Effect of Topographic Data Accuracy on Watershed Management

Evaluation and Mitigation of Flash Flood Risks in Arid Regions: A Case Study of Wadi Sudr in Egypt

Flash floods threaten the lives of people and properties in different regions around the world, especially in arid and semi-arid regions due to infrequent flood events. The current study aims to assess the geomorphological parameters of Wadi Sudr, South Sinai in Egypt to evaluate flash flood risks and provide adequate mitigation methods. This study presents an integrated method that combines geographic information system (GIS) and watershed modeling system (WMS) with HEC-HMS to visualize and assess flood events in the study area. Different morphologic parameters of the watershed were determined, including linear, areal, and relief parameters. GIS was used to analyze the satellite images and determine the characteristics of the valley to get the extension and number of stream orders in the valley, then WMS was used to estimate rainstorms and basin characteristics, as well as estimate the amount of rain that causes flooding. HEC-HMS program was used for hydrological demonstration and precipitation overflow estimation. The morphometric analysis provided a quantitative portrayal of the Wadi Sudr watershed. Wadi Sudr has 4029 streams connected with seventh order of streams spread over an area of 547.45 km2. Based on the results of morphologic and hydraulic parameters of the watershed, two locations of protection dams were suggested. A comparison between the two locations was made to select the best location based on some criteria, including storage capacity, water depth behind the dam, width and shape of the valley, and the area covered by water stored in the reservoir. The comparison between the two locations showed that the first location is more appropriate for dam construction based on the examined criteria. The valley shape in the first location is more regular than in the second. The first location provided higher storage capacity and water depth in front of the dam than the second. The area covered by water and the width of the valley is less than the second. The stability of the dam at the first site could be higher and the cost of construction could cost be less than the second due to these reasons. A comparison was made using the weighted linear combination (WLC) method, which consists of 13 criteria to determine the suitability index (SI) in order to select the best location from the proposed locations. SI proved that the first location is better than the second. The designed dam in the selected site could be cost-efficient to protect the study area from flood risks and harvesting water that can be used in different purposes. This methodology can be applied in different areas for mitigating flash flood risks.

A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test

Identifying the spatial range of mining disturbance on vegetation is of significant importance for the plan of environmental rehabilitation in mining areas. This paper proposes a method to identify the spatial range of mining disturbance (SRMD). First, a non-linear and quantitative relationship between driving factors and fractional vegetation cover (FVC) was constructed by geographically weighted artificial neural network (GWANN). The driving factors include precipitation, temperature, topography, urban activities, and mining activities. Second, the contribution of mining activities (Wmine) to FVC was quantified using the differential method. Third, the virtual contribution of mining activities (V-Wmine) to FVC during the period without mining activity was calculated, which was taken as the noise in the contribution of mining activities. Finally, the SRMD in 2020 was identified by the significance test based on the Wmine and noise. The results show that: (1) the mean RMSE and MRE for the 11 years of the GWANN in the whole study area are 0.0526 and 0.1029, which illustrates the successful construction of the relationship between driving factors and FVC; (2) the noise in the contribution of mining activities obeys normal distribution, and the critical value is 0.085 for the significance test; (3) most of the SRMD are inside the 3 km buffer with an average disturbance distance of 2.25 km for the whole SRMD, and significant directional heterogeneity is possessed by the SRMD. In conclusion, the usability of the proposed method for identifying SRMD has been demonstrated, with the advantages of elimination of coupling impact, spatial continuity, and threshold stability. This study can serve as an early environmental warning by identifying SRMD and also provide scientific data for developing plans of environmental rehabilitation in mining areas.

Progress and Trends in the Application of Google Earth and Google Earth Engine

Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.

Effect of Independent Variables on Urban Flood Models

The simulation accuracy of urban flood models is affected by independent variables describing terrain resolution and artificial land cover. An evaluation of these effects could provide suggestions for the improvement of simulation accuracy when the available terrain resolutions and representation methods of land cover are different. This paper focused on exploring and evaluating these effects on simulation accuracy using two indicators, relative depth accuracy (RDA) and relative area accuracy (RAA). The study area was the Nanjing Jianye district in China, which has experienced extensive urbanization. Designed rainfall (2 and 10 year return periods) and three terrain resolutions (17, 35, and 70 m) were used in this paper. Building blocks (BB), road drainage (RD), and a combination of both (BB + RD) were compared to present the effect of artificial land cover. Real flood events were initially simulated as a model verification case, and hypothetic modeling scenarios were simulated to evaluate the effects of different resolutions and representation methods. The results indicate that the effect of terrain resolutions on simulation accuracy was more obvious than that of artificial land cover in the study area. In this paper, 20–30% higher accuracy could be achieved in the 35 m resolution model with respect to the 70 m resolution model. A relative accuracy of 94% was achieved in the 17 m resolution model when using the BB method, which was 5% higher than that using the RD method. This paper shows that evaluating the effects of terrain resolution and artificial land cover is effective and helpful for improving the simulation accuracy of urban flood models in extensively urbanized districts.

Selected Aspects of Navigation System Synthesis for Increased Flight Safety, Protection of Human Lives, and Health

Accurate navigation systems allow us to optimize the trajectory of flying objects and thus solve environmental problems in aviation and their impact on public health. In this paper, we present one of the methods of assessment of accuracy and resistance to interference of distance-measuring equipment (DME). By using computer technology, the method enables us to determine the potential but also the real error measuring the distance of the flying object from DME. The credibility of the respective results of the solution on the task of DME optimal rangefinder synthesis depends on the accuracy of the previous data used, i.e., mathematical models of the respective flying objects flight dynamics, useful signals, and their parameters and interference. DME systems have an impact on air transport safety, and therefore the impact of interference on their operation must be investigated.

Obtaining elevation of Oncomelania Hupensis habitat based on Google Earth and it's accuracy evaluation: an example from the Poyang lake region, China

Schistosomiasis japonicum is a major zoonosis that seriously harms human health and affects social and economic development in China. The control of Oncomelania Hupensis, the only intermediate host of schistosome japonicum, is one of the integrated measures for schistosomiasis control in China. Acquiring updated elevation data of snail habitat environment, as well as it's spatial analysis, play an important role for the risk evaluation and precise control of schistosomiasis transmission and prevalence. Currently, the elevation database of snail habitat environment in schistosomiasis epidemic areas has not been available in the world, which affects the development of research and application work regarding to snail control. Google Earth(GE) can provide massive information related to topography, geomorphology and ground objects of a region due to its indisputable advantages such as wide use, free charge and rapidly updating. In this paper, taking the Poyang lake region as a example, we extracted elevation data of snail-inhabited environment of the lake from GE and established a elevation correction regression model(CRM) for acquiring accurate geospatial elevations, so as to provide a decision-making reference for snail control and risk evaluation of schistosomiasis in China. We developed a GE Application Programming Interface(API) program to extract elevation data from GE, which was compared with the actual elevation data obtained from topographic map of the Poyang Lake bottom. Then, a correction regression model was established and evaluated by 3 index, Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Index of Agreement(IOA) for the accuracy of the model. The elevation values extracted from GE in 15086 sample grid points of the lake ranged from 8.5 m to 24.8 m. After the sample points were divided randomly to three groups, the mean elevations of three groups were 13.49 m, 13.52 m and 13.65 m, respectively, with standard deviation ranged from 2.04-2.06. The mean elevation among three groups has no statistic difference (F = 1.536, P = 0.215). A elevation correction regression model was established as y = 6.228 + 0.485×. the evaluation results for the accuracy of the model showed that the MAE and RMSE before correction was 1.28 m and 3.95 m respectively, higher than that after correction, which were 0.74 and 1.30 m correspondingly. The IOA before correction (-0.40)was lower than that after correction(0.34). Google Earth can directly or indirectly get access to massive information related to topography, geomorphology and ground objects due to its indisputable advantages. However, it still needs to be converted into more reliable and accurate data by combining with pre-processing tools. This study used self-developed API program to extract elevation data from GE through precisely locating and improved the accuracy of elevation by using a correction regression model, which can provide reliable data sources for all kinds of spatial data researches and applications.