{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:45:34Z","timestamp":1776206734571,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Conventionally, in situ rainfall data are used to develop Intensity Duration Frequency (IDF) curves, which are one of the most effective tools for modeling the probability of the occurrence of extreme storm events at different timescales. The rapid recent technological advancements in precipitation sensing, and the finer spatio-temporal resolution of data have made the application of remotely sensed precipitation products more dominant in the field of hydrology. Some recent studies have discussed the potential of remote sensing products for developing IDF curves. This study employs a 19-year NEXRAD Stage-IV high-resolution radar data (2002\u20132020) to develop IDF curves over the entire state of Texas at a fine spatial resolution. The Annual Maximum Series (AMS) were fitted to four widely used theoretical Extreme Value statistical distributions. Gumble distribution, a unique scenario of the Generalized Extreme Values (GEV) family, was found to be the best model for more than 70% of the state\u2019s area for all storm durations. Validation of the developed IDFs against the operational Atlas 14 IDF values shows a \u00b127% difference in over 95% of the state for all storm durations. The median of the difference stays between \u221210% and +10% for all storm durations and for all return periods in the range of (2\u2013100) years. The mean difference ranges from \u22125% for the 100-year return period to 8% for the 10-year return period for the 24-h storm. Generally, the western and northern regions of the state show an overestimation, while the southern and southcentral regions show an underestimation of the published values.<\/jats:p>","DOI":"10.3390\/rs13152890","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T10:31:44Z","timestamp":1627036304000},"page":"2890","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Development and Assessment of High-Resolution Radar-Based Precipitation Intensity-Duration-Curve (IDF) Curves for the State of Texas"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2550-8786","authenticated-orcid":false,"given":"Dawit T.","family":"Ghebreyesus","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX 78249, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9805-8080","authenticated-orcid":false,"given":"Hatim O.","family":"Sharif","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX 78249, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105203","DOI":"10.1016\/j.atmosres.2020.105203","article-title":"Evaluating intensity-duration-frequency (IDF) curves of satellite-based precipitation datasets in Malaysia","volume":"248","author":"Noor","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s11027-014-9571-6","article-title":"Adaptation to climate change impacts on water demand","volume":"21","author":"Wang","year":"2016","journal-title":"Mitig. Adapt. Strateg. Glob. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.jhydrol.2015.03.027","article-title":"A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model","volume":"524","author":"Abbaspour","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_4","unstructured":"NOAA (2021, April 20). U.S. Climate Extremes Index, Available online: www.ncdc.noaa.gov\/extremes\/cei."},{"key":"ref_5","unstructured":"NOAA (2021, April 20). The Atlantic Hurricane Database Re-Analysis Project, Available online: www.aoml.noaa.gov\/hrd\/hurdat\/comparison_table.html."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.5194\/hess-21-2389-2017","article-title":"Intensity\u2013duration\u2013frequency curves from remote sensing rainfall estimates: Comparing satellite and weather radar over the eastern Mediterranean","volume":"21","author":"Marra","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_7","first-page":"1","article-title":"Intensity-Duration-Frequency (IDF) rainfall curves, for data series and climate projection in African cities","volume":"3","author":"Giugni","year":"2014","journal-title":"SpringerPlus"},{"key":"ref_8","first-page":"1","article-title":"Generation of rainfall intensity duration frequency (IDF) curves for ungauged sites in arid region","volume":"1","author":"Subyani","year":"2017","journal-title":"Earth Syst. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sherif, M., Chowdhury, R., and Shetty, A. (2014, January 1\u20135). Rainfall and intensity-duration-frequency (IDF) curves in the United Arab Emirates. Proceedings of the World Environmental and Water Resources Congress 2014, Portland, OR, USA.","DOI":"10.1061\/9780784413548.231"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1175\/BAMS-D-14-00283.1","article-title":"So, how much of the Earth\u2019s surface is covered by rain gauges?","volume":"98","author":"Kidd","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/S0022-1694(97)00117-0","article-title":"Transformation of point rainfall to areal rainfall: Intensity-duration-frequency curves","volume":"204","author":"Sivapalan","year":"1998","journal-title":"J. Hydrol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Overeem, A., Buishand, T., Holleman, I., and Uijlenhoet, R. (2010). Extreme value modeling of areal rainfall from weather radar. Water Resour. Res., 46.","DOI":"10.1029\/2009WR008517"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1002\/2013WR014224","article-title":"Flood frequency analysis using radar rainfall fields and stochastic storm transposition","volume":"50","author":"Wright","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7752","DOI":"10.1029\/2018WR022929","article-title":"Developing intensity-duration-frequency (IDF) curves from satellite-based precipitation: Methodology and evaluation","volume":"54","author":"Ombadi","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"084045","DOI":"10.1088\/1748-9326\/ab370a","article-title":"Intensity-duration-frequency curves at the global scale","volume":"14","author":"Courty","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40562-019-0147-x","article-title":"Deriving intensity\u2013duration\u2013frequency (IDF) curves using downscaled in situ rainfall assimilated with remote sensing data","volume":"6","author":"Sun","year":"2019","journal-title":"Geosci. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5697","DOI":"10.3390\/rs70505697","article-title":"Assessment and comparison of TMPA satellite precipitation products in varying climatic and topographic regimes in Morocco","volume":"7","author":"Milewski","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.5194\/hess-19-2037-2015","article-title":"Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002\u20132012)","volume":"19","author":"Prat","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"207","DOI":"10.5194\/hess-23-207-2019","article-title":"Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS","volume":"23","author":"Beck","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Furl, C., Ghebreyesus, D., and Sharif, H.O. (2018). Assessment of the performance of satellite-based precipitation products for flood events across diverse spatial scales using GSSHA modeling system. Geosciences, 8.","DOI":"10.3390\/geosciences8060191"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s10584-013-1048-1","article-title":"Climate change impacts on extreme events in the United States: An uncertainty analysis","volume":"131","author":"Monier","year":"2015","journal-title":"Clim. Chang."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1002\/joc.3712","article-title":"Influence of climate change on IDF curves for the metropolitan area of Barcelona (Spain)","volume":"34","author":"Navarro","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shrestha, A., Babel, M.S., Weesakul, S., and Vojinovic, Z. (2017). Developing Intensity\u2013Duration\u2013Frequency (IDF) curves under climate change uncertainty: The case of Bangkok, Thailand. Water, 9.","DOI":"10.3390\/w9020145"},{"key":"ref_24","unstructured":"Perica, S., Pavlovic, S., Laurent, M.S., Trypaluk, C., Unruh, D., and Wilhite, O. (2018). Precipitation-Frequency Atlas of the United States, Texas, in NOAA Atlas 14."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ghebreyesus, D., and Sharif, H.O. (2021). Time Series Analysis of Monthly and Annual Precipitation in The State of Texas Using High-Resolution Radar Products. Water, 13.","DOI":"10.3390\/w13070982"},{"key":"ref_26","unstructured":"USEIA (2020, April 20). Gulf of Mexico Fact Sheet. 20 June 2020, Available online: https:\/\/www.eia.gov\/special\/gulf_of_mexico\/."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1175\/1525-7541(2002)003<0093:RTCOSN>2.0.CO;2","article-title":"Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements","volume":"3","author":"Seo","year":"2002","journal-title":"J. Hydrometeorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1214\/aos\/1176344136","article-title":"Estimating the dimension of a model","volume":"6","author":"Schwarz","year":"1978","journal-title":"Ann. Stat."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1029\/1999WR900330","article-title":"Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data","volume":"36","author":"Martins","year":"2000","journal-title":"Water Resour. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1029\/2001WR000367","article-title":"Generalized maximum likelihood Pareto-Poisson estimators for partial duration series","volume":"37","author":"Martins","year":"2001","journal-title":"Water Resour. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1080\/01621459.1951.10500769","article-title":"The Kolmogorov-Smirnov test for goodness of fit","volume":"46","author":"Massey","year":"1951","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_33","unstructured":"Lukas, J., Wolter, K., Mahoney, K., Barsugli, J., Doesken, N., Ryan, W., and Hoerling, M. (2013). Severe Flooding on the Colorado Front Range, September 2013: A Preliminary Assessment from the CIRES Western Water Assessment at the University of Colorado."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Alsumaiti, T.S., Hussein, K., Ghebreyesus, D.T., and Sharif, H.O. (2020). Performance of the CMORPH and GPM IMERG Products over the United Arab Emirates. Remote Sens., 12.","DOI":"10.3390\/rs12091426"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1175\/2011BAMS3158.1","article-title":"Advanced concepts on remote sensing of precipitation at multiple scales","volume":"92","author":"Sorooshian","year":"2011","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1002\/met.1467","article-title":"Validation of remote-sensing precipitation products for Angola","volume":"22","author":"Pombo","year":"2015","journal-title":"Meteorol. Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2890\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:33:53Z","timestamp":1760164433000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2890"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,23]]},"references-count":36,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13152890"],"URL":"https:\/\/doi.org\/10.3390\/rs13152890","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,23]]}}}