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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. 

Abstract Urgency of Precipitation Intensity-Duration-Frequency (IDF) estimation using the most recent data has grown significantly due to recent intense precipitation and cloud burst circumstances impacting infrastructure caused by climate change. Given the continually available digitized up-to-date, long-term, and fine resolution precipitation dataset from the United States Department of Agriculture Forest Service’s (USDAFS) Experimental Forests and Ranges (EF) rain gauge stations, it is both important and relevant to develop precipitation IDF from onsite dataset (Onsite-IDF) that incorporates the most recent time period, aiding in the design, and planning of forest road-stream crossing structures (RSCS) in headwaters to maintain resilient forest ecosystems. Here we developed Onsite-IDFs for hourly and sub-hourly duration, and 25-yr, 50-yr, and 100-yr design return intervals (RIs) from annual maxima series (AMS) of precipitation intensities (PIs) modeled by applying Generalized Extreme Value (GEV) analysis and L-moment based parameter estimation methodology at six USDAFS EFs and compared them with precipitation IDFs obtained from the National Oceanic and Atmospheric Administration Atlas 14 (NOAA-Atlas14). A regional frequency analysis (RFA) was performed for EFs where data from multiple precipitation gauges are available. NOAA’s station-based precipitation IDFs were estimated for comparison using RFA (NOAA-RFA) at one of the EFs where NOAA-Atlas14 precipitation IDFs are unavailable. Onsite-IDFs were then evaluated against the PIs from NOAA-Atlas14 and NOAA-RFA by comparing their relative differences and storm frequencies. Results show considerable relative differences between the Onsite- and NOAA-Atlas14 (or NOAA-RFA) IDFs at these EFs, some of which are strongly dependent on the storm durations and elevation of precipitation gauges, particularly in steep, forested sites of H. J. Andrews (HJA) and Coweeta Hydrological Laboratory (CHL) EFs. At the higher elevation gauge of HJA EF, NOAA-RFA based precipitation IDFs underestimate PI of 25-yr, 50-yr, and 100-yr RIs by considerable amounts for 12-h and 24-h duration storm events relative to the Onsite-IDFs. At the low-gradient Santee (SAN) EF, the PIs of 3- to 24-h storm events with 100-yr frequency (or RI) from NOAA-Atlas14 gauges are found to be equivalent to PIs of more frequent storm events (25–50-yr RI) as estimated from the onsite dataset. Our results recommend use of the Onsite-IDF estimates for the estimation of design storm peak discharge rates at the higher elevation catchments of HJA, CHL, and SAN EF locations, particularly for longer duration events, where NOAA-based precipitation IDFs underestimate the PIs relative to the Onsite-IDFs. This underscores the importance of long-term high resolution EF data for new applications including ecological restorations and indicates that planning and design teams should use as much local data as possible or account for potential PI inconsistencies or underestimations if local data are unavailable. 

Inuit hunters and meteorologists alike pay close attention to weather and weather changes, with deep understandings. This paper describes a long-time research project based in Kangiqtugaapik (Clyde River), Nunavut, where a research team of Inuit and visiting scientists have combined information and knowledge from a community-based weather station network, on-going interviews and discussions, and extensive travel (both Arctic fieldwork and visits to southern universities) to co-produce knowledge related to human–weather relationships and weather information needs and uses in one Nunavut community. The project uses the concept of “HREVs”, human-relevant environmental variables — complex, synthesis variables that, used in conjunction with a host of social variables, assist in informing safe land travel and activities. This work, including linking Inuit knowledge and environmental modeling, can be expanded to not only understand human–weather relationships more broadly and in other locations but also provide insights into the process of building diverse research teams and knowledge co-production. Inuit angunasuktiit amma silalirijiit tamarmik ujjiqsuttiasuunguvut silamit amma silaup asijjiqpallianingani, tukisiumaniqarjuaqłutik. Una paippaangujuq unikkaarivuq akuniujumi qaujitasaqtaunirmut piliriangujumi Kangiqtugaapik (Clyde River), Nunavummi, qaujisaqtiujuni katinngajuni Inungni amma pularaqtunut qaujisaqtiujunut katirisimajuni uqausiksani amma qaujimaniujumi nunalingni−tunngavilingmi silalirivvingmi tusaumatittiniujumi, apiqsuqtaunginnaqtuni amma uqallangniujuni, amma aullaaqsimarjuaqłutik (tamakkit Ukiuqtaqtumi iniujumi piliriniujumi amma pulararniujunut qallunaat nunanganni silattuqsarvigjuangujunut) saqqitittiqatigiingnirmut qaujimaniujumi pijjutiqaqtumut inungnut−silamut piliriqatigiingniujuni amma silamut uqausiksani pijariaqarniujunut amma aturniujunut atausirmi Nunavummi nunaliujumi. Piliriangujuq atusuunguvuq isumagijauniujumi “HREVs”, inungnut-atuutilingnut avatimut ajjigiinnginniujunut – nalunaqtuni, katinniujuni isumagijauniujuni aaqqiksinirnut piliri−jusiujumi ajjigiinnginniujuni, atuqatiqaqłuni ilagijaujumi inuuqatigiingujunut ajjigiinnginniujunit, ikajuqsuisuunguvuq aaqqiksuinirmi attananngittumi nunami aullaarniujumi amma qanuiliurniujunut. Una piliriniujuq ilaqaqtumi kasuqatiqarnirmi inuit qaujimajanginni amma avatimut uukturautiqarnirmi, angigligiaqtaujunnaqpuq tukisiumanituangunngittumi inungt-silamut piliriqatigiingniujumi tauvunngaujjiniujumi ammalu asinginni iniujunut, kisiani tunisijunnaqpuq tukisirjuarniujuni piliriniujuni sananirmut ajjigiinngiruluujaqtuni qaujisaqtiujunut katinngajuni amma qaujimanirmut saqqitittiqatigiingniujumi. 

Abstract For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals. 

Abstract A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution.