A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters

Markov Chain Monte Carlo (MCMC) methods have become increasingly popular for estimating the posterior probability distribution of parameters in hydrologic models. However, MCMC methods require the a priori definition of a proposal or sampling distribution, which determines the explorative capabilities and efficiency of the sampler and therefore the statistical properties of the Markov Chain and its rate of convergence. In this paper we present an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), which is well suited to infer the posterior distribution of hydrologic model parameters. The SCEM-UA algorithm is a modified version of the original SCE-UA global optimization algorithm developed by Duan et al. [1992]. The SCEM-UA algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Three case studies demonstrate that the adaptive capability of the SCEM-UA algorithm significantly reduces the number of model simulations needed to infer the posterior distribution of the parameters when compared with the traditional Metropolis-Hastings samplers

Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?

In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchment

The role of hydrograph indices in parameter estimation of rainfall-runoff models

A reliable prediction of hydrologic models, among other things, requires a set of plausible parameters that correspond with physiographic properties of the basin. This study proposes a parameter estimation approach, which is based on extracting, through hydrograph diagnoses, information in the form of indices that carry intrinsic properties of a basin. This concept is demonstrated by introducing two indices that describe the shape of a streamflow hydrograph in an integrated manner. Nineteen mid-size (223-4790 km2) perennial headwater basins with a long record of streamflow data were selected to evaluate the ability of these indices to capture basin response characteristics. An examination of the utility of the proposed indices in parameter estimation is conducted for a five-parameter hydrologic model using data from the Leaf River, located in Fort Collins, Mississippi. It is shown that constraining the parameter estimation by selecting only those parameters that result in model output which maintains the indices as found in the historical data can improve the reliability of model predictions. These improvements were manifested in (a) improvement of the prediction of low and high flow, (b) improvement of the overall total biases, and (c) maintenance of the hydrograph's shape for both long-term and short-term predictions. Copyright © 2005 John Wiley & Sons, Ltd

Modification of the National Weather Service Distributed Hydrologic Model for subsurface water exchanges between grids

To account for spatial variability of precipitation, as well as basin physiographic properties, the National Weather Service (NWS) has developed a distributed version of its hydrologic component, termed the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Because channels are the only source of water exchange between neighboring computational elements, the absence of such exchange has been identified as a weakness in the model. The primary objective of this paper is to modify the model structure to account for subsurface water exchanges without dramatically altering the conceptual framework of the water balance module. The subsurface exchanges are established by partitioning the slow response components released from the lower layer storages into two parts: the first part involves the grid's conceptual channel, while the second is added to the lower layer storages of the downstream pixel. Realizing the deficiency of the water balance module to locate the lower zone layers in sufficient depths, a complementary study is conducted to test the feasibility of further improvement in the modified model by equally shifting downward the lower zone layers of all pixels over the basin. The Baron Fork at Eldon, Oklahoma, is chosen as the test basin. Ten years of grid-based multisensor precipitation data are used to investigate the effects of the modification, plus shifting the lower zone layers on model performance. The results show that the modified-shifted HL-RDHM can markedly improve the streamflow simulations at the interior point, as well as very high peak-flow simulations at the basin's outlet. Copyright 2011 by the American Geophysical Union

Practitioner Track Proceedings of the 6th International Learning Analytics & Knowledge Conference (LAK16)

Practitioners spearhead a significant portion of learning analytics, relying on implementation and experimentation rather than on traditional academic research. Both approaches help to improve the state of the art. The LAK conference has created a practitioner track for submissions, which first ran in 2015 as an alternative to the researcher track. The primary goal of the practitioner track is to share thoughts and findings that stem from learning analytics project implementations. While both large and small implementations are considered, all practitioner track submissions are required to relate to initiatives that are designed for large-scale and/or long-term use (as opposed to research-focused initiatives). Other guidelines include: • Implementation track record The project should have been used by an institution or have been deployed on a learning site. There are no hard guidelines about user numbers or how long the project has been running. • Learning/education related Submissions have to describe work that addresses learning/academic analytics, either at an educational institution or in an area (such as corporate training, health care or informal learning) where the goal is to improve the learning environment or learning outcomes. • Institutional involvement Neither submissions nor presentations have to include a named person from an academic institution. However, all submissions have to include information collected from people who have used the tool or initiative in a learning environment (such as faculty, students, administrators and trainees). • No sales pitches While submissions from commercial suppliers are welcome; reviewers do not accept overt (or covert) sales pitches. Reviewers look for evidence that a presentation will take into account challenges faced, problems that have arisen, and/or user feedback that needs to be addressed. Submissions are limited to 1,200 words, including an abstract, a summary of deployment with end users, and a full description. Most papers in the proceedings are therefore short, and often informal, although some authors chose to extend their papers once they had been accepted. Papers accepted in 2016 fell into two categories. • Practitioner Presentations Presentation sessions are designed to focus on deployment of a single learning analytics tool or initiative. • Technology Showcase The Technology Showcase event enables practitioners to demonstrate new and emerging learning analytics technologies that they are piloting or deploying. Both types of paper are included in these proceedings

A Conceptual Framework for Integration Development of GSFLOW Model: Concerns and Issues Identified and Addressed for Model Development Efficiency

In Coupled Groundwater and Surface-Water Flow (GSFLOW) model, the three-dimensional finite-difference groundwater model (MODFLOW) plays a critical role of groundwater flow simulation, together with which the Precipitation-Runoff Modeling System (PRMS) simulates the surface hydrologic processes. While the model development of each individual PRMS and MODFLOW model requires tremendous time and efforts, further integration development of these two models exerts additional concerns and issues due to different simulation realm, data communication, and computation algorithms. To address these concerns and issues in GSFLOW, the present paper proposes a conceptual framework from perspectives of: Model Conceptualization, Data Linkages and Transference, Model Calibration, and Sensitivity Analysis. As a demonstration, a MODFLOW groundwater flow system was developed and coupled with the PRMS model in the Lehman Creek watershed, eastern Nevada, resulting in a smooth and efficient integration as the hydrogeologic features were well captured and represented. The proposed conceptual integration framework with techniques and concerns identified substantially improves GSFLOW model development efficiency and help better model result interpretations. This may also find applications in other integrated hydrologic modelings