The CAMELS data set:Catchment attributes and meteorology for large-sample studies

We present a new data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities. This complements the daily time series of meteorological forcing and streamflow provided by Newman et al. (2015b). To produce this extension, we synthesized diverse and complementary data sets to describe six main classes of attributes at the catchment scale: Topography, climate, streamflow, land cover, soil, and geology. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of the attributes we extracted, makes this new data set well suited for large-sample studies and comparative hydrology. In comparison to the similar Model Parameter Estimation Experiment (MOPEX) data set, this data set relies on more recent data, it covers a wider range of attributes, and its catchments are more evenly distributed across the CONUS. This study also involves assessments of the limitations of the source data sets used to compute catchment attributes, as well as detailed descriptions of how the attributes were computed. The hydrometeorological time series provided by Newman et al

Legacy, rather than adequacy, drives the selection of hydrological models

The findings of hydrological modeling studies depend on which model was used. Although hydrological model selection is a crucial step, experience suggests that hydrologists tend to stick to the model they have experience with, and rarely switch to competing models, although these models might be more adequate given the study objectives. To gain quantitative insights into model selection, we explored the use of seven rainfall-runoff models based on the abstract of 1,529 peer-reviewed papers published between 1991 and 2018. The models selected were the Hydrologiska Byråns Vattenbalansavdelning model (HBV), the Variable Infiltration Capacity model (VIC), the mesoscale Hydrological model (mHM), the TOPography-based hydrologic model (TOPMODEL), the Precipitation Runoff Modelling System (PRMS), the Génie Rural model à 4 paramètres Journaliers (GR4J), and the Sacramento soil moisture accounting model. We provide quantitative evidence of regional preferences in model use across the world and demonstrate that specific models are consistently preferred by certain institutes. Model attachment is particularly strong. In ~74% of the studies, the model selected can be predicted solely based on the affiliation of the first author. The influence of adequacy on the model selection process is less clear. Our data reveal that each model is used across a wide range of purposes, landscapes, and temporal and spatial scales (i.e., as a model of everything and everywhere). Model intercomparisons can provide guidance for model selection and improve model adequacy, but they are still rare (because each model must usually be setup individually) and the insights they provide are currently limited (because they are rarely controlled experiments). We suggest that moving from fixed-structure models to modular modeling frameworks (master templates for model generation) can overcome these issues, enable a more collaborative and responsive model development environment, and result in improved model adequacy

Mapping (dis)agreement in hydrologic projections

Hydrologic projections are of vital socio-economic importance. However, they are also prone to uncertainty. In order to establish a meaningful range of storylines to support water managers in decision making, we need to reveal the relevant sources of uncertainty. Here, we systematically and extensively investigate uncertainty in hydrologic projections for 605 basins throughout the contiguous US. We show that in the majority of the basins, the sign of change in average annual runoff and discharge timing for the period 2070–2100 compared to 1985–2008 differs among combinations of climate models, hydrologic models, and parameters. Mapping the results revealed that different sources of uncertainty dominate in different regions. Hydrologic model induced uncertainty in the sign of change in mean runoff was related to snow processes and aridity, whereas uncertainty in both mean runoff and discharge timing induced by the climate models was related to disagreement among the models regarding the change in precipitation. Overall, disagreement on the sign of change was more widespread for the mean runoff than for the discharge timing. The results demonstrate the need to define a wide range of quantitative hydrologic storylines, including parameter, hydrologic model, and climate model forcing uncertainty, to support water resource planning

Swiss Validation of the Enhanced Recovery After Surgery (ERAS) Database.

Enhanced recovery after surgery (ERAS) pathways have considerably improved postoperative outcomes and are in use for various types of surgery. The prospective audit system (EIAS) could be a powerful tool for large-scale outcome research but its database has not been validated yet. Swiss ERAS centers were invited to contribute to the validation of the Swiss chapter for colorectal surgery. A monitoring team performed on-site visits by the use of a standardized checklist. Validation criteria were (I) coverage (No. of operated patients within ERAS protocol; target threshold for validation: ≥ 80%), (II) missing data (8 predefined variables; target ≤ 10%), and (III) accuracy (2 predefined variables, target ≥ 80%). These criteria were assessed by comparing EIAS entries with the medical charts of a random sample of patients per center (range 15-20). Out of 18 Swiss ERAS centers, 15 agreed to have onsite monitoring but 13 granted access to the final dataset. ERAS coverage was available in only 7 centers and varied between 76 and 100%. Overall missing data rate was 5.7% and concerned mainly the variables "urinary catheter removal" (16.4%) and "mobilization on day 1" (16%). Accuracy for the length of hospital stay and complications was overall 84.6%. Overall, 5 over 13 centers failed in the validation process for one or several criteria. EIAS was validated in most Swiss ERAS centers. Potential patient selection and missing data remain sources of bias in non-validated centers. Therefore, simplified validation of other centers appears to be mandatory before large-scale use of the EIAS dataset

Impact of weekday surgery on application of enhanced recovery pathway: a retrospective cohort study.

To compare the enhanced recovery after surgery (ERAS) protocol compliance and clinical outcomes depending on the weekday of surgery. Cohort of consecutive non-selected patients undergoing elective colorectal surgery from January 2012 to March 2015. This retrospective analysis of our prospective database compared patients operated early in the week (Monday and Tuesday) with patients operated in the second half (late: Thursday, Friday). Compliance with the ERAS protocol, functional recovery, complications and length of stay. Demographic and surgical details were similar between the early (n=352) and late groups (n=204). Overall compliance with the ERAS protocol was 78% vs 76% for the early and late groups, respectively (p=0.009). Significant differences were notably prolonged urinary drainage and intravenous fluid infusion in the late group. Complication rates and length of stay, however, were not different between surgery on Monday or Tuesday and surgery on Thursday or Friday. Application of the ERAS protocol showed only minor differences for patients operated on early or late during the week, and clinical outcomes were similar. A fully implemented ERAS programme appears to work also over the weekend

Evaluation of prenatal diagnosis of congenital heart disease in a regional controlled case study.

AIMS: This study evaluated the evolution of the prenatal diagnosis of congenital heart disease (CHD) between 2003 and 2008 and its repercussion for the CHD prevalence rate at birth in a well-defined population (Canton of Vaud, Switzerland). METHODS AND RESULTS: All 572 cases of CHD reported in the Eurocat Registry of Vaud-Switzerland between 1.5.2003 and 31.12.2008 were analysed and compared with the cases in our clinical database. CHD cases were divided into five different groups according to heart disease severity. The prenatal detection rates increased significantly between 2003 and 2008, with a mean detection rate of 25.2%. There was a significantly higher rate of prenatal diagnosis in the first four groups of CHD severity, with the highest detection rate (87.5%) found in the group with the most severe CHD (group 1). In this group, 85.7% of cases resulted in a termination of pregnancy, and there was a consequent 75% reduction in the prevalence of severe major cardiac malformation at birth. Detection rates were 66% in group 2, 68.6% in group 3, and the lowest in groups 4 and 5, with rates of 25.9% and 12.9%, respectively. CONCLUSION: This study shows that the prenatal detection rate for CHD increased in a well-defined population over the study period. Prenatal diagnosis thus has had a major impact on patients with the most severe types of CHD and has resulted in a significant reduction in severe CHD at birth

A ranking of hydrological signatures based on their predictability in space

Hydrological signatures are now used for a wide range of purposes, including catchment classification, process exploration and hydrological model calibration. The recent boost in the popularity and number of signatures has however not been accompanied by the development of clear guidance on signature selection. Here we propose that exploring the predictability of signatures in space provides important insights into their drivers, their sensitivity to data uncertainties, and is hence useful for signature selection. We use three complementary approaches to compare and rank 15 commonly‐used signatures, which we evaluate in 671 US catchments from the CAMELS data set (Catchment Attributes and MEteorology for Large‐sample Studies). Firstly, we employ machine learning (random forests) to explore how attributes characterizing the climatic conditions, topography, land cover, soil and geology influence (or not) the signatures. Secondly, we use simulations of a conceptual hydrological model (Sacramento) to benchmark the random forest predictions. Thirdly, we take advantage of the large sample of CAMELS catchments to characterize the spatial auto‐correlation (using Moran's I) of the signature field. These three approaches lead to remarkably similar rankings of the signatures. We show i) that signatures with the noisiest spatial pattern tend to be poorly captured by hydrological simulations, ii) that their relationship to catchments attributes are elusive (in particular they are not correlated to climatic indices) and iii) that they are particularly sensitive to discharge uncertainties. We suggest that a better understanding of their drivers and better characterization of their uncertainties would increase their value in hydrological studies

Enhanced Recovery after Elective Colorectal Surgery - Reasons for Non-Compliance with the Protocol.

Enhanced recovery after surgery (ERAS) protocols for elective colorectal surgery reduce the intensity of postoperative complications, hospital stays and costs. Improvements in clinical outcome are directly proportional to the adherence to the recommended pathway (compliance). The aim of the present study was to analyze reasons for the non-compliance of colorectal surgeries with the ERAS protocol. A consecutive cohort of patients undergoing elective colorectal surgery was prospectively analyzed with regards to the surgery's compliance with the ERAS protocol. The reason for every single protocol deviation was documented and the decision was categorized based on whether it was medically justified or not. During the 8-month study period, 76 patients were included. The overall compliance with 22 ERAS items was 76% (96% in the preoperative, 82% in the perioperative, and 63% in the postoperative period). The decision to deviate from the clinical pathway was mainly a medical decision, while patients and nurses were responsible in 26 and 14% of the cases, respectively. However, reasons for non-compliance were medically justified in 78% of the study participants. 'Non-compliance' with the ERAS protocol was observed mostly in the postoperative period. Most deviations from the pathway were decided by doctors and in a majority of cases it appeared that they were due to a medical necessity rather than non-compliance. However, almost a quarter of deviations that were absolutely required are still amenable to improvement

CAMELS-GB : a large sample, open-source, hydro-meteorological dataset for Great Britain

Data underpins our knowledge and understanding of the hydrological system; they are used to drive, test and evaluate hydrological models and advance our understanding of hydrological processes and dynamics. With the increasing availability of observational datasets, the integration of information from many catchments for data and modelling analyses is becoming increasingly common. The production of new, open source, datasets for large samples of catchments is vital to advance knowledge on hydrological processes and to ensure hydrological research is reusable and reproducible through the use of common datasets and code. However, the availability of open source, large-sample catchment datasets is notably sparse. In this study, we present CAMELS-GB, the first large sample, open-source, hydro-meteorological catchment dataset for Great Britain (GB). CAMELS-GB integrates a wealth of different datasets derived from national, continental and global products based on observational, satellite and modelled data. The dataset consists of hydro-meteorological timeseries, catchment attributes and catchment boundaries for >800 catchments that cover a wide range of climatic, hydrological, landscape and human management characteristics across GB. Long daily timeseries is provided for a range of hydro-meteorological data (including rainfall, potential-evapotranspiration, temperature, radiation, humidity and flow) from 1970-2015 covering several major hydrological events. A comprehensive set of catchment attributes are provided describing a range of catchment characteristics including topography, climate, hydrology, land cover, soils and (hydro)-geology. Importantly, we also derive human impact attributes (including abstraction returns, percentage urban and gauge distance from reservoir), as well as attributes describing the quality of the flow data (including discharge uncertainty estimates and out of bank flow). The dataset and code used to derive the data will be made open source and provided with comprehensive metadata to allow its use in a wide range of hydro-meteorological data and environmental modelling analyses