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1) Demonstrate a potential to couple NST with existing landlab models that generate sediment sources or other sediment input conditions: We present here the overall modeling framework linking Landlab NetworkSedimentTransport (NST) component to Landlab SPACE component. We imported DEM generated from running SPACE component with random fire generations over the simulation period and converted to stream network.
2) Run the NetworkSedimentTransporter with pulses of sediment to understand the impact of landscape disturbance on sediment yield: We evaluate sediment delivery and yield across a stream network in response to random pulses of sediment synthetically generated in the NST module. +
<p>''pymt'' provides tools for coupling models with disparate time and space scales that expose a Basic Model Interface (BMI). It includes a collection of Earth-surface models and is an extensible plug-in framework for adding new models. Detailed information on ''pymt'' can be found at https://pymt.readthedocs.io.<p>
<p>''pymt'' is an element of the CSDMS Workbench, an integrated system of software tools, technologies, and standards for building and coupling models. Learn more at https://csdms.colorado.edu/wiki/Workbench.</p> +
<p>GeoTiff is a Python library for accessing data and metadata from a GeoTIFF file through an API or a BMI.</p>
<p>The bmi-geotiff library accepts a filepath or an URL to a GeoTIFF file. Data are loaded into an xarray DataArray using the experimental open_rasterio method. The API is wrapped with a Basic Model Interface (BMI), which provides a standard set of functions for coupling with data or models that also expose a BMI.</p>
<p>More information on GeoTiff can found in its documentation: https://bmi-geotiff.readthedocs.io.</p> +
<p>In this exercise you will modify the code to get a better understanding of how rock uplift rates and patterns and the erodibility coefficient control fluvial channel form.</p>
<p>Landlab is an element of the CSDMS Workbench, an integrated system of software tools, technologies, and standards for building and coupling models. Learn more at https://csdms.colorado.edu/wiki/Workbench.</p> +
<p>Landlab is an open-source Python-language package for numerical modeling of Earth surface dynamics. Landlab was designed for disciplines that quantify Earth surface dynamics such as geomorphology, hydrology, glaciology, and stratigraphy. It can also be used in related fields. Scientists who use this type of model often build their own unique model from the ground up, re-coding the basic building blocks of their landscape model rather than taking advantage of codes that have already been written. Landlab saves practitioners from the need for this kind of re-invention by providing standardized components that they can re-use. Detailed information about Landlab can be accessed at https://landlab.readthedocs.io.</p>
<p>Landlab is an element of the CSDMS Workbench, an integrated system of software tools, technologies, and standards for building and coupling models. Learn more at https://csdms.colorado.edu/wiki/Workbench.</p> +
<p>Meanderpy is a Python package that implements a simple numerical model of meandering, the one described by Howard & Knutson in their 1984 paper "Sufficient Conditions for River Meandering: A Simulation Approach".
This is a kinematic model that is based on computing migration rate as the weighted sum of upstream curvatures; flow velocity does not enter the equation. Curvature is transformed into a 'nominal migration rate' through multiplication with a migration rate (or erodibility) constant; in the Howard and Knutson, 1984 paper this is a nonlinear relationship based on field observations that suggested a complex link between curvature and migration rate.</p>
<p>In the 'meanderpy' package we use this linear relationship between the nominal migration rate and curvature, as recent work using time-lapse satellite imagery suggests that high curvatures result in high migration rates (Sylvester et al., 2019).</p>
<p>You can read more about the model, find references at: https://csdms.colorado.edu/wiki/Model:Meanderpy.<p>
<p>There is a keynote lecture on the concepts behind this model that you can watch here:
https://csdms.colorado.edu/wiki/Presenters-0433</p> +
<p>The Basic Model Interface (BMI) is a set of standard control and query functions that, when added to a model code, make that model both easier to learn and easier to couple with other software elements. Detailed information about BMI can be found at https://bmi.readthedocs.io.</p>
<p>BMI is an element of the CSDMS Workbench, an integrated system of software tools, technologies, and standards for building and coupling models. Learn more at https://csdms.colorado.edu/wiki/Workbench.</p> +
<p>The Kudryavtsev et al. (1974), or Ku model, presents an approximate solution of the Stefan problem. The model provides a steady-state solution under the assumption of sinusoidal air temperature forcing. It considers snow, vegetation, and soil layers as thermal damping to variation of air temperature. The layer of soil is considered to be a homogeneous column with different thermal properties in the frozen and thawed states. The main outputs are annual maximum frozen/thaw depth and mean annual temperature at the top of permafrost (or at the base of the active layer). It can be applied over a wide variety of climatic conditions.</p>
<p>The Ku model is part of the Permamodel Toolbox, see Overeem ''et al.'' 2018.</p> +
<p>The Kudryavtsev et al. (1974), or Ku model, presents an approximate solution of the Stefan problem. The model provides a steady-state solution under the assumption of sinusoidal air temperature forcing. It considers snow, vegetation, and soil layers as thermal damping to variation of air temperature. The layer of soil is considered to be a homogeneous column with different thermal properties in the frozen and thawed states. The main outputs are annual maximum frozen/thaw depth and mean annual temperature at the top of permafrost (or at the base of the active layer). It can be applied over a wide variety of climatic conditions.</p>
<p>The Ku model is part of the Permamodel Toolbox, see Overeem ''et al.'' 2018.</p> +
<p>What is permafrost and how do you make a first-order prediction about permafrost occurrence. This is lesson 1 in a mini-course on permafrost, this lab uses the Air Frost Number and annual temperature data to predict permafrost occurrence.</p>
<p>The Frost Number model is part of the Permamodel Toolbox, see Overeem ''et al.'' 2018.</p> +
Explore coastal processes by 1) a spreadsheet lab or 2) an advanced modeling lab using the CEM model. We look at the effects of waves and river avulson on a coastline. The spreadsheet lab can be downloaded at https://csdms.colorado.edu/csdms_wiki/images/CoastlineEvolutionLab.zip +
Impact craters are important aspects of the landscape on most rocky planetary bodies. The attached presentation introduces the importance of considering impact cratering and the basic formation and resulting morphology of a crater. The associated lab introduces the fundamental concepts of impact cratering distribution and morphology, and how we can recreate the observed distribution and morphology in models, after ideas published by Howard (2007). After investigating impact cratering itself, this lab moves on to explore the roles of diffusion and advection in modifying the cratered surface, using Landlab model components to explore the evolution of cratered landscapes over time. +
In this lab, we learn about river stage and discharge, using gage height data downloaded from the USGS for the upper Colorado River. We use standard Python libraries to read, analyze, and visualize the data. +
In this lab, we use the HydroTrend model to investigate river sediment supply to the ocean by exploring the effects of climate change on river fluxes. We also look at the effect of humans on rivers: the building of a reservoir. +
Landslide susceptibility is the likelihood of a landslide occurring in an area on the basis of local terrain condition to estimate “where” landslides are likely to occur. This lab demonstrates how to use several CSDMS Data Components (https://csdms.colorado.edu/wiki/DataComponents) to download topography and soil datasets to calculate the landslide susceptibility for a study area in Puerto Rico when Hurricane Maria hit the island in 2017. (Picture source: https://www.weather.gov/sju/maria2017) +
Overland flow, particularly the infiltration-excess mechanism, is affected by the nature of water input through precipitation. This lab demonstrates how to use the CSDMS Data Components (https://csdms.colorado.edu/wiki/DataComponents) to download the topography dataset and use the Landlab components (https://landlab.readthedocs.io/) to delineate the watershed and simulate the overland flow for a study area in the Boulder County. +
Permafrost is defined as any material (rock or soil) that remains below 0°C for two or more consecutive years. This lab demonstrates how to use several CSDMS data components (https://csdms.colorado.edu/wiki/DataComponents) to download topography, snow and temperature data and couple them with the Model Components from Landlab and Pymt to calculate the permafrost active layer thickness and simulate the hillslope diffusion process for a study area in Alaska. +
Sandy shorelines are areas of dynamic geomorphic change, evolving on timescales ranging from hours to centuries. As part of the CSDMS ESPIn workshop, this educational lab was designed to allow users to observe firsthand the long-term change of a sandy coast of their choosing and explore the processes driving that change. The CEM was developed by Ashton et al. (2001) as an exploratory model that uses wave climate characteristics to model the evolution of an idealized coastline. In this educational lab, we couple CoastSat (a python tool that extracts shoreline geometry from satellite imagery (Vos et al., 2019)) to the CEM by initializing the model with observed shorelines from anywhere in the world. The CEM is then further driven by an average wave climate derived from local buoy data. This allows users to visualize the evolution of any sandy beach in the world through time. Through an introductory-level coding exercise, users will learn how to extract complex datasets, run a geomorphic model, and explore the impact of different wave climates on a beach they care about. +
The Ganges-Brahmaputra Delta is one of the largest deltas in the world. In Bangladesh alone, 160 million people live in the floodplains. The Ganges and Brahmaputra Rivers and their delta are strongly impacted by the Asian summer monsoon. Widespread hazards relate to the intense seasonal rainfall: flooding, river erosion and channel switches. The people of Bangladesh have adapted to this dynamic delta system by raising villages above the annual flood level and embanking agricultural land.<br><br>
Still, the lowlands of the Ganges-Brahmaputra Delta are considered one of the regions most at risk from climate change, and particularly from sea level rise. About 75 million people live in the GB coastal zone (defined as the region <10 m.a.s.l). It is thought that the impact of relative sea-level change will be profound in Bangladesh where 32% of the country is already affected by tides, salinization, and cyclones/storm surges. At the same time, the Ganges-Brahmaputra river system drains tremendous amounts of sediment (sand, silt and clay) from its steep Himalayan hinterland. We ask: how does this amount of sediment change with a changing monsoonal climate? Does this sediment aggrade fast enough to help counteract rapid sea-level rise? +
