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Collection of Floodhippo resources from the NFIE 2016 Summer Institute

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The accuracy of the standard MODIS daily snow cover products and several of the most common and computationally frugal gap filling methods were validated using 12 years of daily snow observations from over 800 SNOTEL stations. This is the joined csv of those station observations with the various MODIS snow cover datasets tested extracted from Google Earth Engine, and may be used to verify or further explore the accuracies of the MxD10A1 (c5) datasets.

Used in the "Comprehensive Accuracy Assessment of MODIS Daily Snow Cover Products and Gap Filling Methods" paper published in ISPRS Journal of Photogrammetry and Remote Sensing.

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Hello! My name is Jim Coll and I am one of the student course coordinators this summer and was a participant last summer alongside Mike Johnson. I am currently a Ph.D. student at the University of Kansas in the Geography and Atmospheric Science department. My research interests straddles scales; I work with global snow cover and remote sensing and hyper resolution (sub-meter) data collection and modeling of natural stream reaches. Although I currently call Kansas my home, I am originally from New Hampshire and when I can escape work I enjoy snowboarding (and its flat earth cousin longboarding), long walks on the beach, and hiking, I'm looking forward to a fantastic summer!

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Hey UCGIS!

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A ModelMuse archive for the final project. Currently a temp file for demonstration purposes. This will be published (with D.O.I.) when finalized.

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This code creates a daily F-MOD_V product as outlined in the “Comprehensive Accuracy Assessment of MODIS Daily Snow Cover Products and Gap Filling Methods” paper published in (TBD). This code can be copy-pasted into the Google Earth Engine JavaScript code editor (https://code.earthengine.google.com/) to create a gap filled snow cover dataset.

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For Stochastic_Streamflow_HW1

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A python 3 notebook containing code, explanation, and responses to homework 1

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Stochastic_Data_HW2

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HW2 broken example

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Here's a collection of resources related to the TEAM application (https://jamesmcoll.users.earthengine.app/view/team)
Raw Code: https://code.earthengine.google.com/f55a05fbf6e2468e01744d87ca178461

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ET plays a significant role in the terrestrial water cycle, accounting for up to 70% of the water budget depending on the environment. Disturbances in land cover such as urbanization, changes in cropping patterns and fire all impact the amount of water that is removed from the surface via ET. Currently, these disturbances are not assimilated in models where evapotranspiration is a function of land cover such as the North American Land Data Assimilation System (NLDAS) or the National Water Model (NWM). Part of this challenge is in the ability to identify basins that have experience disturbance. Following upon work that highlights the role of ET of post-fire regions, and cropped landscapes we explore how the operational MODIS ET mission can be used to identify basins that have experienced some form of disturbance. Using the 20 year MOD16 record and the computational capacities of Google Earth Engine, we develop ET signatures for all California watersheds and search for how deviations can be used to identify time and types of disturbance.

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Calculate length of a shapefile in python