the false impression of elevation change.▪Emergence of Indonesian islands, more rain over them, a stronger Walker Circulation, and cooler eastern Pacific may have led to ice ages.

Xiaojiang River Basin Ecological Environmental Quality Spatiotemporal Pattern and Evolutionary Trend Analysis Using GEE from 1990 to 2022

Assessment and monitoring of the quality of the ecological environment in the area is a very important fundamental task in the development of ecological civilization in the Xiaojiang River Basin in Yunnan Province, which serves as a demonstration area for ecological restoration in the upper reaches of the Yangtze River. The Landsat remote sensing images from 1990, 1995, 2000, 2005, 2010, 2014, 2018, and 2022 were chosen, and the four indexes of greenness （NDMVI）, humidity （WET）, dryness （NDBSI）, and heat （LST） were extracted. The remote sensing ecological index （RSEI） was created using the principal component analysis method, then the spatial and temporal patterns and trends of ecological quality in the Xiaojiang River Basin between 1990 and 2022 were examined using the GEE platform, ArcGIS 10.7 platform, and Python platform, combining the analysis methods of geographic information mapping, coefficient of variation, Mann-Kendall trend test, Sen's slope estimation, and Hurst's index. The findings demonstrated that： ① the ecological quality of the study area had more obvious geographic differentiation spatially, and by 2022, the areas with excellent and good ecological quality grades were primarily distributed in the areas with better alpine vegetation cover, and those with poor ecological quality were primarily distributed in the areas of the mudslide ravines with relatively low terrain. On a time scale, the study area's RSEI index increased from 0.41 in 1990 to 0.55 in 2022, with a fluctuating overall trend of ecological quality improvement and an average increase of 0.048（10 a） -1； this progress was directly related to a number of ecological construction initiatives that have been energetically carried out, such as converting farms to forests, preventing mudslides, saving soil and water, managing heavy metal contamination, etc. ② The RSEI was more appropriate for the evaluation of ecological quality in alpine ravine areas because, in comparison to the NDVI index, the NDVMI adopted in this study was more sensitive to vegetation information in topographic undulation areas, especially in shaded areas, and could more accurately and quantitatively describe the vegetation information. ③ The RSEI in the Xiaojiang River Basin had a mean coefficient of variation of 0.202. Overall, its volatility was low, and its high volatility was mostly concentrated in the mudslide gully area along both sides of the Xiaojiang River fracture zone, where the surface was made up of bare rocks and sediment that was easily impacted by the changing of the seasons, the climate, and human activity. ④ The quality of the ecological environment in the region was significantly improving, with the rising area reaching 85.72% of the total area and the declining area accounting for approximately 10.15% of the total area. The future trend of change will be dominated by ongoing improvement and future degradation, accounting for 44.75% and 39.97%, respectively. It is important to pay close attention to areas that could potentially degrade. The findings of this study can serve as a theoretical foundation for additional ecological environmental conservation, management, and sustainable development in the Xiaojiang River Basin.

Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models

The Ca Mau Peninsula, situated in the Mekong Delta of Vietnam, features low-lying terrain. In addition to the challenges posed by climate change, land subsidence in the area is exacerbated by the overexploitation of groundwater and intensive agricultural practices. In this study, we assessed the land subsidence susceptibility in the Ca Mau Peninsula utilizing three boosting machine learning models: AdaBoost, Gradient Boosting, and Extreme Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), groundwater depth, digital terrain model (DTM), normalized vegetation index (NDVI), geology, soil composition, distance to roads, and distance to rivers and streams. The dataset includes 2011 points referenced from the Persistent Scattering SAR Interferometry (PSI) method, of which 1011 points are subsidence points and the remaining are non-subsidence points. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the receiver operating characteristic (ROC) curve, area under the curve (AUC), specificity, sensitivity, and overall accuracy (ACC). The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC and ACC above 0.88 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau Peninsula. In addition, 31 subsidence points measured by leveling surveys between 2005 and 2020, provided by the Department of Survey, Mapping and Geographic Information Vietnam, were used for validating the land subsidence susceptibility from the XGB method. The findings indicate a 70.9% accuracy rate in predicting subsidence susceptibility compared to the leveling measurement points.

Landscape ecological risk assessment across different terrain gradients in the Yellow River Basin

The Yellow River Basin is an important ecological barrier zone in China, and the landscape pattern has changed greatly due to intense human activities. It is of great significance to explore the dynamic forecasting of ecological risk based on terrain gradient for the ecological security of the Yellow River Basin. In this study, the distribution characteristics of ecological risk from 2000 to 2040 are evaluated by CA-Markov and ERI models. We put forward a new method of landscape ecological risk assessment based on terrain gradient and further analyzed the relationship between ecological risk and terrain index. The results showed that the proportion of the cultivated land and the grassland in the Yellow River basin is more than 73%, with the largest dynamic change in 2020. The ecological risk in the study area showed a spatial pattern of “high in the northwest and southwest, low in the east and south-central.” During the study period, the overall ecological risk showed a decreasing trend, and the high risk was reduced by four times. The future ecological risk of all terrain gradient will show a decreasing trend, the high risks mainly occurred in areas with “flat terrain with low terrain gradient and low vegetation coverage.” This study will provide a new perspective for the dynamic forecasting of ecological risk and the analysis of the change of ecological risk through terrain gradients.

Spatial high-resolution modelling and uncertainty assessment of forest growing stock volume based on remote sensing and environmental covariates

Accurate forest growing stock (GSV) mapping is crucial for informed land management decisions, climate change mitigation, and sustainable resource utilization. This study aimed to digital mapping and uncertainty assessment of GSV levels in the Bashkiriya Nature Reserve, Russia. A random forest approach was used to predict GSV at a spatial resolution of 10 m (10 ×10 m) using 8395 plots and a set of 36 environmental covariates representing remote sensing data, relief and climate variables. The results revealed that the GSV levels ranged from 1 to 439 m3/ha, with a median of 200 m3/ha. Our model was evaluated using cross-validation and three performance metrics: root mean square error (RMSE=76 m3/ha), coefficient of determination (R2 =0.55) and Nash-Sutcliffe efficiency coefficient (NSE=0.55). In addition, we assessed the uncertainty of our predictions using a 90% prediction interval. According to the assessment of covariate importance, the significance of explanatory variables decreased in the following order: remote sensing > terrain > climate. We found, that Sentinel-2A satellite data, along with spectral indices, played a pivotal role in our spatial prediction model. Among these variables, the SWIR 12 band emerged as a standout contributor, highlighting the importance of this spectral information in GSV mapping. According to the generated map, the highest GSV values were identified primarily in regions characterized by low-lying terrain with mature trees of the reserve, while the lowest levels were distinctly evident on treeless mountain peaks. While it is widely acknowledged that remote sensing constitutes a primary source for modelling forest properties, our research has underscored the significance of the SWIR spectrum and elevation in future forest investigations. An advantage of the produced map of GSV and their associated uncertainties is that the end users can incorporate these uncertainties into decision-making processes involved in forest and environmental planning.

Holocene process-based hydroclimate evolution coupled with human behaviours in Dian Lake basin, Southwest China

Lakes are considered excellent archives for the reconstruction of sedimentary processes and related hydroclimatic variations. Dian Lake in south-western China was selected to demonstrate the impacts of Indian summer monsoon-induced water and sediment availabilities on land surface changes and human behaviours. A new sediment core from Dian Lake in the vicinity of Hebosuo village was analyzed by grain size composition, geochemical content, and minerals to detect land-lake processes that responded to hydroclimatic variations and human impacts through the Holocene. Our data suggest that Dian Lake levels during the last 9.8 cal. ka BP were controlled by the variations of the Indian summer monsoon moisture supply according to the displacements of the Intertropical Convergence Zone. A low lake level stage of ∼ 4 m lower than present was determined between 3.1 and 1.73 cal. ka BP, allowing the residents of the Dian culture to occupy the low terrains for settlement and farmland cultivation. With lake level rise at around 1.73 cal. ka BP (220 CE) the high input of clay-fine silt-sized sediments rich in rubidium and barium indicates intensified soil erosion in the Dian Lake basin. Former paddy farmlands and mountain slopes were drained by artificial and natural channels towards the lake in response to strengthened rainfalls covering low terrains by coarse and red alluvial sediments that fostered the residents to abandon the lowlands in the southern basin until 1368 CE. However, the surface erosion process during this period promoted the elevation of the south-eastern delta so that it could be re-occupied since Ming Dynasty. The Indian summer monsoon influence comparable to the modern pattern was responsible for lake level rise to the present level since then but also triggered high seasonality in flood and drought events to foster manual regulation of flood discharge during the last centuries.

The transboundary contribution of PM10 in Korea depended on the dipole surface temperature pattern in the East Asian region during April of 2021

During the spring of 2021, rapid surface cooling in the Arctic area expanded to most of the East Asian region, featuring surface-temperature dipole patterns of the colder west (Mongolia and northern-central China) and the warmer east (eastern-northeastern China, the Yellow Sea, and Korea). Despite the decreasing trend of dusty days within Korea, a notable rise in the occurrence of transboundary mineral dust-induced dusty days was observed in contributing to an increase in PM10 levels in the spring of 2021. This study investigated the PM10 source apportionment of anthropogenic dust, mineral dust, and biomass burning in the East Asian region and its transboundary impact on Korea, within the context of the surface-temperature dipole pattern observed in April of 2021. Source apportionment of PM10 was determined through a comparative analysis between baseline and sensitivity experiments conducted by means of a WRF-Chem simulation. In these sensitivity simulations, emissions from each source were systematically excluded to assess their individual contributions. PM10 Base refers to baseline simulations, PM10 Anthro to anthropogenic emissions, PM10 Dust to mineral dust, and PM10 Biomass to biomass burning. The colder west pattern was linked to increased synoptic-scale cyclonic activity, marked by higher positive potential vorticity, causing a sudden surge in severe dust storms over Mongolia and northern China. PM10 Base levels were most elevated in the higher terrain of Mongolia, comprising predominantly PM10 Dust (95%). However, the dominance of atmospheric-warming-induced anticyclones, represented by enhanced negative potential vorticity, prevailed over the lower terrain in the warmer east regions, obstructing the transboundary dispersion of PM10 Base to Korea. Nevertheless, Korea experienced a transboundary contribution of PM10 Dust, reaching 35%. In addition, the source apportionments indicated a PM10 Anthro ratio of 55% in eastern China and a PM10 Biomass ratio of 35% in northeastern China, contributing 25% and 5% respectively to Korea. During April 14–30, 2021, the dipole pattern evolved further to the significantly colder west (Mongolia and China) and the moderately warmer east (the Yellow Sea and Korea). The transboundary contributions of PM10 Anthro, PM10 Dust, and PM10 Biomass were intensified as a composite mixture within Korea, an outcome attributed to the intermittent cyclones and subsequent anticyclones.

Land Use Change and Landscape Ecological Risk Assessment Based on Terrain Gradients in Yuanmou Basin

Investigating the distribution characteristics of landscape ecological risk (LER) on terrain gradients is of great significance for optimizing the landscape pattern of ecologically vulnerable areas in mountainous regions and maintaining the sustainable development of the ecological environment. The Yuanmou Basin is a typical ecologically vulnerable area in the southwestern mountainous region of China, where issues such as soil erosion are pronounced, becoming one of the main factors restricting regional economic development. This study selected the Yuanmou Basin as the study area, and, using land use data from 2000, 2010, and 2020, constructed an LER assessment model based on disturbance and vulnerability. By integrating elevation and topographic position index data, we examined the spatiotemporal evolution characteristics of LER under different terrain gradients. The LER assessment results are summarized as follows: (1) From 2000 to 2020, the land use types of the Yuanmou Basin were mainly grassland, forest land and cropland. The land use showed a sharp increase in the cropland area and a simultaneous decrease in the grassland area, indicating a main land use evolution direction from grassland to cropland. (2) Over the span of 20 years, the average landscape ecological risk in the Yuanmou Basin slightly increased, specifically manifesting as a significant reduction in low ecological risk areas, while areas of medium and slightly lower ecological risks saw an increase. (3) The spatial distribution of LER in the Yuanmou Basin presents a pattern of being low on the periphery and high in the center, with significant positive spatial correlation, obvious spatial aggregation, as well as “high-high” and “low-low” clustering. (4) Low- and lower-risk areas in the Yuanmou Basin are distributed in the non-arid thermal zone and the medium–high terrain zone, while areas of medium, higher and high risk levels are mainly distributed in the arid thermal zone and the low terrain zone. The research results provide a scientific basis for optimizing and developing the land resources of the Yuanmou Basin.

Temporal and Spatial Distributions of O3 Concentration and Potential Source Area Analysis of Hexi Corridor Based on Satellite and Ground Monitoring

Based on 2005-2020 O3 column concentration data of OMI remote sensing satellite, combined with air pollutant data from 10 nationally controlled environmental automatic monitoring stations in the Hexi Corridor and global data assimilation system meteorological data, we used Kriging interpolation, correlation analysis, and backward trajectory (HYSPLIT) models to explore the temporal and spatial distribution characteristics, meteorological factors, transmission paths, and potential sources of O3 in the Hexi Corridor. The results showed the following:① in terms of temporal distribution, O3 column concentration showed an upward trend in 2005-2010 and 2014-2020 and downward trend in 2010-2014; the maximum and minimum values were reached in 2010 and 2014 (332.31 DU and 301.00 DU), respectively, and seasonal changes showed that those in spring and winter were significantly higher than those in summer and autumn. ② In terms of spatial distribution, O3 column concentration showed a latitudinal band distribution characteristic of increasing from southwest to northeast; the high-value areas were primarily distributed in urban areas with low terrain, and the median zone was latitudinally striped with the basic alignment of the Qilian foothills. ③ The analysis of meteorological conditions revealed that temperature, wind speed, and sunshine hours were positively correlated with O3, and relative humidity was negatively correlated with O3. ④ By simulating the airflow transportation trajectory of the receiving point in Wuwei City, it was found that the direction of the O3 conveying path was relatively singular; the dominant airflow in each season was primarily in the west and northwest; and the proportions were 71.62%, 66.85%, 61.22%, and 77.78%, respectively. There were certain seasonal differences in the source areas of O3 potential contribution:the high-value areas of O3 potential sources in spring, summer, and autumn were distributed in Baiyin City and Lanzhou City, which were southeast wind sources, and the high-value areas in winter were distributed between the Badain Jaran Desert and the Tengger Desert, which was the north wind source.

Occurrence, spatiotemporal distribution patterns,partitioning and risk assessments of multiple pesticide residues in typical estuarine water environments in eastern China

The low terrain and the prosperous agriculture in the east of China, have caused the accumulation of pesticide residues in the estuaries. Therefore, this study analyzed the spatiotemporal distribution and partition tendency of 106 pesticides based on their abundance, frequencies, and concentrations in the aquatic environment of 16 river estuaries in 7 major basins in the eastern China by using solid-phase extraction (SPE) with high-performance liquid chromatography tandem mass spectrometry (HPLC‒MS/MS) and gas chromatography tandem mass spectrometry (GC‒MS/MS). In addition, potential risk of multiple pesticides was also evaluated. The results showed that herbicides were the dominant pesticide type, while triazines were the predominate substance group of pesticide. In addition, triadimenol, vinclozolin, diethylatrazine, prometryn, thiamethoxam, atrazine, and metalachlor were the major pesticides in the water, while prometryn, metalachlor, and atrazine were the main pesticides in the sediment. The average total concentration of pesticide was 751.15 ng/L in the dry season, 651.17 ng/L in the wet season, and 617.37 ng/L in the normal season, respectively. The estuaries of the Huai River Basin, the Yangtze River Basin, the Hai River Basin, and the Yellow River Basin have been affected by the low pollution treatment efficiency, weak infrastructure, and agricultural/non-agricultural activities in eastern China, resulting in relatively serious pesticide pollution. The estuaries of Huaihe River, Yangtze River, Xiaoqing River, and Luanhe River had large pesticide abundance and comparatively severe pesticide pollution, while the estuaries of Tuhai River and Haihe River had heavy pesticide contamination in the sediment, which might be induced by historical sedimentary factors. The log KOC values showed that except for thioketone, other pesticides were relatively stable due to the adsorption by sediment. The ecological risk assessment results indicated that insecticides had a high risk. Teenagers were the most severely affected by the noncarcinogenic risk of pesticides, while adults were mostly affected by the carcinogenic risk of pesticides. Therefore, pesticide hazards in the water environment of estuaries in eastern China needs to be further close supervision.

Spatiotemporal Information Mining for Emergency Response of Urban Flood Based on Social Media and Remote Sensing Data

The emergency response is crucial in preventing and mitigating urban floods. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring. However, current research lacks a thorough exploration of the application of social media data and remote sensing imagery in the urban flood emergency response. To address this issue, this paper, while extracting disaster information based on social media data, deeply mines the spatiotemporal distribution characteristics and dynamic spatial accessibility of rescue points. Furthermore, SAR imagery and social media data for monitoring urban flooding are compared. This study took the Zhengzhou 7.20 urban flood as a case study and created a methodological framework to quickly extract flood disaster information (flood, landslide, and rescue points) using these two types of data; spatiotemporal analysis and random forest classification were also conducted to mine valuable information. Temporally, the study revealed that disaster information did not increase proportionally with the amount of rainfall during the rainfall process. Spatially, specific regions with higher susceptibility to flooding, landslides, and rescue points were identified, such as the central region characterized by low drainage standards and high-density urban areas, as well as the eastern region with low-lying terrain. Moreover, this study examined the spatial accessibility of rescue resources in real flood scenarios and found that their service coverage varied throughout the day during and after the disaster. In addition, social media excelled in high-density urban areas’ flood point extraction, while SAR performed better in monitoring floods at the edges of low-density urban areas and large water bodies, allowing them to complement each other, to a certain extent. The findings of this study provide scientific reference value for the optimal selection of rescue paths and the allocation of resources in the emergency response to urban floods caused by extreme rainstorms.

HEALTH AND ECONOMIC IMPACTS OF CLIMATE CHANGE IN RURAL BANGLADESH AND OPTIONS TO GO THROUGH

Bangladesh is one of the most vulnerable countries due to its unfavorable geographic location, flat and low-lying terrain, dense population, and high levels of poverty. This research aims to evaluate the effects of climate change on the availability of water, housing, financial support, sanitation, and health status in Bangladesh's southwest coastal area and to investigate adaptation options. A descriptive cross-sectional study was conducted in a disaster-prone village Pratapnagar of Assasuni Upazila in Satkhira, Bangladesh from September to October 2021. A questionnaire survey was conducted to 100 male respondents 30-70 years of age who were suffering because of a breach of the embankment and could not recover from the loss due to supercyclone Amphan (category 5) from May 16 to May 21, 2020. The mean age of the respondents was 42 years (SD = 9.14). Fishing is the main source of income for 46% of the respondents. The study also revealed that 40% of the respondents had lost their dwellings and are still submerged in water, and 32% of the respondents have lost their agricultural land. 55% of respondents have lost their livestock and 35% of the respondents use riverside open toilets; while 55% do use toilets but water submerges the toilets during high tide. During the last 2 months of the study period, 82% of respondents had recurrent diarrhea, 42% had respiratory diseases and 32% have been infected with skin diseases. Assasuni was the victim Upazila, where cyclone Amphan hit, and most of the drinking water sources were devastated. This socio-economic impact falls not only on the people in the coastal belt but also on the people of the whole country.

Research on the Adaptability of Typical Denoising Algorithms Based on ICESat-2 Data

Photon-counting light detection and ranging (LiDAR) emits and receives weak photon signals, which are easily mixed with background noise caused by the sun, the atmosphere, etc., and is thus difficult to distinguish. Therefore, point-cloud denoising is a key step in point-cloud data processing of photon-counting LiDAR. To explore the adaptability of different denoising algorithms for photon-counting LiDAR data in different times and spaces, in this paper, NASA’s official differential, regressive and Gaussian adaptive nearest neighbor (DRAGANN) algorithm; Herzfeld’s radial basis function (RBF) denoising algorithm; and the density-based spatial clustering of applications with noise (DBSCAN) algorithm based on density clustering are used to denoise the ICESat-2 ATL03 photon point-cloud data. Airborne LiDAR data are used to verify the denoising accuracy, and then the adaptability of the three algorithms is discussed. The results show that the DRAGANN algorithm is suitable for data with moderate Fraction Vegetation Coverage (FVC) (45–75%) at night and is less affected by slope; therefore, it is not limited to terrain slope. The denoising accuracy of the RBF algorithm decreases with increasing FVC and decreases with increasing slope. It is suitable for data with low terrain slope (0~55°) and low FVC (0~220°), which is less affected by observation time; therefore, it is suitable for all-day data. The DBSCAN algorithm is suitable for data with moderate FVC (45~75%) at night, regardless of terrain slope. Unlike the DRAGANN algorithm, the DBSCAN algorithm is greatly affected by solar noise photons, but at night, its denoising accuracy is higher than that of the DRAGANN algorithm. The research results have certain reference significance for the subsequent processing and application of ICESat-2 data.

Determination of the Hydrological and Morphometric Characteristics Using GIS

Abstract This paper was conducted to determine the hydrological and morphometric characteristics of the Wad Ramli region, Sudan. The Digital Elevation Model (DEM) was obtained and then processed within the ArcGIS 10.8 program. Hydrological analysis tools were used to determine the hydrological characteristics, and then the quantitative analysis was done to deduce the morphological characteristics. The water flow direction, water streams, stream orders, order 4 watershed, and all water basins were obtained. The best location for the dam's water storage was determined. It was found that the water is flowing normally, the basin is asymmetrical, and the area has low terrain.

PM2.5 concentration assessment based on geographical and temporal weighted regression model and MCD19A2 from 2015 to 2020 in Xinjiang, China.

PM2.5 is closely linked to both air quality and public health. Many studies have used models combined with remote sensing and auxiliary data to inverse a large range of PM2.5 concentrations. However, the data's spatial resolution is limited. and better results might have been obtained if higher resolution data had been used. Therefore, this paper establishes a geographical and temporal weighted regression model (GTWR) and estimates the PM2.5 concentration in Xinjiang from 2015 to 2020 using 1 km resolution MCD19A2 (MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006) data and 9 auxiliary variables. The findings indicate that the GTWR model performs better than the simple linear regression (SLR) and geographically weighted regression (GWR) models in terms of accuracy and feasibility in retrieving PM2.5 concentrations in Xinjiang. Simultaneously, by combining the GTWR model with MCD19A2 data, a spatial distribution map of PM2.5 with better spatial resolution can be obtained. Next, the regional distribution of annual PM2.5 concentrations in Xinjiang is consistent with the terrain from 2015 to 2020. The low value area is primarily found in the mountainous area with higher terrain, while the high value area is primarily in the basin with lower terrain. Overall, the southwest is high and the northeast is low. In terms of time change, the six-year PM2.5 shows a single peak distribution with 2016 as the inflection point. Lastly, from 2015 to 2020, the seasonal average PM2.5 concentration in Xinjiang has a significant difference, thereby showing winter (66.15μg/m3)>spring (52.28μg/m3)>autumn (40.51μg/m3)>summer (38.63μg/m3). The research shows that the combination of MCD19A2 data and GTWR model has good applicability in retrieving PM2.5 concentration.