Uncovering patterns and processes regulating stream water chemistry from catchment to continental scale
Restricted (Penn State Only)
- Author:
- Sadayappan, Kayalvizhi
- Graduate Program:
- Environmental Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 06, 2024
- Committee Members:
- Farshad Rajabipour, Program Head/Chair
Li Li, Co-Chair & Dissertation Advisor
Jay Regan, Co-Chair & Dissertation Advisor
Chaopeng Shen, Outside Field Member
Lauren McPhillips, Major Field Member
Jonathan Duncan, Outside Unit Member - Keywords:
- biogeochemistry
stream water chemistry
reactive transport modeling
intermittent stream
machine learning
modeling
concentration-discharge relationship
nitrate - Abstract:
- Stream water chemistry is essential for healthy aquatic life, water quality, and usability for human or wildlife consumption, recreation, agriculture or industrial purposes. It also plays major role in biogeochemical cycles as streams transport water and elements from terrestrial systems to oceans. Under climate change and other anthropogenic disturbances, stream water chemistry is changing rapidly. It is essential to understand the spatial and temporal patterns of stream water chemistry, and the processes shaping them. This dissertation work addresses three key knowledge gaps: 1) the need for parsimonious and flexible watershed scale Reactive Transport Models (RTM) to tease apart the influence of multiple hydrological and biogeochemical processes that shape solute export and stream water chemistry; 2) understanding patterns and processes shaping stream chemistry in intermittent streams that have become increasingly widespread in a changing climate; 3) identifying large scale drivers of stream chemistry using nitrate, a biogenic solute and nutrient that has become a global problem, as a representative solute. Chapter 2 addresses first knowledge gap by developing a new, parsimonious model BioRT-HBV, a watershed scale RTM that can simulate hydrological processes and biogeochemical reactions involving multiple solutes based on reaction stoichiometry, thermodynamics and kinetics. The model requires minimal data and can serve as an accessible educational tool for researchers with varying levels of expertise and from multiple disciplines. Chapter 3 aims to understand stream chemistry (biogenic and geogenic solutes) and its response to changing stream discharge in an intermittent stream. Biogenic solutes showed flushing pattern (increase in concentration with discharge) under high discharge regimes while both flushing and dilution pattern (decrease in concentration with discharge) during low discharge regimes. They exhibit high variability during baseflow. Geogenic solutes exhibited chemostatic behavior and low concentration variations mainly. BioRT-HBV cannot reproduce the highly variable dynamics of biogenic solutes but captured the chemostatic pattern for calcium and dissolved inorganic carbon, representative geogenic solutes, by considering the different subsurface sources that released solutes to stream based on flow path variations. Chapter 4 identifies large scale controls over long term mean riverine nitrate concentrations across conterminous United States (CONUS) using Boosted Regression Tree, a Machine Learning model. Five drivers – nitrogen application (manure and fertilizer) rates, percent urban area, mean annual precipitation, mean annual temperature, and sand content – alone explained 69% of spatial variability in riverine concentrations across CONUS. BioRT-HBV model developed here is versatile and can simulate a wide array of biogenic and geogenic solutes, of which we have illustrated few in this work. It can be applied to diverse catchments, even those without extensive data, as it requires minimal data. This user-friendly model with easy learning curve can be useful to researchers from hydrology and biogeochemistry fields and can promote interdisciplinary research. The results from this dissertation work suggest that stream and riverine water quality can degrade under future climate. The analysis of intermittent stream chemistry suggests that biogenic solute concentrations in intermittent streams can change significantly during low flows and droughts. This contradicts the common perception that stream solute concentrations are primarily driven by discharge. With projected increases in stream intermittency, more streams can exhibit significant concentration changes during baseflow and even exceed water quality standards. Continental scale study indicates that as discharge reduces (net effect of precipitation and temperature changes) due to global warming, stream nitrate levels will increase even as nitrogen input remain constant. If climate regulates other solute dynamics similarly, then decreasing discharge due to both climate change and water abstractions can endanger stream and riverine water quality.