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Stachelek, Jemma

Publications and source records attributed to Stachelek, Jemma.

16 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High resolution hydrologic routing with machine learning assisted waterbody classification

This software contains the code for a machine learning-based pipeline for creating persistent waterbody databases used in hydrologic routing. It consists of three components, 1) a PyTorch library (TorchWBType) for classifying/labeling arbitrary waterbodies into lakes and non-lakes, 2) a batch processing orchestrator (wbextractor) for delineating waterbodies from remote sensing imagery, and 3) graphing/analysis scripts for reproducing the plots in an associated journal article (LA-UR-24-22590).

Stachelek, Jemma↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

58 GEOSCIENCES↗
DOE OSTI2023

Quantifying co-benefits of water quality policies: An integrated assessment model of land and nitrogen management

Due to the nature of nitrogen cycling, policies designed to address water quality concerns have the potential to provide benefits beyond the targeted water quality improvements. For example, actions to protect water quality by reducing nitrate leaching from agriculture also reduce emissions of nitrous oxide, a potent greenhouse gas. These positive effects, which are incidental to the regulation's intended target, are termed “co-benefits.” To quantify the co-benefits associated with reduced nitrate leaching, we integrate an economic model of farmer decision making with a model of terrestrial nitrogen cycling for the watershed surrounding Lake Mendota, Wisconsin, USA. Our modeling approach provides a framework that links air and water pollutants in an agri-environmental system and offers a direction for future studies. Our model results highlight the finding that the co-benefits from nitrous oxide abatement are substantial, and their inclusion increases the benefit–cost ratio of water quality policies. Consideration of these co-benefits has the potential to reverse the conclusions of benefit–cost analysis in the assessment of current water quality policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Recent streamflow trends across permafrost basins of North America

Introduction Climate change impacts, including changing temperatures, precipitation, and vegetation, are widely anticipated to cause major shifts to the permafrost with resulting impacts to hydro-ecosystems across the high latitudes of the globe. However, it is challenging to examine streamflow shifts in these regions owing to a paucity of data, discontinuity of records, and other issues related to data consistency and accuracy. Methods Recent trends for long-term periods (1990–2021, 1976–2021) in observed minimum, mean, and maximum seasonal and annual streamflow were analyzed for a range of watersheds across North America affected by varying degrees of permafrost coverage. Results Streamflow trend analysis revealed that areas affected by permafrost are changing variably over the periods in terms of maximum, mean, and minimum seasonal and annual streamflow. These changes indicate a significant shift occurring in the most recent 46 years towards increasing mean streamflow for the dominant (> 50%) permafrost systems. Meanwhile, minimum streamflow increases for all permafrost-dominant systems and many of the other permafrost-affected systems across the seasons and annual periods considered, with the greatest number of significant changes in streamflow over other metrics. Maximum streamflow is shifting variably with significant increases in the permafrost-dominant systems in winter and fall over longer time periods of analysis. Our analysis suggests that streamflow trends are driven by climate (precipitation, followed by temperature), while variables such as permafrost coverage only appear important in the most recent 32-year period. Discussion The increases in streamflow trends observed in this study are reflective of deepening active layers and thawing permafrost, indicating that the entire hydrograph is undergoing change within permafrost-dominant streamflow systems as the Arctic moves towards a warmer future under climate change. Despite the many challenges to understanding changing streamflow in cold regions, there are new products and datasets in development that are increasingly allowing researchers to better understand the patterns of change in Arctic and subarctic systems affected by permafrost, offering a range of new tools, which, along with continued observational records, may help in improved understanding of changing Arctic streamflow patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantifying uncertainty in Pareto estimates of global lake area

Abstract Size is a critical factor determining the rate and occurrence of specific lake processes such as carbon sequestration and greenhouse gas emissions and emerging evidence suggests that small lakes in particular have particularly large CO 2 flux rates. Because we do not have a complete census of all lakes, upscaling estimates of such processes to small lakes at broad spatial scales requires the use of lake size‐abundance distributions rather than empirical measurements of area. Existing lake census efforts are incomplete such that as lakes become smaller, they are more likely to be omitted either because they are too small to be resolved from remote sensing products or because of limited ground surveying effort (i.e., “censoring” of small lakes relative to large lakes). The present study explores one potential shortcoming of prior approaches estimating global lake area using lake size‐abundance distributions. Namely, that these prior approaches rely on frequentist curve fitting techniques combined with an ad‐hoc cutoff determination strategy (visual inspection to determine a likely censoring point). This yields an over‐exact lake area estimate that is typically reported with no uncertainty bounds. I show how these shortcomings can be addressed with a Bayesian model that produces larger estimates of lake area uncertainty relative to the typical approach. When used as part of a sensitivity analysis, such an approach has the potential to enable more robust intercomparisons among studies of aquatic processes upscaling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantifying uncertainty in Pareto estimates of global lake area

This software contains the code for Bayesian uncertainty analysis of global lake area. Computed uncertainties are compared against more conventional estimation using lake size-abundance distributions. Routines are included to explore sensitivity to observational errors and ad-hoc "censoring" strategies.

Stachelek, Jemma↗
DOE OSTI2022

Geographically aware estimates of remotely sensed water properties for Chesapeake Bay

This software contains the code for a data-driven model to produce high-resolution (< 1 km2) estimates of temperature, salinity, and turbidity over decadal time scales as required by next-generation Earth Systems Models. Our model fits daily MODIS Aqua reflectance data to surface observations (< 1 m depth) from 2000-2021 in Chesapeake Bay, USA.

Stachelek, Jemma↗
DOE OSTI2022

LANL Data Sprint 2022 - STEM Santa Fe

The Los Alamos National Laboratory (LANL) Data Sprint took place virtually on July 25-29, 2022. Our team worked with data from the STEM Santa Fe (SSF) organization (https://stemsantafe.org) whose mission can be summarized as devoted to “[creating] a world filled with analytical citizens exploring complex issues for the betterment of society”. We worked with SSF data exported from Google Forms with information on (pre/post) participant surveys and registration demographics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

rabpro: global watershed boundaries, river elevation profiles, and catchment statistics

River and Basin Profiler (rabpro) is a Python package to delineate watersheds, extract river flowlines and elevation profiles, and compute watershed statistics for any location on the Earth’s surface. As fundamental hydrologically-relevant units of surface area, watersheds are areas of land that drain via aboveground pathways to the same location, or outlet. Delineations of watershed boundaries are typically performed on digital elevation models (DEMs) that represent surface elevations as gridded rasters. Depending on the resolution of the DEM and the size of the watershed, delineation may be very computationally expensive. With this in mind, we designed rabpro to provide user-friendly workflows to manage the complexity and computational expense of watershed calculations given an arbitrary coordinate pair. In addition to basic watershed delineation, rabpro will extract the elevation profile for a watershed’s mainchannel flowline. This enables the computation of river slope, which is a critical parameter in many hydrologic and geomorphologic models. Finally, rabpro provides a user-friendly wrapper around Google Earth Engine’s (GEE) Python API to enable cloud-computing of zonal watershed statistics and/or time-varying forcing data from hundreds of available datasets. Altogether, rabpro provides the ability to automate or semi-automate complex watershed analysis workflows across broad spatial extents.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Historical and Future Extreme Event Indices for the Colorado River Basin

Extremes events are occurring more frequently and are projected to increase in the future. This data set includes extreme event indices that were generated for the Colorado River basin using data from the Variable Infiltration Capacity hydrologic model. VIC simulations were run using six CMIP5 earth system models (ESMs), two different time periods, (historical, 1970-1999) and future (2070-2099), and for one Representative Concentration Pathway (RCP 8.5). ESMs available are MPI-ESM-LR, MIROC-ESM, IPSL-CM5A-LR, GFDL-ESM2M, and GFDL-ESM2G. The different indicators include temperature (min/max, freezing days, heating days), precipitation (dry days, max), runoff (min, max), soil moisture (min, max), evapotranspiration (max), snow water equivalent (SWE, max), wind speeds (max), and pressure (min). Detailed information on the indicators, file names, and how they were calculated is included in the README.txt document. Data sets were used to calculate results are in related references Bennett et al. 2020 and Talsma et al. 2021. Details on the VIC model configuration is described in related references Bennett et al. 2018 and Bennett et al. 2017.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Imperfect slope measurements drive overestimation in a geometric cone model of lake and reservoir depth

Lake and reservoir (waterbody) depth is a critical characteristic that influences many important ecological processes. Unfortunately, depth measurements are labor-intensive to gather and are only available for a small fraction of waterbodies globally. Therefore, scientists have tried to predict depth from characteristics easily obtained for all waterbodies, such as surface area or the slope of the surrounding land. One approach for predicting waterbody depth simulates basins using a geometric cone model where the nearshore land slope and distance to the center of the waterbody are assumed to be representative proxies for in-lake slope and distance to the deepest point respectively. We tested these assumptions using bathymetry data from ~5000 lakes and reservoirs to examine whether differences in waterbody type or shape influenced depth prediction error. Here, we found that nearshore land slope was not representative of in-lake slope, and using it for prediction increases error substantially relative to models using true in-lake slope for all waterbody types and shapes. Predictions were biased toward overprediction in concave waterbodies (i.e., bowl-shaped; up to 18% of the study population) and reservoir waterbodies (up to 30% of the study population). Despite this systematic overprediction, model errors were fewer (in absolute and relative terms, irrespective of any specific slope covariate) for concave than convex waterbodies, suggesting the geometric cone model is an adequate representation of depth for these waterbodies. But because convex waterbodies are far more common (>72% of our study population), minimizing overall depth prediction error remains a challenge.

54 ENVIRONMENTAL SCIENCES↗