The Global Drought Monitor Portal - The Foundation for a Global Drought Early Warning System
Bibliographic record
Abstract
International workshops and conferences have, for many years, noted the importance of drought monitoring and have called for the creation of drought early warning systems (the 2007 GEO Ministerial Summit in Cape Town, South Africa, the 2009 WMO-sponsored Inter-\nRegional Workshop on Indices and Early Warning Systems for Drought in Lincoln, Nebraska, USA, and the 2010 Global Drought Assessment Workshop [GDAW] in Asheville, North Carolina, USA, are recent examples). Drought monitoring, assessment, response, mitigation, adaptation, and early warning systems have been created in a number of countries around the world, and some regional and continental efforts have been successful, but a global drought early warning system (GDEWS) remains elusive. The National Integrated Drought Information System (NIDIS) U.S. Drought Portal is a web-based information system created to address drought services and early warning in the United States, including drought monitoring, forecasting, impacts, mitigation, research, and education. It was recognized at the April 2010 GDAW that the creation of a Global Drought Monitoring web portal (GDMP) as a clearinghouse for global drought information would be\nhighly beneficial, but neither the World Meteorological Organization (WMO) nor Group on Earth Observations (GEO) has the resources to provide such a program (GEO does not directly fund initiatives but relies upon donated efforts from GEO members). The managers\nof the NIDIS portal agreed to develop a prototype GDMP. The GDMP is made interoperable with the Global Earth Observation System of Systems (GEOSS) by utilizing Open Geospatial Consortium (OGC) Web Mapping Services (WMS) and other web services to exchange\ndrought maps (and other information) among existing continental and regional drought monitoring efforts, including the North American Drought Monitor (which provides coverage for North America, including Canada, USA, and Mexico), the European Drought Observatory\n(which provides coverage for the European continental region), and the Princeton University African Drought Monitor (which provides African continental coverage). The Republic of Argentina is a full member and is currently being integrated into the GDMP, and the\nCommonwealth of Australia is also in the process of having portions of the Australia Water Availability Project being incorporated into the GDMP. The GDMP will provide global coverage of drought indicators computed using a standard methodology, such as the\nStandardized Precipitation Index (SPI) (at time scales corresponding to meteorological and hydrological drought) computed from Global Historical Climatology Network (GHCN) in situ data, deployment of hydrologic drought indicators for full water budget drought assessment\nin semi-arid terrains, and satellite-observed and modeled soil moisture (for agricultural drought). In addition, the OGC and other web services will empower the GDMP with a drill down capability providing access to the regional and continental assessments, national\ndrought products, and local drought analyses produced within the participating countries. This paper will discuss the creation of the GDMP, its functionality, and its potential applications within the context of a GDEWS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.056 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".