These tools are developed to support hydrologic, climate, and data-intensive research. Together they reflect how I work across environmental science, computational modeling, and scientific machine learning.
YAML-driven geospatial workflow combining NDVI, rainfall, soil moisture, and forest-loss layers. Harmonizes inputs to a common monthly grid and applies configurable suitability thresholds. Includes a clearly labeled synthetic demo.
Python · Remote sensing · Raster processing
Explore the repository ↗PostgreSQL analysis of job-posting data to compare skill demand and reported salaries, with reproducible Python visualizations. Results describe the source dataset rather than the current job market.
SQL · PostgreSQL · Data visualization
Explore the repository ↗A Python package to compute long-term and short-term Soil Moisture Memory (SMM) for hydrological and climate research.
A toolkit for Baseflow Index (BFI) analysis and hydrological decomposition of streamflow time series.
A graph-based routing framework for transporting physically generated runoff through directed river networks using topology-aware machine learning.
An interactive application and modeling workflow for comparing LSTM and Transformer approaches to streamflow prediction in CAMELS-style basins.