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CONUS NG-IDF Data Sets

<p>The <strong>NG-IDF datasets</strong>, spanning 1951‒2013, characterize the magnitude, trend, seasonality, and driving mechanism of extreme events relevant to hydrologic design&nbsp;for over 200,000 locations across the CONUS at a 1/16th-degree resolution. These datasets enhance traditional precipitation-based intensity-duration-frequency curves (PREC-IDF)&nbsp;by accounting for total water reaching the land surface from rainfall, snowmelt, and rain-on-snow (ROS), i.e., Next-Generation intensity-duration-frequency analysis (<strong>NG-IDF</strong>).</p><p>PREC-IDF's neglect of snow processes can lead to significant biases in extreme event analysis, especially in snowy regions. NG-IDF addresses this by including snow processes, providing a systematic and consistent technique for all environments from rain-dominated, transitional, to snow-dominated locations. The resulting datasets are spatially continuous, and readily usable for supporting site-specific infrastructure design and for assessing&nbsp;the potential biases and design risks related to the use of PREC-IDF.</p><p>&nbsp;</p><p><strong>Abbreviations:</strong></p><blockquote><p>P = Precipitation</p><p>R = Rainfall</p><p>ROS = Rain-on-snow</p><p>M = Snowmelt&nbsp;</p><p>W = Water reaching the land surface from rain, snowmelt, and ROS events</p></blockquote><p>&nbsp;</p><p><strong>Classification of Driving Mechanism for Extreme W Events:</strong></p><p>For each location, the driving mechanism was determined for extreme W events with different durations and return periods, following the classification criteria as follows:</p><blockquote><p><strong>Rainfall (R):</strong> precipitation on snow-free ground;&nbsp;</p><p><strong>Snowmelt (M): </strong>decreasing SWE, daily rainfall &lt; 10 mm, and the sum of rainfall and snowmelt has &lt; 20% contribution from rainfall.</p><p><strong>Rain-on-snow (ROS):</strong> decreasing SWE with at least 10 mm of daily rainfall falling on a snowpack with at least 10 mm daily SWE, and snowmelt contributes at least 20% of the total of rain and snowmelt</p></blockquote><p>&nbsp;</p><p><strong>The datasets include:</strong></p><p><i>Note: In data format description, <strong>C</strong> = column, <strong>R</strong> = row,&nbsp;<strong>C[i]</strong> indicates the ith column of a data file. All data files are prepared&nbsp;in comma-separated value (.csv) format.</i></p><ul><li><strong>list.csv</strong><ul><li><strong>Description:</strong>&nbsp;The geographic&nbsp;coordinates and cluster ID (used for snow parameterization)&nbsp;of 207,173 locations over the CONUS</li><li><strong>Data Dimension</strong>: 207,173 (R) x 3 (C)</li><li><strong>Format</strong>:&nbsp;C1: Latitude; C2: Longitude;&nbsp;C3: cluster ID (ranging from 1‒5)</li></ul></li></ul><p>&nbsp;</p><ul><li><strong>AMF_WY/</strong><ul><li><strong>Description:</strong>&nbsp;Annual maximum series with durations of 24h, 48h, and 72h, driven by different hydrometeorological mechanisms over water years 1951‒2013 (10/1/1950-09/30/2013). The mechanisms include W, P, R, ROS, and M.</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[mechanism].csv, e.g., 24h/W.csv</li><li><strong>Data Dimension</strong>:&nbsp;207,173 (R) x 65 (C)</li><li><strong>Unit</strong>: mm</li><li><strong>Format</strong>:&nbsp;C1: Latitude; C2: Longitude; C3‒C65: maximum value for each year from 1951-2013</li></ul></li></ul><p>&nbsp;</p><ul><li><strong>AMF_CY/</strong><ul><li><strong>Description:</strong>&nbsp;Annual maximum series with durations of 24h, 48h, and 72h, driven by different hydrometeorological mechanisms&nbsp;over calendar years 1950‒2012 (1/1/1950-12/30/2012). The mechanisms include W, P, R, ROS, and M.</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[mechanism].csv, e.g., 24h/W.csv</li><li><strong>Data Dimension</strong>:&nbsp;207,173 (R) x 65 (C)</li><li><strong>Unit</strong>: mm</li><li><strong>Format</strong>:&nbsp;C1: Latitude; C2: Longitude; C3‒C65: maximum value for each year from 1950-2012</li></ul></li></ul><p>&nbsp;</p><ul><li><strong>IDF/</strong><ul><li><strong>Description:</strong>&nbsp;Discrete IDF values, i.e., the magnitude of extreme events with durations of 24h, 48h, and 72h, driven by different hydrometeorological mechanisms.&nbsp;The mechanisms include W, P, R, ROS, and M.</li><li><strong>Subfolder/File Naming Convention</strong>:&nbsp;[duration]/[mechanism].csv, e.g., 24h/W.csv</li><li><strong>Data Dimension</strong>:&nbsp;207,173 (R) x 9 (C)</li><li><strong>Unit</strong>: mm</li><li><strong>Format</strong>:&nbsp;C1: Latitude; C2: Longitude; C3‒C9: IDF values for the return period of 2, 5, 10, 25, 50, 100, and 500 years. NaN indicates no runoff caused by a given mechanism, such as ROS.</li></ul></li></ul><p>&nbsp;</p><ul><li><strong>IDF_90CI/</strong><ul><li><strong>Description:</strong>&nbsp;90% C.I. for IDF values in the IDF/ folder described above</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[mechanism]_H.csv,&nbsp;e.g., 24h/W_H.csv<ul><li><strong>Data Dimension</strong>:&nbsp;207,173 (R) x 9 (C)</li><li><strong>Unit</strong>: mm</li><li><strong>Format</strong>:&nbsp;C1: Latitude; C2: Longitude; C3‒C9: <i>95% quantile</i> for IDF values with the return period of 2, 5, 10, 25, 50, 100, and 500 years.<strong> </strong>NaN indicates no runoff event caused by a given mechanism.</li></ul></li><li><strong>Subfolder/File Naming Convention</strong>:&nbsp;[duration]/[mechanism]_L.csv,&nbsp;e.g., 24h/W_L.csv<ul><li><strong>Data Dimension</strong>:&nbsp;207,173 (R) x 9 (C)</li><li><strong>Unit</strong>: mm</li><li><strong>Format</strong>:&nbsp;C1: Latitude; C2: Longitude; C3‒C9: <i>5% quantile</i> for IDF values with the return period of 2, 5, 10, 25, 50, 100, and 500 years.<strong> </strong>NaN indicates no runoff event caused by a given mechanism.</li></ul></li></ul></li></ul><p>&nbsp;</p><ul><li><strong>trend/</strong><ul><li><strong>Description</strong>:&nbsp;Sen's slope of Mann-Kendall trend in annual maximum series driven by different hydrometeorological mechanisms over water years 1951‒2013.&nbsp;The mechanisms include W, P, rainfall (R), ROS, and snowmelt (M).</li><li><strong>Subfolder/File Naming Convention</strong>:&nbsp;[duration]/[mechanism].csv, e.g., 24h/W.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 3 (C)</li><li><strong>Unit:</strong>&nbsp;mm/year</li><li><strong>Format</strong>:&nbsp;C1: Latitude; C2: Longitude; C3: Trend indicated by Sen's slope. The value&nbsp;is zero if the trend is not statistically significant.</li></ul></li></ul><p>&nbsp;</p><ul><li><strong>Driver/</strong><ul><li><strong>Description</strong>:&nbsp;Dominant driving mechanism of extreme W events with different durations and return periods</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[return period].csv, e.g., 24h/50y.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 3 (C)</li><li><strong>Format</strong>:&nbsp;C1: Latitude; C2: Longitude; C3: Dominant driver IDs (1=R, 2=ROS, 3=M).</li></ul></li></ul><p>&nbsp;</p><ul><li><strong>risk/</strong><ul><li><strong>Description</strong>:&nbsp;Design risk associated with PREC-IDF estimated 100-year extreme events</li><li><strong>Subfolder/File Naming Convention</strong>:&nbsp;[duration]/100y.csv, e.g., 24h/100y.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 3 (C)</li><li><strong>Unit</strong>: %</li><li><strong>Format</strong>:&nbsp;C1: Latitude; C2: Longitude; C3: Bias in the 100-year event&nbsp;based on PREC-IDF vs. NG-IDF</li></ul></li></ul><p>&nbsp;</p><ul><li><strong>SI/</strong><ul><li><strong>Description</strong>: Seasonality of annual maximum W events with 24-, 48-, and 72-h&nbsp;durations over water years 1951‒2013</li><li><strong>Subfolder/File Naming Convention</strong>:&nbsp;[duration]/W.csv, e.g., 24h/W.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 4 (C)</li><li><strong>Format</strong>:&nbsp;C1: Latitude; C2: Longitude; C3: Mean data (=1 if Oct 1); C4: Seasonality index ranging from 0 to 1</li></ul></li></ul>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0

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