Laurel Larsen, Professor

Closed (1) Modeling the coevolution of vegetation and landforms on floodplains

Closed. This professor is continuing with Spring 2021 apprentices on this project; no new apprentices needed for Fall 2021.

Coevolution is a term that is used to describe the product of feedbacks between vegetation and landscapes. In fluvial environments, vegetation impacts flow velocities and sediment erosion and deposition dynamics, which sculpts the landscape. In turn, the distribution of elevations is a first-order control on vegetation colonization and growth. It is important to understand the interplay between vegetation and landforms to increase the odds of success of river and wetland restoration projects and to better predict and plan for floods.

In the field of fluvial geomorphology, it is common practice to use simulation models to quantify how riverscapes will change with changes in flood frequency and/or climate. However, most of these models assume that vegetation plays a singular role; the diversity of plants within a floodplain is not commonly represented in the model's rules. Increasingly, geomorphologists are learning that different plants play different roles in their control of or response to physical processes. In this project, we use modeling as a tool to understand how diverse communities of vegetation may have different impacts on the evolution of floodplain landscapes.

Specific Tasks:
- Download and become familiar with the CAESAR model for landscape evolution, written in the C programming language. (
- Review strategies used in the literature for representing different types of vegetation communities with different rules
- Work with a senior project member to determine which new rules for vegetation should be incorporated into CAESAR
- Modify the CAESAR source code
- Conduct multiple simulations of a floodplain with and without different types of vegetation represented

Learning outcomes:
Students will gain programming experience with applications to fluvial geomorphology. At the end of the semester, the student will emerge with a much better understanding of the interactions and feedbacks between flow, vegetation, and sediment transport. These processes lie at the heart of the emerging fields of ecohydrology and ecogeomorphology.

Day-to-day supervisor for this project: Jordan Wingenroth, Staff Researcher

Qualifications: Familiarity with programming (C preferred but not required). Interest in earth surface process research. Coursework in the physical sciences. Student must be a self-starter and willing to work and learn independently.

Weekly Hours: to be negotiated

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Closed (2) Influence of coastal marshes on sedimentation, southern Louisiana

Closed. This professor is continuing with Spring 2021 apprentices on this project; no new apprentices needed for Fall 2021.

The ESDL focuses on the interplay between biological, physical, and human aspects of the environment. This internship focuses on a physical laboratory experiment that is well underway, working to understand the effect of submerged vegetation on sediment collection and settling in rivers and wetlands. Interception, the direct capture of particles by plant stems and leaves in vegetated river systems and wetlands, might play an important role in shaping the landscape’s physical form, ecological communities, and water chemistry.

Interception may function as a filter for particle-bound contaminants, and it may play an important role in reversing subsidence and land loss at the coastal margin. However, particle capture rate by “collectors”, a catch-all term for submerged roots, stems, and branches, varies greatly depending on the physical structure of the canopy and its biochemical properties. Experimental methods attempting to bring theory of fluid dynamics to bear on this topic are still being actively developed. One subject of particular recent interest concerns biofilm, the thin layer of algae and other microbiota that live underwater on the surface of plants. In a series of laboratory and field flume experiments (a flume is like a wind tunnel for water), we are building on work recently published by other researchers (e.g., Huang et al. 2008), to better characterize the empirical properties of sediment capture by plants with and without biofilm and to determine mathematical relationships for interception as a function of particle size distribution, water velocity, and vegetation density. The student will work with experimental data collected from field flumes in the Wax Lake Delta of southern Louisiana.

The student will work collaboratively to interpret the results of field flume experiments. Example tasks involved in this project:

- Calibrate instruments measuring fluorescence and volume concentration to estimate mass volume of sediment concentration
- Running samples for suspended sediment size distributions or fluorescence in the wet lab
- Designing new strategies for analyzing samples or visualizing experimental results
- Data processing of existing experiment data
- Data analysis of existing experiment data
- Help create database of published measurements of marsh and/or floodplain vegetation characteristics and sedimentation rates

Learning outcomes include:
- Achieving an improved understanding of hydraulics and hydrology and sediment transport dynamics
- Improving data processing skills, including time series analyses
- Becoming familiar with a major issue in coastal sustainability and the underlying science
- Gaining hands-on experience with experimental approaches to fluvial geomorphology and hydrology

Day-to-day supervisor for this project: Sheila Trampush, Post-Doc

Qualifications: This project will be of interest to students in Earth and Planetary Sciences, Geography, Civil and Environmental Engineering, and potentially CRS (though students from other majors are also welcome to apply). Required: Students should have data-processing capabilities in spreadsheet software and/or Matlab, Python, or R. Students should have completed at least one college-level lab course. Students should demonstrate a strong quantitative background, highlighting the physical science courses they have taken and grades in those courses. Students should be willing to work as a member of a research team and have strong communications skills.

Weekly Hours: to be negotiated

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Closed (3) Improved forecasting of river flow

Applications for fall 2021 are now closed for this project.

River flow forecasting is essential for planning reservoir operations, defense strategies against flooding, and fluvial ecosystems management plans. However, flow forecasting is a highly uncertain science. One of the biggest uncertainties lies in resolving the timescales over which water is stored in the subsurface and time lags between perturbations in hydrometeorological variables and perturbations in streamflow.

To reduce this uncertainty, we are synthesizing data from highly instrumented watersheds throughout the world into a common database. With the organized and cleaned data, we will be applying statistical techniques from information theory to identify the critical timescales over which predictors of streamflow are relevant to streamflow forecasting. We are recruiting students to help in the process of database compilation and analysis of results.

The recruited student will be working with a large and multidisciplinary team of researchers at Berkeley and external institutions. Specific tasks will include implementing a workflow to ensure that datasets downloaded from instrumented watersheds are comparable and operational. This may involve running code to fill gaps in the datasets or aggregate different datapoints in time. Once the complete dataset from a particular site has been compiled, the student will interpret the output of the code that derives time lags between precipitation, other hydroclimatic variables, and discharge, drawing conclusions and syntheses.

Learning outcomes will be improved experience with scientific computing and programming, and exposure to a central challenge in hydrology. Students will also gain familiarity with cutting-edge tools emerging from the discipline of Information Theory and will gain insight into different modeling approaches for forecasting river flow.

Day-to-day supervisor for this project: Edom Moges, Post-Doc

Qualifications: The student should have programming (Python is preferred, though strong programming skills in another language, such as Matlab, will also be viewed favorably) and computing (Github experience is preferred) skills. The student ideally has also had some exposure to statistics and is comfortable interpreting scientific graphs. This would be an ideal opportunity for a statistics, CS, or civil engineering student who would like to gain experience with environmental applications. It could also be a good fit for an earth science or geography student with strong programming skills.

Weekly Hours: to be negotiated

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Closed (4) Understanding physical processes and making environmental predictions using LSTM Neural Networks

Applications for fall 2021 are now closed for this project.

The Environmental Systems Dynamics Laboratory (ESDL) focuses on the interplay between biological, physical, and human aspects of the environment using a combination of physically-based and data-driven models. Research topics include how river deltas grow or shrink, how landslides occur and mobilize, how deforestation affects precipitation, and how to forecast the response of environmental systems under changing forcing scenarios. This internship aims to expand on our current work exploring the use of deep learning (DL) for environmental predictions.
DL methods often outperform other models (including physical ones) in making environmental predictions but are often used as a “black box”, reducing our ability to gain insight into the physical processes involved. For example, Long-Short-Term-Memory (LSTM) networks are extremely effective in making river streamflow predictions, even in watersheds that are snow-dominated, as they can capture the lags between the forcing and response variables. Unlike a physical model, the LSTM does not know that in the winter precipitation turns to snow and does not become streamflow until the melting season. Yet, it learns from data that the system has a memory, and is able in many cases to generate accurate streamflow predictions, based on precipitation and temperature time series. In such cases the state variables indeed track observed snow measurements, even though these have not been provided to the LSTM as input variables. This suggests that the internal states of a trained LSTM represent hydrologic processes that control streamflow, and they can be identified by their correspondence to independent, and collocated observational datasets that the model has not seen. Thus, analyzing the LSTM state variables could provide insight on how the response may change under different climatic regimes, as well as the capability of approximating basin-wide variables that are not measured in many watersheds.
In addition, we seek to introduce physical constraints (such as water balance) to the LSTM, by modifying the optimization loss function and/or by including process-based model outputs among the input variables. This will enable the improvement of streamflow prediction, particularly in non-stationary conditions where out-of-sample data are more frequent, as well as a more robust generalization to other watersheds where data measurements are more sparse.
Similar applications include the prediction of soil moisture, evapo-transpiration, solute concentration, and subsurface pore water pressure. In addition to generating good predictions, we would like to learn how these response times change across time and space. We also want to explore how transferable DL methods are across different landscapes or climate gradients, as transferability is essential in developing larger scale models that can be trained concurrently on many different watersheds in different climatic and topographic settings. Finally,we want to explore how introducing physical constraints using physics-based loss functions and hybrid data-driven and process-based models can aid generalization and performance in non-stationary conditions.

The student will work with a variety of time series data from intensely monitored Critical Zone observatories, as well as from state and national datasets discharge and precipitation. The student will work collaboratively to develop DL models and to interpret the LSTM state variables, and their relative importance. Example tasks involved in this project:

- Experiment with diverse LSTM model architectures

- Parallelize code to tune hyperparameters of the LSTM model at the large scale using UC Berkeley high-performance computing clusters
- Implement physical constraints in the LTSM model structures
- Apply transfer learning in LSTM model
- Analyze the importance of physical inputs in LSTM using method like layer-wise relevance propagation

Learning outcomes include:
- Mastering how to train, calibrate and optimize deep neural networks model
- Learning how to use artificial intelligence to understand physical processes and improve environmental science
- Achieving an improved understanding of environmental systems and hydrology in particular
- Improving data processing skills, including time series analyses
- Becoming familiar with major issues in environmental forecasting and the underlying science
- Gaining hands-on experience with data-driven approaches to catchment hydrology

Day-to-day supervisors for this project: Dino Bellugi, Staff Researcher, Liang Zhang, Graduate Student

Day-to-day supervisor for this project: Dino Bellugi, Staff Researcher

Qualifications: Qualifications: This project will be of interest to students in Computer Science, Data Science, and Statistics (though students from other majors are also welcome to apply) who have an interest in applying their Machine Learning (ML) experience to the domains of Earth Science, Geography, Civil and Environmental Engineering. Required: Students should have programming capabilities in Python and PyTorch in particular. Familiarity with Matlab, and other ML libraries such as Scikit-Learn, Keras, and the Matlab Statistics and Machine Learning Toolbox is desirable. Students should demonstrate a strong ML background, highlighting courses they have taken, and applications developed. Students should be willing to work as a member of a research team and have strong communications skills.

Weekly Hours: to be negotiated

Off-Campus Research Site: Predominantly remote, pending campus re-opening progress.

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