The C-Band All-Sky Survey (C-BASS) is an all-sky full-polarization survey at a frequency of 5 GHz, designed to provide complementary data to the all-sky surveys of WMAP and Planck, and future CMB ...B-mode polarization imaging surveys. The observing frequency has been chosen to provide a signal that is dominated by Galactic synchrotron emission, but suffers little from Faraday rotation, so that the measured polarization directions provide a good template for higher frequency observations, and carry direct information about the Galactic magnetic field. Telescopes in both northern and southern hemispheres with matched optical performance are used to provide all-sky coverage from a ground-based experiment. A continuous-comparison radiometer and a correlation polarimeter on each telescope provide stable imaging properties such that all angular scales from the instrument resolution of 45 arcmin up to full sky are accurately measured. The northern instrument has completed its survey and the southern instrument has started observing. We expect that C-BASS data will significantly improve the component separation analysis of Planck and other CMB data, and will provide important constraints on the properties of anomalous Galactic dust and the Galactic magnetic field.
Watersheds may not recover from drought Peterson, Tim J; Saft, M; Peel, M C ...
Science (American Association for the Advancement of Science),
05/2021, Letnik:
372, Številka:
6543
Journal Article
Recenzirano
The Millennium Drought (southeastern Australia) provided a natural experiment to challenge the assumption that watershed streamflow always recovers from drought. Seven years after the drought, the ...runoff (as a fraction of precipitation) had not recovered in 37% of watersheds, and the number of recovered watersheds was not increasing. When recovery did occur, it was not explained by watershed wetness. For those watersheds not recovered, ~80% showed no evidence of recovering soon, suggesting persistence within a low-runoff state. The post-drought precipitation not going to runoff was found to be likely going to increased evapotranspiration per unit of precipitation. These findings show that watersheds can have a finite resilience to disturbances and suggest that hydrological droughts can persist indefinitely after meteorological droughts.
The choice of hydrological model structure, that is, a model's selection of states and fluxes and the equations used to describe them, strongly controls model performance and realism. This work ...investigates differences in performance of 36 lumped conceptual model structures calibrated to and evaluated on daily streamflow data in 559 catchments across the United States. Model performance is compared against a benchmark that accounts for the seasonality of flows in each catchment. We find that our model ensemble struggles to beat the benchmark in snow‐dominated catchments. In most other catchments model structure equifinality (i.e., cases where different models achieve similar high efficiency scores) can be very high. We find no relation between the number of model parameters and performance during either calibration or evaluation periods nor evidence of increased risk of overfitting for models with more parameters. Instead, the choice of model parametrization (i.e., which equations are used and how parameters are used within them) dictates the model's strengths and weaknesses. Results suggest that certain model structures are inherently better suited for certain objective functions and thus for certain study purposes. We find no clear relationships between the catchments where any model performs well and descriptors of those catchments' geology, topography, soil, and vegetation characteristics. Instead, model suitability seems to relate strongest to the streamflow regime each catchment generates, and we have formulated several tentative hypotheses that relate commonalities in model structure to similarities in model performance. Modeling results are made publicly available for further investigation.
Key Points
Conceptual model structure uncertainty is high across different catchments and objective functions
There is no evidence of systematic overfitting for models with up to 15 calibrated parameters
Model performance relates more to streamflow signatures than to climate or catchment descriptors
The objective of this paper is to identify better performing Coupled Model Intercomparison Project phase 3 (CMIP3) global climate models (GCMs) that reproduce grid-scale climatological statistics of ...observed precipitation and temperature for input to hydrologic simulation over global land regions. Current assessments are aimed mainly at examining the performance of GCMs from a climatology perspective and not from a hydrology standpoint. The performance of each GCM in reproducing the precipitation and temperature statistics was ranked and better performing GCMs identified for later analyses. Observed global land surface precipitation and temperature data were drawn from the Climatic Research Unit (CRU) 3.10 gridded data set and re-sampled to the resolution of each GCM for comparison. Observed and GCM-based estimates of mean and standard deviation of annual precipitation, mean annual temperature, mean monthly precipitation and temperature and Köppen-Geiger climate type were compared. The main metrics for assessing GCM performance were the Nash-Sutcliffe efficiency (NSE) index and root mean square error (RMSE) between modelled and observed long-term statistics. This information combined with a literature review of the performance of the CMIP3 models identified the following better performing GCMs from a hydrologic perspective: HadCM3 (Hadley Centre for Climate Prediction and Research), MIROCm (Model for Interdisciplinary Research on Climate) (Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change), MIUB (Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data group), MPI (Max Planck Institute for Meteorology) and MRI (Japan Meteorological Research Institute). The future response of these GCMs was found to be representative of the 44 GCM ensemble members which confirms that the selected GCMs are reasonably representative of the range of future GCM projections.
Hydrologists are commonly involved in impact, adaption and vulnerability assessments for climate change projections. This paper presents a framework for how such assessments can better differentiate ...between the impacts of climate change and those of natural variability, an important differentiation as it relates to the vulnerability to water availability under change. The key concept involved is to characterize “hydrologic stress” relative to the range of behaviour encountered under baseline conditions, where the degree to which climate change causes the behaviour of a system to shift outside this baseline range provides a non-dimensional measure of stress. The concept is applicable to any system that is subject to climate forcings, though the approach is applied here to a range of examples illustrative of many environmental and engineering applications. These include hydrologic systems that are dependent on the frequency of flows above or below selected thresholds, those that are dominated by storage and those which are sensitive to the sequencing of selected flow components. The analyses illustrate that systems designed or adapted to accommodate high variability are less stressed by a given magnitude of climate impacts than those operating under more uniform conditions. The metrics characterize hydrologic stress in a manner that can facilitate comparison across different regions, or across different assets within a region. Adoption of the approach requires reliance on the use of climate ensembles that represent aleatory uncertainty under both baseline and impacted conditions, and this has implications for how the outputs of climate models are provided and utilized.
•The study assesses the impact of using year-to-year variable monthly LAI to calibrate VIC model and its performance.•VIC model efficiency can be improved by using year-to-year variable monthly LAI ...to calibrate the model.•Leaf area index elasticity of runoff is strongly related to catchment characteristics.•Uses of long-term mean monthly LAI in VIC model tend to underestimate simulate runoff in dry period and overestimate in wet period.
This study assessed the effect of using observed monthly leaf area index (LAI) on hydrological model performance and the simulation of runoff using the Variable Infiltration Capacity (VIC) hydrological model in the Goulburn–Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) leaf area index dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash–Sutcliffe efficiency, the logarithm transformed flow Nash–Sutcliffe efficiency and the percentage bias. Finally, the deviation of the simulated monthly runoff using the observed monthly LAI from simulated runoff using long-term mean monthly LAI was computed. The VIC model predicted monthly runoff in the selected sub-catchments with model efficiencies ranging from 61.5% to 95.9% during calibration (1982–1997) and 59% to 92.4% during validation (1998–2012). Our results suggest systematic improvements, from 4% to 25% in Nash–Sutcliffe efficiency, in sparsely forested sub-catchments when the VIC model was calibrated with observed monthly LAI instead of long-term mean monthly LAI. There was limited systematic improvement in tree dominated sub-catchments. The results also suggest that the model overestimation or underestimation of runoff during wet and dry periods can be reduced to 25 mm and 35 mm respectively by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.
C-Band All-Sky Survey: a first look at the Galaxy Irfan, M. O.; Dickinson, C.; Davies, R. D. ...
Monthly Notices of the Royal Astronomical Society,
04/2015, Letnik:
448, Številka:
4
Journal Article
Recenzirano
Odprti dostop
We present an analysis of the diffuse emission at 5 GHz in the first quadrant of the Galactic plane using two months of preliminary intensity data taken with the C-Band All-Sky Survey (C-BASS) ...northern instrument at the Owens Valley Radio Observatory, California. Combining C-BASS maps with ancillary data to make temperature–temperature plots, we find synchrotron spectral indices of β = −2.65 ± 0.05 between 0.408 and 5 GHz and β = −2.72 ± 0.09 between 1.420 and 5 GHz for −10° < |b| < −4°, 20° < l < 40°. Through the subtraction of a radio recombination line free–free template, we determine the synchrotron spectral index in the Galactic plane (|b| < 4°) to be β = −2.56 ± 0.07 between 0.408 and 5 GHz, with a contribution of 53 ± 8 per cent from free–free emission at 5 GHz. These results are consistent with previous low-frequency measurements in the Galactic plane. By including C-BASS data in spectral fits, we demonstrate the presence of anomalous microwave emission (AME) associated with the H ii complexes W43, W44 and W47 near 30 GHz, at 4.4σ, 3.1σ and 2.5σ, respectively. The CORNISH (Co-Ordinated Radio ‘N’ Infrared Survey for High mass star formation) VLA 5-GHz source catalogue rules out the possibility that the excess emission detected around 30 GHz may be due to ultracompact H ii regions. Diffuse AME was also identified at a 4σ level within 30° < l < 40°, −2° < b < 2° between 5 and 22.8 GHz.
Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is ...the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.
Summary Background Prosthetic joint infection (PJI) is associated with significant costs to the healthcare system. Current literature examines the cost of specific treatment modalities without ...assessing other cost drivers for PJI. Aims To examine the overall cost of the treatment of PJI and to identify factors associated with management costs. Methods The costs of treatment of prosthetic joint infections were examined in 139 patients across 10 hospitals over a 3-year period (January 2006 to December 2008). Cost calculations included hospitalization costs, surgical costs, hospital-in-the-home costs and antibiotic therapy costs. Negative binomial regression analysis was performed to model factors associated with total cost. Findings The median cost of treating prosthetic joint infection per patient was Australian $34,800 (interquartile range: 20,305, 56,929). The following factors were associated with increased treatment costs: septic revision arthroplasty (67% increase in treatment cost; P = 0.02), hypotension at presentation (70% increase; P = 0.03), polymicrobial infections (41% increase; P = 0.009), surgical treatment with one-stage exchange (100% increase; P = 0.002) or resection arthroplasty (48% increase; P = 0.001) were independently associated with increased treatment costs. Culture-negative prosthetic joint infections were associated with decreased costs (29% decrease in treatment cost; P = 0.047). Treatment failure was associated with 156% increase in treatment costs. Conclusions This study identifies clinically important factors influencing treatment costs that may be of relevance to policy-makers, particularly in the setting of hospital reimbursement and guiding future research into cost-effective preventive strategies.
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