Journal: Scientific Reports

Article Title: US Power Production at Risk from Water Stress in a Changing Climate

doi: 10.1038/s41598-017-12133-9

Figure Lengend Snippet: Spatial profile of water scarcity and stream temperature over near-term projection horizons. (A) Projected changes in low surface runoff (10 th percentile of all climate realizations [see Methods]) during 2006–2020 (top), 2011–2025 (middle), and 2021–2035 (bottom), relative to current estimates (1991–2005). Calculations are performed in MATLAB 2015a (Version 8.5, http://www.mathworks.com ) [Software]. Shades of blue show positive changes in future freshwater availability relative to current estimates, but they do not necessarily indicate water surplus. (B) Same as in ( A ) but for projected changes in high stream temperature (90 th percentile of all climate simulations). The red (blue)-colored upward (downward) triangles in ( B ) indicate increase (decrease) in stream temperature. ( C ) Same as in (A ) but for projected changes in 2-meter surface air temperature (90 th percentile). Spatial patterns of current estimates are shown in Figures , and . Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ) and ArcGIS Desktop (Version 10.3.1, http://www.esri.com ). Finally, all these maps are organized and labelled in Adobe Photoshop CS Desktop (Version 5.1, https://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ).

Techniques: Software, Generated

Journal: Scientific Reports

Article Title: US Power Production at Risk from Water Stress in a Changing Climate

doi: 10.1038/s41598-017-12133-9

Figure Lengend Snippet: Association between indicators of water stress. ( A – D ) Correlation coefficient between monthly surface runoff and stream temperature as measured by Kendall’s tau at 145 USGS gauge stations for (A) current (1991–2005) and ( B – D ) future time horizons (2006–2035). Correlations are statistically significant at 5% at all gauge stations for both current and future periods. Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ). Finally, all these maps are organized and labelled in Adobe Photoshop CS Desktop (Version 5.1, https://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ).

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: US Power Production at Risk from Water Stress in a Changing Climate

doi: 10.1038/s41598-017-12133-9

Figure Lengend Snippet: Scatter diagrams between indicators of water stress. ( a – d) Scatter plots showing the relationship between mean surface runoff and maximum stream temperature for nine climatologically homogeneous regions, each shown by different colors, for current (1991–2005) and future time periods (2006–2035). The size of the color-filled circles represents strength of the association, as measured by Kendall’s tau (shown in Fig. ), between surface runoff and water temperature. For a given region, nature of the association is captured by the different shades of the color; darker (lighter) shades or negative (positive) values of Kendall’s tau represent inverse (direct) relationship. The two dotted horizontal lines are drawn at the ensemble mean of stream temperatures and a critical water temperature limit of 27 °C (a limit over which water is not suitable for cooling; it is ~5 °C lower than the Environmental Protection Agency [EPA] prescribed maximum allowed temperature of ~32 °C). The vertical dotted line is drawn at no flow. The left side of the vertical line represents water scare situations, and the side above 27 °C represents warmer. Each of the scatter plots is divided into four quadrants: scarcer, warmer (top left); scarcer, cooler (bottom left); wetter, warmer (top right); and wetter, cooler (bottom right) as shown in ( d ). The partitioning of the scatter diagrams explicitly identify regions with hot spots – a combination of low flow and high temperature. The figure legend at ( a ) indicates negative values of Kendall’s tau whereas at ( b ) shows positive values. Legends are same for all panels. Figures were generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ). Finally, all these figures are organized and labelled in Adobe Photoshop CS Desktop (Version 5.1, https://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ).

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: US Power Production at Risk from Water Stress in a Changing Climate

doi: 10.1038/s41598-017-12133-9

Figure Lengend Snippet: Time series comparison of SWSI relative to univariate water stress indices. Sample time series of 3-month SWSI is compared with standardized low surface runoff flow and high stream temperature indices at 3-month time scale. The top and bottom panel shows selected USGS gauge locations over Southeast (North Carolina, USGS Station ID 02077200, latitude 36.39° and longitude 79.20°) and West (California, USGS Station ID 373822118514401, latitude 37.64° and longitude 118.86°). Figures are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ).

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: US Power Production at Risk from Water Stress in a Changing Climate

doi: 10.1038/s41598-017-12133-9

Figure Lengend Snippet: Spatial trend of Standardized Anomaly of SWSI. Time series of standardized anomaly for each of the nine climatologically homogeneous regions for 45 years (1991–2035). Years that are water stressed (negative values of standardized anomaly) are shown in red. The horizontal dashed lines are drawn at −0.5, −1.0, and −2.0 Standard Deviations (SDs) to indicate three water stress levels: 0.5-, 1-, and 2-SD. The vertical line demarcates current and future time periods. Figures are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ). Finally, all these figures are organized and labelled in Adobe Photoshop CS Desktop (Version 5.1, https://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ).

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: US Power Production at Risk from Water Stress in a Changing Climate

doi: 10.1038/s41598-017-12133-9

Figure Lengend Snippet: Contours of standardized water stress index. ( A – D ) Spatial location, installed power production capacity (in Quad), and primary fuel types of thermoelectric power plants superimposed over contours of decadal mean of standardized water stress index for current (1996–2005) and future (2006–2035) time periods. Size of the filled color circle is directly proportional to the installed production capacity. Different shades of water stress contours indicate risk level due to the joint effects of low flow and high stream temperature. Grey shades in the map indicate regions where data is not available. Figures are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ). Finally, all these maps are organized and labelled in Adobe Photoshop CS Desktop (Version 5.1, https://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ).

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: US Power Production at Risk from Water Stress in a Changing Climate

doi: 10.1038/s41598-017-12133-9

Figure Lengend Snippet: Regional distribution of power production at risk under various water stress levels (WSL). ( a – d ) Bar plots showing the breakup of power production at risk for five different water stress risk levels over nine regions for ( a ) current (1996–2005) and ( b – d ) future (2006–2035) time periods. The annual power production capacity for each region is shown in ( c ). The total production capacity is 11.07 Quad (Table ). The number of power plants in a specific region is shown in ( d ). The total number of power plants is 815 (Table ). Five water stress levels (WSL) are defined as follows: WSL1 (−0.5 ≤ WSI ≤ 0), WSL2 (−0.75 ≤ WSI ≤ −0.5), WSL3 (−1.0 ≤ WSI ≤ −0.75), WSL4 (−1.5 ≤ WSI ≤ −1.0), and WSL5 (WSI ≤ −1.5), where WSI stands for water stress index. WSL1 (WSL5) indicates the less (most) severe condition. WNC: West North Central, SW: Southwest, SE: Southeast, NW: Northwest, NE: Northeast, and ENC: East North Central. Figures are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ). Finally, all these figures are organized and labelled in Adobe Photoshop CS Desktop (Version 5.1, https://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ).

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

doi: 10.1038/s41598-019-49822-6

Figure Lengend Snippet: Dependence between extreme CWL and river peak discharge. (a-c) Spatial maps of correlation and upper tail dependence between annual maxima CWL and d-day lagged daily peak discharge within ± 7 days of the occurrence of the extreme CWL using nonparametric dependence measures. The complete dependence between two variables is established using Kendall’s τ ( a ), while the Upper Tail Dependence Coefficients, UTDC ( b , c ), are computed using two nonparametric upper tail dependence metrics (see Methods). The location of SGs with significant (at 5% level) dependence between compound flood drivers are marked with colours, whereas the location of SGs with insignificant and low values of positive dependence (with values <0.1 and p-values ≥0.05), and negative dependence (values <0) are marked with white ( d ) Kernel density functions of complete and UTDC metrics illustrating the negative skewness in the spatial distribution. The two UTDC distributions are shifted significantly (as indicated by the p- values < 0.05) towards higher values relative to the distribution of complete dependence. The density curve in LOG estimator is flattened, with an elongated right (higher dependence) tail. Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

doi: 10.1038/s41598-019-49822-6

Figure Lengend Snippet: Stronger upper tail dependence relative to complete dependence increases the likelihood of compound flood events: Proof-of-concept illustrations of unconditional ( left panel ) and conditional (on high coastal CWL; right panel ) flood hazards in UK Rivers along the North shields TG: River Ribble (a, top panel) a tidally influenced river located at a geodesic distance of 157 km and in the River South Tyne (b, bottom panel), non-tidally influenced, located at a geodesic distance of 69 km from the TG. ( a ) Kendall’s τ correlation between Annual maxima CWL and peak discharge for River Ribble is 0.16 with p- value = 0.12 [the p- value indicates the evidence against the null hypothesis of independence: the smaller (larger) the p -value, the stronger is the evidence against (for) the null hypothesis; however, a p -value does not indicate the probability that the null hypothesis is true], while empirical upper tail dependence coefficients are \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{CFG}^{Obs}$$\end{document} λ C F G O b s = 0.28 (p-value = 0.0054) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{LOG}^{Obs}$$\end{document} λ L O G O b s = 0.44 (p-value = 0.011). ( b ) Kendall’s τ correlation associated with compound event pairs in River South Tyne is 0.25 with p-value = 0.018, while empirical upper tail dependence coefficients are \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{CFG}^{Obs}$$\end{document} λ C F G O b s = 0.35 (p-value = 0.001) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{LOG}^{Obs}$$\end{document} λ L O G O b s = 0.44 (p-value = 0.013). While circles with shades in yellow and red denote the year of occurrence of the compound event, the one in gray indicates copula-simulated samples. For clarity, return level estimates are rounded to their nearest decimal numbers. Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

doi: 10.1038/s41598-019-49822-6

Figure Lengend Snippet: Spatial Variations in compound flood hazards for selected winter storm events. Spatial distribution of CHR index showing compound flooding hotspots for three winter storm episodes: Capella (1 st –5 th January, 1976; a and d ), Xynthia (26 th February–7 th March, 2010; b and e ), and Xaver (4 th –11 th December, 2013; c and f ) for T = 10- ( top panel ) and 50-year ( bottom panel ) return periods. The triangles indicate locations of TG. The colours in the TGs indicate the standardized anomaly of annual maximum CWL, while the size of the triangle is proportional to its value. The upward (positive) and downward (negative) triangles indicate the sign of the standardized anomaly at each TG location. The circles show SG locations where CHR is calculated. The darker shade represents a high value indicating a greater hazard, while a lighter shade denotes low hazard associated with the compound event. Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

doi: 10.1038/s41598-019-49822-6

Figure Lengend Snippet: The fraction of TG-SG pairs showing an increase in the likelihood of compound flood hazards for the three winter storm episodes ( a ) Fraction (expressed as a percentage) of TG-SG pair with an increase in T-year peak discharge associated with compound event relative to at-site peak discharge. Percentage relative increase in T- year peak discharge for ( b ) 10- and ( c ) 50-year events. The increase in discharge is quantified as the relative difference between the magnitude of the T- year flood peak conditional on AMWL and the seasonal maxima (November-March) at-site T- year peak discharge expressed as a percentage. The horizontal bars in red (figure b) and blue (Figure c) show TG-SG pairs with an increase in flood hazard. The dotted vertical line (in black) indicates the relative increase of the order of 50%. Figures are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India

doi: 10.1038/s41598-017-15896-3

Figure Lengend Snippet: Trends in frequency of 85 th percentile heatwaves over India during the period 1951–2010 for different durations. ( a ) 3-day heatwaves ( b ) 5-day heatwaves and ( c ) 10-day heatwaves. Trends are detected using Mann-Kendall test and the magnitude of trend is quantified using Sen’s slope method at 95% significance level. This figure is plotted in Matlab R2014a (Version 8.3.0.532, URL: https://in.mathworks.com ).

Article Snippet: The maps are prepared in Matlab R2014a (Version 8.3.0.532, URL: https://in.mathworks.com ).

Techniques:

Journal: Scientific Reports

Article Title: Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India

doi: 10.1038/s41598-017-15896-3

Figure Lengend Snippet: Major heatwaves in India during 1951–2010. The maps show the spatial distribution of HWMId for ( a ) 1998 heatwaves with maximum HWMId 55.8 at the grid 12.5 N, 76.5E ( b ) 2003 heatwaves with maximum HWMId 33.5 at the grid 14.5 N, 78.5E and ( c ) 1973 heatwaves with maximum HWMId 28.8 at the grid 16.5 N, 75.5E. This figure is created in Matlab R2014a (Version 8.3.0.532, URL: https://in.mathworks.com ).

Article Snippet: The maps are prepared in Matlab R2014a (Version 8.3.0.532, URL: https://in.mathworks.com ).

Techniques:

Journal: Scientific Reports

Article Title: Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India

doi: 10.1038/s41598-017-15896-3

Figure Lengend Snippet: Percentage area in meteorological droughts ( a ) Meteorologically homogeneous regions of India and ( b – g ). Percentage area of different homogeneous regions in moderate droughts defined below SPI < −1.3. Mann-Kendall trend test results in terms of p-values are also shown here. Statistically significant trends are observed in the spatial extent of droughts from 1951 to 2010 for West Central India and Central Northeast India. The map of IMD homogeneous regions in this figure is prepared in QGIS (Version 2.14.0 ‘Essen’, free and open source geographic information system, QGIS Development Team (2016), URL: http://changelog.qgis.org/en/qgis/version/2.14.0/ ) and all other plots are generated using Matlab R2014a (Version 8.3.0.532, URL: https://in.mathworks.com ).

Article Snippet: The maps are prepared in Matlab R2014a (Version 8.3.0.532, URL: https://in.mathworks.com ).

Techniques: Generated

Journal: Scientific Reports

Article Title: Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India

doi: 10.1038/s41598-017-15896-3

Figure Lengend Snippet: Percentage changes in concurrent moderate droughts defined below SPI < −1.3 and heatwaves during 1981–2010 with respect to the base period 1951–1980. Droughts with longer and severe heatwaves have increased much more than droughts with shorter duration and less severe heatwaves. Droughts and heatwaves are increasing across whole India and notably decreasing in Rajasthan and West Bengal. The maps are prepared in Matlab R2014a (Version 8.3.0.532, URL: https://in.mathworks.com ).

Article Snippet: The maps are prepared in Matlab R2014a (Version 8.3.0.532, URL: https://in.mathworks.com ).

Techniques:

Journal: Scientific Reports

Article Title: Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India

doi: 10.1038/s41598-017-15896-3

Figure Lengend Snippet: Spatial area of India in concurrent moderate droughts defined below SPI < −1.3 and heatwaves. Percentage area of India in concurrent moderate droughts (SPI < −1.3) and ( a ) 85 th percentile heatwaves ( b ) 90 th percentile heatwaves and ( c ) 95 th percentile heatwaves. Empirical CDFs of area affected by concurrent droughts (SPI < −1.3) and heatwaves for the periods 1951 to 1980 in blue and 1981 to 2010 in red ( d – i ). Significant departure is observed in CDFs during the period 1951 to 1980 compared to the base period for all combinations and divergence is highest in upper tail of 10-day heatwaves. These figures are generated using Matlab R2014a (Version 8.3.0.532, URL: https://in.mathworks.com ).

Article Snippet: The maps are prepared in Matlab R2014a (Version 8.3.0.532, URL: https://in.mathworks.com ).

Techniques: Generated