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Skookum Creek Watershed Observatory with upscaled hydrometeorology and downscaled gridded climate forcings
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| Type: | Resource | |
| Storage: | The size of this resource is 585.7 KB | |
| Created: | Apr 17, 2020 at 4:48 p.m. | |
| Last updated: | Aug 04, 2020 at 5:38 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 952 |
| Downloads: | 12 |
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Abstract
Evaluating the future change in the hydrologic response of rivers is typically carried out using a complex sequence of linked numerical models starting with climate projections by general circulations models (GCMs) with various assumptions about emissions scenarios on radiation (representative concentration pathways (RCPs), downscaling using statistical methods, or dynamic downscaling using nested regional climate models (RCMs), bias correction of selected downscaled hydrometeorological variables of interest using observed data sets (e.g. precipitation and temperature from gridded station data), and finally use of the bias corrected meteorological forcing as inputs to hydrologic models to simulate hydrologic change and various impacts. To address the issues encountered in the Pacific Northwest, Skagit and Nooksack basin studies, and mountain environments in general we have developed a hybrid approach which bias-corrects and combines simulated data from high-resolution regional climate models (RCMs) with long-term gridded interpolations of in situ data from weather stations. This is achieved by focusing on two primary objectives: 1) removing bias in the atmospheric model, while preserving the temperature and precipitation gradients in the physically based simulations, and 2) preserving the spatio-temporal correlations and time series characteristics of the gridded meteorological records based on station observations. The computational methods employed are intended to be flexible and (where the supporting data sets are available) can be broadly applied in support of hydrologic modeling in mountain environments. Substantial improvements in streamflow simulations in the Skagit case study provide proof of concept that temperature and precipitation bias at moderate to high elevation is effectively reduced by the new hybrid data processing approach and greatly improved model predictions, but the size of the dataset is massively large for investigating the details of the methods. This dataset and code is designed for tutorial and illustrations using the Skookum Creek watershed.
!! Disclaimer: Work in progress. !!
Sample dataset for demonstrating the methods in the upscaling-downscaling paper by
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Subject Keywords
Coverage
Spatial
Content
README.md
Software Instructions: Online and Personal Computer
INTERACTIVE! You won't regret clicking on this badge.
To open interactive Jupyter Notebooks with Binder JupyterHub server. You will be connected to a virtual machine with the software environment required to execute the models.
Online Modeling Instructions
To open interactive Jupyter Notebooks with the CUAHSI JupyterHub server, go to the upper right corner of the resource page and click on 'Open With'. Select CUAHSI JupyterHub. You will be connected to a virtual machine with the software environment required to execute the models.
Notebook 1: Learn about Observatory functions applied to Skookum Creek in the Nooksack Watershed. OGH_skookum_hybrid_2020417.ipynb
Personal Computer Installation Instructions
Packages
The notebooks included in this resource require the following Python 3 packages:
``` geopandas==0.5.0
dask==1.2.0
hs_restclient==1.3.5
hstools==0.0.1
descartes==1.1.0
landlab==1.10.0
```
To ensure that you have the correct packages and versions, run the following command(s) inside a Python terminal:
$ conda list
or
$ pip list
Creating a Working Environment
We recommend using Anaconda to create a fresh Python environment with all dependencies installed. After installing Anaconda, simply run the commands below with your desired environment name in place of MY_ENVIRONMENT_NAME:
conda create -n MY_ENVIRONMENT_NAME --file requirements.txt
activate the environment and start a jupyter server
source activate MY_ENVIRONMENT_NAME
jupyter notebook
Debugging a Working Environment
Are you getting errors? Here are some suggested steps. If you still have issues, email help@cuahsi.org or reach out to us (comment on this resource or see emails in HydroShare profiles) and we will invite you to the HydroShare Slack #landlab channel.
Bug: PackagesNotFound
PackagesNotFoundError: The following packages are not available from current channels.
Reduce the number of packages that were not available by running the following command
conda config --append channels conda-forge
Bug: Conda vs.Pip Install
If you get errors for a few packages, remove them from the requirements.txt file until you successfully created the conda environment.
conda create -n MY_ENVIRONMENT_NAME --file requirements.txt
Any packages that didn't get installed during creation of conda environment can be pip installed separately in the newly created conda environment. for example:
pip install hs-restclient==1.3.5
Reproducible Quote of the Day:
"The product of mental labor - science - always stands far below its value, because the labor-time necessary to reproduce it has no relation at all to the labor-time required for its original production." Karl Marx
Additional Metadata
| Name | Value |
|---|---|
| appkey | mybinder |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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