ABSTRACT:

This is a shapefile for Withlacochee River. This was provided by our instructor, Dr. Venkatesh Merwade in EAPS 59100 class on FAIR Science for downloading DEM for this area.

ABSTRACT:

This is a geotiff file for Digital Elevation Model (DEM) for Withlacochee River extracted from the National Elevation Dataset (NED).

This was created using a qgis and python based code for automated download of NED for given watershed boundaries. The watershed boundary is available at: https://www.hydroshare.org/resource/4c0ecdf634474776b181abab0d863adb/

The watershed boundary was provided by our instructor, Dr. Venkatesh Merwade in EAPS 59100 class on FAIR Science.

ABSTRACT:

This resource contains a flood frequency analysis for Eel River at North Manchester, IN (USGS Station Number: 03328000). The data download and analysis is completely automated using Python 3 code written in Jupyter Notebook. The resource contains three files:
1) Tutorial_Flood_Frequency_Analysis.pdf: A step by step instruction of the entire methodology leading to flood frequency analysis outputs. It assumes that the user has preliminary knowledge of the mathematics behind flood frequency analysis.
2) code02_rev04.ipynb: A python notebook with the code for implementing flood frequency analysis for any USGS gauge station
3) Flood_Frequency_Table.pdf: A pdf file containing the output of the flood frequency analysis for the above mentioned table.

This resource is created as part of a course , EAPS 59100, on FAIR Science at Purdue University.

ABSTRACT:

Modeling riverine processes require accurate representation of topography. However, Digital Elevation Models (DEMs) do not have complete bathymetric representation and need to be augmented with additional bathymetry data. SPRING is a conceptual bathymetry generation tool for creating 3D representation of river channel geometry that can be incorporating into traditional DEMs to develop a complete a more accurate "topo-bathy" DEM. SPRING has an automated framework for processing entire river network in a watershed with minimal user intervention, thereby, enabling it to process large watersheds efficiently. This is a significant advantage over other currently available river bathymetry generation tools which can only process single reaches. Additionally, most of the conceptual bathymetric models currently available to fluvial modelers create symmetric functional surfaces, which do not reflect the anisotropic characteristics of the river channel. SPRING captures the anisotropy in river geometry due to a meandering thalweg, thereby, creating asymmetric river channels that are more representative of natural river systems.

This resource contains an initial release of SPRING. It is available to users as a toolbar in ArcMap, which deploys intuitive Graphic User Interfaces (GUIs) to ensure that no programming (coding) background is required for implementing SPRING. Following files are included with this resource:
1) Installation File: This folder contains a zipped file of the SPRING windows installer (.msi)
2) SPRING_short_instructions.pdf: As the name suggests, these are concise instructions to set up and get SPRING running for the sample data
3) SPRING_User_Manual.pdf: These are slightly more detailed instructions about getting SPRING to run on the user’s dataset. It has more background and troubleshooting information on SPRING.
4) Sample Data: This folder contains a set of sample data. It has a DEM (“sampledem”) and a file geodatabase (“Sample_Data.gdb”). The file geodatabase contains all the input, intermediate and output feature classes that are needed by SPRING.

Please direct all your queries to Sayan Dey (dey6@purdue.edu) and Dr. Venkatesh Merwade (vmerwade@purdue.edu).

ABSTRACT:

This resource is a part of course on FAIR Science (EAPS 59100) offered at Purdue University by Dr. Merwade and Dr. Huber in Fall 2019.

This resource uses machine learning and a moving window analysis to predict the streamflow at a given location (USGS gauge) based on historical flows at the same location. This analysis is performed in python using Jupyter Notebook. This resource contains the python code, analysis (contained in jupyter notebook) and the observed streamflow timeseries (downloaded by the code automatically as csv file). The study site for this resource is the Eel River at North Manchester, IN (USGS 03328000).

ABSTRACT:

This resource contains the data used in the study "Incorporating Network Scale River Bathymetry to Improve Characterization of Fluvial Processes in Flood Modeling" published in Water Resources Research (doi: 10.1029/2020WR029521)

Abstract of the journal article:
Several studies have focused on the importance of river bathymetry (channel geometry) in hydrodynamic routing along individual reaches. However, its effect on other watershed processes such as infiltration and surface water (SW)‐groundwater (GW) interactions has not been explored across large river networks. Surface and sbsurface processes are interdependent, therefore, errors due to inaccurate representation of one watershed process can cascade across other hydraulic or hydrologic processes. This study hypothesizes that accurate bathymetric representation is not only essential for simulating channel hydrodynamics but also affects subsurface processes by impacting SW‐GW interactions. Moreover, quantifying the effect of bathymetry on surface and subsurface hydrological processes across a river network can facilitate an improved understanding of how bathymetric characteristics affect these processes across large spatial domains. The study tests this hypothesis by developing physically based distributed models capable of bidirectional coupling (SW‐GW) with four configurations with progressively reduced levels of bathymetric representation. A comparison of hydrologic and hydrodynamic outputs shows that changes in channel geometry across the four configurations has a considerable effect on infiltration, lateral seepage, and location of water table across the entire river network. For example, when using bathymetry with inaccurate channel conveyance capacity but accurate channel depth, peak lateral seepage rate exhibited 58% error. The results from this study provide insights into the level of bathymetric detail required for accurately simulating flooding‐related physical processes while also highlighting potential issues with ignoring bathymetry across lower order streams such as spurious backwater flow, inaccurate water table elevations, and incorrect inundation extents.