Modelling the shrub encroachment in a grassland with a Cellular Automata Model

Abstract. Arid and semi-arid grasslands of southwestern North America have changed dramatically over the last 150 years as a result of shrub encroachment, i.e. the increase in density, cover and biomass of indigenous shrubby plants in grasslands. Numerous studies have documented the expansion of shrublands in the southwestern American grasslands; in particular shrub encroachment has occurred strongly in part of the northern Chihuahuan desert since 1860. This encroachment has been simulated using an ecohydrological Cellular Automata model, CATGraSS. It is a spatially distributed model driven by spatially explicit irradiance and runs on a fine-resolution gridded domain. Plant competition is modelled by keeping track of mortality and establishment of plants; both are calculated probabilistically based on soil moisture stress. For this study CATGraSS has been improved with a stochastic fire module and a grazing function. The model has been implemented in a small area in Sevilleta National Wildlife Refuge (SNWR), characterized by two vegetation types (grass savanna and creosote bush shrub), considering as encroachment causes the fire return period increase, the grazing increase, the seed dispersal caused by animals, the role of wind direction and plant type competition. The model is able to reproduce the encroachment that has occurred in SNWR, simulating an increase of the shrub from 2% in 1860 to the current shrub percentage, 42%, and highlighting among the most influential factors the reduced fire frequency and the increased grazing intensity

Effect of water stress on growth components of winter safflower (Carthamus tinctorius L.)

Abstract A field study was carried out in order to determine the effect of irrigation, water stress imposed at different development stages on seed yield, seasonal evapotranspiration (ET), crop-water relationships, oil yield and plant growth components of safflower (Carthamus tinctorius L.) for winter sowing at Thrace Region in Turkey. The field trials were conducted on a loam Entisol soil, using cv. Dincer, the most popular variety in the region. A randomised complete block design with three replications was used. Three known growth stages of the plant were considered and a total of 8 (including rain fed) irrigation treatments were applied. Results of this study show that safflower is significantly affected by water shortage in the soil profile due to omitted irrigation during the sensitive vegetative stage. Highest yields were observed in the fully irrigated control. An evapotranspiration of 728 mm were calculated for non-stressed production for winter sowing. Safflower seed yield of this treatment was 4.05 ton per hectare

CellLab-CTS 2015: continuous-time stochastic cellular automaton modeling using Landlab

CellLab-CTS 2015 is a Python-language software library for creating two-dimensional, continuous-time stochastic (CTS) cellular automaton models. The model domain consists of a set of grid nodes, with each node assigned an integer state code that represents its condition or composition. Adjacent pairs of nodes may undergo transitions to different states, according to a user-defined average transition rate. A model is created by writing a Python code that defines the possible states, the transitions, and the rates of those transitions. The code instantiates, initializes, and runs one of four object classes that represent different types of CTS models. CellLab-CTS provides the option of using either square or hexagonal grid cells. The software provides the ability to treat particular grid-node states as moving particles, and to track their position over time. Grid nodes may also be assigned user-defined properties, which the user can update after each transition through the use of a callback function. As a component of the Landlab modeling framework, CellLab-CTS models take advantage of a suite of Landlab's tools and capabilities, such as support for standardized input and output

The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds

Representation of flowing water in landscape evolution models (LEMs) is often simplified compared to hydrodynamic models, as LEMs make assumptions reducing physical complexity in favor of computational efficiency. The Landlab modeling framework can be used to bridge the divide between complex runoff models and more traditional LEMs, creating a new type of framework not commonly used in the geomorphology or hydrology communities. Landlab is a Python-language library that includes tools and process components that can be used to create models of Earth-surface dynamics over a range of temporal and spatial scales. The Landlab OverlandFlow component is based on a simplified inertial approximation of the shallow water equations, following the solution of de Almeida et al. (2012). This explicit two-dimensional hydrodynamic algorithm simulates a flood wave across a model domain, where water discharge and flow depth are calculated at all locations within a structured (raster) grid. Here we illustrate how the OverlandFlow component contained within Landlab can be applied as a simplified event-based runoff model and how to couple the runoff model with an incision model operating on decadal timescales. Examples of flow routing on both real and synthetic landscapes are shown. Hydrographs from a single storm at multiple locations in the Spring Creek watershed, Colorado, USA, are illustrated, along with a map of shear stress applied on the land surface by flowing water. The OverlandFlow component can also be coupled with the Landlab DetachmentLtdErosion component to illustrate how the nonsteady flow routing regime impacts incision across a watershed. The hydrograph and incision results are compared to simulations driven by steady-state runoff. Results from the coupled runoff and incision model indicate that runoff dynamics can impact landscape relief and channel concavity, suggesting that on landscape evolution timescales, the OverlandFlow model may lead to differences in simulated topography in comparison with traditional methods. The exploratory test cases described within demonstrate how the OverlandFlow component can be used in both hydrologic and geomorphic applications

Enabling collaborative numerical modeling in earth sciences using knowledge infrastructure

Knowledge Infrastructure is an intellectual framework for creating, sharing, and distributing knowledge. In this paper, we use Knowledge Infrastructure to address common barriers to entry to numerical modeling in Earth sciences: computational modeling education, replicating published model results, and reusing published models to extend research. We outline six critical functional requirements: 1) workflows designed for new users; 2) a community-supported collaborative web platform; 3) distributed data storage; 4) a software environment; 5) a personalized cloud-based high-performance computing platform; and 6) a standardized open source modeling framework. Our methods meet these functional requirements by providing three interactive computational narratives for hands-on, problem-based research demonstrating how to use Landlab on HydroShare. Landlab is an open-source toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for the sharing of data and models. We describe the methods we are using to accelerate knowledge development by providing a suite of modular and interoperable process components that allows students, domain experts, collaborators, researchers, and sponsors to learn by exploring shared data and modeling resources. The system is designed to support uses on the continuum from fully-developed modeling applications to prototyping research software tools

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

On transient semi‐arid ecosystem dynamics using Landlab: vegetation shifts, topographic refugia, and response to climate

Projecting how arid and semi‐arid ecosystems respond to global change requires the integration of a wide array of analytical and numerical models to address different aspects of complex ecosystems. We used the Landlab earth surface modeling toolkit (Hobley et al., 2017, https://doi.org/10.5194/esurf-5-21-2017) to couple several ecohydrologic and vegetation dynamics processes to investigate the controls of exogenous drivers (climate, topography, fires, and grazing) and endogenous grass‐fire feedback mechanisms. Aspect‐controlled ecosystems and historical woody plant encroachment (WPE) narratives in central New Mexico, USA are used to construct simulations. Modeled ecosystem response to climatic wetness (i.e., higher precipitation, lower potential evapotranspiration) on topography follows the Boyko's “geo‐ecological law of distribution.” Shrubs occupy cooler pole‐facing slopes in the dry end of their ecoclimatic range (Mean Annual Precipitation, MAP ≤ 200 mm), and shift toward warmer equator‐facing slopes as regional moisture increases (MAP > 250 mm). Trees begin to occupy pole‐facing slopes when MAP > 200 mm, and gradually move to valleys. Pole‐facing slopes increase species diversity at the landscape scale by hosting relict populations during dry periods. WPE observed in the region since the middle 1800s is predicted as a three‐phase phenomenon. Phase II, rapid expansion, requires the removal of the positive grass‐fire feedback by livestock grazing or fire suppression. Regime shifts from grassland to shrubland are marked by critical thresholds that involve grass cover remaining below 40%, shrub cover increasing to 10%–20% range, and the grass connectivity, Cg, remaining below 0.15. A critical transition to shrubland is predicted when grazing pressure is not removed before shrub cover attains 60%

A mechanistic ecohydrological model to investigate complex interactions in cold and warm water‐controlled environments: 1. Theoretical framework and plot‐scale analysis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95321/1/jame60.pd