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CivilFlow.ai — A bias-corrected, basin-operational climate-and-hydrology knowledgebase assembled for reservoir decision support: a pre-loaded CMIP6 dataset of 13 General Circulation Models x 4 SSP scenarios (SSP126/245/370/585) at daily timestep and 0.25-degree resolution covering South Asia, bias-corrected by Empirical Quantile Mapping against IMD gridded observations, fused with continuously ingested satellite precipitation, evapotranspiration and temperature (ESA Sentinel, Copernicus, NASA MODIS/TRMM, SRTM), and bound to calibrated hydrological models and reservoir-balance simulations for …
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A bias-corrected, basin-operational climate-and-hydrology knowledgebase assembled for reservoir decision support: a pre-loaded CMIP6 dataset of 13 General Circulation Models x 4 SSP scenarios (SSP126/245/370/585) at daily timestep and 0.25-degree resolution covering South Asia, bias-corrected by Empirical Quantile Mapping against IMD gridded observations, fused with continuously ingested satellite precipitation, evapotranspiration and temperature (ESA Sentinel, Copernicus, NASA MODIS/TRMM, SRTM), and bound to calibrated hydrological models and reservoir-balance simulations for a multi-purpose dam portfolio of 1,440+ large dams. The latent licensable asset is not the raw CMIP6 or IMD data (both open) but the bias-corrected, basin-aligned, demand-segmented, operations-ready derivative layer plus the calibrated model and operating-rule library per reservoir.
From 50 yearsCoverage UtilitiesAsset class Equities · Commodities
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CivilFlow.ai — A bias-corrected, basin-operational climate-and-hydrology knowledgebase assembled for reservoir decision support: a pre-loaded CMIP6 dataset of 13 General Circulation Models x 4 SSP scenarios (SSP126/245/370/585) at daily timestep and 0.25-degree resolution covering South Asia, bias-corrected by Empirical Quantile Mapping against IMD gridded observations, fused with continuously ingested satellite precipitation, evapotranspiration and temperature (ESA Sentinel, Copernicus, NASA MODIS/TRMM, SRTM), and bound to calibrated hydrological models and reservoir-balance simulations for …
CivilFlow.ai offers (Alternative, Geospatial, ESG, Fundamental) — est. 0.5-1 billion climate grid-cells in the hosted knowledgebase: 13 GCMs x 4 SSP scenarios x daily timestep x ~2,000-4,000 South Asia cells at 0.25-degree x ~100+ years per run = roughly 10-100M values per GCM-scenario pair; plus per-reservoir calibrated hydrology, storage and demand series across a portfolio designed to scale to 1,440+ large dams, currently deployed on a ~30,800 km2 sub-basin — so the operational layer is in the millions of records today and multiplies with each templated dam configured.
Climate-adjusted hydropower inflow and availability risk for Indian basins under SSP pathways (the commercially sharpest use — India's hydro fleet governed by operating rules written on stationarity assumptions); drought early warning and trigger calibration for basin authorities; inter-sector water allocation modelling (irrigation, drinking, industrial, hydropower) for concession and PPA risk; physical-climate-risk underwriting of Indian water and hydropower assets at reservoir resolution; calibration and downscaling of Indian hydrological models on an already-bias-corrected, IMD-anchored layer (saving the single most labour-intensive step in any Indian basin study); training of reservoir-operation and allocation policies for reinforcement learning; and infrastructure investment screening via scenario NPV comparisons.
The data is with 50 years of history.
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