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Irene Garousi-Nejad

Utah State University | Graduate Research Assistant

Subject Areas: Hydrology, Water Resources

 Recent activity

ABSTRACT:

Objective: To be able to use the Terrain Analysis Using Digital Elevation Models (TauDEM) tools to derive hydrologically useful information from Digital Elevation Models (DEMs).

Jupyter Notebook TauDEM was used for watershed delineation and calculation of Height Above Nearest Drainage in the Logan River Watershed in Utah. To start, "logan.tif" Digital Elevation Model (DEM) data and "LoganOultet.shp" Logan Outlet were used as the main inputs. The final results were "loagnw.tif" subwatershed, "logannet.shp" stream networks, and 'loganhand.tif' HAND map. This resource includes both the inputs to and the outputs from Jupyter Notebook TauDEM used for hydrologic terrain analysis in the Logan River Watershed in Utah.

To use the Jupyter Notebook, click on the "Open With" blue bottom at the top right of this page and choose "Jupyter". Then, click on "TauDEM.ipynb" to see the code and run it.

Most part of this jupyter notebook is adopted from Tarboton and Garousi-Nejad (2017).

Tarboton, D., I. Garousi-Nejad (2017). UCGIS 2017 Hydrologic Terrain Analysis Using TauDEM Start, HydroShare, http://www.hydroshare.org/resource/d4ed65b0c3c5475aa40af88c4d627c63

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ABSTRACT:

This resource contains the input/output and scripts used for Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method research which is submitted to the peer-reviewed journal of Water Resources Research and is not formally published on HydroShare in case any change are needed during the review process. Once the paper is accepted, a DOI will be used to cite this resource in the paper.

The abstract from the paper follows:
Flood inundation remains challenging to map, model, and forecast because it requires detailed representations of hydrologic and hydraulic processes. Recently, Continental-Scale Flood Inundation Mapping (CFIM), an empirical approach with fewer data demands, has been suggested. This approach uses National Water Model forecast discharge with Height Above Nearest Drainage (HAND) calculated from a digital elevation model to approximate reach-averaged hydraulic properties, estimate a synthetic rating curve, and map near real-time flood inundation from stage. In 2017, a record flood occurred on the Bear River in northern Utah, USA, due to rapid snowmelt. In this study, we evaluated the CFIM method over the river section where this flooding occurred. We compared modeled flood inundation with the flood inundation observed in high-resolution Planet RapidEye satellite imagery. Differences were attributed to discrepancies between observed and forecast discharges but also notably due to shortcomings in the derivation of HAND from the National Elevation Dataset as implemented in CFIM, and possibly due to sub optimal Manning’s n hydraulic roughness parameter. Examining these differences highlights limitations in the HAND terrain analysis methodology. We present a set of improvements developed to overcome some of the limitations and advance CFIM outcomes. These include conditioning the topography using high-resolution hydrography, dispersing nodes used to subdivide the river into reaches and catchments, and using a high-resolution digital elevation model. We also suggest an approach to obtain a reach specific Manning’s n from observed inundation. The methods developed have the potential to improve CFIM.

There are four main directories in this resource, each of which is described in details in Readme.md, which we recommend reading the Readme.md prior to running scripts to reproduce results.
To reproduce results:
1- Select which scenario you want to run (more details on scenarios can be found in the paper)
2- Download the directory of the selected scenario from this HydroShare resource (for example ./Processed_Data/Scenario6). This contains required inputs (including terrain analysis and HAND rasters) to run python scripts. If one also wants to reproduce including terrain analysis and HAND rasters, we recommend obtaining them as described in the methodology section of the paper.
3- Download the directory including scripts (./Scripts). Note that some functions used in these scripts require the arcpy library. In addition, note that addresses and inputs defined in scripts are based on Scenario 6. Scenario 6 is the scenario where we applied our developed etching approach to the 3(m) DEM using the high-resolution hydrography dataset along with all other improvements evaluated in scenarios 2-5. If using these scripts with another scenario, one needs to change addresses and name of inputs.
4- The easiest way to start working with these scenarios and scripts, is to do step 2 and 3 above and put them in the following address on your local machine: C:\Scenario6\
5- You can evaluate reproduced results with those we put into ./Output_Data directory of this HydroShare resource.

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ABSTRACT:

Flood inundation remains stubbornly challenging to map, model, and forecast with high precision for decision making because it requires a detailed
representation of the hydrologic and hydraulic processes, which are computationally demanding, and data limited. Recently, an empirical approach,
Continental-Scale Flood Inundation Mapping (CFIM), having fewer data demands and perhaps offering a more practical alternative, has been
presented as a scientific workflow where a Height Above Nearest Drainage (HAND) terrain model along with the National Water Model (NWM)
forecast discharge is employed for near real-time flood inundation mapping. In February 2017, a record flood occurred on the Bear River in Box
Elder County due to rapid snowmelt and rain on snow. In this study, we evaluated the CFIM method over the reach of the Bear River where this
flooding occurred. We evaluated the performance of the CFIM in terms of its accuracy in representing flooded and non-flooded areas when
comparing the results with flood inundation observed in imagery from the high-resolution Planet CubeSat RapidEye Satellites. The results indicate
that there were differences between CFIM flood inundation predictions and flooded area recorded by CubeSat Imagery. We used evaluation of these
differences to address challenges of CFIM and present a set of improvements to overcome some of the limitations and advance the outcome of
CFIM. The improvements utilize (1) the high-resolution (1:24,000) National Hydrography Dataset (NHD) to provide an obstacle-removed and
hydrologically conditioned topography, and (2) a higher-resolution Digital Elevation Model (DEM) dataset available for this area. The results indicate
that differences between CFIM flood inundation predictions and flooded area recorded by CubeSat Imagery were attributed to differences in observed
and forecast discharges, but also notably due to shortcomings in the HAND method and the derivation of HAND from the national elevation dataset
as implemented in CFIM. Examination of the causes for these differences has led us to develop proposed improvements to the CFIM methods,
which in this study were evaluated only for this single location. Nonetheless, the proposed improvements have the potential, following further
evaluation, to improve the broad application of the CFIM methodology.

PLAIN LANGUAGE SUMMARY:
Flood inundation is difficult to map, model, and forecast because of the data needed and computational demand. Recently an approach based on
the Height Above Nearest Drain (HAND) derived from a digital elevation model along with using the National Water Model forecasts has been
suggested, for both flood mapping and obtaining reach hydraulic properties. This approach was tested for a recent snowmelt flood on the Bear River
and compared to inundated area mapped using CubeSat satellite imagery. Initial differences found were reduced by addressing shortcomings in the
terrain analysis evaluation of HAND both in terms of the digital elevation model resolution and method used to condition the digital elevation model
using streamline information.

Slides for AGU Fall Meeting 2018 presentation H34G-08 at Washington D.C., December 12, 2018
Session: H34G: Research, Development, and Evaluation of the National Water Model and Facilitation of Community Involvement II

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ABSTRACT:

This resource includes the code (written in Python 3.6) and the documentation of a technique which is presented for adjusting the slopes of a Digital Elevation Model (DEM) derived drainage network where the slope is zero. The procedure uses the stream river network delineated from the grid-based DEM using Terrain analysis using Digital Elevation Models (TauDEM) software and re-compute the slopes considering the length and slope of all the upstream, downstream, and side entrance reaches. The results of this procedure is that all of the DEM-derived drainage network will have a positive (“downhill”) slope which are constrained to be greater than 0 m/m even when the elevation smoothing process produces equal upstream and downstream elevations on a flow line.

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ABSTRACT:

This resource includes the script, called script_NWM_dl_thredds.py, written in Python 3.6 to download the National Water Model products (specifically the analysis and assimilation) from HydroShare THREDDS data server. The other script, called script_NWM_readncfile.py, is also written in Python 3.6 to read the streamflow values from downloaded NetCDF files for a specific period (which is set to be February 15, 2017, but can be set to any other time if needed).

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 Contact

Resources
All 0
Collection 0
Composite Resource 0
Generic 0
Geographic Feature 0
Geographic Raster 0
HIS Referenced Time Series 0
Model Instance 0
Model Program 0
MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
Script Resource 0
SWAT Model Instance 0
Time Series 0
Web App 0
Generic Generic

ABSTRACT:

This proposal represents the need of using GIS as a tool to prepare inputs data of WRF-Hydro hydrologic model to simulate and predict streamflow in a small watershed in the GSL. WRF-Hydro, developed by National Center for Atmospheric Research ( NCAR), is the underlying hydrologic model implemented in National Water Model (NWM). The goal of this work is to use WRF-Hydro for a small watershed and compare the outputs with those of NWM.

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Generic Generic
NWM_USGS_retrieval
Created: Dec. 6, 2016, 5:13 p.m.
Authors: Irene Garousi-Nejad

ABSTRACT:

In hydrology, water data and specifically streamflow, has been an interesting issue, and historical observations of streamflow are collected by the United States Geological Survey (USGS). Additionally, several hydrologic models are used to produce forecasts of streamflow conditions in the future. Among efforts to forecast streamflow, the most recent endeavors to predict streamflow have led to the development, launch, and unveiling of America’s first National Water Model (NWM) on August 16, 2016. This model forecasts more precise, detailed, frequent, and expanded water information that can be utilized by various communities to improve water-related decisions. However, researchers who aim to use NWM forecast data may face some problems due to the retrieval, management, and analysis of these data. To cope with these challenges, a retrieval code (NWM_USGS_retrieval) that facilitates and automates the process of querying and retrieving data was generated in this project using the Python scripting language and demonstrated in a Jupyter IPython Notebook.

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Generic Generic

ABSTRACT:

Population growth and socioeconomic changes in developing countries over the past few decades have created sever stress on the available water resources across the world, particularly in semiarid regions, such as Utah. Hence, the optimal management of water resources is imperative. This study aimed to explore opportunities to provide the optimal reservoir operation rules for the Hyrum Reservoir, located on the Little Bear River in Utah, considering the reliability and vulnerability as the objective functions. Solving the multi-objective (herein two-objective) problem contributed us to investigate the interaction between reliability and vulnerability in this project. Modified Firefly Algorithm (MFA) was implemented as the optimization tool and three different problems, namely (1) single objective problem with reliability as the objective function, (2) single objective problem with vulnerability as the objective function, and (3) multi-objective problem with reliability and vulnerability as the objective functions, were solved. The results demonstrate the trade-off between the two objectives in the multi-objective problem. It also manifest that considering a multi-objective problem provide solutions whose the reliability and vulnerability values are within the upper and lower ranges calculated in the single objective problems.

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Geographic Feature (ESRI Shapefiles) Geographic Feature (ESRI Shapefiles)
iGarousi_homewatershed
Created: April 10, 2017, 4:26 p.m.
Authors: Irene Garousi-Nejad

ABSTRACT:

My name is Irene Garousi-Nejad. I am a graduate student in Civil and Environmental Engineering at Utah State University working with David G. Tarboton. My research is exploring options for improving flood and water supply forecasting in the Western United States, such as the Great Salt Lake and Colorado River basins, using physically-based distributed hydrologic modeling.

"Models are undeniably beautiful; however, they may have their hidden vices. The question is not only whether they are good to look at, but whether we can live happily with them" -- A. Kaplan, 1964 --

Outside of academics, I enjoy mountain climbing, playing and listening to music, and making Papier-Mache art.
You can contact me at: i.garousi@aggiemail.usu.edu

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Composite Resource Composite Resource

ABSTRACT:

This presentation is provided for the attendees of the Global Academy program at Utah State University in summer 2018 and talks about HydroShare, a web-based collaboration environment to enable more rapid advances in hydrologic understanding through collaborative data sharing, analysis, and modeling.

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Composite Resource Composite Resource

ABSTRACT:

This resource includes the script, called script_NWM_dl_thredds.py, written in Python 3.6 to download the National Water Model products (specifically the analysis and assimilation) from HydroShare THREDDS data server. The other script, called script_NWM_readncfile.py, is also written in Python 3.6 to read the streamflow values from downloaded NetCDF files for a specific period (which is set to be February 15, 2017, but can be set to any other time if needed).

Show More
Composite Resource Composite Resource

ABSTRACT:

This resource includes the code (written in Python 3.6) and the documentation of a technique which is presented for adjusting the slopes of a Digital Elevation Model (DEM) derived drainage network where the slope is zero. The procedure uses the stream river network delineated from the grid-based DEM using Terrain analysis using Digital Elevation Models (TauDEM) software and re-compute the slopes considering the length and slope of all the upstream, downstream, and side entrance reaches. The results of this procedure is that all of the DEM-derived drainage network will have a positive (“downhill”) slope which are constrained to be greater than 0 m/m even when the elevation smoothing process produces equal upstream and downstream elevations on a flow line.

Show More
Composite Resource Composite Resource

ABSTRACT:

Flood inundation remains stubbornly challenging to map, model, and forecast with high precision for decision making because it requires a detailed
representation of the hydrologic and hydraulic processes, which are computationally demanding, and data limited. Recently, an empirical approach,
Continental-Scale Flood Inundation Mapping (CFIM), having fewer data demands and perhaps offering a more practical alternative, has been
presented as a scientific workflow where a Height Above Nearest Drainage (HAND) terrain model along with the National Water Model (NWM)
forecast discharge is employed for near real-time flood inundation mapping. In February 2017, a record flood occurred on the Bear River in Box
Elder County due to rapid snowmelt and rain on snow. In this study, we evaluated the CFIM method over the reach of the Bear River where this
flooding occurred. We evaluated the performance of the CFIM in terms of its accuracy in representing flooded and non-flooded areas when
comparing the results with flood inundation observed in imagery from the high-resolution Planet CubeSat RapidEye Satellites. The results indicate
that there were differences between CFIM flood inundation predictions and flooded area recorded by CubeSat Imagery. We used evaluation of these
differences to address challenges of CFIM and present a set of improvements to overcome some of the limitations and advance the outcome of
CFIM. The improvements utilize (1) the high-resolution (1:24,000) National Hydrography Dataset (NHD) to provide an obstacle-removed and
hydrologically conditioned topography, and (2) a higher-resolution Digital Elevation Model (DEM) dataset available for this area. The results indicate
that differences between CFIM flood inundation predictions and flooded area recorded by CubeSat Imagery were attributed to differences in observed
and forecast discharges, but also notably due to shortcomings in the HAND method and the derivation of HAND from the national elevation dataset
as implemented in CFIM. Examination of the causes for these differences has led us to develop proposed improvements to the CFIM methods,
which in this study were evaluated only for this single location. Nonetheless, the proposed improvements have the potential, following further
evaluation, to improve the broad application of the CFIM methodology.

PLAIN LANGUAGE SUMMARY:
Flood inundation is difficult to map, model, and forecast because of the data needed and computational demand. Recently an approach based on
the Height Above Nearest Drain (HAND) derived from a digital elevation model along with using the National Water Model forecasts has been
suggested, for both flood mapping and obtaining reach hydraulic properties. This approach was tested for a recent snowmelt flood on the Bear River
and compared to inundated area mapped using CubeSat satellite imagery. Initial differences found were reduced by addressing shortcomings in the
terrain analysis evaluation of HAND both in terms of the digital elevation model resolution and method used to condition the digital elevation model
using streamline information.

Slides for AGU Fall Meeting 2018 presentation H34G-08 at Washington D.C., December 12, 2018
Session: H34G: Research, Development, and Evaluation of the National Water Model and Facilitation of Community Involvement II

Show More
Composite Resource Composite Resource

ABSTRACT:

This resource contains the input/output and scripts used for Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method research which is submitted to the peer-reviewed journal of Water Resources Research and is not formally published on HydroShare in case any change are needed during the review process. Once the paper is accepted, a DOI will be used to cite this resource in the paper.

The abstract from the paper follows:
Flood inundation remains challenging to map, model, and forecast because it requires detailed representations of hydrologic and hydraulic processes. Recently, Continental-Scale Flood Inundation Mapping (CFIM), an empirical approach with fewer data demands, has been suggested. This approach uses National Water Model forecast discharge with Height Above Nearest Drainage (HAND) calculated from a digital elevation model to approximate reach-averaged hydraulic properties, estimate a synthetic rating curve, and map near real-time flood inundation from stage. In 2017, a record flood occurred on the Bear River in northern Utah, USA, due to rapid snowmelt. In this study, we evaluated the CFIM method over the river section where this flooding occurred. We compared modeled flood inundation with the flood inundation observed in high-resolution Planet RapidEye satellite imagery. Differences were attributed to discrepancies between observed and forecast discharges but also notably due to shortcomings in the derivation of HAND from the National Elevation Dataset as implemented in CFIM, and possibly due to sub optimal Manning’s n hydraulic roughness parameter. Examining these differences highlights limitations in the HAND terrain analysis methodology. We present a set of improvements developed to overcome some of the limitations and advance CFIM outcomes. These include conditioning the topography using high-resolution hydrography, dispersing nodes used to subdivide the river into reaches and catchments, and using a high-resolution digital elevation model. We also suggest an approach to obtain a reach specific Manning’s n from observed inundation. The methods developed have the potential to improve CFIM.

There are four main directories in this resource, each of which is described in details in Readme.md, which we recommend reading the Readme.md prior to running scripts to reproduce results.
To reproduce results:
1- Select which scenario you want to run (more details on scenarios can be found in the paper)
2- Download the directory of the selected scenario from this HydroShare resource (for example ./Processed_Data/Scenario6). This contains required inputs (including terrain analysis and HAND rasters) to run python scripts. If one also wants to reproduce including terrain analysis and HAND rasters, we recommend obtaining them as described in the methodology section of the paper.
3- Download the directory including scripts (./Scripts). Note that some functions used in these scripts require the arcpy library. In addition, note that addresses and inputs defined in scripts are based on Scenario 6. Scenario 6 is the scenario where we applied our developed etching approach to the 3(m) DEM using the high-resolution hydrography dataset along with all other improvements evaluated in scenarios 2-5. If using these scripts with another scenario, one needs to change addresses and name of inputs.
4- The easiest way to start working with these scenarios and scripts, is to do step 2 and 3 above and put them in the following address on your local machine: C:\Scenario6\
5- You can evaluate reproduced results with those we put into ./Output_Data directory of this HydroShare resource.

Show More
Composite Resource Composite Resource

ABSTRACT:

Objective: To be able to use the Terrain Analysis Using Digital Elevation Models (TauDEM) tools to derive hydrologically useful information from Digital Elevation Models (DEMs).

Jupyter Notebook TauDEM was used for watershed delineation and calculation of Height Above Nearest Drainage in the Logan River Watershed in Utah. To start, "logan.tif" Digital Elevation Model (DEM) data and "LoganOultet.shp" Logan Outlet were used as the main inputs. The final results were "loagnw.tif" subwatershed, "logannet.shp" stream networks, and 'loganhand.tif' HAND map. This resource includes both the inputs to and the outputs from Jupyter Notebook TauDEM used for hydrologic terrain analysis in the Logan River Watershed in Utah.

To use the Jupyter Notebook, click on the "Open With" blue bottom at the top right of this page and choose "Jupyter". Then, click on "TauDEM.ipynb" to see the code and run it.

Most part of this jupyter notebook is adopted from Tarboton and Garousi-Nejad (2017).

Tarboton, D., I. Garousi-Nejad (2017). UCGIS 2017 Hydrologic Terrain Analysis Using TauDEM Start, HydroShare, http://www.hydroshare.org/resource/d4ed65b0c3c5475aa40af88c4d627c63

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