Practitioner Interview

Phone interview with Guillermo Martinez Baquero from Interra by David Watkins, Jr. Interview questions asked inquired about (i) practitioner’s professional background, (ii) practitioner’s personal experience with systems analysis techniques and software in their job, (iii) role, benefits, and challenges in using systems analysis concepts in the water resources engineering profession, and (iv) recommendations for improving education of environmental and water resources systems analysis in universities

Decomposition of the Mean Squared Error and NSE Performance Criteria: Implications for Improving Hydrological Modelling

The mean squared error (MSE) and the related normalization, the Nash-Sutcliffe efficiency (NSE), are the two criteria most widely used for calibration and evaluation of hydrological models with observed data. Here, we present a diagnostically interesting decomposition of NSE (and hence MSE), which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and show how model calibration problems can arise due to interactions among these components. The analysis is illustrated by calibrating a simple conceptual precipitation-runoff model to daily data for a number of Austrian basins having a broad range of hydro-meteorological characteristics. Evaluation of the results clearly demonstrates the problems that can be associated with any calibration based on the NSE (or MSE) criterion. While we propose and test an alternative criterion that can help to reduce model calibration problems, the primary purpose of this study is not to present an improved measure of model performance. Instead, we seek to show that there are systematic problems inherent with any optimization based on formulations related to the MSE. The analysis and results have implications to the manner in which we calibrate and evaluate environmental models; we discuss these and suggest possible ways forward that may move us towards an improved and diagnostically meaningful approach to model performance evaluation and identification

Common variants in Alzheimer's disease and risk stratification by polygenic risk scores.

Funder: Funder: Fundación bancaria ‘La Caixa’ Number: LCF/PR/PR16/51110003 Funder: Grifols SA Number: LCF/PR/PR16/51110003 Funder: European Union/EFPIA Innovative Medicines Initiative Joint Number: 115975 Funder: JPco-fuND FP-829-029 Number: 733051061Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer's disease

Diagnostic Evaluation of Watershed Models

With increasing model complexity there is a pressing need for new methods that can be used to mine information from large volumes of model results and available data. This work explores strategies to identify and evaluate the causes of discrepancy between models and data related to hydrologic processes, and to increase our knowledge about watershed input-output relationships. In this context, we evaluate the performance of the abcd monthly water balance model for 764 watersheds in the conterminous United States. The work required integration of the Hydro-Climatic Data Network dataset with various kinds of spatial information, and a diagnostic approach to relating model performance with assumptions and characteristics of the basins. The diagnostic process was implemented via classification of watersheds, evaluation of hydrologic signatures and the identification of dominant processes. Knowledge acquired during this process was used to test modifications of the model for hydrologic regions where the performance was "poor"

CONTINENTAL SCALE DIAGNOSTIC EVALUATION OF MONTHLY WATER BALANCE MODELS FOR THE UNITED STATES

Water balance models are important for the characterization of hydrologic systems, to help understand regional scale dynamics, and to identify hydro-climatic trends and systematic biases in data. Because existing models have, to-date, only been tested on data sets of limited spatial representativeness and extent, it has not yet been established that they are capable of reproducing the range of dynamics observed in nature. This dissertation develops systematic strategies to guide selection of water balance models, establish data requirements, estimate parameters, and evaluate performance. Through a series of three papers, these challenges are investigated in the context of monthly water balance modeling across the conterminous United States. The first paper reports on an initial diagnostic iteration to evaluate relevant components of model error, and to examine details of its spatial variability. We find that to conduct a robust model evaluation it is not sufficient to rely upon conventional NSE and/or r^2aggregate statistics of performance; to have reasonable confidence that the model can provide hydrologically consistent simulations, it is also necessary to examine measures of water balance and hydrologic variability. The second paper builds upon the results of the first, and evaluates the suitability of several candidate model structures, focusing specifically snow-free catchments. A diagnostic Maximum-Likelihood model evaluation procedure is developed to incorporate the notion of `Hydrological Consistency' and controls for structural complexity. The results confirm that the evaluation of hydrologic consistency, based on benchmark comparisons and on stringent analysis of residuals, provides a robust basis for guiding model selection. The results reveal strong spatial persistence of certain model structures that needs to be understood in future studies. The third paper focuses on understanding and improving the procedure for constraining model parameters to provide hydrologically consistent results. In particular, it develops a penalty-function based modification of the Mean Squared Error estimation to help ensure proper reproduction of system behaviors by minimizing interaction of error components and by facilitating inclusion of relevant information. The analysis and results provide insight into the identifiability of model parameters, and further our understanding of how performance criteria should be applied during model identification.Embargo: Release after 5/3/201

Comparison of 1-year outcome in patients with severe aorta stenosis treated conservatively or by aortic valve replacement or by percutaneous transcatheter aortic valve implantation (data from a multicenter Spanish registry)

The factors that influence decision making in severe aortic stenosis (AS) are unknown. Our aim was to assess, in patients with severe AS, the determinants of management and prognosis in a multicenter registry that enrolled all consecutive adults with severe AS during a 1-month period. One-year follow-up was obtained in all patients and included vital status and aortic valve intervention (aortic valve replacement [AVR] and transcatheter aortic valve implantation [TAVI]). A total of 726 patients were included, mean age was 77.3 ± 10.6 years, and 377 were women (51.8%). The most common management was conservative therapy in 468 (64.5%) followed by AVR in 199 (27.4%) and TAVI in 59 (8.1%). The strongest association with aortic valve intervention was patient management in a tertiary hospital with cardiac surgery (odds ratio 2.7, 95% confidence interval 1.8 to 4.1, p <0.001). The 2 main reasons to choose conservative management were the absence of significant symptoms (136% to 29.1%) and the presence of co-morbidity (128% to 27.4%). During 1-year follow-up, 132 patients died (18.2%). The main causes of death were heart failure (60% to 45.5%) and noncardiac diseases (46% to 34.9%). One-year survival for patients treated conservatively, with TAVI, and with AVR was 76.3%, 94.9%, and 92.5%, respectively, p <0.001. One-year survival of patients treated conservatively in the absence of significant symptoms was 97.1%. In conclusion, most patients with severe AS are treated conservatively. The outcome in asymptomatic patients managed conservatively was acceptable. Management in tertiary hospitals is associated with valve intervention. One-year survival was similar with both interventional strategies