Framework for Incorporating Downscaled Climate Output into Existing Engineering Methods: Application to Precipitation Frequency Curves

<div><p>To improve the resiliency of designs, particularly for long-lived infrastructure, current engineering practice must be updated to incorporate a range of future climate conditions that are likely to be different from the past. However, a considerable mismatch exists between climate model outputs and the data inputs needed for engineering designs. This paper provides a framework for incorporating climate trends into design standards and applications, including selecting the appropriate climate model source based on the intended application, understanding model performance and uncertainties, addressing differences in temporal and spatial scales, and interpreting results for engineering design. The framework is illustrated through an application to depth-duration-frequency curves, which are commonly used in stormwater design. A change factor method is used to update the curves in a case study of Pittsburgh. Extreme precipitation depth is expected to increase in the future for Pittsburgh for all return periods and durations examined, requiring revised standards and designs. Doubling the return period and using historical, stationary values may enable adequate design for short-duration storms; however, this method is shown to be insufficient to enable protective designs for longer-duration storms.</p><div><br></div></div><div></div

Percent difference in (A) annual precipitation; (B) PET averaged by land use categories in switchgrass altered regions in Kansas and Oklahoma as shown in <b>Figure 1</b>.

<p>Box top and bottom edges are the interquartile range of percent difference for each year, and whiskers are maximum and minimum annual values. X-axis labels are land use categories: No Change (NC), Switchgrass/Grassland (S/G), Switchgrass/Cropland (S/C), Grassland/Switchgrass (G/S), Cropland/Switchgrass (C/S), and average over all categories (Avg).</p

Ethanolamine Signaling Promotes <i>Salmonella</i> Niche Recognition and Adaptation during Infection

<div><p>Chemical and nutrient signaling are fundamental for all cellular processes, including interactions between the mammalian host and the microbiota, which have a significant impact on health and disease. Ethanolamine is an essential component of cell membranes and has profound signaling activity within mammalian cells by modulating inflammatory responses and intestinal physiology. Here, we describe a virulence-regulating pathway in which the foodborne pathogen <i>Salmonella enterica</i> serovar Typhimurium (<i>S</i>. Typhimurium) exploits ethanolamine signaling to recognize and adapt to distinct niches within the host. The bacterial transcription factor EutR promotes ethanolamine metabolism in the intestine, which enables <i>S</i>. Typhimurium to establish infection. Subsequently, EutR directly activates expression of the <i>Salmonella</i> pathogenicity island 2 in the intramacrophage environment, and thus augments intramacrophage survival. Moreover, EutR is critical for robust dissemination during mammalian infection. Our findings reveal that <i>S</i>. Typhimurium co-opts ethanolamine as a signal to coordinate metabolism and then virulence. Because the ability to sense ethanolamine is a conserved trait among pathogenic and commensal bacteria, our work indicates that ethanolamine signaling may be a key step in the localized adaptation of bacteria within their mammalian hosts.</p></div

Effect of ethanolamine and EutR on SPI-1.

<p>(<b>A</b>) qRT-PCR of <i>sipC</i> from WT <i>S</i>. Typhimurium (SL1344) grown in LB or LB supplemented with 5 mM ethanolamine (EA). (<b>B</b>) qRT-PCR of <i>sipC</i> from WT <i>S</i>. Typhimurium (SL1344) grown in DMEM or DMEM supplemented with ethanolamine (EA) as indicated. For (<b>A</b>) and (<b>B</b>), n = 3; error bars represent the geometric mean ± SD. Statistical significance is shown relative to cells grown without EA supplementation; <i>strB</i> was used as the endogenous control. (<b>C</b>) Invasion of HeLa cells by WT (SL1344) and the Δ<i>eutR</i> (CJA009) strains. Mean ± SE of nine independent experiments. (<b>D</b>) Invasion of HeLa cells by WT (SL1344) and the Δ<i>eutR</i> (CJA009) strains. Mean ± SE of six independent experiments with supplementation of 5 mM EA. **, <i>P</i> ≤ 0.005; <i>P</i> > 0.05 = ns.</p

Hydro-climatology of the conterminous US; (A) Precipitation elasticity of streamflow (ε_p) and (B) Evapotranspiration elasticity of streamflow (ε_pet).

<p>Hydro-climatology of the conterminous US; (A) Precipitation elasticity of streamflow (ε_p) and (B) Evapotranspiration elasticity of streamflow (ε_pet).</p

EutR in pathogen-microbiota-host interactions.

<p>(<b>A</b>) Schematic of the <i>eut</i> operon. (<b>B</b>) <i>In vitro</i> growth curve of S. Typhimurium WT (SL1344), Δ<i>eutR</i> (CJA009), or Δ<i>eutB</i> (CJA020) strains in LB without or with supplementation of 5 mM ethanolamine (EA). Each data point shows the average of three independent experiments. (<b>C</b>) qRT-PCR of <i>eutR</i> in WT or the Δ<i>eutB</i> (CJA020) <i>S</i>. Typhimurium strains grown in Dulbecco’s Modified Eagle Medium (DMEM) or DMEM supplemented with 5 mM EA. n = 3; error bars represent the geometric mean ± standard deviation (SD); <i>strB</i> was used as the endogenous control. (<b>D-F</b>) Competition assays between (<b>D</b>) Δ<i>eutB</i>::Cm^R (CJA018) and WT strains; (<b>E</b>) Δ<i>eutR</i>::Cm^R (CJA007) and WT strains; or (<b>F</b>) Δ<i>eutR</i>::Cm^R (CJA007) and Δ<i>eutB</i> (CJA020) strains. Mice were orogastrically inoculated with 1:1 mixtures of indicated strains. Colony forming units (cfu) were determined at indicated time points. Each bar represents a competition index (CI). Horizontal lines represent the geometric mean value ± standard error (SE) for each group (n = 2 litters (6–8 animals)). *, <i>P</i> ≤0.05; **, <i>P</i> ≤ 0.005; ***, <i>P</i> ≤0.0005; <i>P</i> > 0.05 = ns.</p

EutR in <i>S</i>. Typhimurium niche adaptation.

<p>(<b>A</b>) EutR senses ethanolamine to activate transcription. (<b>B</b>) In the intestine, EutR promotes expression of the <i>eut</i> operon that encodes ethanolamine metabolism, thereby enhancing <i>S</i>. Typhimurium growth. (<b>C</b>) EutR expression in macrophages activates expression of genes in SPI-2, which are required for intramacrophage survival and dissemination.</p

Default land use categories in the WRF model (A); new land use categories defined for the biofuel scenario (B); and fraction of land use that is switchgrass in the biofuel scenario (C).

<p>Default land use categories in the WRF model (A); new land use categories defined for the biofuel scenario (B); and fraction of land use that is switchgrass in the biofuel scenario (C).</p

Change in mean annual precipitation and potential evapotranspiration for 1981–2004 expressed in A) percentage change in mean annual precipitation; B) change in mean annual precipitation (millimeters); C) percentage change in mean annual PET; and D) change in PET (millimeters) under the biofuel scenario.

<p>Percent change under the biofuel scenario relative to the baseline scenario for a given location is estimated as the mean of: where x is either P or PET and i is year between 1981 and 2004.</p

Streamflow Impacts of Biofuel Policy-Driven Landscape Change

<div><p>Likely changes in precipitation (P) and potential evapotranspiration (PET) resulting from policy-driven expansion of bioenergy crops in the United States are shown to create significant changes in streamflow volumes and increase water stress in the High Plains. Regional climate simulations for current and biofuel cropping system scenarios are evaluated using the same atmospheric forcing data over the period 1979–2004 using the Weather Research Forecast (WRF) model coupled to the NOAH land surface model. PET is projected to increase under the biofuel crop production scenario. The magnitude of the mean annual increase in PET is larger than the inter-annual variability of change in PET, indicating that PET increase is a forced response to the biofuel cropping system land use. Across the conterminous U.S., the change in mean streamflow volume under the biofuel scenario is estimated to range from negative 56% to positive 20% relative to a business-as-usual baseline scenario. In Kansas and Oklahoma, annual streamflow volume is reduced by an average of 20%, and this reduction in streamflow volume is due primarily to increased PET. Predicted increase in mean annual P under the biofuel crop production scenario is lower than its inter-annual variability, indicating that additional simulations would be necessary to determine conclusively whether predicted change in P is a response to biofuel crop production. Although estimated changes in streamflow volume include the influence of P change, sensitivity results show that PET change is the significantly dominant factor causing streamflow change. Higher PET and lower streamflow due to biofuel feedstock production are likely to increase water stress in the High Plains. When pursuing sustainable biofuels policy, decision-makers should consider the impacts of feedstock production on water scarcity.</p></div