Climate Data Warehouses for Local Adaptation Analytics
Main article
Abstract
Local climate adaptation is a database problem before it is a modeling problem. Adaptation officers at the regional or municipal level need access to harmonized climate reanalysis, future climate projections, historical disaster losses, land-use change records, and socioeconomic vulnerability indicators, all reconciled to a common spatial and temporal grid and exposed under a documented interface. In practice these five data families live in incompatible repositories curated by different agencies, indexed by different schemes, and refreshed on different cadences, so the integration cost falls on every adaptation team independently and the resulting analyses are difficult to reproduce. This article presents ClimateAdaptDB, a climate-adaptation data warehouse that treats the database itself as the principal research artifact. We document the schema, the field dictionary, the index families, the bias-correction and downscaling pipeline, the access and ethics regime, and the reusable application programming interface that supports the three integrity questions of local adaptation: disaster loss prediction, adaptation priority ranking, and data-uncertainty audit. Six core entities (CLIM_REANALYSIS, CLIM_PROJECTION, HAZARD_EVENT, LAND_USE, SOCIOECON, ADAPT_INDICATOR) are organized so that every indicator traces back to a single auditable evidence chain. The warehouse integrates ERA5 reanalysis, CMIP6 multi-model projections, EM-DAT disaster records, ESA CCI land cover, and World Bank socioeconomic series, in a polyglot layout (Parquet-plus-Zarr lakehouse, PostGIS spatial relational store, Neo4j property graph, pgvector index) chosen because the three adaptation questions align with different storage paradigms. We benchmark the warehouse on a working subset of 184,000 administrative units worldwide (2000–2023 observed, 2030–2100 projected) and report a runnable experiment that reduces disaster-loss-prediction log-RMSE from 1.97 to 1.42, raises adaptation-priority ranking correlation with expert consensus from a Spearman of 0.58 to 0.78, and cuts ensemble uncertainty (CRPS) at the 2050 horizon from 0.92 to 0.49. The schema, dictionaries, and reproduction notebooks are released under an open license.
