This study investigated the effects of polypropylene fibre (PF) reinforcement on the mechanical behaviour of clay soil. Using clay soil and polypropylene fibres from China’s Inner Mongolia and Hebei Provinces, respectively, a series of soil samples with 0%, 1.5%, 2.25%, and 3% PF content by soil weight were subjected to compaction, shear strength, consolidation, California bearing ratio, and microstructure analyses. The study results indicate improved compaction, shear strength, consolidation, and the bearing ratio of the PF-stabilised clay soil. As the PF content increased, its maximum dry density increased and its optimum moisture content decreased; its angle of internal friction increased and its cohesion coefficient decreased; and its void ratio, consolidation coefficient, and hydraulic conductivity all decreased. Comparing the unstabilised (0% PF) and stabilised (3% PF) clay soil, the void ratio, consolidation coefficient, and hydraulic conductivity decreased from 0.96 to 0.93, from 2.52 to 2.34 cm²/s, and from 1.12 to 1.02 cm/s, respectively. The optimum PF content was determined to be 3% by the weight of the soil, as this quantity resulted in the best improvement in soil properties.

The construction of buildings on expansive soils poses considerable risk of damage or collapse due to soil shrinkage or swelling made likely by the remarkable degree compressibility and weak shear resistance of such soils. In this research, rice husk ash (RHA) was added to expansive soil samples in different quantities of 0%, 4%, 8%, 12%, and 16% by weight of soil to determine their effects on the plasticity index, compaction parameters, consolidation performance, and California bearing ratio (CBR)of clay soil. The results show that the use of RHA increases the effective stress and decreases the void ratio and coefficient of consolidation. Adding 16% RHA resulted in the greatest reduction in the hydraulic conductivity, void ratio, and coefficient of consolidation. The void ratio decreased from 0.96 to 0.93, consolidation coefficient decreased from 2.52 to 2.33 cm²/s, and hydraulic conductivity decreased from 1.12 to 0.80 cm/s. The addition of RHA improved the soil properties and coefficient of consolidation due to the high density and cohesiveness of RHA. The results of this study can be used to provide a suitable basis for the treatment of expansive soil to provide improved conditions for infrastructure construction.

Drought is an unpredictable hydrological phenomenon, and climate change has made it difficult to predict and analyze droughts. Nyala city airport metrological station rainfall records from 1943 to 2017 (75 years) were investigated. Four statistical drought indices were used; the standardized precipitation index (SPI), the rainfall anomaly index (RAI), the rainfall decile percent index (RDI), and the percent normal precipitation index (PNI). The study analyzes, assesses, compares, and determines the proper drought index. Results show that annual normal drought class (DC4) percentages for PNI, RDI, and RAI are not significantly different at an average of 42% and 65.3% for SPI at a frequency of 49 years. In comparing the average monthly and yearly drought frequency values and considering the historical dry and wet droughts, results showed the indices performance rank as: SPI, RAI, RDI, and PNI. Result reveals that the SPI was superior in all analyses, but it had some defects in detecting monthly dry drought when precipitation is dominated by rare or zero values (start and end of the rainy season). This was concluded and revealed by conducting a zone chart showing the deviations of standard deviation about the mean. Thus, the SPI index outperforms the other three indices.

Satellite-based rainfall estimates (SREs) represent a promising alternative dataset for climate and hydrological studies, where gauge observations are insufficient. However, these datasets are accompanied by significant uncertainties. Therefore, this study aims to minimize the systematic bias of Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS), Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Global Precipitation Climatology Project (GPCP) rainfall estimates using a quantile mapping (QM) method with climatic zones (CZs). The adjusted rainfall estimates were evaluated for the period from 2003–2017; data from 2003 to 2016 were used for calibration, and data from 2017 were used for validation. The results revealed significant improvements for the adjusted PERSIANN-CCS, PERSIANN-CDR, CHIRPS, and GPCP monthly time series in terms of all statistical measures and evaluation of overall CZs. In terms of Root Mean Square Errors (RMSEs), the adjusted CHIRPS did not show an improvement. This method successfully removed the mean bias of the daily time series for all SREs. The findings suggest that this method can be applied to correct the systematic bias of all SREs in the monthly time series in the future without the need for further gauge measurements over Sudan.