## Taher Chegini

#### University of Houston

Subject Areas: | Hydrology, Hydraulic, Numerical modeling, Data Analysis |

### Recent Activity

**ABSTRACT: **

For hydrological and hydraulic modeling, the bankfull width and depth are

important parameters that are challenging to measure in the field and/or

estimate a priori. Many studies suggested different methodologies for estimating

these parameters based on various geomorphological and hydrological characteristics.

One such study is by [Bieger et al. (2015)](https://doi.org/10.1111/jawr.12282)

which proposed a methodology to estimate bankfull width and depth for the conterminous

United States (CONUS) using regression equations. They gathered hydraulic geometry

data from hundreds of sites across the CONUS and developed regional regression

equations for the

[Physiographic Regions of CONUS](https://www.sciencebase.gov/catalog/item/631405bbd34e36012efa304e).

The general form of the regression equations is:

\begin{equation}

Y = a * A^b,

\end{equation}

where `Y` is the bankfull width or depth, `A` is the drainage area, and `a` and `b`

are the coefficients of the regression equation. The coefficients `a` and `b` are

specific to each physiographic region. Thus, bankfull width and depth can be

estimated for any location in the CONUS using the drainage area and the coefficients

of the corresponding physiographic region.

However, the dataset provided by Bieger et al. (2015) is not readily available

for use in hydrological and hydraulic modeling. This repository aims to provide

a methodology to generate a geospatial dataset for the bankfull width and depth of the CONUS using the regression equations provided by Bieger et al. (2015).

So, users can easily query the bankfull width and depth for any location in the

CONUS using the drainage area and the coefficients of the corresponding physiographic.

**ABSTRACT: **

The CAMELS datasets are not provided in an ideal format and takes a bit of data processing to convert them to useful and convenient forms for geospatial analyses. So, I decided to use the beloved netcdf and feather formats to make the dataset more accessible while taking care of some small annoyances! Three data sources are available from the CAMELS dataset:

1. Observed Flow: Streamflow observations for all 671 stations.

2. Basin Geometries: Polygons representing basins' boundaries for all 671 stations.

3. Basin Attributes: 60 Basin-level attributes for all 671 stations.

Two files are available:

1. camels_attributes_v2.0.feather: Includes basin geometries and 60 basin-level attributes that are available in CAMELS.

2. camels_attrs_v2_streamflow_v1p2.nc: Includes observed flows for all 671 stations, as well as the 60 basin-level attributes. It has two dimensions (station_id and time) and 60 data variables.

Additionally, some small annoyances in the original dataset are taken care of:

1. Station names didn't have a consistent format and there were some missing commas and extra periods! Now, the names have a consistent format (title) and there is comma before the states.

2. Station IDs and HUC 02 are strings with leading zeros if needed.

The code that was used to generate the dataset can be found at https://github.com/cheginit/camels_netcdf.

### Contact

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**Resource**

**ABSTRACT: **

The CAMELS datasets are not provided in an ideal format and takes a bit of data processing to convert them to useful and convenient forms for geospatial analyses. So, I decided to use the beloved netcdf and feather formats to make the dataset more accessible while taking care of some small annoyances! Three data sources are available from the CAMELS dataset:

1. Observed Flow: Streamflow observations for all 671 stations.

2. Basin Geometries: Polygons representing basins' boundaries for all 671 stations.

3. Basin Attributes: 60 Basin-level attributes for all 671 stations.

Two files are available:

1. camels_attributes_v2.0.feather: Includes basin geometries and 60 basin-level attributes that are available in CAMELS.

2. camels_attrs_v2_streamflow_v1p2.nc: Includes observed flows for all 671 stations, as well as the 60 basin-level attributes. It has two dimensions (station_id and time) and 60 data variables.

Additionally, some small annoyances in the original dataset are taken care of:

1. Station names didn't have a consistent format and there were some missing commas and extra periods! Now, the names have a consistent format (title) and there is comma before the states.

2. Station IDs and HUC 02 are strings with leading zeros if needed.

The code that was used to generate the dataset can be found at https://github.com/cheginit/camels_netcdf.

**Resource**

**ABSTRACT: **

For hydrological and hydraulic modeling, the bankfull width and depth are

important parameters that are challenging to measure in the field and/or

estimate a priori. Many studies suggested different methodologies for estimating

these parameters based on various geomorphological and hydrological characteristics.

One such study is by [Bieger et al. (2015)](https://doi.org/10.1111/jawr.12282)

which proposed a methodology to estimate bankfull width and depth for the conterminous

United States (CONUS) using regression equations. They gathered hydraulic geometry

data from hundreds of sites across the CONUS and developed regional regression

equations for the

[Physiographic Regions of CONUS](https://www.sciencebase.gov/catalog/item/631405bbd34e36012efa304e).

The general form of the regression equations is:

\begin{equation}

Y = a * A^b,

\end{equation}

where `Y` is the bankfull width or depth, `A` is the drainage area, and `a` and `b`

are the coefficients of the regression equation. The coefficients `a` and `b` are

specific to each physiographic region. Thus, bankfull width and depth can be

estimated for any location in the CONUS using the drainage area and the coefficients

of the corresponding physiographic region.

However, the dataset provided by Bieger et al. (2015) is not readily available

for use in hydrological and hydraulic modeling. This repository aims to provide

a methodology to generate a geospatial dataset for the bankfull width and depth of the CONUS using the regression equations provided by Bieger et al. (2015).

So, users can easily query the bankfull width and depth for any location in the

CONUS using the drainage area and the coefficients of the corresponding physiographic.