Near-term spatial hydrologic forecasting in Everglades, USA for landscape planning and ecological forecasting

Leonard G. Pearlstine; James M. Beerens; Gregg Reynolds; Saira Haider; Mark McKelvy; Kevin Suir; Stephanie Romanach; Jennifer H. Nestler

doi:10.1016/j.envsoft.2020.104783

USGS · 70210888

Near-term spatial hydrologic forecasting in Everglades, USA for landscape planning and ecological forecasting

Leonard G. Pearlstine·James M. Beerens·Gregg Reynolds·Saira Haider·Mark McKelvy·Kevin Suir·Stephanie Romanach·Jennifer H. Nestler

View original source ↗Export metadata ↓

Abstract

Operational ecological forecasting is an emerging field that leverages ecological models in a new, cross-disciplinary way – using a real-time or nearly real-time climate forecast to project near-term ecosystem states. These applications give decision-makers lead time to anticipate and manage state changes that degrade ecosystem functions or directly impact humans. The Everglades Forecasting model (EverForecast) is an operational forecast model designed specifically for conservation management purposes including water management. It provides up to six-month forecasts of daily projected, spatially continuous stage values across the Everglades. We validated EverForecast quarterly to measured historical values at 207 gages (1 Jan 2000 – 31 Dec 2019). EverForecast hindcasted water stage accurately captured measured stage variation, with a low percentage of measured stage exceeding hindcasted values. Over the whole spatial extent, the mean RMSE is 20.98 cm, the mean MAE is 14.42 cm, and the mean MBE is 0.91 cm.

Explore related subjects

Environmental Modelling and Software Article

Source-reported bounding extent: 25.090573819461° to 26.775039386999605° latitude; -81.97174072265625° to -80.15899658203125° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Leonard G. Pearlstine, James M. Beerens, Gregg Reynolds, Saira Haider, Mark McKelvy, Kevin Suir, Stephanie Romanach, Jennifer H. Nestler. 2020. Near-term spatial hydrologic forecasting in Everglades, USA for landscape planning and ecological forecasting. https://doi.org/10.1016/j.envsoft.2020.104783

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

A catalogue of Do's and Don'ts in the modeling of environmental systems

Modeling plays a vital role in understanding and managing complex environmental systems, but its credibility and quality depend heavily on a comprehensive set of defensible model activities and practices, especially when the system of interest is plagued with uncertainties and conflicting stakeholder perspectives. This paper proposes a catalogue of Do's and Don'ts to guide modelers in addressing the many pertinent considerations through the whole modeling cycle. This practical tool provides advice on approaching modeling effectively through adhering to good modeling practice. It emphasizes model choices that align with the model purpose and context, and the justification and documentation of modeling decisions and assumptions. Managing uncertainty is a core consideration. The identification, assessment and reporting of these uncertainties is important across the entire modeling process, which spans problem framing, technical design, implementation and application phases. Such good practices are critical for transparency and reliability of the modeling.

Environmental Modelling and Software↗

USGS2025

Automating physics-based models to estimate thermoelectric-power water use

Thermoelectric (TE) power plants withdraw more water than any other sector of water use in the United States and consume water at rates that can be significant especially in water-stressed regions. Historical TE water-use data have been inconsistent, incomplete, or discrepant, resulting in an increased research focus on improving the accuracy and availability of TE water-use data using modeling approaches. This paper describes and benchmarks new code that was developed to automate and update a physics-based TE water use model that was previously published. Utilizing the automated physics-based model, monthly TE-power water withdrawal and consumption were calculated for a total of 1341 TE power plants for the 2008–2020 historical reanalysis. The updated and automated physics-based thermoelectric-power water-use model provides spatially and temporally relevant TE water-use estimates that are consistent, reproducible, transparent, and can be generated efficiently for water-using, utility-scale TE-power plants across conterminous United States (CONUS).

Environmental Modelling and Software↗

USGS2024

Transfer learning with convolutional neural networks for hydrological streamline delineation

Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline delineation. We evaluate the performance of eleven image-based pre-trained models and a baseline model using datasets from Rowan County, North Carolina, and Covington River, Virginia in the USA. Our results demonstrate that when models are adapted to a new area, the fine-tuned ImageNet pre-trained model exhibits superior predictive accuracy, markedly higher than the models trained from scratch or those only fine-tuned on the same area. Moreover, the pre-trained model achieves better smoothness and connectivity between classified streamline channels. These findings underline the effectiveness of transfer learning in enhancing the delineation of hydrological streamlines across varied geographies, offering a scalable solution for accurate and efficient environmental modelling.

North Carolina, Virginia↗