The Soil and Water Assessment Tool (SWAT) was used to explore the potential impact of four climate change scenarios on discharge from the Simiyu River in Tanzania, located in the Lake Victoria watershed in Africa. The SWAT model used in this study was calibrated and verified by comparing model output with historic stream flow data for 1973 1976 as well as 1970 1971. SWAT was operated at daily and monthly time steps during both calibration and verification. For the daily time step verification, the model had a Nash Sutcliffe coefficient of efficiency (NSE) of 0.52 and a correlation coefficient (R2 ) of 0.72. For the monthly time step verification, the recorded NSE and R2 values were 0.66 and 0.70. In developing climate change scenarios within the general patterns defined by the Intergovernmental Panel on Climate Change, predicted increases in CO2 concentrations were implemented within the constraints of the model ’s parameterisation by raising, in a seasonally specific manner, the values of two proxy parameters: daily baseline temperature and precipitation. Under all scenarios, Simiyu River discharge increased (24 45%), showing the highest increase in the rainy season (March to May), with the greatest increase occurring during the rainy season (March to May). Discharge was influenced to a much greater degree by increases in precipitation rather than by temperature. The increase in river flow predicted by the model suggests that the potential increase in heavy flood damage during the rainy season will increase, which could, in turn, have significant adverse effects on infrastructure, human health, and the environment in the watershed. The SWAT predictions provide an important insight into the magnitude of stream flow changes that might occur in the Simiyu River in Tanzania as a result of future climatic change.
Yanagita, T., Nemoto, T., Satoh, S., Yoshikawa, N., Maruta, T., Shiraishi, S., … Murakami, M. (2013). Neuronal Insulin Receptor Signaling: A Potential Target for the Treatment of Cognitive and Mood Disorders. In Mood Disorders. InTech. https://doi.org/10.5772/54389