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Management of water resources and impacts of climate change in the Upper Pungwe River Basin
MENVSCDepartment of Hydrology and Water ResourcesDeveloping countries are largely characterized by rural-based communities often settled in headwater catchments whose livelihoods are dependent on natural resources available. As the climate changes, hydrological regimes are also altered affecting these communities. Assessing available water resources and their management becomes crucial to inform sustainable resources management, planning and development. This study quantified water resources in ten selected headwater sub-catchments of the Pungwe River Basin using the Pitman model in (SPatial And Time Series Information Model) SPATSIM_V3 and ten statistically downscaled climate datasets from the Climate Systems Analysis Group forced with RCP 4.5 and RCP 8.5 emission scenarios. Rainfall and potential evaporation data were used to setup the model while streamflow data was used for model calibration. The calibrated model parameters were used to project future water resources using stochastic rainfall ensembles derived from the delta change method in SPATSIM. Interviews were also carried out with natural resources managers to understand how headwater sub-catchments were being managed using a case of Pungwe Sub-Catchment. The interviews revealed that headwater catchment management is not yet incorporated in the management procedures of water resources in the sub-catchment, but the principles of integrated water resources management are being fully implemented. The Pitman- SPATSIM showed that water resources in the headwater sub-catchments to be adequate to meet ecological and human needs. Near-future (2020-2060) and far-future (2061-2099) projections using RCP 4.5 varied from the current period (1960-2010) with a percentage difference in mean monthly flow within the range of -9% to 7% for all sub-catchments. Under RCP 8.5, the near and far-future had similar projections, with both periods showing a minor reduction in water availability with a few subcatchments showing a reduction as high as 71% (sub-catchment E72) which could possibly be attributed to streamflow datasets used for the calibration process. It was concluded that future water resources availability in the study area will be stable, with the key assumption that climate change is the sole variable driving water availability. To fully understand the water resources availability in the future, other factors such as land use changes need to be incorporated in the simulation of future water resources.NR
A Systematic Study Site Selection Protocol to Determine Environmental Flows in the Headwater Catchments of the Vhembe Biosphere Reserve
Developing nations will be worst hit by the impacts of climate change because limited resources hinder the spatial reach of climate studies, effort, and subsequent implementation to help with the improvement of livelihoods. Therefore, finding the best-case study is an essential undertaking in environmental assessments. This study explains one systematic approach to selecting a study site for an environmental assessment project. A desktop review of relevant literature, a simple factor scoring assessment process, reliance on expert opinion, and a field survey for ground-truthing were conducted. The desktop review showed the most critical factors to site selection. The scoring of these factors selected those that were crucial for the study. Experts validated the results and suggested the best study site among the ones identified. While the design is simplified, the proposed approach selects the most appropriate study site for environmental assessments
Evaluation of the FACSPresto, a New Point of Care Device for the Enumeration of CD4% and Absolute CD4+ T Cell Counts in HIV Infection
Introduction: Enumeration of CD4+ T lymphocytes is important for pre-ART disease staging and screening for opportunistic infections, however access to CD4 testing in resource limited settings is poor. Point of care (POC) technologies can facilitate improved access to CD4 testing. We evaluated the analytical performance of a novel POC device the FACSPresto compared to the FACSCalibur as a reference standard and to the PIMA, a POC device in widespread use in sub-Saharan Africa. Method Specimens were obtained from 253 HIV infected adults. Venous blood samples were analyzed on the FACSPresto and the FACSCalibur, in a subset of 41 samples additional analysis was done on the PIMA. Results: The absolute CD4 count results obtained on the FACSPresto were comparable to those on the FACSCalibur with low absolute (9.5cells/μl) and relative bias (3.2%). Bias in CD4% values was also low (1.06%) with a relative bias of 4.9%. The sensitivity was lower at a CD4 count threshold of ≤350cells/μl compared with ≤500cells/μl (84.9% vs. 92.8%) resulting in a high upward misclassification rate at low CD4 counts. Specificity at thresholds of ≤350cells/μl and ≤500cells/μl were 96.6% and 96.8% respectively. The PIMA had a high absolute (-68.6cells/μl) and relative bias (-10.5%) when compared with the FACSCalibur. At thresholds of ≤350cells/μl and ≤500cells/μl the sensitivity was 100% and 95.5% respectively; specificity was 85.7% and 84.2% respectively. The coefficients of repeatability were 4.13%, 5.29% and 9.8% respectively. Discussion The analytic performance of the FACSPresto against the reference standard was very good with better agreement and precision than the PIMA. The FACSPresto had comparable sensitivity at a threshold of 500 cells/μl and better specificity than the PIMA. However the FACSPresto showed reduced sensitivity at low CD4 count thresholds. Conclusion: The FACSPresto can be reliably used as a POC device for enumerating absolute CD4 count and CD4% values
Comparison between FACSPresto and FACSCalibur.
<p>Passing-Bablok regression plot comparison of (a) absolute CD4 count and (c) CD4% values obtained from FACSPresto with the FACSCalibur as reference standard. The solid line represents the regression line and dashed line the 95%CI. Pollock plots indicating %mean bias between (b) absolute CD4 count and (d) CD4% values obtained on FACSPresto compared with those obtained on the FACSCalibur. The solid line represents the mean bias, the dashed line represents mean bias ±1.96SD.</p
Comparison between PIMA and FACSCalibur and FACSPresto.
<p>(a) Passing-Bablok regression plot comparison of absolute CD4 count between PIMA and FACSCalibur; (b) Pollock plot indicating %mean bias between PIMA and FACSCalibur</p
Sensitivity, Specificity, Positive (PPV) and Negative Predictive values (NPV) and misclassification rates of absolute CD4 counts at thresholds of 350 cells/μl and 500 cells/μl for FACSPresto and PIMA with FACSCalibur as the reference standard.
<p>Sensitivity, Specificity, Positive (PPV) and Negative Predictive values (NPV) and misclassification rates of absolute CD4 counts at thresholds of 350 cells/μl and 500 cells/μl for FACSPresto and PIMA with FACSCalibur as the reference standard.</p