Supplementary dataset for "Disentangling the roles of future scenarios and climate/hydrological models in global water gap projections"
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The folder contains the output data relative to the paper:Citrini, A., Sangiorgio, M., and Rosa, L. Disentangling the roles of future scenarios and climate/hydrological models in global water gap projections Contact: lrosa@carnegiescience.edu List of files: Figure_3_S3.xlsx: Seasonal water gap spreads (in km³/month) for North Temperate, Tropics, and South Temperate zones across scenario combinations: historical period (1985–2014), and future projections for mid-century (2035–2064) and end-century (2071–2100) under SSP1-2.6 and SSP5-8.5. Figure_4_S4.xlsx: Monthly water gap spreads (in km³/month) for India across scenario combinations: historical period (1985–2014), and future projections for mid-century (2035–2064) and end-century (2071–2100) under SSP1-2.6 and SSP5-8.5. Figure_5_S5.xlsx: Monthly water gap spreads (in km³/month) for USA across scenario combinations: historical period (1985–2014), and future projections for mid-century (2035–2064) and end-century (2071–2100) under SSP1-2.6 and SSP5-8.5. Figure_6_S6.xlsx: Monthly water gap spreads (in km³/month) for China across scenario combinations: historical period (1985–2014), and future projections for mid-century (2035–2064) and end-century (2071–2100) under SSP1-2.6 and SSP5-8.5. Figure_7.xlsx: Shapley attribution of inter-model water-gap divergence for the historical baseline period (1985–2014) for the whole globe, India, USA, and China. The file reports annual contribution shares (%) and monthly Shapley contributions (km³/month) of irrigation consumption and routed renewable blue-water availability to the H08–WaterGAP2-2e divergence in water gap. Figure_S8.xlsx: Monthly irrigation water consumption (km³/month) for the whole globe, India, USA, and China simulated by the two Global Hydrological Models for the historical baseline period (1985–2014) from ISIMIP project. The dates are reported in YYYYMM format. Figure_S9.xlsx: Shapley attribution of inter-model water-gap divergence at end-century (2071–2100) under SSP1-2.6 for the whole globe, India, USA, and China. The file reports annual contribution shares (%) and monthly Shapley contributions (km³/month) of irrigation consumption and routed renewable blue-water availability to the H08–WaterGAP2-2e divergence in water gap. Figure_S10.xlsx: Shapley attribution of inter-model water-gap divergence at end-century (2071–2100) under SSP5-8.5 for the whole globe, India, USA, and China. The file reports annual contribution shares (%) and monthly Shapley contributions (km³/month) of irrigation consumption and routed renewable blue-water availability to the H08–WaterGAP2-2e divergence in water gap. Figure_S13.xlsx: Monthly water-gap distributions (km³/month) under alternative environmental-flow requirement (EFR) benchmarks for the whole globe, India, USA, and China, under baseline conditions (1985–2014) and end-century projections (2071–2100) under SSP5-8.5. The file includes the three EFR benchmarks compared: Variable Monthly Flow (VMF), fixed 60% environmental-flow allocation, and Tennant 30% of mean annual flow GHM_diff_baseline_VMF.tif: Raster layer of spatially explicit difference in median annual irrigation water gap (H08 – WaterGAP2-2e, in km³/year) for the baseline period (1985-2014), using VMF as the EFR method. Positive values indicate grid cells where H08 simulates larger water gaps than WaterGAP2-2e, whereas negative values indicate grid cells where WaterGAP2-2e simulates larger water gaps than H08. EPSG:4326. median_H08_baseline_VMF.tif: Raster layer of median annual water gap simulated by H08 (km³/year) for the baseline period (1985-2014), using VMF as the EFR method. EPSG:4326. median_WaterGAP_baseline_VMF.tif: Raster layer of median annual water gap simulated by WaterGAP2-2e (km³/year) for the baseline period (1985-2014), using VMF as the EFR method. EPSG:4326. median_baseline_VMF.tif: Raster layer of median annual water gap (km³/year) for the baseline period (1985–2014), computed using the Variable Monthly Flow (VMF) environmental-flow requirement method. EPSG:4326. median_2100_SSP585_VMF.tif: Raster layer of median annual water gap (km³/year) for the end-century period (2071–2100) under SSP5-8.5, computed using the Variable Monthly Flow (VMF) environmental-flow requirement method. EPSG:4326. median_baseline_fixed60.tif: Raster layer of median annual water gap (km³/year) for the baseline period (1985–2014), computed using a fixed 60% environmental-flow allocation. EPSG:4326. median_2100_SSP585_fixed60.tif: Raster layer of median annual water gap (km³/year) for the end-century period (2071–2100) under SSP5-8.5, computed using a fixed 60% environmental-flow allocation. EPSG:4326. median_baseline_tennant30.tif: Raster layer of median annual water gap (km³/year) for the baseline period (1985–2014), computed using the Tennant 30% of mean annual flow environmental-flow benchmark. EPSG:4326. median_2100_SSP585_tennant30.tif: Raster layer of median annual water gap (km³/year) for the end-century period (2071–2100) under SSP5-8.5, computed using the Tennant 30% of mean annual flow environmental-flow benchmark. EPSG:4326.
