Monitoring natural and anthropogenic induced variations in water availability across Africa
Tuesday, 16 December 2014: 4:30 PM
Africa, the second-driest continent in the world after Australia, is one of the most vulnerable continents to climate change. Understanding the impacts of climatic and anthropogenic factors on Africa’s hydrologic systems is vital for the assessment and utilization of Africa’s water resources. In this study, we utilize the Gravity Recovery and Climate Experiment (GRACE) and land surface models (LSM; GLDAS and CLM4.5) in conjunction with other readily-available temporal climatic and remote sensing, geological and hydrological datasets for monitoring the spatial and temporal trends in Terrestrial Water Storage (TWS) over a time period of 10 years (01/2003–12/2012) over the African continent and to investigate the nature (e.g., climatic and/or human pressures-related) of, and the controlling factors causing, these variations. Spatial and temporal (i.e., time series analysis) correlations of the trends extracted from GRACE-derived (TWSGRACE) and LSM-derived (TWSLSM) TWS indicate the following: (1) Large (≥ 90 % by area) sectors of Africa are undergoing statistically significant TWSGRACE and TWSLSM variations due to natural and anthropogenic causes; (2) a general correspondence between TWSGRACE and TWSLSM over areas (e.g., Niger and Mozambique NE basins in eastern and western Africa) largely controlled by natural (i.e., increase/decrease in precipitation and/or temperature) causes; (3) discrepancies are observed over areas that witnessed extensive anthropogenic effects measured by TWSGRACE but unaccounted for by TWSLSM. Examples include: (a) strong (compared to that observed by TWSLSM) negative TWSGRACE trends were observed over areas that witnessed heavy groundwater extraction (e.g., Western, Desert, Egypt); (b) strong (compared to that observed by TWSLSM) positive TWSGRACE over Lake Volta reservoir; and (c) strong (compared to that observed by TWSLSM) negative trends over areas undergoing heavy deforestation (e.g., northern and NW Congo Basin); (4) additional discrepancies in other areas (e.g., Zambezi and the Okavango basins) are attributed to models being uncalibrated and not simulating all of the TWS components (e.g., river storage and groundwater in GLDAS; lakes and reservoirs in GLDAS and CLM4.5) . Future work should focus on using TWSGRACE to calibrate TWSLSM.