JiangchaoQiu's repositories
GRA-2020-SALib
Materials for GRA short course at UC Davis, Jan. 13 2020
HURDAT2_processor
# Best Track Data (HURDAT2) Atlantic hurricane database (HURDAT2) 1851-2018 (5.9MB download) This dataset was provided on 10 May 2019 to include the 2018 update to the best tracks. This dataset (known as Atlantic HURDAT2) has a comma-delimited, text format with six-hourly information on the location, maximum winds, central pressure, and (beginning in 2004) size of all known tropical cyclones and subtropical cyclones. The original HURDAT database has been retired. Detailed information regarding the Atlantic Hurricane Database Re-analysis Project is available from the Hurricane Research Division. ref:https://www.nhc.noaa.gov/data/ #https://www.nhc.noaa.gov/data/hurdat/hurdat2-format-atlantic.pdf # HURDAT2 Processor This is a python script that convert your HURDAT to a dataframe and generate a CSV file for you to eaily process this data. This work is part of trajectory segmentation research[1]. We use this dataset for evaluation purposes. If you are going to apply this script please cite to our work. Thanks. [1]: Etemad, Mohammad, et al. "A Trajectory Segmentation Algorithm Based on Interpolation-based Change Detection Strategies." EDBT/ICDT Workshops. 2019.
pyro2
A simple python-based tutorial on computational methods for hydrodynamics
GlobalHydro
Global high-resolution hydrologic modeling with 2.94 million river reaches (~90 m DEM)
SWAN-Support
A collection of resources to help use the SWAN wave model.
visual
visualization for "Marsooli, R., N. Lin, K. Emanuel, and K. Feng (2019). Climate change exacerbates hurricane flood hazards along US Atlantic and Gulf Coasts in spatially varying patterns. Nature Communications."
climada_module_tropical_cyclone
tropical cyclone hazard event sets, incl. surge and rain
Radial_convergence_plot
Script to produce radial convergence plots (or chord plots) for Sobol sensitivity analysis results or other purposes
FigureGen
Visualization of ADCIRC Model Data in Raster Formats
SWAN_example
Example Matlab scripts for prepping and viewing SWAN model runs.
fvcom-toolbox
Fork of the fvcom-toolbox (original available at https://github.com/GeoffCowles/fvcom-toolbox)
Coastal-Surge-Flood-Height
Given a map of the land-ocean interface, a DEM of the land, and a specified free water surface height, this code will fill up the land on the coastal fringe with water up to the height of this water surface (as if a bath), and output a raster map with the inundation depth.
return_curves
Codes to generate historical and future coastal flood return curves
river_delineation
A curated list of GIS tools and resources for river delineation
Typhoon_Stats
Python code to estimate return period of tropical cyclone winds in the northern west pacific ocean.
flood-map
A simple web map to visualize flood information on maps
subdomainPy
Python scripts for Subdomain Modeling in ADCIRC+SWAN
houston_street_flooding
Experimenting with predicting street flooding during the 2017 Subsurface Hackathon in Houston
LISFLOOD-FP_MATLAB
MATLAB (and some Python and R) scripts for pre- and post-processing LISFLOOD-FP inputs and outputs.
GPM-Downscaling
Machine learning based methods for satellite precipitation downscaling
CoinCalc
CoinCalc R Package for Event Coincidence Analysis
tropical-cyclone
Statistical Study of Tropical Cyclone in North Western Pacific Region
TCwindgen
Quickly generate wind field from hurricane/Tropical cyclones based on track parameter. C CUDA implementation of TCRM wind model
Desink-Python-script-calling-arcGIS-desink-tool-
A Python script that calls the arcGIS desink tool as a preprocessing step in calculating hydrological connectivity for the fluvial bathtub inundation model
PolyADCIRC
Python-based framework for running batches of parallel ADCIRC simulations with varying parameters (Manning’s n and limited variable bathymetry, etc).
stormtracks
Detection and Tracking of Tropical Cyclones
SMT
Adcirc Subdomain Modeling Tool
Modeling-uncertainty-in-the-Earth-Sciences
teaching material for the book "Modeling Uncertainty in the Earth Sciences", Jef Caers, 2011.