Proposal of a Python interface to OpenMI, as the base for open source hydrological framework

Abstract. State of the art challenges in sustainable management of water resources have created demand for integrated, flexible and easy to use hydrological models which are able to simulate the quantitative and qualitative aspects of the hydrological cycle with a sufficient degree of certainty. Existing models which have been de-veloped to fit these needs are often constrained to specific scales or purposes and thus can not be easily adapted to meet different challenges. As a solution for flexible and modularised model development and application, the Object Modeling System (OMS) has been developed in a joint approach by the USDA-ARS, GPSRU (Fort Collins, CO, USA), USGS (Denver, CO, USA), and the FSU (Jena, Germany). The OMS provides a modern modelling framework which allows the implementation of single process components to be compiled and applied as custom tailored model assemblies. This paper describes basic principles of the OMS and its main components and explains in more detail how the problems during coupling of models or model components are solved inside the system. It highlights the integration of different spatial and temporal scales by their representation as spatial modelling entities embedded into time compound components. As an exam-ple the implementation of the hydrological model J2000 is discussed.

This study reports on the integration of the J2K model (an object-oriented, hydrological system for fully distributed simulation of the water balance in large watersheds) under the Object Modeling System (OMS) environmental modeling framework and subsequent evaluation of OMS-J2K performance in the Cedar Creek watershed (CCW) in northeastern Indiana. The CCW is one of 14 benchmark watersheds in the USDA-ARS Conservation Effects Assessment Project (CEAP) watershed assessment study. Two input parameter sets were developed for OMS-J2K evaluation: (1) a parameter with parameter values taken from previous simulation studies where J2K was applied to watersheds with characteristics similar to the CCW, and (2) an parameter with modifications to input parameters related to evapotranspiration, soil water storage, and soil water lateral flow. Comparisons of daily, average monthly, and annual average simulated and observed flows for the 1997-2005 simulation period using the base parameter set resulted in Nash-Sutcliffe efficiency (E NS ), root mean square deviation (RMSD), and relative error (PBIAS) coefficients of 0.34 to 0.48 for E NS , 1.50 to 8.79 m 3 s -1 for RMSD, and -18.43% for PBIAS. All statistical evaluation coefficients improved for the adjusted parameter set (e.g., 0.44 to 0.59 for E NS , 0.87 to 7.73 m 3 s -1 for RMSD, and -8.59% for PBIAS). The ranges of E NS and PBIAS values for uncalibrated or manually adjusted streamflow predictions in this study (using both parameter sets) were similar to others reported in the literature for various watershed models. This study represents the first attempt to develop and apply a complex natural resource system model under the OMS. The results indicate that the OMS-J2K watershed model was able to reproduce the hydrological dynamics of the CCW and should serve as a foundation on which to build a more comprehensive model to better quantify water quantity and quality at the watershed scale. In particular, the topological routing scheme employed by OMS-J2K (thus allowing the simulation of lateral processes vital for the modeling of runoff concentration dynamics) is much more robust than the quasi-distributed routing schemes used by other watershed-scale natural resource models and represents a noteworthy advancement in hydrological modeling toward deriving suitable conservation management scenarios.

The Fortran (FORmula TRANslating) computer language was the result of a project begun by John Backus at IBM in 1954. The goal of this project was to provide a way for programmers to express mathematical formulas through a formalism that computers could translate into machine instructions. Initially there was a great deal of skepticism about the efficacy of such a scheme. “How,’’ the scientists asked, “would anyone be able to tolerate the inefficiencies that would result from compiled code?’’ But, as it turned out, the first compilers were surprisingly good, and programmers were able, for the first time, to express mathematics in a high-level computer language. Fortran has evolved continually over the years in response to the needs of users, particularly in the areas of mathematical expressivity, program maintainability, hardware control (such as I/O), and, of course, code optimizations. In the meantime, other languages such as C and C11 have been designed to better meet the nonmathematical aspects of software design, such as graphical interfaces and complex logical layouts. These languages have caught on and have gradually begun to erode the scientific/engineering Fortran code base. By the 1980s, pronouncements of the “death of Fortran” prompted language designers to propose extensions to Fortran that would incorporate the best features of other high-level languages and, in addition, provide new levels of mathematical expressivity popular on supercomputers such as the CYBER 205 and the CRAY systems. This language became standardized as Fortran 90 (ISO/IEC 1539: 1991; ANSI X3.1981992). At the present time, Fortran 95, which includes many of the parallelization features of High Performance Fortran discussed later in this paper, is in the final stages of standardization. It is not yet clear whether the modernization of Fortran can, of itself, stem the C tide. However, I will demonstrate in this paper that modern Fortran is a viable mainstream language for parallelism. It is true that parallelism is not yet part of the scientific programming mainstream. However, it seems likely that, with the scientists’ never-ending thirst for affordable performance, parallelism will become much more common—especially

Computers and computing are topics of discussion in many curriculum areas in secondary school. The four courses recommended by this task group, however, have computing as their primary content. The courses are: Introduction to Computer Science I (a full year course)Introduction to Computer Science II (a full year course)Introduction to a High-level Computer Language (a half-year course)Applications and Implications of Computers (a half-year course)Courses 1 and 2 are designed for students with a serious interest in computer science. Course 1 can serve as a single introductory course for some students and also act as a prerequisite for Course 2. At the end of two years of study, students should be prepared to be placed in second level computer science classes in post-secondary educational institutions or to take the Advanced Placement Exam available through the College Entrance Exam Board.Courses 1 and 2 will require a significant amount of equipment for each student enrolled. Computers, either microcomputers or a timesharing system with convenient storage for files (disks) and high-speed printers are necessary.Courses 3 and 4 are designed to be of general interest to any student at the secondary level. Course 3 is a course about programming. It is a general introduction to the process of writing programs in a high-level computer language. It requires extensive hardware facilities, as students will need to practice using whichever computer language is chosen for the instruction.Course 4 includes information about the ways computers are used and about the impact of computer use on people's lives. Because students in this course will be using a computer to learn about applications, teaching this course requires extensive software in addition to hardware.Courses 1, 3 and 4 have been designed assuming no prerequisite courses and are not interdependent.Teachers of all four of these courses should be qualified in the content area and preferably certified to teach about computing and computer science. The discipline of computer science has been evolving over several years and will continue to change. Courses in computer science will similarly continue to change. Well trained teachers are the cornerstone of effective instruction in such a fluid environment.

Integrated urban hydrologic and hydraulic modelling in Chicago, Illinois

Advanced application of rule nets to the automation of chemical analysis systems

Over 90 percent of all microprocessors are now used for real-time and embedded applications, and the behavior of many of these applications is constrained by the physical world. Higher-level programming languages and middleware are needed to robustly and productively design, implement, compose, integrate, validate, and enforce real-time constraints along with conventional functional requirements and reusable components. Designing real-time and embedded systems that implement their required capabilities, are dependable and predictable, and are parsimonious in their use of limited computing resources is hard; building them on time and within budget is even harder. Moreover, due to global competition for market share and engineering talent, companies are now also faced with the problem of developing and delivering new products in short time frames. Therefore it is essential that the production of real-time embedded systems can take advantage of languages, tools, and methods that enable higher software productivity. Ideally, developers should use a programming language that shields them from many accidental complexities, such as type errors, memory management, and steep learning curves. The Java programming language has become an attractive choice because of its safety, productivity,its relatively low maintenance costs, and the availability of well trained developers. Although it has good software engineering characteristics, Java is unsuitable for developing real-time embedded systems, mainly due to under-specification of thread scheduling and the presence of garbage collection. Recently, to address these problems, a number of extensions to Java have been proposed by the Java Community Process Expert Group for the Real-Time Specification for Java (RTSJ) among others. The intent of these specifications is the development of real-time applications by providing several additions such as extending the Java memory model, providing stronger semantics in thread scheduling, and so on. Interest in real-time Java in both the research community and industry, because of its challenges and its potential impact on the development of embedded and real-time applications, has recently increased, significantly. This interest in the industry comes not only from traditional applications, such as industrial control, but also for business and financial applications. The goal of the proposed workshop is to gather researchers working on real-time and embedded Java to identify the challenging problems that still need to be properly resolved in order to assure the success of the of real-time Java as a technology, and to report results and experience gained by researchers.

Abstract. This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ∼ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

In this dissertation we develop numerical algorithms and software tools to facilitate parallel solutions of nonlinear programming (NLP) problems. In particular, we address large-scale, block-structured problems with an intrinsic decomposable configuration. These problems arise in a great number of engineering applications, including parameter estimation, optimal control, network optimization, and stochastic programming. The structure of these problems can be leveraged by optimization solvers to accelerate solutions and overcome memory limitations, and we propose variants to two classes of optimization algorithms: augmented Lagrangian (AL) schemes and Schur-complement interior-point methods. The convergence properties of augmented Lagrangian decomposition schemes like the alternating direction method of multipliers (ADMM) and progressive hedging (PH) are well established for convex optimization but convergence guarantees in non-convex settings are still poorly understood. In practice, however, ADMM and PH often perform satisfactorily in complex non-convex NLPs. In this work, we study connections between the method of multipliers (MM), ADMM, and PH to derive benchmarking metrics that explain why PH and ADMM work in practice. We illustrate the concepts using challenging dynamic optimization problems. Our exposition seeks to establish more formalism in benchmarking ADMM, PH, and AL schemes and to motivate algorithmic improvements. The effectiveness of nonlinear interior-point solvers for solving large-scale problems relies quite heavily on the solution of the underlying linear algebra systems. The schur-complement decomposition is very effective for parallelizing the solution of linear systems with modest coupling. However, for systems with large number of coupling variables the schur-complement method does not scale favorably. We implement an approach that uses a Krylov solver (GMRES) preconditioned with ADMM to solve block-structured linear systems that arise in the interior-point method. We show that this ADMM-GMRES approach overcomes the well-known scalability issues of Schur decomposition. One important drawback of using decomposition approaches like ADMM and PH is their convergence rate. Unlike Schur-complement interior-point algorithms that have super-linear convergence, augmented Lagrangian approaches typically exhibit linear and sublinear rates. We exploit connections between ADMM and the Schur-complement decomposition to derive an accelerated version of ADMM. Specifically, we study the effectiveness of performing a Newton-Raphson algorithm to compute multiplier estimates for augmented Lagrangian methods. We demonstrate using two-stage stochastic programming problems that our multiplier update achieves convergence in fewer iterations for MM on general nonlinear problems. In the case of ADMM, the newton update significantly reduces the number of subproblem solves for convex quadratic programs (QPs). Moreover, we show that using newton multiplier updates makes the method robust to the selection of the penalty parameter. Traditionally, state-of-the-art optimization solvers are implemented in low-level programming languages. In our experience, the development of decomposition algorithms in these frameworks is challenging. They present a steep learning curve and can slow the development and testing of new numerical algorithms. To mitigate these challenges, we developed PyNumero, a new open source framework implemented in Python and C++. The package seeks to facilitate development of optimization algorithms for large-scale optimization within a high-level programming environment while at the same time minimizing the computational burden of using Python. The efficiency of PyNumero is illustrated by implementing algorithms for problems arising in stochastic programming and optimal control. Timing results are presented for both serial and parallel implementations. Our computational studies demonstrate that with the appropriate balance between compiled code and Python, efficient implementations of optimization algorithms are achievable in these high-level languages.

The past decade has seen explosive growth in digitized medical data. This trend offers medical practitioners an unparalleled opportunity to identify effectiveness of treatments for patients using summary statistics and to offer patients more personalized medical treatments based on predictive analytics. To exploit this opportunity, statisticians and computer scientists need to work and communicate effectively with medical practitioners to ensure proper measurement data, collection of sufficient volumes of heterogeneous data to ensure patient privacy, and understanding of probabilities and sources of errors associated with data sampling. Interdisciplinary collaborations between scientists are likely to lead to the development of more effective methods for explaining probabilities, possible errors, and risks associated with treatment options to patients. This chapter introduces some online resources to help medical practitioners with little or no background in summary and predictive statistics learn basic statistical concepts and implement data analysis on their personal computers using R, a high-level computer language that requires relatively little training. Readers who are only interested in understanding basic statistical concepts may want to skip the subsection on R.

Traditionally, vibration signal processing has been performed using analog and digital signal analyzers or writing code in intermediate and high-level computer languages. However, the advent of higher-level interpretive-based signal processing software products such as MATLAB has added a new dimension to vibration signal processing. This paper presents a method for analyzing motor vibration data using MATLAB. The method first pre-processes the vibration data to remove noise and baseline wander. Then, the frequency spectrum of the vibration signal is calculated using the Fourier transform. The frequency spectrum is then used to identify the dominant frequencies in the vibration signal. These dominant frequencies can be used to identify potential problems with the motor, such as bearing defects or misalignment. The method was studied on a set of vibration data collected from open source online data of a real motor. The results showed that the method was able to identify the dominant frequencies in the vibration signal accurately. The method was also able to identify the potential problems with the motor. This paper demonstrates the effectiveness of using MATLAB for analyzing motor vibration data. The method presented in this paper can be used to improve the reliability and efficiency of motor maintenance.

Jeneberang watershed management plays an important role to the function of Bili-Bili Dam as a multipurpose dam in maintaining year-round water availability (quantity and distribution), agricultural irrigation, water supply for the local freshwater provider (PDAM), demand on drinking water, and hydroelectric power plants in Gowa, Takalar, and Makassar. The study tried to answer the emerging problem of how to predict the hydrological state and water availability in the Jeneberang watershed throughout the year. This study applied the SWAT (Soil and Water Assessment Tool) model, a distributed model connected to the Geographic Information System (GIS) developed by Jeff Arnold for the USDA ARS (US Department of Agriculture - Agriculture Research Service). The hydrological model would be applied to evaluate a scenario of the climate change effects on the hydrology state. Understanding the watershed hydrological response to the changes in physics (land use) and climate (rainfall and air temperature) is an important component of water resource planning and management (Vorosmarty et al., 2000). One of the performance indicators for the watershed water balance is river discharge fluctuation (Surahman, 2016). The river regime coefficient demonstrates fluctuation in river discharge, a number showing the ratio between maximum discharge (Qmax) and minimum discharge (Qmin). SWAT model analysis showed the highest discharge (Qmax) of 30,805 m³/sec and lowest discharge (Qmin) of 994 m³/sec. The analysis employed the data of 2006 and 2009, resulted in the Nash-Sutcliffe (NS) Efficiency values of 0.65 (satisfied) and R2 of 0.88 that in line with a study of Yusuf (2010) at the Cirasea watershed (0.74) and Junaidi (2011) at the Cisadane watershed (0.70). This value indicates that the SWAT method can predict the hydrological state of watersheds in Indonesia, including the Jeneberang watershed.

The SYMBOL 2R Computing System represents an attempt to bridge the gap between machine languages and higher level programming languages. This gap has always limited the users' ability to communicate with the machine, thus creating a communication problem. Early attempts at solving this communication problem consisted of developing a series of user-oriented programming languages, i.e., assembler languages and higher level languages, supplemented with a host of translator programs that would transform the higher level languages into machine language. For several years it seemed that this approach represented the only economically feasible solution. In 1965, W. H. Burkhardt (2) suggested the development of high-level language computers Burkhardt did suggest that the availability of low cost components would make high-level language computers an economically feasible solution. In fact, the recent decline in the cost of components as compared with the steady, if not increasing cost of the software needed to develop and maintain compilers for high-level languages, has made the high-level language computer solution seem even more attractive.

High-level-language (HLL) computer architecture refers to those architecture which can accept one or more high-level languages (such as Fortran, Alogl, Snobol, etc.). This paper classifies the HLL computer architecture into four types (von Neumann architecture, syntax-oriented architecture, indirect execution architecture, and direct execution architecture) based on the criterion of proximity in lexicality, syntax and semantics between the high-level language and the machine language. It then introduce es concepts of these high-level-language architecture. A simple example is employed to illustrate the differences among these four types of architecture.

<p>Hydrological models represent a fundamental tool for linking data with theories in scientific studies. Conceptual models are among the most frequently used type of models in catchment scale studies, due to their low computational requirements and ease of interpretation. Model selection requires the comparison of model alternatives, which is complicated by differences in conceptualization, implementation, and source code availability of the models present in the literature. For this reason, several model-building frameworks have been introduced in the last decade, which facilitate model comparisons by enabling different model alternatives within the same software and numerical architecture. These frameworks, however, have their own limitations, including the difficulty of extension from a user perspective, the requirement of long set-up procedures, and the need of customized input files.<br>Building on the decennial experience with the development and usage of Superflex, a flexible modeling framework for conceptual model building, so far implemented in FORTRAN language and not available as open source, we propose SuperflexPy, an open source Python framework for building conceptual hydrological models. SuperflexPy allows the user to build fully customized models using generic elements (i.e. reservoirs, splitters, junctions, lag functions, etc.) and to arrange them as desired, for example to reflect lumped or semi-distributed model configurations. SuperflexPy is easy to configure through modular initialization scripts, easy to extend with custom functionalities, and easy to interface with other frameworks, making it an essential element for creating a continuous and reproducible pipeline that goes from raw data to model results and interpretation.<br>In this presentation, we will introduce this framework, showcasing some applications and highlighting its potential in the context of open science.</p>