Increasing Parameter Certainty and Data Utility Through Multi-Objective Calibration of a Spatially Distributed Temperature and Solute Model

To support the goal of distributed hydrologic and instream model predictions based on physical processes, we explore multi-dimensional parameterization determined by a broad set of observations. We present a systematic approach to using various data types at spatially distributed locations to decrease parameter bounds sampled within calibration algorithms that ultimately provide information regarding the extent of individual processes represented within the model structure. Through the use of a simulation matrix, parameter sets are first locally optimized by fitting the respective data at one or two locations and then the best results are selected to resolve which parameter sets perform best at all locations, or globally. This approach is illustrated using the Two-Zone Temperature and Solute (TZTS) model for a case study in the Virgin River, Utah, USA, where temperature and solute tracer data were collected at multiple locations and zones within the river that represent the fate and transport of both heat and solute through the study reach. The result was a narrowed parameter space and increased parameter certainty which, based on our results, would not have been as successful if only single objective algorithms were used. We also found that the global optimum is best defined by multiple spatially distributed local optima, which supports the hypothesis that there is a discrete and narrowly bounded parameter range that represents the processes controlling the dominant hydrologic responses. Further, we illustrate that the optimization process itself can be used to determine which observed responses and locations are most useful for estimating the parameters that result in a global fit to guide future data collection efforts

Short communication: Landlab v2.0: a software package for Earth surface dynamics

umerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon–Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components – modular implementations of single physical processes – and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components – for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab

Short communication: Landlab v2.0: a software package for Earth surface dynamics

Numerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon-Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components - modular implementations of single physical processes - and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components - for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab.</p

Application of TOPNET in the Distributed Model Intercomparison Project

This paper describes the application of a networked version of TOPMODEL, TOPNET, as part of the Distributed Model Intercomparison Project (DMIP). The model implementation is based on a topographically derived river network with spatially distributed sub-basins draining to each network reach. The river network is mapped from the US National Elevation Dataset Digital Elevation Model (DEM) using procedures that objectively estimate drainage density from geomorphic principles. Rainfall inputs are derived from NEXRAD (radar) for each sub-basin. For each sub-basin, the wetness index distribution is derived from the DEM. The initial model parameters for each sub-basin are estimated using look up tables based on soils (STATSGO) and vegetation (1-km AVHRR). These initial model parameters provide the spatially distributed pattern of parameters at the scale of each sub-basin. Calibration uses a multiplier for each parameter to adjust the parameters while retaining the relative spatial pattern obtained from the soils and vegetation data. Parameter multipliers were calibrated using the shuffled complex evolution algorithm [J. Optim. Theory Appl. 61 (1993)] with the objective to minimize the mean square error between observed and modeled hourly streamflows. We describe the model and calibrated results submitted for all basins for the time periods involved in the DMIP study. We were encouraged by the relatively good performance of the model, especially in comparison to streamflow from smaller interior watersheds not used in calibration and simulated as ungaged basins. The limited resources used to achieve these results show some of the potential for distributed models to be useful operationally

User Needs Assessment, Chapter 4

This chapter reports on a data collection effort targeting the Hydrologic Information System (HIS) User Community: who they are, what they do, and how they do it. Here we present the results of a web-based survey of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) members and their affiliates which has clarified important HIS development issues, informed HIS project decision-making and will help create an effective, efficient, and functional HIS. CUAHSI is an organization representing more than 100 universities, sponsored by the National Science Foundation to develop infrastructure and services for the advancement of hydrologic science and education in the United States. The CUAHSI Hydrologic Information System (HIS) project is a component of CUAHSI’s mission that is intended to improve infrastructure and services for hydrologic information acquisition and analysis. You can learn more about CUAHSI from the website www.cuahsi.org