the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Apr 2026
Brief Communication: Rise of the Guadalupe River- A Multifaceted Post Event Analysis of the July 4, 2025, Flood in Central Texas
Abstract. The flash flooding across Central Texas on July 4th, 2025, caused more than 130 fatalities and property losses exceeding 20 billion dollars. The objective of this study is to diagnose the drivers of this catastrophic event and to analyze the temporal variability in forecasting flood inundation dynamics in the hours leading up to and during the event. Using the Operational National Water Model short-range streamflow forecast product, we generated 306 forecasted flood inundation maps between July 3rd and July 4th, 2025. For evaluation, we constructed an inundation extent benchmark derived from USGS high water marks. Both impact-based and pixel-based assessments are presented.
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Status: open (until 14 May 2026)
- RC1: 'Comment on egusphere-2026-1847', Anonymous Referee #1, 16 Apr 2026 reply
Data sets
FIMBench Dipsikha Devi et al. https://github.com/sdmlua/fimbench
Model code and software
FIMserv Anupal Baruah et al. https://github.com/sdmlua/FIMserv
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#1
The study concludes that underprediction and lag in the National Water Model (NWM) short-range forecasts are primarily due to errors in rainfall estimation from the HRRR model. While this is a common bottleneck in hydrologic forecasting, the paper would benefit from a deeper analysis of the hydro-meteorological mechanisms causing these errors. I suggest the authors include a brief diagnostic of the HRRR’s atmospheric environment (e.g., moisture convergence or instability biases). Since rainfall intensities surpassed 100-year return period thresholds and reached localized rates of 100mm/h, understanding the model's failure to resolve these convective heavy rainfall is as critical as the flood analysis itself.
#2
A standout finding in this paper is the failure of the USGS gauge during the peak flow. The paper should emphasize the need for hardened sensor networks or alternative data sources (like satellite-derived flow) for nudging.
#3
The authors propose a probabilistic forecast as a solution to represent forecast uncertainty in their concluding remarks. Although such probabilistic ensemble information has great potential in its use, it often confuses operational sides as it is difficult to make a decision with uncertain information. The authors should expand their discussion on how such probabilistic information can be used as specific action triggers.