ABSTRACT:

This is a tutorial that will walk you through the steps to import a DEM into Landlab and create a raster model grid.

Note: To use this tutorial, you should have Landlab (version 1.0) installed on your computer. To install Landlab, please follow the instructions @ https://landlab.github.io/#/#install

The starting point is to obtain a Digital Elevation Model (DEM) in ESRI's Ascii Grid format, or convert it to this format. The format consists of a 6-line header followed by elevation values. Here we'll work with a small example DEM file 'west_bijou_gully.asc' (attached in this resource). The header for this file is as follows:
ncols 43
nrows 89
xllcorner 559705.000000000000
yllcorner 4380220.000000000000
cellsize 3
NODATA_value 0
Here, our objective is to translate the contents of this file into a Landlab RasterModelGrid.

This tutorial also illustrates setting boundary conditions on DEMs and using the halo option for reading in DEMs without nodata values around the edges.
For this we will use the resource 'synthetic_landsacpe.asc'.

Citing Landlab:
Tucker, G.E., Gasparini, N.M., Istanbulluoglu, E., Hobley, D.E.J., Nudurupati, S.S., Adams, J.M., and Hutton, E. (2013). Landlab v1.0. https://landlab.github.io.

ABSTRACT:

This tutorial demonstrates implementation of the Cellular Automaton Tree-GRass-Shrub Simulator (CATGRaSS) [Zhou et al., 2013] on a flat domain. This model is built using components from the Landlab component library. CATGRaSS is spatially explicit model of plant coexistence. It simulates local ecohydrologic dynamics (soil moisture, transpiration, biomass) and spatial evolution of tree, grass, and shrub Plant Functional Types (PFT) driven by rainfall and solar radiation.

Each cell in the model grid can hold a single PFT or remain empty. Tree and shrub plants disperse seeds to their neighbors. Grass seeds are assumed to be available at each cell. Establishment of plants in empty cells is determined probabilistically based on water stress of each PFT. Plants with lower water stress have higher probability of establishment. Plant mortality is simulated probabilistically as a result of aging and drought stress. Fires and grazing will be added to this model soon.

This model (driver) contains:
- A local vegetation dynamics model that simulates storm and inter-storm water balance and ecohydrologic fluxes (ET, runoff), and plant biomass dynamics by coupling the following components:
- PrecipitationDistribution
- Radiation
- PotentialEvapotranspiration
- SoilMoisture
- Vegetation

- A spatially explicit probabilistic cellular automaton component that simulates plant competition by tracking establishment and mortality of plants based on soil moisture stress:
- VegCA

To run this Jupyter notebook, please make sure that the following files are in the same folder:
- cellular_automaton_vegetation_flat_domain.ipynb (this notebook)
- Inputs_Vegetation_CA.txt (Input parameters for the model)
- Ecohyd_functions_flat.py (Utility functions)

[Ref: Zhou, X, E. Istanbulluoglu, and E.R. Vivoni. "Modeling the ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate." Water Resources Research 49.5 (2013): 2872-2895]

ABSTRACT:

This tutorial demonstrates implementation of the Cellular Automaton Tree-GRass-Shrub Simulator (CATGRaSS) [Zhou et al., 2013] on a digital elevation model (DEM). This model is built using components from the Landlab component library. CATGRaSS is a spatially explicit model of plant coexistence. It simulates local ecohydrologic dynamics (soil moisture, transpiration, biomass) and spatial evolution of tree, grass, and shrub Plant Functional Types (PFT) driven by rainfall and solar radiation.

Each cell in the model grid can hold a single PFT or remain empty. Tree and shrub plants disperse seeds to their neighbors. Grass seeds are assumed to be available at each cell. Establishment of plants in empty cells is determined probabilistically based on water stress for each PFT. Plants with lower water stress have higher probability of establishment. Plant mortality is simulated probabilistically as a result of aging and drought stress. Fires and grazing will be added to this model soon.

This model (driver) contains:
- A local vegetation dynamics model that simulates storm and inter-storm water balance and ecohydrologic fluxes (ET, runoff), and plant biomass dynamics by coupling the following components:
- PrecipitationDistribution
- Radiation
- PotentialEvapotranspiration
- SoilMoisture
- Vegetation

- A spatially explicit probabilistic cellular automaton component that simulates plant competition by tracking establishment and mortality of plants based on soil moisture stress:
- VegCA

To run this Jupyter notebook, please make sure that the following files are in the same folder:
- cellular_automaton_vegetation_DEM.ipynb (this notebook)
- Inputs_Vegetation_CA.txt (Input parameters for the model)
- Ecohyd_functions_DEM.py (Utility functions)

[Ref: Zhou, X, E. Istanbulluoglu, and E.R. Vivoni. "Modeling the ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate." Water Resources Research 49.5 (2013): 2872-2895]

ABSTRACT:

This tutorial demonstrates implementation of the Cellular Automaton Tree-GRass-Shrub Simulator (CATGRaSS) [Zhou et al., 2013] on a flat domain. This model is built using components from the Landlab component library. CATGRaSS is spatially explicit model of plant coexistence. It simulates local ecohydrologic dynamics (soil moisture, transpiration, biomass) and spatial evolution of tree, grass, and shrub Plant Functional Types (PFT) driven by rainfall and solar radiation.

Each cell in the model grid can hold a single PFT or remain empty. Tree and shrub plants disperse seeds to their neighbors. Grass seeds are assumed to be available at each cell. Establishment of plants in empty cells is determined probabilistically based on water stress of each PFT. Plants with lower water stress have higher probability of establishment. Plant mortality is simulated probabilistically as a result of aging and drought stress. Fires and grazing will be added to this model soon.

This model (driver) contains:
- A local vegetation dynamics model that simulates storm and inter-storm water balance and ecohydrologic fluxes (ET, runoff), and plant biomass dynamics by coupling the following components:
- PrecipitationDistribution
- Radiation
- PotentialEvapotranspiration
- SoilMoisture
- Vegetation

- A spatially explicit probabilistic cellular automaton component that simulates plant competition by tracking establishment and mortality of plants based on soil moisture stress:
- VegCA

To run this Jupyter notebook, please make sure that the following files are in the same folder:
- cellular_automaton_vegetation_flat_domain.ipynb (this notebook)
- Inputs_Vegetation_CA.txt (Input parameters for the model)
- Ecohyd_functions_flat.py (Utility functions)

[Ref: Zhou, X, E. Istanbulluoglu, and E.R. Vivoni. "Modeling the ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate." Water Resources Research 49.5 (2013): 2872-2895]

ABSTRACT:

This Landlab driver illustrates the use of Landlab ecohydrology components to model semi-arid ecohydrological dynamics driven by a storm pulse and solar radiation. Components (names given in parenthesis) we will use are:
* Precipitation Distribution (PrecipitationDistribution)
* Solar radiation (Radiation)
* Potential Evapotranspiration (PotentialEvapotranspiration)
* Soil Moisture (SoilMoisture)
* Vegetation (Vegetation)
A digital elevation model (DEM) of a headwater region in central New Mexico (latitude 34N) will be used as input.

This tutorial is an extension of Ecohydrologic Mapping Tutorial (Istanbulluoglu, E., S. S. Nudurupati (2016). Landlab Ecohydrologic Mapping Tutorial, HydroShare, http://www.hydroshare.org/resource/15d0a79514c44a59b41b68ad74496d0f)

ABSTRACT:

SN - 22Sep17: Debugging 'observatory_gridded_hydromet.py'.

ABSTRACT:

This resource contains a tutorial that intends to serve as an ice breaker to Python's Numpy and Landlab packages. This tutorial has been developed by Landlab team and has been downloaded from https://github.com/landlab/landlab/wiki/Tutorials.

ABSTRACT:

Presentations created by participants of the GSA 2017 meeting short course, Landlab Earth Surface Modeling Toolkit: Building and Applying Models of Coupled Earth Surface Processes.

Participants selected a tutorial group to join in the second part of the course. Throughout the afternoon, groups explored the topic they chose with a Landlab developer. At the end of the day groups shared what they did with Landlab using these presentations.

ABSTRACT:

This resource contains a jupyter notebook that uses landlab to illustrate the sensitivity of detachment-limited stream-power controlled rivers to changes in rock uplift and erodibility. This notebook was designed as a homework assignment in an upper level geomorphology course.

ABSTRACT:

Landlab is an open-source Python toolkit that provides a modeling environment for building 2D numerical Earth surface models. This toolkit is designed to accelerate the development of new process-based models by providing: tools to create a grid; data structures for storing and managing data on the grid; and easy-to-use plotting and visualization functions. In this cyberseminar, we will walk through the basics of Landlab using HydroShare, including a simple example of an Earth surface model. In addition, we will provide two more water-specific examples—an Overland Flow example and an Ecohydrology example—for further independent exploration. We will discuss the two additional examples in detail during WaterHackWeek. No prior experience is required, but a basic understanding of HydroShare and Python is helpful.

ABSTRACT:

This Landlab driver illustrates the use of Landlab ecohydrology components to model semi-arid ecohydrological dynamics driven by a storm pulse and solar radiation. Components (names given in parenthesis) we will use are:
* Solar radiation (Radiation)
* Potential Evapotranspiration (PotentialEvapotranspiration)
* Soil Moisture (SoilMoisture)
* Vegetation (Vegetation)
A digital elevation model (DEM) of a headwater region in central New Mexico (latitude 34N) will be used as input.
Components will be introduced step by step. First, we will start with mapping solar radiation and potential evapotranspiration (PET). Note that some of the commands used are only to provide information about the in/outputs of components and can be deleted or not run. We will then run soil moisture and vegetation modules and show how to write outputs in a file.

ABSTRACT:

Collection of resources for Landlab tutorials and presentations at WaterHackWeek2019.

ABSTRACT:

In this resource, you will find the slides and expanded Jupyterhub notebooks of the tutorials presented during Landlab lecture on 28th March 2019, for Waterhackweek 2019.