An uncontrolled sediment influx from the watershed upstream is a known threat to dam stability, while the pattern and amount of sediment yield are influenced by the predominant upstream land-use and land cover (LULC) types, precipitation amount, and intensity. Hence, the need to monitor sediment yield accumulation and its controlling factors in dam operation becomes crucial. In this paper, the Soil and Water Assessment Tool (SWAT) was used to assess the roles of land-use change, land cover area, and runoff on watershed’s sediment yield based on change detection analysis between 1975 and 2013 in the Kaduna Watershed (Nigeria), Western Africa. The SWAT standard procedures for the simulation of hydrological characteristics and sediment yields prediction were adopted. The datasets were calibrated for a period of 46 years and validated using 2015–2017 measured flow data, and suspended sediments concentration (SSC) acquired between March and October 2018. The model function was statistically determined using the Nash-Sutcliffe (NS), the coefficient of determination (r²) and the percentage of observed data (p-factor). The evaluation results of the SWAT model yielded NS, r² and p-factor of 0.71, 0.80, and 0.86, respectively. These data suggest that the model performed satisfactorily for streamflow and sediment yield predictions. Findings suggest that the extinction of evergreen forests and a significant change in land-use from range grasses and forest to agriculture generic and residential types between 1975 and 2013, which resulted in surface runoff, sediment yield, and flow alteration. Evapotranspiration increased by 22.40% between 1975 and 2013. These changes have negatively impacted the watershed runoff by 56.00% and model sediment yield by 68.00% at the end of 2013. Thus, these variations can influence various human activities in the watershed, such as food security, livestock, energy production and water supply. It is hypothesized from the presented data that land use types exact a more dominant control on runoff and sediment yield than land cover area, although climatic influence may not be ruled out.

Background: The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors. Methods: The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020. Results: Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali. Conclusion: Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW.

Understanding of shoreline dynamics is essential to coastal management and the development of tropical archipelagic countries. Therefore, this study aims to analyze the factors affecting shoreline changes and their future predictions in Bengkayang Regency, West Kalimantan. Research data used includes historical maps, remote sensing imagery, total suspended solids, underwater slope and depth, distance from the estuaries, wind speed, and distance from the coastal constructions. The multiple linear regression models and digital shoreline analysis system were applied. Findings showed accretion occurred in 1981, 1991, and 2021 while abrasion was detected in 2001 and 2011. The shorelines gained 37.05 meters between 1945 and 2021 under intensive accretion in segments 2-4 while abrasion recorded in segments 6-8. Furthermore, the shorelines are projected to be abraded between 2021 and 2041. These findings are recommended to be considered in the development of coastal areas that are adaptive to climate change, sea-level rise, and land subsidence.

Fine particulate matter is one of the atmospheric contaminants that exist in the atmosphere. The purpose of this study is to evaluate spatial–temporal changes in PM_2.5 concentrations in the central and south zones of Peninsular Malaysia from 2019 to 2020. The study area involves twenty-one monitoring stations in the central and south zones of Peninsular Malaysia, using monthly and annual means of PM_2.5 concentrations. The spatial autocorrelation of PM_2.5 is calculated using Moran’s I, while three semi-variogram models are used to measure the spatial variability of PM_2.5. Three kriging methods, ordinary kriging (OK), simple kriging (SK), and universal kriging (UK), were used for interpolation and comparison. The results showed that the Gaussian model was more appropriate for the central zone (MSE = 14.76) in 2019, while the stable model was more suitable in 2020 (MSE = 19.83). In addition, the stable model is more appropriate for both 2019 (MSE = 12.68) and 2020 (8.87) for the south zone. Based on the performance indicator, universal kriging was chosen as the best interpolation method in 2019 and 2020 for both the central and south zone. In conclusion, the findings provide a complete map of the variations in PM_2.5 for two different zones, and show that interpolation methods such as universal kriging are beneficial and could be extended to the investigation of air pollution distributions in other areas of Peninsular Malaysia.

Landscape dynamics reflected in changes in land use have been proven to have changed environmental quality in many countries (Roy et al. 2022). Landscape dynamics occur massively in many countries ...