Occurrence of Extreme Rainfall and Flood Risks in Yopougon, Abidjan, Southeast Côte d’Ivoire from 1971 to 2022

Yopougon, located in the western part of the Autonomous District of Abidjan, is the most heavily populated municipality in Côte d’Ivoire. However, this area is prone to floods and landslides during the rainy season. The study aims to assess recent flood risks in the municipality of Yopougon of the Autonomous District of Abidjan. To achieve this objective, the study analyzed two types of data: daily rainfall from 1971 to 2022 and parameters derived from a Numerical Field and Altitude Model (NFAM). The study examined six rainfall parameters using statistical analysis and combined land use maps obtained from the NFAM of Yopougon. The results indicated that, in 67% of cases, extreme rainfall occurred mainly between week 3 of May and week 1 of July. The peak of extreme rainfall was observed in week 2 of June with 15% of cases. These are critical periods of flood risks in the Autonomous District of Abidjan, especially in Yopougon. In addition, there was variability of rainfall parameters in the Autonomous District of Abidjan. This was characterized by a drop of annual and seasonal rainfall, and an increase of numbers of rainy days. Flood risks in Yopougon are, therefore, due to the regular occurrence of rainy events. Recent floods in Yopougon were caused by normal rains ranging from 55 millimeters (mm) to 153 mm with a return period of less than five years. Abnormal heavy rains of a case study on June 20-21, 2022 in Yopougon were detected by outputs global climate models. Areas of very high risk of flood covered 18% of Yopougon, while 31% were at high risk. Climate information from this study can assist authorities to take in advance adaptation and management measures.

Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

In this paper,we describe and analyze two datasets entitled“Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021”and“Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”.These datasets provide the homogenised monthly and daily mean(TG),minimum(TN),and maximum(TX)temperature and precipitation(RR)records since 1960 at 366 stations in China and 56stations in Greece.The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720.For China,the regional mean annual TG,TX,TN,and RR series during 1960–2021 showed significant warming or increasing trends of 0.27℃(10 yr)^(-1),0.22℃(10 yr)^(-1),0.35℃(10 yr)^(-1),and 6.81 mm(10 yr)-1,respectively.Most of the seasonal series revealed trends significant at the 0.05level,except for the spring,summer,and autumn RR series.For Greece,there were increasing trends of 0.09℃(10 yr)-1,0.08℃(10 yr)^(-1),and 0.11℃(10 yr)^(-1)for the annual TG,TX,and TN series,respectively,while a decreasing trend of–23.35 mm(10 yr)^(-1)was present for RR.The seasonal trends showed a significant warming rate for summer,but no significant changes were noted for spring(except for TN),autumn,and winter.For RR,only the winter time series displayed a statistically significant and robust trend[–15.82 mm(10 yr)^(-1)].The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

Coastline Change Modelling Induced by Climate Change Using Geospatial Techniques in Togo (West Africa)

Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline leads to coastal erosion with significant damage on socio-economic infrastructures and human habitats. This research, basing on geospatial techniques, focuses on coastal erosion monitoring from 1988 to 2018 in Togo. It is interested in the extraction of shoreline and in the analysis of change. Various satellite images indexes have been developed for shoreline extraction but the major scientific problem concerns the precision of the different classification algorithms methods used for the extraction of the shoreline from these water index. This study used NDWI index from multisource satellite images. It assesses the performance of Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Support Vector Machine (SVM) Supervised Classification methods for the extraction of the shoreline on NDWI index. The topographic morphology such as linear and non-linear coastal surfaces have been considered. The estimation of the rates of change of the shoreline was performed using the statistical linear regression method (LRR). The results revealed that the SVM Supervised Classification method showed good performance on linear and non-linear coastal surface than the other methods. For the kinematics of the shoreline, the southwest of the Togolese coast has an average erosion rate ranging from 2.49 to 5.07 m per year. The results obtained will serve as decision-making support tools for the design and implementation of appropriate adaptations plans to avoid the immersion of the asphalt road by sea, displacement of population and disturbance of human habitats.

Segmentation of thermal infrared images of cucumber leaves using K-means clustering for estimating leaf wetness duration

Leaf wetness duration(LWD)is a critical parameter used to predict plant disease,but its determination under actual field conditions is a major challenge.In this study,a method for determining LWD using thermal infrared imaging was developed and applied to cucumber plants grown in a solar greenhouse.Thermal images of the plant leaves were captured using an infrared scanning camera,and a leaf wetness area segmentation method consisting of two procedures was applied.First,a color space conversion was performed automatically by an image-processing algorithm.Then,the K-means clustering algorithm was applied to enable the segmentation of the wetness area on the thermal image.Subsequently,to enable overall thermal image analysis,an initial leaf wetness threshold(LWT)of 5%was defined(where wetness values higher than 5%indicated that the leaf was in a wet state).The results of comparative experiments conducted using thermal images of plant leaves captured using an infrared scanning camera and human visual observation indicated that the estimated LWD values were generally higher than the observed LWD values,because slight leaf wetness condensations were overlooked by the human eye but detected by the infrared scanning camera.While these differences were not found to be statistically significant in this study,the proposed method for determining LWD using thermal infrared imaging may provide a new LWD detection method for cucumber and other plants grown in solar greenhouses.