RESUMO
The current global climate has shown a significant change, mostly resulting from human-induced activities. Frequent experiences of extreme rainstorms, deadly landslides, and floods followed by the destruction of roads, bridges, drainage, buildings, agriculture, and other infrastructures have been appearing across the globe along with extensive socio-economic effects including human lives losses whereby tropical Africa is among the greatly affected regions. Several studies in the region acclaim the increase of climate-related extremes due to a gradual climate variation. Hence, this study aimed to evaluate how existing water structures might respond to the future climate, which is getting more severe and frequent in the region. The study was conducted on the Nyabugogo River catchment (NRC), covering a huge part of the Kigali metropolitan area. It was carried out through a downscaled global climate model (CMIP6 GCM) projection coupled with a joint SWAT + hydrological model and HEC-RAS hydrodynamic simulation. The study showed that the annual precipitation in Kigali might keep increasing, resulting in increased risks of extreme weather events. The study identified up to 38% (+514.9 mm) annual precipitation increment, which resulted in more than a doubled flow rate (+28.0 m3/s) increment by the end of the century under a high greenhouse gas emission scenario (ssp585). As a result, hydrodynamic simulations revealed that the Bridge-1 in NRC might fail to accommodate the 50-year return peak storm under ssp585. Henceforth, there is a need to adopt high GHG emission scenarios in critical infrastructure development. Further, enforcing green-grey infrastructures in flood risk-low resilient areas is recommended to improve climate resilience. Thus, the results of this study might prove useful in climate-resilient infrastructure development and other pre-emptive adaptation practices, most importantly building anticipated resilience against climate-related hazards.
RESUMO
The characteristics of fecal sludge delivered to treatment plants are highly variable. Adapting treatment process operations accordingly is challenging due to a lack of analytical capacity for characterization and monitoring at many treatment plants. Cost-efficient and simple field measurements such as photographs and probe readings could be proxies for process control parameters that normally require laboratory analysis. To investigate this, we evaluated questionnaire data, expert assessments, and simple analytical measurements for fecal sludge collected from 421 onsite containments. This data served as inputs to models of varying complexity. Random forest and linear regression models were able to predict physical-chemical characteristics including total solids (TS) and ammonium (NH4+-N) concentrations, and solid-liquid separation performance including settling efficiency and filtration time (R2 from 0.51-0.66) based on image analysis of photographs (sludge color, supernatant color, and texture) and probe readings (conductivity (EC) and pH). Supernatant color was the best predictor of settling efficiency and filtration time, EC was the best predictor of NH4+-N, and texture was the best predictor of TS. Predictive models have the potential to be applied for real-time monitoring and process control if a database of measurements is developed and models are validated in other cities. Simple decision tree models based on the single classifier of containment type can also be used to make predictions about citywide planning, where a lower degree of accuracy is required.