Arash Modaresi Rad
Boise State University
Recent Activity
ABSTRACT:
This dataset focuses on reach-averaged estimation of river channel geometry, including top-width and depth, crucial for water flow prediction and flood mapping. Leveraging HYDRoacoustic data from the Surface Water Oceanographic Topography (HYDRoSWOT) program, we develop a machine learning model to predict channel geometry using data from the National Water Model, National Hydrologic Geospatial Fabric network, and other geospatial datasets. Our model demonstrates good fit within the Continental United States, with better performance observed in flatter regions. Covering nearly 2.7 million reaches in the US, this dataset is indexed to the National Hydrologic Geospatial Fabric network. However, in estuaries, particularly near river mouths where it widens into the coastal zone, there are no recorded Acoustic Doppler Current Profiler (ADCP) measurements in HYDRoSWOT, leading to unreliable model accuracy. Additionally, limitations in the training dataset, particularly the primary significant feature of ML models—100% annual exceedance probability discharge derived from the NWM—diminish skill in this exceedance probability, impacting the overall model goodness-of-fit. We provide estimates of channel geometry for two conditions: 100% and 50% annual exceedance probability, based on NWM historical retrospective data.
ABSTRACT:
This App provides 30 m resolution map of soil textures based on USDA soil classification for contiguous United States.
US soil texture classifications (defined by the USDA) derived from 30-m POLARIS Soil dataset Over the Contiguous United States.
The app is available at: https://water-delineation.users.earthengine.app/view/soil-texture-for-contiguous-united-states
and the home page url: https://water-delineation.users.earthengine.app/
ABSTRACT:
A Google Earth Engine App developed to delineate water bodies around the globe from 1984 until present and to provide 16 day estimates of surface area of water bodies as well as shapefiles to the user.
The app uses a novel framework to filters only those images that cloud is on top of the water body and allows users to choose from a list of spectral water indices to map water bodies. The app also allows users to select the choice of threshold (i.e., a fixed zero threshold or dynamic threshold to separate water form non-water background).
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ABSTRACT:
A Google Earth Engine App developed to delineate water bodies around the globe from 1984 until present and to provide 16 day estimates of surface area of water bodies as well as shapefiles to the user.
The app uses a novel framework to filters only those images that cloud is on top of the water body and allows users to choose from a list of spectral water indices to map water bodies. The app also allows users to select the choice of threshold (i.e., a fixed zero threshold or dynamic threshold to separate water form non-water background).
Created: March 6, 2022, 10:46 p.m.
Authors: Modaresi Rad, Arash
ABSTRACT:
This App provides 30 m resolution map of soil textures based on USDA soil classification for contiguous United States.
US soil texture classifications (defined by the USDA) derived from 30-m POLARIS Soil dataset Over the Contiguous United States.
The app is available at: https://water-delineation.users.earthengine.app/view/soil-texture-for-contiguous-united-states
and the home page url: https://water-delineation.users.earthengine.app/
Created: May 10, 2024, 5:49 p.m.
Authors: Modaresi Rad, Arash
ABSTRACT:
This dataset focuses on reach-averaged estimation of river channel geometry, including top-width and depth, crucial for water flow prediction and flood mapping. Leveraging HYDRoacoustic data from the Surface Water Oceanographic Topography (HYDRoSWOT) program, we develop a machine learning model to predict channel geometry using data from the National Water Model, National Hydrologic Geospatial Fabric network, and other geospatial datasets. Our model demonstrates good fit within the Continental United States, with better performance observed in flatter regions. Covering nearly 2.7 million reaches in the US, this dataset is indexed to the National Hydrologic Geospatial Fabric network. However, in estuaries, particularly near river mouths where it widens into the coastal zone, there are no recorded Acoustic Doppler Current Profiler (ADCP) measurements in HYDRoSWOT, leading to unreliable model accuracy. Additionally, limitations in the training dataset, particularly the primary significant feature of ML models—100% annual exceedance probability discharge derived from the NWM—diminish skill in this exceedance probability, impacting the overall model goodness-of-fit. We provide estimates of channel geometry for two conditions: 100% and 50% annual exceedance probability, based on NWM historical retrospective data.