Irene Garousi-Nejad

Stanford University | Postdoctoral Scholar

Subject Areas: Hydrology, Water Resources Management

 Recent Activity

ABSTRACT:

This resource contains files required to retrieve precipitation accumulation (PREC), precipitation increment (PRCP), snow-adjusted precipitation increment (PRCPSA), snow water equivalent (WTEQ), and snow rain ratio (SNRR) from the SNOTEL network for a specified group of sites and a period of interest. A Jupyter Notebook (Main.ipynb) first reads the geographical information of SNOTEL sites (latitude and longitude values from the NRCS_SNOTEL_Locations_noAlaska.csv file) and gets other required parameters (including the start time, end time, and the state abbreviation), and then runs the python script (getData.py) to retrieve data.

The getData.py can be changed depending on the variable of your interest. For more details on what other variables are available, visit:
https://wcc.sc.egov.usda.gov/reportGenerator/
https://www.wcc.nrcs.usda.gov/web_service/AWDB_Web_Service_Reference.htm

This has been tested using CyberGIS-Jypyter for Water. To start working with these scripts, click on the Open With button on the top right and select CyberGIS-Jypyter for Water. From that page, open the Main.ipynb and follow the steps.

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ABSTRACT:

The Jupyter Notebook shared here determines X and Y indices of the National Water Model grid cells that contain snow telemetry (SNOTEL) sites. It uses two inputs: one CSV file that includes SNOTEL site information and one NetCDF file that is a land surface model output of the NWM reanalysis results. You can open this resource with CUAHSI JupyterHub and run the notebook within the code folder. The output is a CSV file that gives X and Y indices of the National Water Model grid cells associated with each SNOTEL site.

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ABSTRACT:

This resource contains Jupyter Notebooks used to create Figures of Garousi-Nejad, I. and D. G. Tarboton, (2022), "A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States," Hydrological Processes, 36(1): e14469, https://doi.org/10.1002/hyp.14469.

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ABSTRACT:

This resource includes Jupyter Notebooks that combine (merge) model results with observations. There are four folders:

- NWM_SnowAssessment: This folder includes codes required for combining model results with observations. It also has an output folder that contains outputs of running five Jupyter Notebooks within the code folder. The order to run the Jupyter Notebooks is as follows.
First run Combine_obs_mod_[*].ipynb where [*] is P (precipitation), SWE (snow water equivalent), TAir (air temperature), and FSNO (snow covered area fraction). This combines the model outputs and observations for each variable. Then, run Combine_obs_mod_P_SWE_TAir_FSNO.ipynb.

- NWM_Reanalysis: This folder contains the National Water Model version 2 retrospective simulations that were retrieved and pre-processed at SNOTEL sites using https://doi.org/10.4211/hs.3d4976bf6eb84dfbbe11446ab0e31a0a and https://doi.org/10.4211/hs.1b66a752b0cc467eb0f46bda5fdc4b34.

- SNOTEL: This folder contains preprocessed SNOTEL observations that were created using https://doi.org/10.4211/hs.d1fe0668734e4892b066f198c4015b06.

- GEE: This folder contains MODIS observations that we downloaded using https://doi.org/10.4211/hs.d287f010b2dd48edb0573415a56d47f8. Note that the existing CSV file is the merged file of the downloaded CSV files.

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ABSTRACT:

This resource contains Jupyter Notebooks that are used for post-processing the retrieved National Water Model retrospective simulations (NWM-R2), which are geospatial gridded results with a spatial resolution of 1 km and temporal resolution of 3 h. The NWM-R2 grid cells used were from https://doi.org/10.4211/hs.7839e3f3b4f54940bd3591b24803cacf and snow water equivalent and snow-covered area fraction at these grid cells from https://doi.org/10.4211/hs.3d4976bf6eb84dfbbe11446ab0e31a0a that retrieved this information from the NOAA Google Cloud.

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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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Resource Resource
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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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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Resource Resource
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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Resource 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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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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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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Resource 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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Physical Hydrology Homework 01
Created: Aug. 5, 2019, 7:47 p.m.
Authors: Garousi-Nejad, Irene · Lane, Belize

ABSTRACT:

Hydrologic Data Analysis, and Conservation Laws

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Resource Resource

ABSTRACT:

This resource contains the data and scripts used for: Garousi-Nejad, I., D. G. Tarboton, M. Aboutalebi and A. F. Torres-Rua, (2019), "Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method," Water Resources Research, http://doi.org/10.1029/2019WR024837.

Abstract from the paper:
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, rapid snowmelt resulted in a record flood on the Bear River in Utah, USA. 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 National Elevation Dataset as implemented in CFIM, and possibly due to sub optimal hydraulic roughness parameter. Examining these differences highlights limitations in the HAND terrain analysis methodology. We present a set of improvements developed to overcome some 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 and validated improvements for the flood of March 2019 in the Ocheyedan River, Iowa. The methods developed have the potential to improve CFIM.

The file Readme.md describes the contents and steps for reproducing the analyses in the paper.

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Resource Resource

ABSTRACT:

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data.
- There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects).
- If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS.
- You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

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ABSTRACT:

Nowadays, there is a growing tendency to use Python and R in the analytics world for physical/statistical modeling and data visualization. As scientists, analysts, or statisticians, we oftentimes choose the tool that allows us to perform the task in the quickest and most accurate way possible. For some, that means Python. For others, that means R. For many, that means a combination of the two. However, it may take considerable time to switch between these two languages, passing data and models through .csv files or database systems. There's a solution that allows researchers to quickly and easily interface R and Python together in one single Jupyter Notebook. Here we provide a Jupyter Notebook that serves as a tutorial showing how to interface R and Python together in a Jupyter Notebook on CUAHSI JupyterHub. This tutorial walks you through the installation of rpy2 library and shows simple examples illustrating this interface.

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Resource Resource
Calculating Runoff using TOPMODEL (M6)
Created: Oct. 21, 2019, 6:07 p.m.
Authors: Garousi-Nejad, Irene · Lane, Belize

ABSTRACT:

This resource contains data inputs and an iPython Jupyter Notebook used to simulate semi-distributed variable source area runoff generation in a tributary to the Logan River. This resource is part of the HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about.

In this activity, the student learns how to (1) calculate the topographic wetness index using digital elevation models (DEMs) following up on a previous module on DEMs and GIS in Hydrology; (2) apply TOPMODEL concepts and equations to estimate soil moisture deficit and runoff generation across a watershed given necessary watershed and storm characteristics; and (3) critically assess concepts and assumptions to determine if and why TOPMODEL is an appropriate tool given information about a specific watershed.

Please note that this exercise sets up the data needed to estimate runoff in the Spawn Creek watershed using TOPMODEL. Spawn Creek is a tributary of the Logan River, Utah. This exercise uses some of the same data as the Logan River Exercise in Digital Elevation Models and GIS in Hydrology at https://www.hydroshare.org/resource/9c4a6e2090924d97955a197fea67fd72/. If running the TOPMODEL for other study sites, you need to prepare a DEM TIF file and an outlet shapefile for the area of interest.
- There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects).
- If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS.

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Resource Resource
Physical Hydrology Homework 10
Created: Nov. 7, 2019, 11:20 p.m.
Authors: Garousi-Nejad, Irene · Lane, Belize

ABSTRACT:

This resource includes data required for physical hydrology homework 10 describe on the following HydroLearn module.
https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/course/

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Examples of CUAHSI Services
Created: Dec. 13, 2019, 2:45 p.m.
Authors: Garousi-Nejad, Irene

ABSTRACT:

This resource contains a powerpoint that is prepared for CUASHSI Town Hall at AGU19. The presentation outlines some of the CUAHSI services that help researchers to (1) publish and share their results and codes through HydroShare, (2) retrieve water-related data (such as streamflow observations) for a specified time and region through CUAHSI HydroClient and CUAHSI Subsetter tool, and (3) develop and run simple hydrologic models with available tools on HydroShare.

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Resource Resource

ABSTRACT:

This notebook has been developed to download specific variables at specific sites from National Water Model (NWM) Retrospective run results in Google Cloud. It has been set up to retrieve data at SNOTEL sites. An input file SNOTEL_indices_at_NWM.csv maps from SNOTEL site identifiers to NWM X and Y indices (Xindex and Yindex). A shell script (gget.sh) uses Google utilities (gsutil) to retrieve NWM grid file results for a fixed (limited) block of time. A python function then reads a set of designated variables from a set of designated sites from NWM grid files into CSV files for further analysis.

The input file SNOTEL_indices_at_NWM.csv was generated using Garousi-Nejad and Tarboton (2022), https://www.hydroshare.org/resource/7839e3f3b4f54940bd3591b24803cacf/.

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Resource Resource

ABSTRACT:

This resource contains a Jupyter Notebook that is used to introduce hydrologic data analysis and conservation laws. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) calculate the residence time of water in land and rivers for the global hydrologic cycle; (2) quantify the relative and absolute uncertainties in components of the water balance; (3) navigate public websites and databases, extract key watershed attributes, and perform basic hydrologic data analysis for a watershed of interest; (4) assess, compare, and interpret hydrologic trends in the context of a specific watershed.

Please note that in problems 3-8, the user is asked to use an R package (i.e., dataRetrieval) and select a U.S. Geological Survey (USGS) streamflow gage to retrieve streamflow data and then apply the hydrological data analysis to the watershed of interest. We acknowledge that the material relies on USGS data that are only available within the U.S. If running for other watersheds of interest outside the U.S. or wishing to work with other datasets, the user must take some further steps and develop codes to prepare the streamflow dataset. Once a streamflow time series dataset is obtained for an international catchment of interest, the user would need to read that file into the workspace before working through subsequent analyses.

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ABSTRACT:

Hydrologic Data Analysis, and Conservation Laws

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Resource Resource

ABSTRACT:

This JavaScript code has been developed to retrieve NDSI_Snow_Cover from MODIS version 6 for SNOTEL sites using the Google Earth Engine platform. To successfully run the code, you should have a Google Earth Engine account. An input file, called NWM_grid_Western_US_polygons_SNOTEL_ID.zip, is required to run the code. This input file includes 1 km grid cells of the NWM containing SNOTEL sites. You need to upload this input file to the Assets tap in the Google Earth Engine code editor. You also need to import the MOD10A1.006 Terra Snow Cover Daily Global 500m collection to the Google Earth Engine code editor. You may do this by searching for the product name in the search bar of the code editor.

The JavaScript works for s specified time range. We found that the best period is a month, which is the maximum allowable time range to do the computation for all SNOTEL sites on Google Earth Engine. The script consists of two main loops. The first loop retrieves data for the first day of a month up to day 28 through five periods. The second loop retrieves data from day 28 to the beginning of the next month. The results will be shown as graphs on the right-hand side of the Google Earth Engine code editor under the Console tap. To save results as CSV files, open each time-series by clicking on the button located at each graph's top right corner. From the new web page, you can click on the Download CSV button on top.

Here is the link to the script path: https://code.earthengine.google.com/?scriptPath=users%2Figarousi%2Fppr2-modis%3AMODIS-monthly

Then, run the Jupyter Notebook (merge_downloaded_csv_files.ipynb) to merge the downloaded CSV files that are stored for example in a folder called output/from_GEE into one single CSV file which is merged.csv. The Jupyter Notebook then applies some preprocessing steps and the final output is NDSI_FSCA_MODIS_C6.csv.

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Resource Resource

ABSTRACT:

The notebooks in this resource have been developed to retrieve precipitation, air temperature, and snow water equivalent measured at Natural Resources Conservation Service (NRCS) SNOTEL sites by calling associated Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI) web services.

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Collection Collection

ABSTRACT:

The HydroShare resources in this collection contain the data and scripts used for: Garousi-Nejad, I. and Tarboton, D. (2022), "A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States", Hydrological Processes, https://doi.org/10.1002/hyp.14469.

Abstract from the paper:
This study compares the US National Water Model (NWM) reanalysis snow outputs to observed snow water equivalent (SWE) and snow‐covered area fraction (SCAF) at snow telemetry (SNOTEL) sites across the Western United States SWE was obtained from SNOTEL sites, while SCAF was obtained from moderate resolution imaging spectroradiometer (MODIS) observations at a nominal 500 m grid scale. Retrospective NWM results were at a 1000 m grid scale. We compared results for SNOTEL sites to gridded NWM and MODIS outputs for the grid cells encompassing each SNOTEL site. Differences between modelled and observed SWE were attributed to both model errors, as well as errors in inputs, notably precipitation and temperature. The NWM generally under‐predicted SWE, partly due to precipitation input differences. There was also a slight general bias for model input temperature to be cooler than observed, counter to the direction expected to lead to under‐modelling of SWE. There was also under‐modelling of SWE for a subset of sites where precipitation inputs were good. Furthermore, the NWM generally tends to melt snow early. There was considerable variability between modelled and observed SCAF as well as the binary comparison of snow cover presence that hampered useful interpretation of SCAF comparisons. This is in part due to the shortcomings associated with both model SCAF parameterization and MODIS observations, particularly in vegetated regions. However, when SCAF was aggregated across all sites and years, modelled SCAF tended to be more than observed using MODIS. These differences are regional with generally better SWE and SCAF results in the Central Basin and Range and differences tending to become larger the further away regions are from this region. These findings identify areas where predictions from the NWM involving snow may be better or worse, and suggest opportunities for research directed towards model improvements.

Order to follow the developed scripts:
1. Notebook to get the indices of National Water Model grid cells containing SNOTEL sites
2. Notebook for retrieval of National Water Model Retrospective run results at SNOTEL sites
3. Notebooks for post-processing the retrieved National Water Model Retrospective run results and inputs at SNOTEL sites
4. Notebook for retrieval of precipitation, air temperature, and snow water equivalent measurements at SNOTEL sites
5. JavaScript code for retrieval of MODIS Collection 6 NDSI snow cover at SNOTEL sites to be run using Google Earth Engine
6. Notebooks for combining the National Water Model results/inputs with observations from SNOTEL and MODIS at SNOTEL sites
7. Notebooks for visualizations reported at A Comparison of National Water Model Retrospective Analysis Snow Outputs at SNOTEL Sites Across the Western U.S.

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Resource Resource

ABSTRACT:

This resource contains Jupyter Notebooks that are used for post-processing the retrieved National Water Model retrospective simulations (NWM-R2), which are geospatial gridded results with a spatial resolution of 1 km and temporal resolution of 3 h. The NWM-R2 grid cells used were from https://doi.org/10.4211/hs.7839e3f3b4f54940bd3591b24803cacf and snow water equivalent and snow-covered area fraction at these grid cells from https://doi.org/10.4211/hs.3d4976bf6eb84dfbbe11446ab0e31a0a that retrieved this information from the NOAA Google Cloud.

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Resource Resource

ABSTRACT:

This resource includes Jupyter Notebooks that combine (merge) model results with observations. There are four folders:

- NWM_SnowAssessment: This folder includes codes required for combining model results with observations. It also has an output folder that contains outputs of running five Jupyter Notebooks within the code folder. The order to run the Jupyter Notebooks is as follows.
First run Combine_obs_mod_[*].ipynb where [*] is P (precipitation), SWE (snow water equivalent), TAir (air temperature), and FSNO (snow covered area fraction). This combines the model outputs and observations for each variable. Then, run Combine_obs_mod_P_SWE_TAir_FSNO.ipynb.

- NWM_Reanalysis: This folder contains the National Water Model version 2 retrospective simulations that were retrieved and pre-processed at SNOTEL sites using https://doi.org/10.4211/hs.3d4976bf6eb84dfbbe11446ab0e31a0a and https://doi.org/10.4211/hs.1b66a752b0cc467eb0f46bda5fdc4b34.

- SNOTEL: This folder contains preprocessed SNOTEL observations that were created using https://doi.org/10.4211/hs.d1fe0668734e4892b066f198c4015b06.

- GEE: This folder contains MODIS observations that we downloaded using https://doi.org/10.4211/hs.d287f010b2dd48edb0573415a56d47f8. Note that the existing CSV file is the merged file of the downloaded CSV files.

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Resource Resource

ABSTRACT:

This resource contains Jupyter Notebooks used to create Figures of Garousi-Nejad, I. and D. G. Tarboton, (2022), "A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States," Hydrological Processes, 36(1): e14469, https://doi.org/10.1002/hyp.14469.

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Resource Resource

ABSTRACT:

The Jupyter Notebook shared here determines X and Y indices of the National Water Model grid cells that contain snow telemetry (SNOTEL) sites. It uses two inputs: one CSV file that includes SNOTEL site information and one NetCDF file that is a land surface model output of the NWM reanalysis results. You can open this resource with CUAHSI JupyterHub and run the notebook within the code folder. The output is a CSV file that gives X and Y indices of the National Water Model grid cells associated with each SNOTEL site.

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Resource Resource
Retrieve SNOTEL Data
Created: Nov. 5, 2021, 3:36 p.m.
Authors: Garousi-Nejad, Irene · Tarboton, David

ABSTRACT:

This resource contains files required to retrieve precipitation accumulation (PREC), precipitation increment (PRCP), snow-adjusted precipitation increment (PRCPSA), snow water equivalent (WTEQ), and snow rain ratio (SNRR) from the SNOTEL network for a specified group of sites and a period of interest. A Jupyter Notebook (Main.ipynb) first reads the geographical information of SNOTEL sites (latitude and longitude values from the NRCS_SNOTEL_Locations_noAlaska.csv file) and gets other required parameters (including the start time, end time, and the state abbreviation), and then runs the python script (getData.py) to retrieve data.

The getData.py can be changed depending on the variable of your interest. For more details on what other variables are available, visit:
https://wcc.sc.egov.usda.gov/reportGenerator/
https://www.wcc.nrcs.usda.gov/web_service/AWDB_Web_Service_Reference.htm

This has been tested using CyberGIS-Jypyter for Water. To start working with these scripts, click on the Open With button on the top right and select CyberGIS-Jypyter for Water. From that page, open the Main.ipynb and follow the steps.

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