The Next Frontier: Making Research More Reproducible

Science and engineering rest on the concept of reproducibility. An important question for any study is: are the results reproducible? Can the results be recreated independently by other researchers or professionals? Research results need to be independently reproduced and validated before they are accepted as fact or theory. Across numerous fields like psychology, computer systems, and water resources there are problems to reproduce research results (Aarts et al. 2015; Collberg et al. 2014; Hutton et al. 2016; Stagge et al. 2019; Stodden et al. 2018). This editorial examines the challenges to reproduce research results and suggests community practices to overcome these challenges. Coordination is needed among the authors, journals, funders and institutions that produce, publish, and report research. Making research more reproducible will allow researchers, professionals, and students to more quickly understand and apply research in follow-on efforts and advance the field

Comparación de Métodos de Interpolación para la Estimación de Temperatura del Reservorio CEASA

La interpolación de temperatura en cuerpos de agua permite realizar predicciones de puntos de muestreo que no presentan datos. En la presente investigación se evaluaron 12 métodos de interpolación para estimar la temperatura del reservorio del Centro de Experimentación Académica Salache (CEASA) de la Universidad Técnica de Cotopaxi. Los datos recolectados en campo fueron interpolados aleatoriamente y comparados con los reales en base al error medio (EM), error absoluto medio (MAE), error medio cuadrático (MSE), raíz del error cuadrático (RMSE) y coeficiente de determinación (R2). La interpolación más apropiada para la representación de la variable temperatura en el reservorio fue el del método del Polinomio Local con un MSE de 0,22 y RMSE de 0,47 y R2 de 0,53. Este método se puede utilizar para obtener datos de temperatura del reservorio, disminuyendo costos de tiempo y dinero que demandaría el levantamiento de información en campo.  Palabras clave: Interpolación, Temperatura, Polinomio Local, Reservorio CEASA.    ABSTRACT The interpolation of temperature in bodies of water allows making predictions of sampling points that do not present data. In the present investigation, 12 interpolation methods were evaluated to estimate the reservoir temperature of the Salache Academic Experimentation Center (CEASA) at the Technical University of Cotopaxi. The data collected in the field were randomly interpolated and compared with the real ones based on the mean error (MS), mean absolute error (MAE), mean square error (MSE), the root of the quadratic error (RMSE) and coefficient of determination (R2). The most appropriate interpolation for the representation of the variable temperature in the reservoir was the Local Polynomial method with an MSE of 0.22 and RMSE of 0.47 and R2 of 0.53. This method can be used to obtain reservoir temperature data, decreasing the time and money costs that gathering information would require in the field require. Key words: Interpolation, Temperature, Local Polynomial, CEASA Reservoir

Sensor data analytics and web applications to improve monitoring and understanding of lake processes

Lakes are complex systems that involve numerous physical, chemical and biological processes. With modern sensor technology, large amounts of sensor data on lake water chemistry are being generated to help researchers understand the spatial and temporal patterns of these lake processes. Each sensor generates different datasets and effectively utilizing the resulting large and diverse datasets to improve understanding of lake processes and optimize sampling strategies is essential to protect and improve lake resources. For example, in the Great Lakes, the case study in this thesis, the US Environmental Protection Agency (USEPA) conducts several monitoring programs with various sensors, including the TRIAXUS undulating vehicle, the Sea-Bird CTD (Conductivity, Temperature, Depth) depth profiler, and a dissolved oxygen (DO) logger network that are the focus of this study. In this work, we develop three data analysis frameworks to support limnologists in more effectively collecting and analyzing these types of datasets, providing a lake system perspective. The frameworks have been made available to the research community as open-source code, including three prototype interactive Web applications. For towed undulating vehicles such as TRIAXUS, we propose a geospatial analysis framework and software to interpret water-quality sampling data in near-real time. The framework includes data quality assurance and quality control processes, automated kriging interpolation along undulating paths, and local hotspot and cluster analyses. The approach is demonstrated using historical sampling data from an undulating vehicle deployed at three rivermouth sites in Lake Michigan during 2011. The normalized root-mean-square error (NRMSE) of the interpolation averages approximately 10% in 3-fold cross validation. The results show that the framework can be used to track river plume dynamics and provide insights on mixing, which could be related to wind and seiche events. Next, we develop and test algorithms for rapid and consistent analysis of depth profiling data sampled from CTD profilers to identify lake stratifications and deep chlorophyll layers (DCL). We develop a segmentation method to approximate vertical temperature profiles with linear segments using Piecewise Linear Representation (PLR) algorithm, from which stratification patterns can be extracted. We also propose an automated peak detection algorithm to identify the fluorescence peak where the DCL lies. Testing the algorithms with data from the Great Lakes, we obtained similar results to human judgments from historical surveys. The algorithms are able to reveal spatial and temporal trends of the thermocline and DCL, as well as analyzing the shape of temperature and fluorescence profiles to detect unusual patterns such as a double thermocline. Finally, we develop a spatio-temporal interpolation framework that identifies the spatially varying temporal trend and estimates hourly hypoxia extent (dissolved oxygen [DO] concentration lower than 2mg/L) with estimation uncertainty. The framework is used to analyze spatio-temporal datasets of dissolved oxygen in Lake Erie, which were sampled from a logger network placed at the lake bottom in 2014, 2015, and 2016. The results show that hypoxia developed differently in these years. The locations with longest total hypoxic duration and longest continuous hypoxic duration are also different. Based on cross-validation results and DO time series patterns, some implications for optimizing logger locations are discussed

The Ecological Importance of Deep Chlorophyll Maxima in the Laurentian Great Lakes

Deep chlorophyll maxima (DCM) are common in stratified lakes and oceans, and phytoplankton growth in DCM can contribute significantly to total ecosystem production. Understanding the drivers of DCM formation is important for interpreting their ecological importance. The overall objective of this research was to assess the food web implications of DCM across a productivity gradient, using the Laurentian Great Lakes as a case study. First, I investigated the driving mechanisms of DCM formation and dissipation in Lake Ontario during April–September 2013 using in situ profile data and phytoplankton community structure. Results indicate that in situ growth was important for DCM formation in early- to mid-summer but settling and photoadaptation contributed to maintenance of the DCM late in the stratified season. Second, I expanded my analysis to all five of the Great Lakes using a time series generated by the US Environmental Protection Agency (EPA) long-term monitoring program in August from 1996-2017. The cross-lake comparison showed that DCM were closely aligned with deep biomass maxima (DBM) and dissolved oxygen saturation maxima (DOmax) in meso-oligotrophic waters (eastern Lake Erie and Lake Ontario), suggesting that DCM are productive features. In oligotrophic to ultra-oligotrophic waters (Lakes Michigan, Huron, Superior), however, DCM were deeper than the DBM and DOmax, indicating that photoadaptation was of considerable importance. Across lakes, euphotic depth was a significant predictor of both DCM depth and chlorophyll concentration, with greater water clarity associated with deeper and weaker DCM. Lastly, I investigated how DCM formation affects zooplankton diel vertical migration (DVM) by comparing the diel movements of different zooplankton size groups across three transects in southern Lake Michigan during summer 2015. Using taxonomy data from stratified net tows to inform our interpretation of laser optical plankton (LOPC) data, I concluded that phytoplankton distributions are an important determinant of zooplankton weighted mean depth. Trade-offs between optimal temperature, access to food resources, and predator avoidance contributed to differences in DVM among zooplankton size groups and regions of the lake. Overall, DCM production likely contributes significantly to phytoplankton biomass in oligotrophic lakes, causing selection pressure toward cold-adapted zooplankton that can effectively utilize DCM resources