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1. A watershed-scale multi-approach assessment of design flood discharge estimates used in hydrologic risk analyses for forest road stream crossings and culverts

   https://doi.org/10.1016/j.jhydrol.2024.130698  

   Mukherjee, Sourav; Amatya, Devendra M; Campbell, John L; Gryczkowski, Landon; Panda, Sudhanshu; Johnson, Sherri L; Elder, Kelly; Jalowska, Anna M; Caldwell, Peter; Grace, Johnny M; et al (January 2024, Journal of hydrology)

   Flood peak magnitudes and frequency estimates are key components of any effective nationwide flood risk management and flood damage abatement program. In this study, we evaluated normalized peak design discharges (Qp) for 1,387 hydrologic unit code 16 to 20 (HUC16-20) watersheds in the White Mountain National Forest (WMNF), New Hampshire and in five Experimental Forest (EF) regions across the United States managed by USDA Forest Service (USDA-FS). Nonstationary regional frequency analysis (RFA) and single site frequency analysis (FA) with long-term high-resolution observed streamflow data along with the deterministic Rational Method (RM) and semi-empirical United States Geological Survey regional regression equation (USGS-RRE) were used. Additionally, a hydrologic vulnerability assessment was performed for 194 road culverts as a result of extreme precipitation-induced flooding on gauged and ungauged watersheds in the Hubbard Brook EF (HBR) within the WMNF. The RM outperformed the USGS-RRE in predicting Qp in the gauged and ungauged HUC16-20 watersheds of WMNF and in three other small, high-relief forest headwater watersheds—Coweeta Hydrologic Lab EF’s watershed-14, and watershed-27 in North Carolina and HJ Andrews EF’s watershed 8 in Oregon. However, the USGS-RRE performed better for larger watersheds, such as the Fraser EF’s St. Louis watershed in Colorado and the Santee EF’s watershed 80 in South Carolina. About 31 %, 26 %, and 56 % of the culverts at the HBR site could not accommodate the 100-yr Qp estimated by RFA, RM and USGS-RRE, respectively. Based on the chosen RIs and techniques, it is determined that except for one culvert with diameter = 0.91 m (36 in.), none of the culverts with diameter of 0.75 m (30 in.) or larger are hydrologically vulnerable. Our results suggest that the observation based RFA works best where multiple gauges are available to extrapolate information for ungauged watersheds, otherwise, RM is best-suited for smaller headwater watersheds and USGS-RRE for larger watersheds. Results from the hydrologic vulnerability analysis revealed that replacing undersized culverts with new culverts of diameter ≥ 0.75-m will improve flood resiliency, provided that the structure is geomorphologically safe (with minimal effects of debris flow, erosion, and sedimentation) and allows for both bank-full discharge and necessary fish passage within that design limit. This study has implications in managing road culverts and crossings at Forest Service and other forested lands for their resiliency to extreme precipitation and flooding hazards induced by climate change. 

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2. Water level data for historic and future ADvanced CIRCulation (ADCIRC) model simulations of Hurricanes Isabel, Sandy, and Dorian, for Norfolk, Virginia, USA

   https://doi.org/10.6073/pasta/73c0fb5b68aaba3cedc137481189f7ab  

   Weghorst, Rachel; Zhu, Quingguang; Christensen, Heather; Reidenbach, Matthew (January 2026, Environmental Data Initiative)

   This dataset consists of water level elevation relative to mean sea level (in meters) for historic and future ADvanced CIRCulation (ADCIRC) model simulations of three major storm events: Hurricane Isabel (2003), Hurricane Sandy (2012), and Hurricane Dorian (2019). ADCIRC is a hydrodynamic which integrates atmospheric and hydrologic data to predict water surface elevation and flow patterns during extreme storm events. The historic simulations contain water level elevation output under the recorded storm conditions for each of the hurricanes, simulating the storms as they originally occurred. The output from these simulations was validated against measured tidal records from multiple NOAA tide gauge stations including Wachapreague (ID 8631044), Kiptopeke (ID 8632200), and Sewells Point (ID 8638610). The future scenarios are simulations of these storms if they were to occur 40 years after the original date of the storm event to forecast the potential impacts of climate change. The parameters for the future simulations included a sea level rise of 0.2 m, and storm winds increasing by a factor of 1.1 (a 10% increase in wind speed). These parameters were based on historical trends of a 5 mm/year sea level rise at the Wachapreague tidal gauge station and a wind speed change rate over a 40-year period from the offshore CHLV2 NOAA buoy station. Included in this dataset are a time series of water level elevation throughout the duration of each storm simulation at each Norfolk location, and spatial data for the peak time of the storm where water levels are the highest (to represent peak storm surge) for each storm simulation. The spatial data includes coordinates on the model mesh and the corresponding water level elevation at each point. If no water was present NaN (missing) was recorded for that location. 

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3. Comparison of Local, Regional, and Scaling Models for Rainfall Intensity–Duration–Frequency Analysis

   https://doi.org/10.1175/JAMC-D-20-0094.1  

   Mascaro, Giuseppe (September 2020, Journal of Applied Meteorology and Climatology)

   Abstract Intensity–duration–frequency (IDF) analyses of rainfall extremes provide critical information to mitigate, manage, and adapt to urban flooding. The accuracy and uncertainty of IDF analyses depend on the availability of historical rainfall records, which are more accessible at daily resolution and, quite often, are very sparse in developing countries. In this work, we quantify performances of different IDF models as a function of the number of available high-resolution (Nτ) and daily (N24h) rain gauges. For this aim, we apply a cross-validation framework that is based on Monte Carlo bootstrapping experiments on records of 223 high-resolution gauges in central Arizona. We test five IDF models based on (two) local, (one) regional, and (two) scaling frequency analyses of annual rainfall maxima from 30-min to 24-h durations with the generalized extreme value (GEV) distribution. All models exhibit similar performances in simulating observed quantiles associated with return periods up to 30 years. When Nτ > 10, local and regional models have the best accuracy; bias correcting the GEV shape parameter for record length is recommended to estimate quantiles for large return periods. The uncertainty of all models, evaluated via Monte Carlo experiments, is very large when Nτ ≤ 5; however, if N24h ≥ 10 additional daily gauges are available, the uncertainty is greatly reduced and accuracy is increased by applying simple scaling models, which infer estimates on subdaily rainfall statistics from information at daily scale. For all models, performances depend on the ability to capture the elevation control on their parameters. Although our work is site specific, its results provide insights to conduct future IDF analyses, especially in regions with sparse data. 

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4. Evaluating IMERG Satellite Precipitation-Based Design Storms in the Conterminous U.S. Using NOAA Atlas Datasets

   https://doi.org/10.3390/w17172602  

   Ekpetere, Kenneth Okechukwu; Li, Xingong; Kastens, Jude; Roundy, Joshua K; Mechem, David B (September 2025, Water)

   Probable Maximum Storms (PMS) are synthetic design storms represented by idealized hyetographs. They play a critical role in assessing extreme rainfall events over extended durations and are widely applied in the hydraulic design of infrastructure such as dams, culverts, and bridges. PMS provide essential input for estimating Probable Maximum Floods (PMF), vital for analyzing worst-case flood scenarios with the potential to cause catastrophic loss of life and property. Despite their importance, the estimation of design storms at ungauged locations, particularly across synoptic scales, remains a major scientific and engineering challenge. This study addresses this gap by utilizing the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) dataset, which provides near-global estimated precipitation coverage. IMERG’s 24 h design storm hyetographs (expressed as cumulative percentage of precipitation throughout a 24 h period) were modeled and compared with similar reference data from NOAA Atlas 14 across twenty-eight regions and seven larger zones covering most of the conterminous United States (CONUS). Across the regions, the average root mean square error (RMSE) was 3.7%, with a mean relative bias (RB) of 1.4%. The mean normalized storm loading index (NSLI) from NOAA Atlas 14 was −7.7%, indicating that 57.7% of the total precipitation was received during the first 12 h of the storm, whereas IMERG storms exhibited a mean NSLI of −4.1%, suggesting they are also frontloaded but to a lesser extent. Across the broader zones, the mean RMSE was 4.8% and the mean RB was 1.1%. The mean NSLI values were −9.7% for NOAA Atlas 14 and −5.7% for IMERG, again indicating that IMERG storms are less frontloaded. When design storm families were estimated corresponding with different degrees of frontloading (corresponding to the 10, 20, …, 90% deciles of NSLI), the 40th to 60th percentile range exhibited the strongest agreement between IMERG and NOAA Atlas 14 hyetographs. 

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5. Enhanced nitrous oxide emission factors due to climate change increase the mitigation challenge in the agricultural sector

   https://doi.org/10.1111/gcb.17472  

   Li, Linchao; Lu, Chaoqun; Winiwarter, Wilfried; Tian, Hanqin; Canadell, Josep G; Ito, Akihiko; Jain, Atul K; Kou‐Giesbrecht, Sian; Pan, Shufen; Pan, Naiqing; et al (August 2024, Global Change Biology)

   Abstract Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N₂O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N₂O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process‐based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low‐ambition nitrogen regulation policies, EF would increase from 1.18%–1.22% in 2010 to 1.27%–1.34% by 2050, representing a relative increase of 4.4%–11.4% and exceeding the IPCC tier‐1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%–0.35% (relative increase of 11.9%–17%). In contrast, high‐ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying–wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N₂O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N₂O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio‐economic assessments and policy‐making efforts. 

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