Flash Flood Risk Estimation of Wadi Qena Watershed, Egypt Using GIS Based Morphometric Analysis

Flash flooding is one of the periodic geohazards in the eastern desert of Egypt where many parts of Upper Egypt, Sinai, and Red Sea areas were hit by severe flash floods, for example in 1976, 1982, 1996 and January 2010. The hazard degree for each sub-basin was determined using the approach developed by El-Shamy for assessing susceptibility of sub-basins to flash flooding risk. To identify at-risk sub-basins, two different methods were applied. The first method is based on the relationship between the drainage density and bifurcation ratio, and the second one uses the relationship between drainage frequency and bifurcation ratio. The three morphometric parameters (the bifurcation ratio, drainage density, and stream frequency) were extracted and calculated for each sub-basin of the watershed. Based on the final hazard degree resulting from the two methods, a detailed hazard degree map was extracted for all sub-basins. The results illustrate that there are no sub-basins with low risk of flooding. The sub-basins with the highest hazard degree are concentrated in the middle of the watershed although they have smaller areas compared with the surrounding sub-basins. The sub-basins located at the boundary of the watershed have an intermediate risk of flooding and moderate potential for groundwater recharge. This constructed map can be used as a basic data for assessment of flood mitigation and planning

Flood Hazard Mapping and Assessment of Precipitation Monitoring System Using GIS-Based Morphometric Analysis and TRMM Data: A Case Study of the Wadi Qena Watershed, Egypt

Wadi Qena is one of the Nile Valley areas particularly at risk of severe flash flooding, located in Egypt. The study aims to verify TRMM rainfall data (TRMM 3B42), using eight stations across Egypt as well as relies on morphometric analysis to generate a flood risk map based on the ranking method. Three process could be recognized through the study, calibration, correction and verification processes. The results discuss the match daily rainfall trends of TRMM and observed data, producing a correction equation for TRMM data with root mean square error (RMSE) value of 0.837 mm d-1 and R2= 0.238 (calibration process). On the other hand, a verification process, using the developed correction equation, obtain RMSE value of 1.701 mm d-1 and R2= 0.601. The morphometric analysis shows 32 sub-basins with a hazard degree from moderate to high, amounting to 50.3% of the watershed area. Conclusively, this study confirms that the current monitoring system is not enough to cover the whole area, especially the high-risk sub-basins, and TRMM data could provide key information for water-related applications in Egypt

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Flash Flood Susceptibility Mapping in Sinai, Egypt Using Hydromorphic Data, Principal Component Analysis and Logistic Regression

Flash floods in the Sinai often cause significant damage to infrastructure and even loss of life. In this study, the susceptibility to flash flooding is determined using hydro-morphometric characteristics of the catchments. Basins and their hydro-morphometric features are derived from a digital elevation model from NASA Earthdata. Principal component analysis is used to identify principal components with a clear physical meaning that explains most of the variation in the data. The probability of flash flooding is estimated by logistic regression using the principal components as predictors and by fitting the model to flash flood observations. The model prediction results are cross validated. The logistic model is used to classify Sinai basins into four classes: low, moderate, high and very high susceptibility to flash flooding. The map indicating the susceptibility to flash flooding in Sinai shows that the large basins in the mountain ranges of the southern Sinai have a very high susceptibility for flash flooding, several basins in the southwest Sinai have a high or moderate susceptibility to flash flooding, some sub-basins of wadi El-Arish in the center have a high susceptibility to flash flooding, while smaller to medium-sized basins in flatter areas in the center and north usually have a moderate or low susceptibility to flash flooding. These results are consistent with observations of flash floods that occurred in different regions of the Sinai and with the findings or predictions of other studies

Flash Flood Susceptibility Mapping in Sinai, Egypt Using Hydromorphic Data, Principal Component Analysis and Logistic Regression

Flash floods in the Sinai often cause significant damage to infrastructure and even loss of life. In this study, the susceptibility to flash flooding is determined using hydro-morphometric characteristics of the catchments. Basins and their hydro-morphometric features are derived from a digital elevation model from NASA Earthdata. Principal component analysis is used to identify principal components with a clear physical meaning that explains most of the variation in the data. The probability of flash flooding is estimated by logistic regression using the principal components as predictors and by fitting the model to flash flood observations. The model prediction results are cross validated. The logistic model is used to classify Sinai basins into four classes: low, moderate, high and very high susceptibility to flash flooding. The map indicating the susceptibility to flash flooding in Sinai shows that the large basins in the mountain ranges of the southern Sinai have a very high susceptibility for flash flooding, several basins in the southwest Sinai have a high or moderate susceptibility to flash flooding, some sub-basins of wadi El-Arish in the center have a high susceptibility to flash flooding, while smaller to medium-sized basins in flatter areas in the center and north usually have a moderate or low susceptibility to flash flooding. These results are consistent with observations of flash floods that occurred in different regions of the Sinai and with the findings or predictions of other studies

Scrutinizing the performance of GIS-based analytical Hierarchical process approach and frequency ratio model in flood prediction – Case study of Kakegawa, Japan

Floods are one of the most common catastrophes in the world. This study generates the flood susceptibility maps (FSM) using AHP and FR in Kakegawa, Japan. A set of 100 flood points were presented in an ArcGIS environment where 70 points were chosen at random as a training dataset while 30 ones were used for validation. Eleven flood causative factors were calculated and utilized to generate the flood vulnerability maps. For the validation 30% data sub-sample set, FSM was completed by creating the receiver operating characteristic curve and the area under the curve (AUC). The results indicate that the two methods show sensible accuracy since the AUC for FR and AHP are 67% and 85.5% respectively. AHP showed higher accuracy due to the expert opinion that being shared while FR achieved lower precision because of its simple arithmetic procedures. The results help decision-makers in determining the locations vulnerable to flooding