Measuring the vulnerability of sub-national regions in South Africa

A small but growing literature has been concerned about the economic (and
environmental) vulnerability on the level of countries. Less attention is paid to the economic vulnerability of different regions within countries. By focusing on the vulnerability of subnational regions, this paper contributes to the small literature on the “vulnerability of place”. They authors see the vulnerability of place as being due to vulnerability in various domains, such as economic vulnerability, vulnerability of environment, and governance, demographic and health fragilities. They use a subnational data set on 354 magisterial districts from South Africa, recognize the potential relevance of measuring vulnerability on a subnational level, and construct a Local Vulnerability Index for the various districts. They condition this index on district per capita income and term this a Vulnerability Intervention Index, interpreting this as an indicator of where higher income per capita, often seen in the literature as a measure of resilience, will in itself be unlikely to reduce vulnerability.

Flood forecasting in large rivers with data-driven models

Results from the application of adaptive neuro-fuzzy inference system (ANFIS) to forecast water levels at 3 stations along the mainstream of the Lower Mekong River are reported in this paper. The study investigated the effects of including water levels from upstream stations and tributaries, and rainfall as inputs to ANFIS models developed for the 3 stations. When upstream water levels in the mainstream were used as input, improvements to forecasts were realized only when the water levels from 1 or at most 2 upstream stations were included. This is because when there are significant contributions of flow from the tributaries, the correlation between the water levels in the upstream stations and stations of interest decreases, limiting the effectiveness of including water levels from upstream stations as inputs. In addition, only improvements at short lead times were achieved. Including the water level from the tributaries did not significantly improve forecast results. This is attributed mainly to the fact that the flow contributions represented by the tributaries may not be significant enough, given that there could be large volume of flow discharging directly from the catchments which are ungauged, into the mainstream. The largest improvement for 1-day forecasts was obtained for Kratie station where lateral flow contribution was 17 %, the highest for the 3 stations considered. The inclusion of rainfall as input resulted in significant improvements to long-term forecasts. For Thakhek, where rainfall is most significant, the persistence index and coefficient of efficiency for 5-lead-day forecasts improved from 0.17 to 0.44 and 0.89 to 0.93, respectively, whereas the root mean square error decreased from 0.83 to 0.69 m.