Mapping Arsenic Risks in the Ayeyarwady (Irrawaddy) Delta, Myanmar: Implications for Public Health

<strong class="journal-contentHeaderColor">Abstract.</strong> With their low lying, flat topography, river deltas and coastal plains are extremely prone to relative sea level rise and other water related hazards. This calls for accurate elevation data for flood risk assessments, especially in the densely populated Southeast Asian deltas. However, in data-poor countries such as Myanmar, where high accuracy elevation data is not accessible, often only global satellite based digital elevation models (DEMs), suffering from low vertical accuracy and remote sensing artefacts, can be used by the public and scientific community. As the lack of accurate elevation data hampers the assessment of flood risk, studying available information on land elevation and its reliability is essential, particularly in the context of sea level rise impact. Here, we assess the performance of ten global DEMs in the Ayeyarwady Delta (Myanmar) against the new, local, so called AD-DEM, which was generated based on topographical map elevation data. To enable comparison, all DEMs were converted to a common vertical datum tied to local sea level. While both CoastalDEM v2.1 and FABDEM, perform comparably well, showing the highest correspondence in comparison with AD-DEM and low elevation spot heights, the FABDEM outperforms the CoastalDEM v2.1 by the absence of remote sensing artefacts. The AD-DEM provides a high accuracy, open source and freely available, independent elevation dataset suitable for evaluating land elevation data in the Ayeyarwady Delta and studying topography and flood risk at large scale, while small scale investigations may benefit from a FABDEM locally improved with data from the AD-DEM. Based on latest IPCC projections of sea level rise, the consequences of DEM selection for assessing the impact of sea level rise in the Ayeyarwady Delta are shown. We highlight the need for addressing particularly low lying populated areas within the most seaward districts with risk mitigation and adaptation strategies while also more inland delta population should be made aware to face a higher risk of flooding due to relative sea level rise in the next ~100 years.

<strong class="journal-contentHeaderColor">Abstract.</strong> With their low lying, flat topography, river deltas and coastal plains are extremely prone to relative sea level rise and other water related hazards. This calls for accurate elevation data for flood risk assessments, especially in the densely populated Southeast Asian deltas. However, in data-poor countries such as Myanmar, where high accuracy elevation data is not accessible, often only global satellite based digital elevation models (DEMs), suffering from low vertical accuracy and remote sensing artefacts, can be used by the public and scientific community. As the lack of accurate elevation data hampers the assessment of flood risk, studying available information on land elevation and its reliability is essential, particularly in the context of sea level rise impact. Here, we assess the performance of ten global DEMs in the Ayeyarwady Delta (Myanmar) against the new, local, so called AD-DEM, which was generated based on topographical map elevation data. To enable comparison, all DEMs were converted to a common vertical datum tied to local sea level. While both CoastalDEM v2.1 and FABDEM, perform comparably well, showing the highest correspondence in comparison with AD-DEM and low elevation spot heights, the FABDEM outperforms the CoastalDEM v2.1 by the absence of remote sensing artefacts. The AD-DEM provides a high accuracy, open source and freely available, independent elevation dataset suitable for evaluating land elevation data in the Ayeyarwady Delta and studying topography and flood risk at large scale, while small scale investigations may benefit from a FABDEM locally improved with data from the AD-DEM. Based on latest IPCC projections of sea level rise, the consequences of DEM selection for assessing the impact of sea level rise in the Ayeyarwady Delta are shown. We highlight the need for addressing particularly low lying populated areas within the most seaward districts with risk mitigation and adaptation strategies while also more inland delta population should be made aware to face a higher risk of flooding due to relative sea level rise in the next ~100 years.

<strong class="journal-contentHeaderColor">Abstract.</strong> With their low lying, flat topography, river deltas and coastal plains are extremely prone to relative sea level rise and other water related hazards. This calls for accurate elevation data for flood risk assessments, especially in the densely populated Southeast Asian deltas. However, in data-poor countries such as Myanmar, where high accuracy elevation data is not accessible, often only global satellite based digital elevation models (DEMs), suffering from low vertical accuracy and remote sensing artefacts, can be used by the public and scientific community. As the lack of accurate elevation data hampers the assessment of flood risk, studying available information on land elevation and its reliability is essential, particularly in the context of sea level rise impact. Here, we assess the performance of ten global DEMs in the Ayeyarwady Delta (Myanmar) against the new, local, so called AD-DEM, which was generated based on topographical map elevation data. To enable comparison, all DEMs were converted to a common vertical datum tied to local sea level. While both CoastalDEM v2.1 and FABDEM, perform comparably well, showing the highest correspondence in comparison with AD-DEM and low elevation spot heights, the FABDEM outperforms the CoastalDEM v2.1 by the absence of remote sensing artefacts. The AD-DEM provides a high accuracy, open source and freely available, independent elevation dataset suitable for evaluating land elevation data in the Ayeyarwady Delta and studying topography and flood risk at large scale, while small scale investigations may benefit from a FABDEM locally improved with data from the AD-DEM. Based on latest IPCC projections of sea level rise, the consequences of DEM selection for assessing the impact of sea level rise in the Ayeyarwady Delta are shown. We highlight the need for addressing particularly low lying populated areas within the most seaward districts with risk mitigation and adaptation strategies while also more inland delta population should be made aware to face a higher risk of flooding due to relative sea level rise in the next ~100 years.

<strong class="journal-contentHeaderColor">Abstract.</strong> With their low lying, flat topography, river deltas and coastal plains are extremely prone to relative sea level rise and other water related hazards. This calls for accurate elevation data for flood risk assessments, especially in the densely populated Southeast Asian deltas. However, in data-poor countries such as Myanmar, where high accuracy elevation data is not accessible, often only global satellite based digital elevation models (DEMs), suffering from low vertical accuracy and remote sensing artefacts, can be used by the public and scientific community. As the lack of accurate elevation data hampers the assessment of flood risk, studying available information on land elevation and its reliability is essential, particularly in the context of sea level rise impact. Here, we assess the performance of ten global DEMs in the Ayeyarwady Delta (Myanmar) against the new, local, so called AD-DEM, which was generated based on topographical map elevation data. To enable comparison, all DEMs were converted to a common vertical datum tied to local sea level. While both CoastalDEM v2.1 and FABDEM, perform comparably well, showing the highest correspondence in comparison with AD-DEM and low elevation spot heights, the FABDEM outperforms the CoastalDEM v2.1 by the absence of remote sensing artefacts. The AD-DEM provides a high accuracy, open source and freely available, independent elevation dataset suitable for evaluating land elevation data in the Ayeyarwady Delta and studying topography and flood risk at large scale, while small scale investigations may benefit from a FABDEM locally improved with data from the AD-DEM. Based on latest IPCC projections of sea level rise, the consequences of DEM selection for assessing the impact of sea level rise in the Ayeyarwady Delta are shown. We highlight the need for addressing particularly low lying populated areas within the most seaward districts with risk mitigation and adaptation strategies while also more inland delta population should be made aware to face a higher risk of flooding due to relative sea level rise in the next ~100 years.

Stream power–based models of bedrock landscape development are effective at producing synthetic topography with realistic fl uvial-network topology and three-dimensional topography, but they are diffi cult to calibrate. This paper examines ways in which fi eld observations, geochronology, and digital elevation model (DEM) data can be used to calibrate a bedrock landscape development model for a specifi c study site. We fi show how uplift rate, bedrock erodibility, and landslide threshold slope are related to steady-state relief, hypsometry, and drainage density for a wide range of synthetic topographies produced by a stream power–based planform landscape development model. Our results indicate that low uplift rates and high erodibility result in low-relief, high drainage density, fl uvially dominated topography, and high uplift rates and low erodibility leads to high-relief, low drainage density, mass wasting–dominated topography. Topography made up of a combination of flchannels and threshold slopes occurs for only a relatively narrow range of model parameters. Using measured values for hypsometric integral, drainage density, and relief, quantitative values of bedrock erodibility can be further constrained, particularly if uplift rates are independently known. We applied these techniques to three sedimentary rock units in the western Transverse Ranges in California that have experienced similar climate, uplift, and incision histories. The 10 Be surface exposure datincision of initially low-relief topography there occurred during the last ~60 k.y. We estimated the relative dependence of drainage area and channel slope on erosion rate in the stream power law from slope-area data, and inferred values for bedrock erodibility ranging from 0.09 to 0.3 m (0.2–0.4) k.y. –1 for the rock types in this study area.

Soil erosion in the agricultural area of a hill slope is a fundamental issue for crop productivity and environmental sustainability. Building terrace is a very popular way to control soil erosion, and accurate assessment of the soil erosion rate is important for sustainable agriculture and environmental management. Currently, many soil erosion estimations are mainly based on the freely available medium or coarse resolution digital elevation model (DEM) data that neglect micro topographic modification of the agriculture terraces. The development of unmanned aerial vehicle (UAV) technology enables the development of high-resolution (centimeter level) DEM to present accurate topographic features. To demonstrate the sensitivity of soil erosion estimates to DEM resolution at this high-resolution level, this study tries to evaluate soil erosion estimation in the Middle Hill agriculture terraces in Nepal based on UAV derived high-resolution (5 × 5 cm) DEM data and make a comparative study for the estimates by using the DEM data aggregated into different spatial resolutions (5 × 5 cm to 10 × 10 m). Firstly, slope gradient, slope length, and topographic factors were calculated at different resolutions. Then, the revised universal soil loss estimation (RUSLE) model was applied to estimate soil erosion rates with the derived LS factor at different resolutions. The results indicated that there was higher change rate in slope gradient, slope length, LS factor, and soil erosion rate when using DEM data with resolution from 5 × 5 cm to 2 × 2 m than using coarser DEM data. A power trend line was effectively used to present the relationship between soil erosion rate and DEM resolution. The findings indicated that soil erosion estimates are highly sensitive to DEM resolution (from 5 × 5 cm to 2 × 2 m), and the changes become relatively stable from 2 × 2 m. The use of DEM data with pixel size larger than 2 × 2 m cannot detect the micro topography. With the insights about the influencing mechanism of DEM resolution on soil erosion estimates, this study provides important suggestions for appropriate DEM data selection that should be investigated first for accurate soil erosion estimation.

Abstract. With their low lying, flat topography, river deltas and coastal plains are extremely prone to relative sea level rise and other water-related hazards. This calls for accurate elevation data for flood risk assessments, especially in the densely populated Southeast Asian deltas. However, in data-poor countries such as Myanmar, where high accuracy elevation data are not accessible, often only global satellite-based digital elevation models (DEMs), suffering from low vertical accuracy and remote sensing artefacts, can be used by the public and scientific community. As the lack of accurate elevation data hampers the assessment of flood risk, studying available information on land elevation and its reliability is essential, particularly in the context of sea level rise impact. Here, we assess the performance of 10 global DEMs in the Ayeyarwady Delta (Myanmar) against the new, local, so-called AD-DEM, which was generated based on topographical map elevation data. To enable comparison, all DEMs were converted to a common vertical datum tied to local sea level. While both CoastalDEM v2.1 (Kulp and Strauss, 2021) and FABDEM (Hawker et al., 2022) perform comparably well, showing the highest correspondence in comparison with AD-DEM and low-elevation spot heights, FABDEM outperforms CoastalDEM v2.1 by the absence of remote sensing artefacts. The AD-DEM provides a high-accuracy, open and freely available, and independent elevation dataset suitable for evaluating land elevation data in the Ayeyarwady Delta and studying topography and flood risk at large scale, while small-scale investigations may benefit from a FABDEM locally improved with data from the AD-DEM. Based on the latest Intergovernmental Panel on Climate Change (IPCC) projections of sea level rise, the consequences of DEM selection for assessing the impact of sea level rise in the Ayeyarwady Delta are shown. We highlight the need for addressing particularly low-lying populated areas within the most seaward districts with risk mitigation and adaptation strategies while also the more inland delta population should be made aware of facing a higher risk of flooding due to relative sea level rise in the next ∼ 100 years.

High-resolution Digital Elevation Model (DEM) data provides essential information for pluvial flood simulation. Although the increased accessibility and quality of publicly available DEM datasets can facilitate geospatial analysis at various scales, existing DEM datasets with global coverage mostly lack sufficient spatial resolution for pluvial flood simulations, which require detailed topographic information to be included in the simulation. Simulating flood scenarios with low-resolution DEMs (&gt;30m) can result in substantial deviations from real cases. This issue becomes even more severe for flood-prone areas in data-scarce developing countries.Image super-resolution is a technique for reconstructing low-resolution information into high-resolution data. Various deep-learning models have been employed for this task, primarily focusing on generating high-resolution natural-colour images. However, the effects of these deep learning models on enhancing the resolution of DEM data have not been extensively investigated. One of the state-of-the-art super-resolution models, the Residual Channel Attention Network (RCAN), has gained popularity due to its accuracy and efficiency. Leveraging publicly available low-resolution global DEM data and high-resolution regional DEM data, this study assesses the performance of RCAN models in a DEM super-resolution task. The experimental results suggest that, compared to conventional interpolation methods, the tested RCAN model exhibits superior performance in constructing high-resolution DEM data. The generated super-resolution DEM data were then tested in pluvial flood simulations and achieved substantially higher realism in modelling floodwater distribution. The proposed method for constructing super-resolution DEMs opens up the possibility of simulating flooding at hyper-resolution globally.

Digital elevation model (DEM) data describe the information of ground elevation. So it is important to protect the copyright of digital elevation model data. A lossless visible 3-D watermarking algorithm to protect DEM data is proposed in this paper. The copyright watermarking is embedded in the DEM data by an visible way. The original data, blocked by 3-D visible watermarking, are hidden in the watermarked DEM data by a generalized histogram algorithm. Because the visible watermark blocks a part of DEM data, illegal users are restricted to retrieve. At the same time, 3-D visible watermark can identify the copyright. Without original 3-D watermark data, authorized users can eliminate the 3-D visible watermark and restore the original DEM data lossless by applying the proposed algorithm. It is a blind watermarking algorithm. Experiments demonstrate that the proposed algorithm has satisfactory security and can effectively protect the copyright of DEM data.

DEM Explorer: An online interoperable DEM data sharing and analysis system

A common method of channel network extraction from digital elevation model (DEM) data is based on specification of a threshold area Ath, that is, the minimum support area required to drain to a point for a channel to form. Current efforts to predict Ath from DEM data are inconclusive, and usually an arbitrary constant Ath value is chosen for channel network extraction. In this paper, we study the effects of threshold area selection on the morphometric properties (such as drainage density, length of drainage paths, and external and internal links) and scaling properties (such as Horton's laws and fractal dimension) of a channel network. We also study the related problem of DEM data resolution and its effect on estimation of scaling properties. The results indicate that morphometric properties vary considerably with Ath, and thus values reported without their associated Ath are meaningless and should be used in hydrologic analysis with caution. Also, the “completeness” of a channel network (in terms of having the outlet stream flowing directly into a higher‐order stream) is found to depend on Ath in a random unpredictable way. Even if only the complete channel networks are used in the analysis, the statistical variability of scaling properties estimates due to Ath selection is significant and can be of comparable size to the variability due to DEM resolution and variability between estimates of different river networks. Our analysis highlights the need to carefully study the problem of network source representation or channel initiation scale from DEMs which will point to an appropriate Ath for channel network extraction and estimation of morphometric properties.

Background: Morphometry is the quantitative assessment and mathematical analysis of landforms, essential for understanding watershed dynamics. The Dzuza, Dhansiri and Khova river basins in Nagaland, India, were analysed to comprehend their shape, drainage network and ecological importance. These watersheds are crucial for the sustainable management of water and land resources, particularly considering Nagaland’s diverse topography and hydrological circumstances. Methods: A field-laboratory study was performed utilising GIS and remote sensing technologies. Digital Elevation Model (DEM) data were acquired and analysed utilising ArcGIS 10.5 software to identify watersheds and calculate morphometric characteristics including drainage density, stream frequency, relief and slope. The data were examined to clarify the linear, relief and aerial characteristics of the three basins. Result: Dzuza Basin, a sixth-order system with 2815 m elevation and 6.44 ruggedness, is prone to erosion. Dhansiri, a seventh-order basin with a 4116.50-km stream network, has a complex drainage pattern, while the smaller Khova Basin also has distinct hydrological characteristics. High drainage densities (2.29-2.43 km/km2) suggest runoff potential, but north/northeast-facing slopes usually retain rainfall. Sustainable management requires customised watershed methods for erosion mitigation in Dzuza, water retention in Dhansiri and integrated soil-water conservation in Khova.

ABSTRACTThe calculation of surface area is meaningful for a variety of space-filling phenomena, e.g., the packing of plants or animals within an area of land. With Digital Elevation Model (DEM) data we can calculate the surface area by using a continuous surface model, such as by the Triangulated Irregular Network (TIN). However, just as the triangle-based surface area discussed in this paper, the surface area is generally biased because it is a nonlinear mapping about the DEM data which contain measurement errors. To reduce the bias in the surface area, we propose a second-order bias correction by applying nonlinear error propagation to the triangle-based surface area. This process reveals that the random errors in the DEM data result in a bias in the triangle-based surface area while the systematic errors in the DEM data can be reduced by using the height differences. The bias is theoretically given by a probability integral which can be approximated by numerical approaches including the numerical integral and the Monte Carlo method; but these approaches need a theoretical distribution assumption about the DEM measurement errors, and have a very high computational cost. In most cases, we only have variance information on the measurement errors; thus, a bias estimation based on nonlinear error propagation is proposed. Based on the second-order bias estimation proposed, the variance of the surface area can be improved immediately by removing the bias from the original variance estimation. The main results are verified by the Monte Carlo method and by the numerical integral. They show that an unbiased surface area can be obtained by removing the proposed bias estimation from the triangle-based surface area originally calculated from the DEM data.

Mountainous areas in synthetic aperture radar (SAR) images suffer severe geometric distortions caused by different look directions. Consequently, ground control point (GCP) extraction hardly obtains accurate results for aircraft positioning. Based on the digital elevation model (DEM) and polarimetric SAR (PolSAR) data, we propose a method for extracting GCPs in mountainous areas by introducing the polarization orientation angle shift (POAS) to minimize geometric distortions. In this method, DEM data are used as the reference map by providing POASs at arbitrary look directions to make up for the look-direction sensitivity of POASs transformed from PolSAR data. The geometric distortions between POASs transformed from DEM and PolSAR data are effectively reduced by calculating the POASs from DEM data at the same look direction and look angles of the PolSAR data. In contrast to the SAR data, which have a large dynamic range, the values of POAS are limited to a small interval. Therefore, the illumination distortions induced by visualization can be reduced. Finally, the GCP extraction between the POAS images is conducted by bilateral filter scale-invariant feature transform. Experiments using various data at different look directions demonstrate that the proposed method obtains better-quality GCPs but less invalid keypoints than the method using only intensity images for mountainous areas.

The issue of unsustainable groundwater utilization is becoming increasingly an evident problem and the key concern for many developing countries. One of the problems is the absence of updated spatial information on the quantity and distribution of groundwater resource. Like the other developing countries, groundwater evaluation in Ethiopia has been usually conducted using field survey which is not feasible in terms of time and resource. This study was conducted in Northern Ethiopia, Wollo Zone, in Gerardo River Catchment district to spatially delineate the groundwater potential areas using geospatial and MCDA tools. To do so, eight major biophysical and environmental factors like geomorphology, lithology, slope, rainfall, land use land cover (LULC), soil, lineament density and drainage density were considered. The sources of these data were satellite image, digital elevation model (DEM), existing thematic maps and metrological station data. Landsat image was used in ERDAS Imagine to drive the LULC of the area, while the geomorphology, soil, and lithology of the area were identified and classified through field survey and digitized from existing maps using the ArcGIS software. The slope, lineament and drainage density of the area were derived from DEM using spatial analysis tools. The rainfall surface map was generated using the thissen polygon interpolation. Finally, after all these thematic maps were organized, weighted value determination for each factor and its field value was computed using IDRSI software. At last, all the factors were integrated together and computed the model using the weighted overlay so that potential groundwater areas were mapped. The findings depicted that the most potential groundwater areas are found in the central and eastern parts of the study area, while the northern and western parts of the Gerado River Catchment have poor potential of groundwater availability. This is mainly due to the cumulative effect of steep topographic and high drainage density. At last, once the potential groundwater areas were identified, cross validation of the resultant model was carefully carried out using existing data of dung wells and bore holes. The point data of dung wells and bore holes were overlaid on groundwater potential suitability map and coincide with the expected values. Generally, from this study, it can be concluded that RS and GIS with the help of MCDA are important tools in monitoring and evaluation of groundwater resource potential areas.