Tseganeh Gichamo

USU

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ABSTRACT:

The Utah Energy Balance (UEB) Snowmelt Model Coupled to the Research Distributed Hydrologic Model (RDHM) with Parallel Processing using CUDA GPU.

This is the model used in the following paper
Gichamo, T. Z., & Tarboton, D. G. (2019). Ensemble streamflow forecasting using an energy balance snowmelt model coupled to a distributed hydrologic model with assimilation of snow and streamflow observations. Water Resources Research, 55. https://doi.org/10.1029/2019WR025472

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ABSTRACT:

Inputs to Research Distributed Hydrologic Model (RDHM) spatially distributed hydrologic model incorporating the UEB snowmelt model that evaluates the effect of snow and streamflow assimilation on streamflow forecasting.

This is data for the following paper
Gichamo, T. Z., & Tarboton, D. G. (2019). Ensemble streamflow forecasting using an energy balance snowmelt model coupled to a distributed hydrologic model with assimilation of snow and streamflow observations. Water Resources Research, 55. https://doi.org/10.1029/2019WR025472

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ABSTRACT:

This script executes the HydroDS tasks required to prepare TOPNET inputs for the use case reported in

Gichamo, T. Z., N. S. Sazib, D. G. Tarboton and P. Dash, (2020), "HydroDS: Data Services in Support of Physically Based, Distributed Hydrological Models," Environmental Modelling & Software: 104623, https://doi.org/10.1016/j.envsoft.2020.104623.

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ABSTRACT:

The Utah Energy Balance (UEB) Snowmelt Model Coupled to the Research Distributed Hydrologic Model (RDHM) with Parallel Processing using CUDA GPU.

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ABSTRACT:

Inputs to a spatially distributed hydrologic model incorporating the UEB snowmelt that evaluates the effect of snow and streamflow assimilation in streamflow forecasting.

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ABSTRACT:

The HydroDS tasks required to be executed to get complete UEB model inputs for an example watershed are given in the Python file “HydroDS_UEB_Setup”. This file calls functions from the other file, "hydrods_python_client" that has declarations for data service functions available from HydroDS.

To run the workflow for a different watershed in the Western US, modify the coordinates of the watershed boundary, outlet location, the start and end time of model period, and the spatial reference (projection) information in the form of EPSG Code (http://spatialreference.org/ref/epsg/). The commands in the workflow script can also be called interactively from any Python command line, or from a user application that uses incorporates the Python Client Library.

For watersheds outside of the Western US, but in the CONUS, you need to upload your own DEM. The services are currently limited to the US.
You need to have a HydroDS account to use these services.

These scripts are for the following paper
Gichamo, T. Z., N. S. Sazib, D. G. Tarboton and P. Dash, (2020), "HydroDS: Data Services in Support of Physically Based, Distributed Hydrological Models," Environmental Modelling & Software, https://doi.org/10.1016/j.envsoft.2020.104623.

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HydroDS Documentation
Created: March 15, 2017, 6:30 p.m.
Authors: Tseganeh Gichamo

ABSTRACT:

This document provides brief descriptions of HydroDS, a data processing web-based service for distributed/gridded hydrological models. HydroDS enables the generation of distributed (gridded) data for variables commonly used in hydrologic models in three widely used file formats: GeoTiff raster, shapefile, and multi-dimensional NetCDF. HydroDS provides functions for watershed delineation, terrain processing, estimation of canopy variables, and retrieval of climate data. The functions can be used independently or chained together to form a workflow that performs a set of related tasks.

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Composite Resource Composite Resource

ABSTRACT:

Logan River Watershed data used for testing parallel implementations of Utah Energy Balance Snowmelt Model reported in:

Gichamo, T. Z. and D. G. Tarboton, (2020), "UEB parallel: Distributed snow accumulation and melt modeling using parallel computing," Environmental Modelling & Software, 125: 104614, https://doi.org/10.1016/j.envsoft.2019.104614.

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Model Instance Resource Model Instance Resource

ABSTRACT:

Inputs to a spatially distributed hydrologic model incorporating the UEB snowmelt that evaluates the effect of snow and streamflow assimilation in streamflow forecasting.

Show More
Model Program Resource Model Program Resource

ABSTRACT:

The Utah Energy Balance (UEB) Snowmelt Model Coupled to the Research Distributed Hydrologic Model (RDHM) with Parallel Processing using CUDA GPU.

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Composite Resource Composite Resource
HydroDS TOPNET model input setup Python scripts
Created: May 30, 2019, 5:57 p.m.
Authors: Nazmus Sazib · David Tarboton

ABSTRACT:

This script executes the HydroDS tasks required to prepare TOPNET inputs for the use case reported in

Gichamo, T. Z., N. S. Sazib, D. G. Tarboton and P. Dash, (2020), "HydroDS: Data Services in Support of Physically Based, Distributed Hydrological Models," Environmental Modelling & Software: 104623, https://doi.org/10.1016/j.envsoft.2020.104623.

Show More
Model Instance Resource Model Instance Resource

ABSTRACT:

Inputs to Research Distributed Hydrologic Model (RDHM) spatially distributed hydrologic model incorporating the UEB snowmelt model that evaluates the effect of snow and streamflow assimilation on streamflow forecasting.

This is data for the following paper
Gichamo, T. Z., & Tarboton, D. G. (2019). Ensemble streamflow forecasting using an energy balance snowmelt model coupled to a distributed hydrologic model with assimilation of snow and streamflow observations. Water Resources Research, 55. https://doi.org/10.1029/2019WR025472

Show More
Model Program Resource Model Program Resource

ABSTRACT:

The Utah Energy Balance (UEB) Snowmelt Model Coupled to the Research Distributed Hydrologic Model (RDHM) with Parallel Processing using CUDA GPU.

This is the model used in the following paper
Gichamo, T. Z., & Tarboton, D. G. (2019). Ensemble streamflow forecasting using an energy balance snowmelt model coupled to a distributed hydrologic model with assimilation of snow and streamflow observations. Water Resources Research, 55. https://doi.org/10.1029/2019WR025472

Show More