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The California-Nevada 1997 New Year’s flood was an atmospheric river (AR)-driven rain-on-snow (RoS) event and remains the costliest in their history. The joint occurrence of saturated soils, rainfall, and snowmelt generated inundation throughout northern California-Nevada. Although AR RoS events are projected to occur more frequently with climate change, the warming sensitivity of their flood drivers across scales remains understudied. We leverage the regionally refined mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) to recreate the 1997 New Year’s flood with horizontal grid spacings of 3.5 km across California, with forecast lead times of up to 4 days, and across six warming levels ranging from pre-industrial conditions to +3.5∘C. We describe the sensitivity of the flood drivers to warming including AR duration and intensity, precipitation phase, intensity and efficiency, snowpack mass and energy changes, and runoff efficiency. Our findings indicate current levels of climate change negligibly influence the flood drivers. At warming levels ≥1.7∘C, AR hazard potential increases, snowpack nonlinearly decreases, antecedent soil moisture decreases (except where the snowline retreats), and runoff decreases (except in the southern Sierra Nevada where antecedent snowpack persists). Storm total precipitation increases, but at rates below warming-induced increases in saturation-specific humidity. Warming intensifies short-duration, high-intensity rainfall, particularly where snowfall-to-rainfall transitions occur. This study highlights the nonlinear tradeoffs in 21st-century RoS flood hazards with warming and provides water management and infrastructure investment adaptation considerations.

Streamflow predictions are critical for managing water resources and for environmental conservation, especially in the water-short Western United States. Land surface models (LSMs), such as the variable infiltration capacity (VIC) model and the Noah LSM with multiparameterization options (Noah-MP), play an essential role in providing comprehensive runoff predictions across the region. Virtually all LSMs require parameter estimation (calibration) to optimize their predictive capabilities. Here, we focus on the calibration of VIC and Noah-MP models at a 1/16 ° latitude–longitude resolution across the Western United States. We first performed global optimal calibration of parameters for both models for 263 river basins in the region. We find that the calibration significantly improves the models' performance, with the median daily streamflow Kling–Gupta efficiency (KGE) increasing from 0.37 to 0.70 for VIC, and from 0.22 to 0.54 for Noah-MP. In general, post-calibration model performance is higher for watersheds with relatively high precipitation and runoff ratios, and at lower elevations. At a second stage, we regionalize the river basin calibrations using the donor-basin method, which establishes transfer relationships for hydrologically similar basins, via which we extend our calibration parameters to 4816 hydrologic unit code (HUC)-10 basins across the region. Using the regionalized parameters, we show that the models' capabilities to simulate high and low flow conditions are substantially improved following calibration and regionalization. The refined parameter sets we developed are intended to support regional hydrological studies and hydrological assessments of climate change impacts. [ABSTRACT FROM AUTHOR]

Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate. [ABSTRACT FROM AUTHOR]

Predicting future climate change over a region of complex terrain, such as the western United States (U.S.), remains challenging due to the low resolution of global climate models (GCMs). Yet climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality predictions. Here, we present an ensemble of dynamically downscaled simulations over the western U.S. from 1980-2100 at 9-km grid spacing, driven by sixteen latest-generation GCMs. This dataset is titled the Western U.S. Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography: (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven intensified local atmospheric updrafts tied to topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western U.S. climate. [ABSTRACT FROM AUTHOR]

Streamflow forecasts are critical for water and environmental management, especially in the water-short Western U.S.. Land Surface Models (LSMs), such as the Variable Infiltration Capacity (VIC) model and the Noah-Multiparameterization (Noah-MP) play an essential role in providing comprehensive runoff forecasts across the region. Virtually all LSMs require parameter estimation to optimize their predictive capabilities. We describe a systematic calibration of parameters for VIC and Noah-MP over 263 river basins in the Western U.S., and distribution of the calibrated parameters over the entire region. Post-calibration results showed a notable improvement in model accuracy in the calibration basins: the median daily streamflow Kling-Gupta Efficiency (KGE) for VIC rose from 0.37 to 0.70, and for Noah-MP, from 0.22 to 0.54. Employing the donor-basin regionalization method, we developed transfer relationships to hydrologically similar basins and extended the calibrated parameters to ungauged basins and the entire region. We assessed factors that influence calibration efficiency and model performance using regional parameter estimates. We evaluated high and low flow simulation capabilities of the two models and observed marked improvements after calibration and regionalization. We also generated gridded parameter sets for both models across all 4816 HUC-10 basins in the Western U.S., a data set that is intended to support regional hydrologic studies and hydrologic climate change assessments. [ABSTRACT FROM AUTHOR]

Streamflow forecasts are critical for water and environmental management, especially in the water-short Western U.S.. Land Surface Models (LSMs), such as the Variable Infiltration Capacity (VIC) model and the Noah-Multiparameterization (Noah-MP) play an essential role in providing comprehensive runoff forecasts across the region. Virtually all LSMs require parameter estimation to optimize their predictive capabilities. We describe a systematic calibration of parameters for VIC and Noah-MP over 263 river basins in the Western U.S., and distribution of the calibrated parameters over the entire region. Post-calibration results showed a notable improvement in model accuracy in the calibration basins: the median daily streamflow Kling-Gupta Efficiency (KGE) for VIC rose from 0.37 to 0.70, and for Noah-MP, from 0.22 to 0.54. Employing the donor-basin regionalization method, we developed transfer relationships to hydrologically similar basins and extended the calibrated parameters to ungauged basins and the entire region. We assessed factors that influence calibration efficiency and model performance using regional parameter estimates. We evaluated high and low flow simulation capabilities of the two models and observed marked improvements after calibration and regionalization. We also generated gridded parameter sets for both models across all 4816 HUC-10 basins in the Western U.S., a data set that is intended to support regional hydrologic studies and hydrologic climate change assessments. [ABSTRACT FROM AUTHOR]

Sow feed intake during lactation is crucial to the maintenance of sow body condition, milk production, and litter performance. Loss of body weight or condition during this time could compromise future reproductive performance. Two groups (n = 52 total) of breeding age gilts and sows were used to evaluate whether a sensory additive would improve intake during the lactation period. Gilts/Sows were weighed, and back fat depth was measured, at 110 d of gestation prior to entering the lactation room, and again at weaning. The sows were sorted by parity and then allotted by BW at d 110 to Control or 0.075% sensory additive (Luctamax® SowVive, Lucta S.A., Barcelona, Spain). Experimental lactation diets were offered upon entry to the lactation room and fed through weaning. During this time feed was weighed back every three days. Gilts/sows were fed 2.72 kg/head/day until farrowing, and sows were placed on full feed 24 hours post-farrowing and allowed to eat ad libitum. Data was analyzed using the PROC MIXED of SAS as an RCBD with treatment as the fixed effect and group as a random effect. During lactation sows on the sensory additive diet showed an increase in intake during days 10 to 21 when compared to sows on the control diet (Table 1). Although it was not significantly different, sows fed sensory additive had numerically heavier weaning BW (239.4 vs 236.2 kg, P = 0.42) and increased litter weight gain (51.96 vs 48.69 kg, P = 0.24) compared to control fed sows. However, backfat thickness change during lactation did not differ between control and sensory additive fed sows (-4.74 vs -5.81 mm, P = 0.65). In conclusion, in the current study lactation diets supplemented with a sensory additive stimulated sow appetite during the lactation period. [ABSTRACT FROM AUTHOR]

The addition of a sensory additive to sow diets during the summer improved feed intake during lactation, and litter performance. The aim of this study was to evaluate the impact of the sensory additive on sow lactation performance during cooler temperatures. Three consecutive groups (n = 61 total) of sows were farrowed during winter (October, December and January). Sows were weighed, and back fat depth was measured, at 110 d of gestation prior to entering the lactation room, and again at weaning. The sows were sorted by parity, and then allotted by BW at d 110 to Control or 0.075% sensory additive (Luctamax® SowVive, Lucta S.A., Spain). Experimental diets were offered upon entry to the lactation room and fed through weaning. Feed disappearance was recorded every three days. Sows were fed 2.72 kg/head/day until farrowing, and then allowed to eat ad libitum from 24 hours postfarrowing until weaning. Data were analyzed using the PROC MIXED procedure of SAS as an RCBD with treatment as the fixed effect and group as a random effect. No differences were observed on sows feed intake (P > 0.4), 10th rib backfat thickness change (P = 0.632) and body weight change from d 110 of gestation to weaning (P = 0.65, Table 1). Sows fed the sensory additive lowered preweaning mortality by 4.9% (P = 0.051), and weaned an additional 1.42 piglets (P = 0.026) with similar weaning weights (P = 0.86) when compared to control. In conclusion, in the current study, lactation diets supplemented with a sensory additive improved litter performance and helped maintain sow body condition. [ABSTRACT FROM AUTHOR]

Capsicum oleoresin has recognized effects on digestion and fat metabolism. A total of 100 PIC x Danbred female pigs were divided in two experimental groups (10 pens/group) from 60Kg body weight (BW) to slaughter: one control group (T1) and another group (T2) where the sensorial additive (350ppm) was added from 90Kg to 130Kg BW. The study was carried out in Mediterranean summer conditions. Experimental rooms turned off their cooling systems at 90Kg BW to the end of the study. Temperature and humidity were recorded using dataloggers (HOBO®). Pig BW and intake were recorded before exposure to the additive and at the end of the administration of experimental feeds. Additionally, backfat thickness (BF) and loin depth (LD) were measured. Respiration rate (RR) and rectal temperature (RT) were also recorded at 0800, 1100, 1400 and 1700h. Carcass and meat quality parameters were measured in the slaughterhouse. Data was analyzed using Proc GLM (performance, carcass and meat quality) and Proc MIXED of repeated measures (RR, RT) of SAS. Environmental data confirmed heat stress conditions for finishing pigs (THI >71 from 0800 to 2200h). Growth performance, BF and LD did not differ between groups. Animals from T2 showed higher (P< 0.05) RR and lower (P< 0.05) RT compared to T1 at 17:00h. At slaughter, animals from T2 tended (P< 0.10) to have a higher fat content (3-4th rib level and ham) compared to T1. Ham pH at 25min and at 20h post-mortem, and drip losses at 48h post-mortem were lower (P< 0.05) in T2 group vs. T1. Concerning the fatty acid profile, the T2 showed a greater concentration of C18:1 and C20:1 compared to T1. In conclusion, the addition of the sensorial additive at the end of the growing period changed carcass composition and quality traits under heat stress conditions. [ABSTRACT FROM AUTHOR]

OutPace® Feed Additive (OP), contains a carefully researched blend of activated medium chain fatty acids (MCFAs; PMI, Arden Hills, MN), formulated to help mitigate the effects of stress in nursery pigs. Several studies using OP in both late and full nursery periods resulted in improved pig performance. A meta-analysis using 9 studies (4 studies during late nursery [15 to 26.8 kg BW] and 5 studies during the full nursery [5.9 to 25.4 kg]) was done to determine the impact of OP (included at 0.25% Phase 1 and 2; 0.125% Phase 3) on average daily gain, average daily feed intake, and feed conversion. The combined data was considered a randomized complete block design. Analysis of variance was completed with mixed models using the GLIMMIX procedure of SAS (SAS 9.4, SAS Institute Inc., Cary, NC) and least squares means were compared using Fisher's least significant difference (P < 0.05). In the analysis of 5 studies conducted in late nursery (45 pens/treatment of 6 to 20 pigs/pen), pigs provided OP had higher average daily gain (0.67 vs 0.63 kg/d; P < 0.05), increased average daily feed intake (0.99 vs 0.97 kg/d; P < 0.05), and improved feed efficiency (0.67 vs 0.65 kg gain/kg feed intake; P < 0.05) compared to pigs fed control diets. Additionally, when pigs were provided OP throughout the nursery period (20 pens/treatment of 7 to 20 pigs/pen), average daily gain was increased 6.1% (0.48 vs 0.45 kg/d; P < 0.05), average daily feed intake tended to be increased 2.2% (0.62 vs 0.61 kg/d; P < 0.1), and feed efficiency was improved 2.7% (0.76 vs 0.74 kg gain/kg feed intake; P < 0.05) compared to pigs provided control diets. In conclusion, providing OP to pigs during the nursery period improved ADG and feed efficiency. [ABSTRACT FROM AUTHOR]

Four experiments were conducted to evaluate: 1) medium chain fatty acids (MCFA) application to swine feed pre- or post-viral contamination with porcine epidemic diarrhea virus (PEDV) measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR), 2) MCFA levels and combinations measured by qRT-PCR, and 3) selected MCFA in bioassay. In Exp. 1, treatments were a 2x2 + 1 factorial with the main effects of chemical treatment (0.3% commercial formaldehyde (CF), Sal CURB [Kemin Industries, Des Moines, IA] or 1% MCFA blend (Blend) of 1:1:1 C6:C8:C10 [PMI, Arden Hills, MN]) and timing of application pre- or post-inoculation with PEDV; plus a positive control (PC; feed inoculated with PEDV and no chemical treatment). All combinations of treatment and timing decreased detectable PEDV compared to PC (P< 0.05). Pre-inoculation had decreased PEDV detection compared to post-inoculation (P=0.009). Commercial formaldehyde decreased PEDV detection compared to MCFA (P< 0.001). In Exp. 2 and 3, preinoculation treatments consisted of: 1) PC, 2) 0.3% CF, and varying levels (0.125-0.66%) and combinations of MCFA (C5:0, C6:0, C8:0, or C10:0). In Exp. 2, treating feed with 0.33% C8:0 decreased (P< 0.05) PEDV detection compared to all levels of MCFA and PC. In Exp. 3, treating feed with CF, 0.5-1% Blend, all levels of C6:0+C8:0, 0.25% C6:0+C10:0, 0.33% C6:0+C10:0, 0.25% C8:0+C10:0, or 0.33% C8:0 + 0.33% C10:0 resulted in decreased PEDV detection compared to PC (P< 0.05). In Exp. 4, feed was treated pre-inoculation with either 1) no treatment (PC), 2) 0.3% CF, 3) 0.5% Blend, or 4) 0.3% C8:0 and analyzed via qRT-PCR and bioassay. Adding 0.5% Blend or 0.3% C8:0 resulted in decreased PEDV detection compared to PC. All chemical treatments resulted in no evidence of infectivity in the bioassay while the positive control did produce evidence of infectivity. In conclusion, lower levels of MCFA than previously evaluated may provide in-feed protection against PEDV. [ABSTRACT FROM AUTHOR]