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.

Over the years, sedimentation has posed a great danger to the storage capacity of hydropower reservoirs. Good understanding of the transport system and hydrological processes in the dam is very crucial to its sustainability. Under optimal functionality, the Shiroro dam in Northern Nigeria can generate ∼600 MW, which is ideally sufficient to power about 404,000 household. Unfortunately, there have not been reliable monitoring measures to assess yield in the upstream, where sediments are sourced into the dam. In this study, we applied the Soil and Water Assessment Tool (SWAT) to predict the hydrological processes, the sediment transport mechanism and sediment yield between 1990 and 2018 in Kaduna watershed (32,124 km 2 ) located upstream of the dam. The model was calibrated and validated using observed flow and suspended sediment concentration (SSC) data. Performance evaluation of the model was achieved statistically using Nash-Sutcliffe (NS), coefficient of determination (r 2 ) and percentage of observed data (p-factor). SWAT model evaluation using NS (0.71), r 2 (0.80) and p-factors of 0.86 suggests that the model performed satisfactorily for streamflow and sediment yield predictions. The model identified the threshold depth of water (GWQMN.gw) and base flow (ALPHA_BF.gw) as the most sensitive parameters for streamflow and sediment yield estimation in the watershed. Our finding showed that an estimated suspended sediment yield of about 84.1 t/ha/yr was deposited within the period under study. Basins 67, 71 and 62 have erosion prone area with the highest sediment values of 79.4, 75.1 and 73.8 t/h respectively. Best management practice is highly recommended for the dam sustainability, because of the proximity of erosion-prone basins to the dam.

Water constitutes a major environmental and public health concerns worldwide. A large proportion of global water consumption is sourced from surface water. The dependency level on surface water is higher in developing countries, especially in rural-to-semi-urban areas, where subsurface water is not accessible. Presented in this paper is a spatiotemporal and hydrochemical quality assessment of the spring-originated Landzun Stream in Bida, Nigeria; which is usually consumed in its untreated state. Water samples were systematically collected in eighteen locations along the stream channel in both rainy and dry seasons at an equidistance interval of 500m. On-site and laboratory measurement of important physical and hydrochemical parameters were carried out using standard procedures. Water temperature in the rainy season (34–37 °C) slightly exceeds measured values in the dry season (29–33 °C). 72.22% (rainy) and 83.33% (dry) of collected samples did not meet the odourless requirement for drinking water. Similarly, estimated percentages of 66.67 and 94.44 of collected samples in rainy and dry seasons respectively have a taste. Contrary to data in the rainy season, 89%, 11%, 67% and 56% of the dry season's samples were enriched in magnesium (Mg), lead (Pb), potassium (K) and iron (Fe) respectively above the 2018 World Health Organisation guidelines for drinking water. This study further established that seasonal variation plays a major role in altering the aesthetic surface water quality. The intake of untreated surface water is a vehicle for potential water-borne diseases and allergies, hence alternative sources of drinking water for the populace dependent on the Landzun Stream is recommended to reduce risks and possible dangers of consuming the stream water.

Spatial interpolation of rain gauge data is important for ecohydrology study or modelling of land degradation. The monthly rainfall data recorded at 68 stations in Jornada basin was analysed to study the spatial patterns of rainfall. The inverse distance weighting spatial interpolation method was applied to model the spatial variability of rainfall for a wet (1992) and dry (1994) years. The rainfall interpolation was tested for the basin wide region and by constraining the rainfall-interpolation within the study area boundary. The accuracy of the interpolation result was measured by adopting the leave-one-out cross validation method. The result indicates that the rainfall displayed a strong spatial variability trend from the southwest to the northeast. The result from the CV analysis of the total data points for both year showed that the IDW interpolation method produced from data points within the study area boundary produced better fits compared to the CV result from all the data points within the