dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulations were carried out for five precipitation products -CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure.
, 2015 (Mei et al. , , 2016;; Seyyedi et al. , 2014 Seyyedi et al. , , 2015;; Bhuiyan et al. , 2017; Nikolopoulos et al. , 2013). Over the last decade, an increasing number of studies have contributed to the development of global precipitation estimation  (Pan et al. , 2010; Beck et al. , 2017a; Kirstetter et al. , 2014; Carr et al. , 2015; Dee et al. , 2011)  aiming at the overall improvement of the hydrological applications and global water resource reanalysis. Numerous models of varying complexity can be used to generate an array of hydrological products from precipitation forcing datasets  (Vivoni et al. , 2007; Ogden and Julien, 1994; Carpenter et al. , 2001; Borga, 2002; Schellekens et al. , 2017). Different hydrological models have different applications depending on the spatial and temporal scales of interest as well as the simulated variables of interest, such as subsurface runoff, surface runoff, and evapotranspiration.
Past studies  (Fekete et al. , 2004; Biemans et al. , 2009)  have revealed that the uncertainty in simulated hydrological variables mainly depends on the uncertainty in precipitation and model parametrization and suggested subsequent exploration of different model structures as part of the overall modeling approach. So far there are several studies that have analyzed uncertainty in precipitation forcing and its impact on hydrologic simulations by usually evaluating hydrologic simulations based on multiple forcing applied to a single model  (Falck et al. , 2015; Bitew et al. , 2012; Behrangi et al. , 2011; Mei et al. , 2016; Bhuiyan et al. , 2018; Gelati et al. , 2018 among others). On the other hand, there are also past studies that have evaluated the model structural uncertainty and its impact on hydrologic simulations, usually by analyzing the simulation outputs from multiple models and a single forcing dataset  (Breuer et al.
, 2017) , to investigate the impact of precipitation uncertainty propagation and its dependence on model structure and hydrologic variables. In this investigation, we used different precipitation forcing datasets based on (i) reanalysis, (ii) satellite estimates, and (iii) a "combined" stochastic precipitation dataset  (Bhuiyan et al. , 2018). To consider model structure and parameters, we examined simulations from five state-of-the-art global-scale hydrological and land surface models (LSMs). With regard to water cycle variables, we evaluated precipitation uncertainty propagation to surface runoff, subsurface runoff, and evapotranspiration fluxes. The study area for this investigation is the Iberian Peninsula, which has precipitation and climate variability due to complex orography influenced by both Atlantic and Mediterranean climates  (Rodríguez-Puebla et al.
, 2001; de Luis et al. , 2010; Herrera et al. , 2012). The analysis comprised two main parts: (1) performance and sensitivity evaluation of the different model-forcing scenarios and (2) precipitation uncertainty propagation to the hydrological variables. We analyzed hydrological simulation with a comparative assessment of the hydrological products and provided a detailed analysis of uncertainty in hydrological simulations for the different global hydrological and land surface models used in the multi-model global water resource reanalysis. Finally, we examined the performance of precipitation products in hydrological applications and potential uncertainty attributed to precipitation error propagations. The paper is structured as follows. Section 2 presents the different types of forcing datasets used for the study, and Sect.
Reference precipitation (SAFRAN) The reference precipitation dataset, hereafter referred to as SAFRAN (Système d'analyse fournissant des renseignements atmosphériques à la neige), was recently created by  Quintana-Seguí et al. (2016 , 2017)  using the SAFRAN meteorological analysis system  (Durand et al. , 1993). Spatially, SAFRAN precipitation data are presented at an hourly timescale on a regular grid with 5 km resolution, spanning 35 years and covering mainland Spain, Portugal, and the Balearic Islands  (Quintana-Seguí et al. , 2016). SAFRAN used an optimal interpolation algorithm  (Gandin, 1966)  to produce a quality-controlled gridded dataset of precipitation which combines ground observations and outputs of a meteorological model  (Quintana-Seguí et al. , 2017). Quintana-Seguí et al.
(  2017 ) also compared the different precipitation analyses with rain gauge data and successfully evaluated their temporal and spatial similarities to the observations by obtaining higher correlation ( > 0. 75) than other precipitation products. The validation of SAFRAN with independent ground observations proved that SAFRAN is a robust product. On the other hand, several factors -including rainfall intermittency, discrete temporal sampling, and censoring of reference values for required quality -reduce the number of comparison samples for reference and satellite estimates. Therefore, the quality-controlled SAFRAN dataset which is designed to force the land surface model is chosen as a reference dataset for the study area  (Quintana-Seguí et al. , 2017).
Combined product The combined product is based on the application of a nonparametric statistical technique for blending multiple satellite-reanalysis precipitation datasets. Specifically, a machine-learning technique, quantile regression forests (QRF;  Meinshausen, 2006) , was used to generate stochastically an improved precipitation ensemble at the spatiotemporal resolution of 0. 25 • , 3 h. The technique optimally merged global precipitation datasets and characterized the uncertainty of the combined product. Details on the methodology and data used to develop the combined product are presented in  Bhuiyan et al. (2018).
The timescale of the evaluation was mostly driven by the data availability. All the land surface models in the study were global models, built originally to work in coupled mode with atmospheric models. The "regionalization", or calibration of hydrological parameters at particular catchments or regions of these models, is an exercise that the different modeling groups or communities are certainly performing but was out of the scope of this study. All models were forced with the various precipitation datasets described in the previous section for an 11-year period (March 2000-December 2010). A summary of some basic characteristics of the models structure is presented in Table  1  and a short description is provided below. For more details on the modeling systems, the interested reader is referred to  Schellekens et al.
Saturation excess runoff is based on sub-grid soil moisture variability, as a fraction of the grid is saturated and water flux over this fraction is converted to surface runoff (probability distribution model;  Blyth, 2002). Once infiltrated into the soil, water flows through the column, resolved using Darcy's law and Richards' equation. Subsurface runoff is calculated using the free drainage approach, with water flowing at the bottom of the resolved soil column at a rate determined by the soil hydraulic conductivity. There was no groundwater table in this version of JULES. The condition at the bottom of the resolved soil layers (3 m) was assumed to be free drainage. The soil hydraulic characteristics were calculated applying pedotransfer functions to the soil texture data from the Harmonized World Soil Database (HWSD; FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012).
ORCHIDEE ORCHIDEE  (Krinner et al. , 2005)  is a complex land surface scheme that consists of a hydrological module, a routing module  (Ngo-Duc et al. , 2007) , and a floodplain module (d  'Orgeval et al. , 2008). It also describes the vegetation dynamics and biological cycles, but these features were not activated for the present study. The most relevant parametrization of ORCHIDEE for the sensitivity of the model to rainfall is the one for partitioning between infiltration and surface runoff. In order to represent correctly the fast progression of the moisture front during a rainfall event when the time step is above 15 min, a time-splitting procedure is used (d  'Orgeval, 2006). The parametrization also takes into account reinfiltration in the case of slopes below 0. 5 % or dense vegetation.
Evaluation metrics To examine the magnitude and variability of differences among hydrological variables, we used the relative difference (RD), defined as RD = ŷi -y i y i , (1) where y i denotes reference variables (SAFRAN-driven simulations) and ŷi denotes simulated variables (based on the other forcing data considered) for each time step i. RD indicates the magnitude and direction of error with positive (negative) value indicating overestimation (underestimation). The RD of annual average estimates of the precipitation forcing and different hydrological variables was calculated using daily datasets at the spatial resolution of 0. 25 •. Moreover, cumulative probability of estimated annual average relative differences among precipitation forcings and the simulated hydrological variables were calculated using same spatial resolutions of 0.
25 •. To collectively assess the performance of various precipitation forcing datasets, models, and simulated hydrological variables, we used a normalized version of the Taylor diagram  (Taylor, 2001). Specifically, we normalized the values of the centered root-mean-square error (CRMSE) and the standard deviation with the standard deviation of the reference. Therefore, the reference (that is, the target point to which the model outputs should be closest) corresponds to the point on the graph with the normalized CRMSE equal to zero, while both the correlation coefficient and normalized standard deviation equal 1. The normalized Taylor diagrams summarized model results for two different temporal scales (3-hourly and daily) at the spatial resolution of 0. 25 •. To evaluate the degree of variation of various precipitation datasets and simulated hydrological variables, we used the coefficient of variation (CV) and coefficient of variation ratio (CV r ).
Variability of multiple hydrological model simulations To examine the magnitude and variability of the differences among both models and forcing datasets, we analyzed the multi-model simulation results for three hydrological variables, including surface runoff (Q s ), subsurface runoff (Q sb ), and evapotranspiration (ET). Throughout this analysis, we used the SAFRAN-based simulation as the reference for comparison. Figures 2 to 5 present spatial maps of annual average values for each model, along with the relative differences of annual average estimates of precipitation forcing and the different hydrological variables for all the precipitation forcing datasets and models. The relative differences in precipitation forcing (Fig. 2 ) exhibited considerable spatial variability for satellite precipitation forcing (relative difference > 20 %) and relatively lower variability for EI_GPCC and the combined product.
This suggests a distinct structural difference in the way precipitation is partitioned into surface-subsurface runoff between the two groups. Looking at the variability of results for combined and reanalysis (EI_GPCC) forcing datasets, no substantial differences occurred between reference and simulated surface runoff (Q s ). However, for the satellite-based simulations, there were significant deviations. Specifically, the CMORPH-based simulation showed significant overestimation for ORCHIDEE and HTESSEL, but this pattern was reversed for JULES, SURFEX, and WaterGAP3, an outcome that highlights the impact of model structure on precipitation error propagation. For subsurface runoff, similar spatial patterns (with respect to Q s ) were exhibited for the reference and the rest of simulations (Fig.
The CDF of precipitation relative differences shows that CMORPH deviated significantly from the other precipitation products (Fig. 6 ). The surface runoff based on ORCHIDEE and HT-ESSEL displayed a clear separation of the CDF for the combined product and EI_GPCC and satellite-based precipitation forcing (Fig. 7 ). Specifically, it is interesting to note that 3B42V(7) responds very differently to other precipitation forcing datasets for ORCHIDEE, highlighting again the sensitivity of runoff response to precipitation structure (space-time variability) and its dependence on the rainfallrunoff generation mechanism. Box plots of the relative difference of different hydrological variables for the various forcing datasets and models at the daily scale are shown in Fig. 8. Note the inclusion of the relative difference of precipitation forcing to allow the comparison between relative differences in precipitation with those in the other hydrological variables.
Simulations based on the combined product and EI_GPCC were always consistent, with significantly reduced NCRMSE values in the range of 0. 25-0. 8 for all the hydrological models. Results for simulated ET are more consistent among the various precipitation forcing datasets, exhibiting normalized standard deviations in the range of 0. 8-1. 2. NCRMSE reduced significantly (< 0. 35) for each forcing dataset; accordingly, the correlation coefficient (CC) also raised considerably (> 0. 9), showing a very high degree of agreement with reference-based simulations. For surfacesubsurface runoff, the SURFEX and WaterGAP3 models performed comparatively better than other models by reducing NCRMSE values, especially for the combined product and EI_GPCC. To illustrate the relative variability between precipitation and individual hydrological variables, we calculated the co-efficient of variation (CV) and the coefficient of variation ratio (CV r ) for all the hydrological models.
To provide an understanding of the impact of precipitation uncertainty in hydrological simulations, we produced box plots of the CV and CV r for precipitation forcing datasets and individual hydrological variables for all the models, as shown in Fig. 11. A precipitation-forcing-wise comparison indicates that the combined product and reanalysis underestimated precipitation variability more than other precipitation forcings, which affected the corresponding variability in Q s for all the models except ORCHIDEE. Although there were no significant differences in terms of variability for combined product and reanalysis-based simulations for the four models (JULES, SURFEX, WaterGAP3, and HTESSEL), substantial differences in variability between precipitation and Q s were observed for ORCHIDEE model.
Satellite products overestimated precipitation variability, leading to overestimation of the variability of surface and subsurface runoff. The variability of ET was much lower than that of the other vari-  ables examined and well captured in all the simulation scenarios. From the box plots of CV from reference-based simulations, the distributions of ET showed low variability (CV < 1), while the variability for all the other hydrological variables was high (CV > 1). In terms of CV r , the SURFEX model performed very well by producing medians close to 1 (CV r = 1 means ideal consistency) for all the precipitation forcing datasets but CMORPH.
Discussion Precipitation from different satellite-reanalysis datasets exhibits considerable differences in pattern and magnitude, which results in significant differences in hydrologic simulations. Results presented in this paper demonstrated clearly that magnitude and dynamics of uncertainty in hydrologic simulations depend not only on the uncertainty of the forcing variable but also on the model and examined hydrologic variable. For example, surface runoff (Q s ) appears to be highly sensitive to precipitation differences, while ET was not for this semi-arid study region  (Figs. 3 to 5). Particularly, ET exhibited reduced sensitivity to precipitation forcing, which potentially suggests that the water volume available for conversion to ET did not deviate significantly among the precipitation scenarios.
Similarly, if we look at the distribution of precipitation relative difference, CMORPH tends to decrease in magnitude compared to other precipitation products. Therefore, for subsurface runoff, CMORPH-based simulations displayed a gross underestimation compared to other precipitation forcing. Precipitation-to-surface-runoff sensitivity is strongly controlled by the corresponding runoff generation scheme in each model. For example, in the case of HTESSEL and OR-CHIDEE, precipitation intensity has a great effect on the generation of surface runoff. The satellite precipitation datasets have higher precipitation intensities (Fig. 6 ) when compared to the remaining datasets, which explains the different behaviour of these two models. However, in the case of JULES, the infiltration excess mechanism is rarely invoked when the drivers are provided at a 3-hourly time step, as the maximum infiltration rate is not reached.
Conclusions This study investigated the propagation of precipitation uncertainty in hydrological simulations and its interaction with hydrologic modeling, which was based on satellitereanalysis precipitation forcing of a number of global hydrological and land surface models for the Iberian Peninsula. The following are the major conclusions from this study. Simulation of surface runoff was shown to be highly sensitive to precipitation forcing, but the direction (that is, overestimation or underestimation) and the magnitude of relative differences indicated strong dependence on the modeling system. Hydrological simulations based on reanalysis and combined product forcing datasets performed better overall than satellite precipitation-driven simulations. Moreover, simulation results using CMORPH as forcing exhibit overall overestimation for ORCHIDEE and HTESSEL, which is totally the opposite to the results from the other models (JULES, SURFEX, and WaterGAP3).
These types of differences highlight the complexity of the interaction between precipitation characteristics and different modeling schemes and should be used as a "reference for caution" when generalizing findings produced from single model simulations. Modeling uncertainty appeared to be much less important for evapotranspiration than for surface and subsurface runoff. The sensitivity of hydrological simulations to different precipitation forcing datasets was shown to depend on the hydrological variable use and model parameterization scheme. Finally, based on our evaluation of the performance of the different hydrological models and five precipitation products -CMORPH, PERSIANN, 3B42V(7), reanalysis, and the combined product -we could not identify a single model that consistently outperformed others, i.
Data availability. The datasets are available online for SAFRAN (http://mistrals. sedoo. fr/Data-Download-IPSL/ ?datsId=1388&search=0&project_name=HyMeX, last access: 31 March 2019), CMORPH (ftp://ftp. cpc. ncep. noaa. gov/precip/ CMORPH_V1. 0/RAW/0. 25deg-3HLY/, last access: 31 March 2019), PERSIANN (http://fire. eng. uci. edu/PERSIANN/data/ 3hrly_adj_cact_tars/, last access: 31 March 2019), 3B42V(7) (https://mirador. gsfc. nasa. gov, last access: 31 March 2019), the atmospheric reanalysis dataset (https://wci. earth2observe. eu/portal/, last access: 31 March 2019), and the combined product (https://sites. google. com/uconn. edu/ehsanbhuiyan/research, last access: 31 March 2019).
Image post-processing, visualisation, and quantification Several software solutions, available from commercial and academic sources, offer semi-automatic and automated processing of cardiac imaging data. Most software products on the market focus on either CMR or PET separately, but there are increasingly software packages such as OsiriX (OsiriX Foundation, Geneva, Switzerland), Munich-Heart (TU München, Munich, Germany), and syngo. via (Siemens Healthineers, Erlangen, Germany) available utilising both PET and MRI data. However, software solutions for fully integrated PET/MRI analyses are still missing. The following sections provide an overview of potential clinical indications for cardiac PET/MRI, which are also summarised in Table  1.
Statistical analysis The variables' distribution was assessed using a Shapiro-Wilk test. Data were expressed as the number, proportion (in percent), mean ± standard deviation (SD) or the median [interquartile range (IQR)], as appropriate. Patients were classified as fluid responders or non-responders as a function of the effect of FC on the SV. An FC response was defined as an increase of more than 15% in the SV after FC  [21]. Patients were classified as VO 2 responders or non-responders as a function of the effect of FC on VO 2. A VO 2 response was defined as an increase of more than 15% in the VO 2 after FC  [7]. The non-parametric Wilcoxon rank sum test, Student's paired t test, Student's t test, and the Mann-Whitney test were used to assess statistical significance, as appropriate. Linear correlations were tested using Pearson's or Spearman's rank method.
At the T2 site, the gases and particles in ambient air and after the OFR were sampled using an Aerodyne Aerosol Chemical Speciation Monitor (ACSM;  Ng et al. , 2011)  and a unit-resolution quadrupole PTR-MS (IONICON;  Lindinger et al. , 1998). Additionally, the analysis herein uses concentrations of sesquiterpenes (SQTs) and several biomass burning tracers, which were measured in ambient air using the semivolatile thermal desorption aerosol gas chromatograph (SV-TAG;  Williams et al. , 2006; Zhao et al. , 2013; Isaacman et al. , 2014). Measurement details for the SV-TAG during GoAmazon2014/5 can be found in  Yee et al. (2017). At both sites, a system of automated valves (Aerodyne Au-toValve) cycled by custom LabVIEW (National Instruments, Inc. ) software was used to alternate sampling between ambient and oxidized air.
The OFR aerosol was sampled for the last 4 min of every step, allowing time for the OFR conditions to stabilize before measurement. An alternative method was also used, where the oxidant concentrations were held constant. In this manner, the OA enhancement from a constant amount of oxidation could be sampled every 16-24 min or faster rather than once every 2-3 h. For example, the concentration that typically produces the maximum amount of SOA formation could be sampled, or the UV lights could be set to achieve the highest oxidant concentrations in order to investigate heterogeneous oxidation. The aerosol data at the T3 site were corrected for diffusive particle losses in the sampling line (an average correction of 3 %) estimated using the Max Planck Particle Loss  Calculator (von der Weiden et al.