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

Modeling the Effects of Forecasted Climate Change on Streamflow in the Nooksack River Basin

The Nooksack River drains an approximately 2000 km2 watershed in the North Cascades in Whatcom County, Washington and is a valuable freshwater resource for regional municipalities, industry, and agriculture, and provides critical habitat for endangered salmon species. With a maritime climate and a high relief basin with glacial ice (3400 hectares), the streamflow response in the Nooksack River is sensitive to increases in temperature, thus forecasting the basins response to future climate is of vital importance for water resources planning purposes, especially during low-flow months in the summer when precipitation is minimal. We apply the Distributed Hydrology Soil Vegetation Model (DHSVM; Wigmosta et al., 1992) with newly developed coupled dynamic glacier model (Clarke et al., 2015) to simulate hydrologic processes in the Nooksack River basin. We calibrate and validate the DHSVM to observed glacial mass balance and glacial ice extent as well as to observed daily streamflow and SNOTEL data in the Nooksack basin using a gridded meteorological forcing data set (1950-2010; Livneh et al., 2013). We simulate forecasted climate change impacts, including glacial recession on streamflow, using gridded daily statically downscaled data from global climate models of the CMIP5 with RCP4.5 and RCP8.5 forcing scenarios developed using the multivariate adaptive constructed analogs method (Abatzoglou and Brown, 2011). Simulation results project an increase in winter streamflows due to more rainfall rather than snow, a decrease in spring snow-melt runoff with peaks that occurring earlier in the year, and lower summer flows. Glacier melt contribution to streamflow initially increases throughout the first half of the 21st century and decreases in the latter half after glacier ice volume decreases substantially