Application of CHIRPS dataset in the selection of rain-based indices for drought assessments in Johor River Basin, Malaysia.

This paper aims to select the most appropriate rain-based meteorological drought index for detecting drought characteristics and identifying tropical drought events in the Johor River Basin (JRB). Based on a multi-step approach, the study evaluated seven drought indices, including the Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), China-Z Index (CZI), Modified China-Z Index (MCZI), Percent of Normal (PN), Deciles Index (DI), and Z-Score Index (ZSI), based on the CHIRPS rainfall gridded-based datasets from 1981 to 2020. Results showed that CZI, MCZI, SPI, and ZSI outperformed the other indices based on their correlation and linearity (R2 = 0.96-0.99) along with their ranking based on the Compromise Programming Index (CPI). The historical drought evaluation revealed that MCZI, SPI, and ZSI performed similarly in detecting drought events, but SPI was more effective in detecting spatial coverage and the occurrence of 'very dry' and 'extremely dry' drought events. Based on SPI, the study found that the downstream area, north-easternmost area, and eastern boundary of the basin were more prone to higher frequency and longer duration droughts. Furthermore, the study found that prolonged droughts are characterized by episodic drought events, which occur with one to three months of 'relief period' before another drought event occurs. The study revealed that most drought events that coincide with El Niño, positive Indian Ocean Dipole (IOD), and negative Madden-Julian Oscillation (MJO) events, or a combination of these events, may worsen drought conditions. The application of CHIRPS datasets enables better spatiotemporal mapping and prediction of drought for JRB, and the output is pertinent for improving water management strategies and adaptation measures. Understanding spatiotemporal drought conditions is crucial to ensuring sustainable development and policies through better regulation of human activities. The framework of this research can be applied to other river basins in Malaysia and other parts of Southeast Asia.

An integrated investigation of hydrocarbon pollution in Ahoada area, Niger Delta Region, Nigeria.

This study investigates hydrocarbon pollution in the Ahoada community of the Niger Delta region of Nigeria. The study uses a geographic information system (GIS) for mapping oil spill hotspots in the region. The resistivity method was used to delineate the extent of hydrocarbon pollution to a depth of 19.7 m in the Ahoada area of the region. Three categories of soil samples, impacted soil (IMS), remediated soil (RS), and control soil (CS), were collected and analyzed for the presence of BTEX, PAH, TPH, TOC, and TOG. The concentrations of the samples from the IMS and RS were compared to that of the CS to determine the extent of pollution. The GIS mapping shows that the most polluted areas in the Niger Delta Region are Rivers, Bayelsa, and Delta states. Results of the geophysical images revealed contaminants' presence to depths beyond 20 m at some locations in the study area. The highest depth of contaminant travel was at Ukperede. Soil samples' analysis showed that the range of concentrations of TPH in IMS at Oshie was 17.27-58.36 mg/kg; RS was 11.73-50.78 mg/kg which were higher than the concentrations of 0.68 mg/kg in the CS. PAHs are more prevalent in Ukperede, ranging from 54.56 to 77.54 mg/kg. BTEX concentrations ranged from 0.02 to 0.38 mg/kg for IMP and 0.01-2.7 mg/kg for RS against a CS value of 0.01 mg/kg. The study revealed that there are characteristically high resistivity values in the samples which were corroborated by the findings from the resistivity survey. TOC was found to be higher in the IMS and RS than in the CS, demonstrating that a significant quantity of the hydrocarbon has undergone appreciable decomposition.

Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios.

Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. Three entropy-based methods, namely symmetrical uncertainty, gain ratio, and entropy gain were used in a multi-criteria decision-making framework to select the best performing General Circulation Models (GCMs) for the projection of rainfall and temperature. Performance of four widely used bias correction methods was compared to identify a suitable method for correcting bias in GCM projections for the period 2010-2099. A machine learning technique was then used to generate a multi-model ensemble (MME) of the bias-corrected GCM projection for different RCP scenarios. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Finally, trends in the SPEI, temperature and rainfall, and return period of droughts for different growing seasons were estimated using a 50-year moving window, with a 10-year interval, to understand driving factors accountable for future changes in droughts. The analysis revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0, and CESM1-CAM5 are the most appropriate GCMs for projecting rainfall and temperature, and the linear scaling (SCL) is the best method for correcting bias. The MME mean of bias-corrected GCM projections revealed an increase in rainfall in the south-south, southwest, and parts of the northwest whilst a decrease in the southeast, northeast, and parts of central Nigeria. In contrast, rise in temperature for entire country during most of the cropping seasons was projected. The results further indicated that increase in temperature would decrease the SPEI across Nigeria, which will make droughts more frequent in most of the country under all the RCPs. However, increase in drought frequency would be less for higher RCPs due to increase in rainfall.