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Wei Zhi

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I am an assistant research professor in the Department of Civil & Environmental Engineering at Penn State University, University Park. My research interests focus on Critical Zone Science and Watershed Hydro-biogeochemistry. More specifically, I am interested in understanding how water moves and interacts with other components in natural environments. Ultimately, I aim to understand and forecast water quantity and quality at the watershed- and continental-scale, using both the process-based reactive transport model and the data-driven machine (deep) learning model.


Academic Profiles:


Research

Continential-scale water quality modelling & analysis

  • 2021, From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?
  • 2020, The Shallow and Deep Hypothesis: Subsurface Vertical Chemical Contrasts Shape Nitrate Export Patterns from Different Land Uses

Watershed-scale hydro-biogeochemicy & reactive transport modelling

Wetland biogeochemistry, nitrogen transformation, and microbial community analysis

Wei Zhi's Projects

biort-flux-pihm icon biort-flux-pihm

Watershed Biogeochemistry Model (the biogeochemical reactive transport model of PIHM family code)

camels-chem-do-dataset icon camels-chem-do-dataset

The newly developed dataset, CAMELS-Chem, compiles USGS water chemistry and instantaneous discharge from 1980 through 2014 in 493 headwater catchments. It includes common stream water chemistry related constituents, as well as an overlapping set of annual wet deposition load from the National Atmospheric Deposition Program.

deepwater icon deepwater

A deep-learning model for river water quality

distinct-source-water-chemistry-shapes-contrasting-concentration-discharge-patterns icon distinct-source-water-chemistry-shapes-contrasting-concentration-discharge-patterns

This work hypothesizes that seemingly disparate C‐Q patterns are driven by switching dominance of end‐member source waters and their chemical contrasts arising from subsurface biogeochemical heterogeneity. We use data from Coal Creek, a high‐elevation mountainous catchment in Colorado, and a recently developed watershed reactive transport model (BioRT‐Flux‐PIHM).

gramm icon gramm

Gramm is a powerful visualization toolbox which allows to quickly create complex, publication-quality figures in Matlab, and is inspired by R's ggplot2 library. As a reference to this inspiration, gramm stands for GRAMmar of graphics for Matlab.

mcd10a1 icon mcd10a1

a robust MODIS snow cover and phenology product (Google Earth Engine codebase)

mm-pihm icon mm-pihm

Multi-Modular Penn State Integrated Hydrologic Model

significant-stream-chemistry-response-to-temperature-variations-in-a-mountain-watershed icon significant-stream-chemistry-response-to-temperature-variations-in-a-mountain-watershed

High-elevation mountain regions, central to global freshwater supply, are experiencing more rapid warming than low-elevation locations. High-elevation streams are therefore potentially critical indicators for earth system and water chemistry response to warming. Here we present concerted hydroclimatic and biogeochemical data from Coal Creek, Colorado in the central Rocky Mountains at elevations of 2700 to 3700 m, where air temperatures have increased by about 2 °C since 1980. We analyzed water chemistry every other day from 2016 to 2019. Water chemistry data indicate distinct responses of different solutes to inter-annual hydroclimatic variations.

snotelr icon snotelr

a snow data network (SNOTEL) R package

the-shallow-and-deep-hypothesis-subsurface-vertical-chemical-contrasts-shape-nitrate-export-pattern icon the-shallow-and-deep-hypothesis-subsurface-vertical-chemical-contrasts-shape-nitrate-export-pattern

This work tests the shallow and deep hypothesis: subsurface vertical chemical contrasts regulate nitrate export patterns under different land use conditions. We synthesized data from 228 watersheds and used reactive transport modeling (500 simulations) under broad land use, climate, and geology conditions. Data synthesis indicated that human perturbation has amplified chemical contrasts in shallow water (e.g., soil water) versus deep waters (e.g., groundwater), inducing primarily flushing patterns in agriculture lands and dilution patterns in urban watersheds.

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