ISIMIP2a Simulation Data from Water (regional) Sector
Bibliographic record
Abstract
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This will serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. In the regional water sector, future simulations of climate-change impacts were also carried out, using climate data from five global climate models (GCMs: HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M and NorESM1-M) for the four Representative Concentration Pathways (RCPs: RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from regional hydrology models (river basins in brackets): HBV-CMA (Yangtze) HBV-IWW (Tagus) HBV-JLU (Rhine, Ganges, Mississippi) HBV-PIK (Rhine, Niger, Yellow, Blue Nile, Amazon) HYMOD-JLU (Rhine, Ganges, Mississippi) HYMOD-UFZ (Rhine, Niger, Blue Nile, Ganges, Yellow, Darling, Mississippi, Amazon) HYPE (Rhine, Tagus, Niger, Ganges, Lena, Mackenzie) mHM (Rhine, Niger, Blue Nile, Ganges, Yellow, Darling, Mississippi, Amazon) SWAP (Rhine, Tagus, Niger, Ganges, Yellow, Yangtze; Lena, Darling, MacKenzie, Mississippi, Amazon) SWAT (Yangtze; Darling; Blue Nile; Amazon; Mississippi; Niger) SWIM (Rhine, Yellow, Mississippi; Niger; Lena; Tagus; Blue Nile; Yangtze; Ganges, Amazon) VIC (Tagus, Blue Nile, Yellow, Lena, Darling, Amazon, MacKenzie; Rhine, Niger, Mississippi; Ganges; Yangtze) VIP (Yellow) WaterGAP3 (Rhine, Tagus, Niger, Blue Nile, Ganges, Yellow, Lena, Mississippi) ECOMAG (Lena, MacKenzie)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.174 | 0.399 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".