Geology topics

Nicholas Aumen

Publications and source records attributed to Nicholas Aumen.

6 recordsLinked to original sources

USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗

USGS2023

Forecasting water levels using machine (deep) learning to complement numerical modelling in the southern Everglades, USA

Water level is an important guide for water resource management and wetland ecosystems, defining one of the most basic processes in hydrology. This research seeks to investigate the possibility of complementing numerical modeling with a Machine Learning (ML) model to forecast daily water levels in the southern Everglades in Florida, USA. An exact analytical solution to water level may not be possible, but using the computational methods afforded by ML, the traditional numerical techniques may be enhanced to generate more robust, scalable predictions. Five locations were chosen for application of the Time-Delayed Neural Network (TDNN) and Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) ML models, which were built to estimate water level with 1, 2, 3, 7 and 10 day forecasts using a simulation step of 1 day. The results showed that rainfall forecasts from weather models could improve water-level forecasts if the accuracy and performance of the weather models can be improved. The ML models presented here improve water-level predictions from a historical hydrologic model for a 24 hour forecast horizon.

Florida↗

USGS2023

Burmese pythons in Florida: A synthesis of biology, impacts, and management tools

Burmese pythons ( Python molurus bivittatus ) are native to southeastern Asia, however, there is an established invasive population inhabiting much of southern Florida throughout the Greater Everglades Ecosystem. Pythons have severely impacted native species and ecosystems in Florida and represent one of the most intractable invasive-species management issues across the globe. The difficulty stems from a unique combination of inaccessible habitat and the cryptic and resilient nature of pythons that thrive in the subtropical environment of southern Florida, rendering them extremely challenging to detect. Here we provide a comprehensive review and synthesis of the science relevant to managing invasive Burmese pythons. We describe existing control tools and review challenges to productive research, identifying key knowledge gaps that would improve future research and decision making for python control.

Florida↗

USGS2020

Aligning climate models with stakeholder needs: Advances in communicating future rainfall uncertainties for south Florida decision makers

Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. Geological Survey workshop, “Increasing Confidence in Precipitation Projections for Everglades Restoration,” highlighted a gap between standard climate model outputs and the climate information needs of some key Florida natural resource managers. These natural resource managers (hereafter broadly defined as “climate data users”) need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate‐model simulations based on user‐defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a “decision matrix” that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high‐resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.

South Florida↗

USGS2019

Assessment of uncertainty in multi-model means of downscaled south Florida precipitation for projected (2019-2099) climate

South Florida resource management, particularly the Everglades restoration effort, is beginning to consider projections of precipitation from multiple climate models for decision-making. Because precipitation changes can significantly affect the Everglades ecosystem, characterization of precipitation projection uncertainty is important for resource management decisions, and reduction of uncertainty is desired for better decision-making. Though uncertainty of precipitation projections has been characterized for many regions, uncertainty has not been sufficiently quantified for south Florida. This study builds upon prior results for projected Florida precipitation by adding recent climate model simulations, seasonal and spatial information, and uncertainty quantification and reduction. We identify the multi-model mean change in south Florida precipitation and characterize the uncertainty of 37 statistically downscaled Coupled Model Intercomparison Project Phase 5 models. For 2019−45, there is a likely (over 60% of ensemble members) increase in south Florida annual mean precipitation owing to a likely to very likely (near 90% of ensemble members) increase in dry season (November, December, January) precipitation, while wet season (June, July, August) shows a more likely than not (over 50% of ensemble members) decrease in precipitation in southern region and increase in precipitation in northern region. As south Florida agencies are on the verge of including precipitation projections in their upcoming planning horizon, this information will aid south Florida practitioners in decisions influenced by future rainfall.

South Florida↗

USGS2011

Recent and historic drivers of landscape change in the Everglades ridge, slough, and Tree Island mosaic

More than half of the original Everglades extent formed a patterned peat mosaic of elevated ridges, lower and more open sloughs, and tree islands aligned parallel to the dominant flow direction. This ecologically important landscape structure remained in a dynamic equilibrium for millennia prior to rapid degradation over the past century in response to human manipulation of the hydrologic system. Restoration of the patterned landscape structure is one of the primary objectives of the Everglades restoration effort. Recent research has revealed that three main drivers regulated feedbacks that initiated and maintained landscape structure: the spatial and temporal distribution of surface water depths, surface and subsurface flow, and phosphorus supply. Causes of recent degradation include but are not limited to perturbations to these historically important controls; shifts in mineral and sulfate supply may have also contributed to degradation. Restoring predrainage hydrologic conditions will likely preserve remaining landscape pattern structure, provided a sufficient supply of surface water with low nutrient and low total dissolved solids content exists to maintain a rainfall-driven water chemistry. However, because of hysteresis in landscape evolution trajectories, restoration of areas with a fully degraded landscape could require additional human intervention.

Critical Reviews in Environmental Science and Tech↗