Abstract
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 (2002–2020) 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’s area for all storm durations. Validation of the developed IDFs against the operational Atlas 14 IDF values shows a ±27% difference in over 95% of the state for all storm durations. The median of the difference stays between −10% and +10% for all storm durations and for all return periods in the range of (2–100) years. The mean difference ranges from −5% 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.
Highlights
The non-stationarity nature of climatic variables requires constant updating of engineering design parameters, especially those that are sensitive to weather and climate extremes
The spatio-temporal variability of the Next Generation Weather Radar (NEXRAD) Stage-IV data was analyzed to capture the variability of the extreme rainfall events across the state
The temporal evolution of the extreme events was analyzed using the Mann–Kendall trend test to evaluate the significance of the trend in the six major metropolitan areas, namely, Dallas-Fort Worth, Houston, San Antonio, McAllen, El Paso, and Amarillo
Summary
The non-stationarity nature of climatic variables requires constant updating of engineering design parameters, especially those that are sensitive to weather and climate extremes. This issue has become more clear than ever with the increased frequency of extreme weather events such as floods and droughts as a result of the changing climate [1]. The conterminous United States experienced a rapid rise in the number of extreme one-day rainfall events with 9 out of the top 10 years of record-breaking one-day events occurring after 1990 [4]. The aforementioned anomalies require the adjustment of the current design parameters with up-to-date rainfall datasets with adequate spatio-temporal resolutions
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