A high-resolution projected climate dataset for Vietnam: Construction and preliminary application in assessing future change is a dataset published in Journal of Water and Climate Change (2022). On theSindex it has a DataRank of 0.646, placing it in the top 46.8% of the data-sharing corpus. It has been cited 22 times, with 14 citing works in its 1-hop citation network.
Abstract This study generates a daily temperature and precipitation dataset over Vietnam at a high resolution of 0.1° for the historical period 1980–2005 and the future period 2006–2100 under four representative concentration pathway (RCP) scenarios, namely RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The bias correction (BC) and spatial disaggregation (SD) method is applied to the outputs of 31 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to create the new dataset called CMIP5-VN. To guide the BC and SD steps, gridded temperature and precipitation data interpolated from daily observations of 147 and 481 stations respectively are used. Results with the CMIP5-VN show that warming over Vietnam is projected to continue till the end of the 21st century under all four RCPs. The average temperature is projected to increase by 1.3±0.52 °C under RCP2.6 and by 3.85±0.85 °C under RCP8.5 between 2080–2099 and 1986–2005. The future increase is more intense in the northern regions than in the south and higher in summer than in winter. Precipitation is projected to increase by 1.16±7.1% under RCP2.6 and by 4.41±9.2% under RCP8.5. In Central Vietnam, there is a consistent rainfall increase in the future rainy season.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.470
From this paper's citation signal
Citation Network Contribution
0.175
From 7 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 73% comes from its base citations and 27% from the citation network (7 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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