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This website provides an application for exploring modeling results from a U.S. Geological Survey (USGS) project titled Mapping Climate Change Resistant Vernal Pools in the Northeastern U.S. The purpose of this project was to improve understanding of the factors that control inundation patterns in vernal pools of the northeastern United States, so as to identify pools that might function as hydrologic refugia under climate change.
Geographic distribution data were collected based on county level occurrences (or converted from point occurrences to county level occurrences) within the five focal states (Minnesota, North Dakota, South Dakota, Nebraska & Iowa) and each U.S. state or Canadian province bordering those focal states (Wisconsin, Illinois, Missouri, Kansas, Wyoming, & Montana in the USA and Saskatchewan, Ontario & Manitoba in Canada).
Although scientists have identified many ways to reduce the negative effects of climate change on wildlife, this information is not readily available to natural resource managers. For successful wildlife adaptation to climate change, natural resource managers should have current, peerreviewed information to guide their decisions. We conducted a review of over 1300 publications for recommendations to manage wildlife in the face of climate change. We then summarized the findings as the wildlife adaptation menu, a tool to inform planning and decision-making in an accessible format.
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Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added...
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This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs...
With global efforts to restore grassland ecosystems, researchers and land management practitioners are working to reconstruct habitat that will persist and withstand stresses associated with climate change. Part of these efforts involve movement of plant material potentially adapted to future climate conditions from native habitat or seed production locations to a new restoration site. Restoration practice often follows this plant-centered, top-down approach. However, we suggest that restoration of belowground interactions, namely between plants and arbuscular mycorrhizal fungi or rhizobia, is important for restoring resilient grasslands. In this synthesis we highlight these interactions and offer insight into how...
Categories: Publication; Types: Citation
Abstract (from Ecological Society of America): Successful management of natural resources requires local action that adapts to larger‐scale environmental changes in order to maintain populations within the safe operating space (SOS) of acceptable conditions. Here, we identify the boundaries of the SOS for a managed freshwater fishery in the first empirical test of the SOS concept applied to management of harvested resources. Walleye (Sander vitreus) are popular sport fish with declining populations in many North American lakes, and understanding the causes of and responding to these changes is a high priority for fisheries management. We evaluated the role of changing water clarity and temperature in the decline...
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This dataset provides model specifications used to estimate water temperature from a process-based model (Hipsey et al. 2019). The format is a single JSON file indexed for each lake based on the "site_id". This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
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This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of...
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Vernal pools are small, seasonal wetlands that provide critically important seasonal habitat for many amphibian species of conservation concern. Natural resource managers and scientists in the Northeast, as well as the Northeast Refugia Research Coalition, coordinated by the Northeast CSC, recently identified vernal pools as a priority ecosystem to study, and recent revisions to State Wildlife Action Plans highlighted climate change and disease as primary threats to key vernal pool ecosystems. Mapping out the hydrology of vernal pools across the Northeast is an important step in informing land management and conservation decision-making. Project researchers modeled the hydrology of roughly 450 vernal pools from...
Abstract (from PLOS ONE): Adequate diversity and abundance of native seed for large-scale grassland restorations often require commercially produced seed from distant sources. However, as sourcing distance increases, the likelihood of inadvertent introduction of multiple novel, non-native weed species as seed contaminants also increases. We created a model to determine an “optimal maximum distance” that would maximize availability of native prairie seed from commercial sources while minimizing the risk of novel invasive weeds via contamination. The model focused on the central portion of the Level II temperate prairie ecoregion in the Midwest US. The median optimal maximum distance from which to source seed was...
Categories: Publication; Types: Citation
The attached file includes a data-processing script for a study to model the hydrology of several hundred vernal pools (i.e., seasonal pools or ephemeral wetlands) across the northeastern United States. See https://doi.org/10.5066/P9CP2NUD for more information.
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The dataset provided here and described in this metadata document consists of several components: (1) pool-specific attributes including name and geographic location, (2) time-varying inundation observations collected between May 2004 and July 2016; (3) landscape attributes associated with pool locations including geologic, soil, and landcover characteristics; (4) short- and medium-term weather and climate variables for time periods (for example, 5-days and 6-months) immediately preceding the dates of inundation observations; and (5) long-term (30-year average) climate variables associated with pool locations.
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This data release includes data-processing scripts, data products, and associated metadata for a study to model the hydrology of several hundred vernal pools (i.e., seasonal pools or ephemeral wetlands) across the northeastern United States. More information on this study is available from the project website. This data release consists of several components: (1) an input dataset and associated metadata document ("pool_inundation_observations_and_climate_and_landscape_data"); (2) an annotated R script which processes the input dataset, performs inundation modeling, and generates model predictions ("annotated_R_script_for_pool_inundation_modeling.R"); and (3) a model prediction dataset and associated metadata document...
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This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
Abstract (from arXiv): This paper introduces a novel framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This framework, termed as physics-guided neural network (PGNN), leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network architecture. Further, this paper presents a novel framework for using physics-based loss functions in the learning objective of neural networks, to ensure that the model predictions not only show lower errors on the training set but are also scientifically consistent with the known physics on the unlabeled set. We illustrate the effectiveness...
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Climate change has been shown to influence lake temperatures in different ways. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we focused on improving prediction accuracy for daily water temperature profiles in 68 lakes in Minnesota and Wisconsin during 1980-2018. The data are organized into these items: Spatial data - One shapefile of polygons for all 68 lakes in this study (.shp, .shx, .dbf, and .prj files) Model configurations - Model parameters and metadata used to configure models (1 JSON file, with metadata for each of 68 lakes, indexed by "site_id") Model inputs - Data formatted as model inputs for predicting temperature a. Lake...
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This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North Temperate Lakes Long-TERM Ecological Research Program (NTL-LTER; https://lter.limnology.wisc.edu/). The buoy is supported by both the Global Lake Ecological Observatory Network (gleon.org) and the NTL-LTER. This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
Abstract (from arXiv): This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physical models, while generating outputs consistent with physical laws, and achieving good generalizability. Standard RNNs, even when producing superior prediction accuracy, often produce physically inconsistent results and lack generalizability. We further enhance this approach by using a pre-training method that leverages the simulated data from a physics-based model to address the scarcity of observed...
map background search result map search result map Mapping Climate Change Resistant Vernal Pools in the Northeastern U.S. Process-guided deep learning predictions of lake water temperature Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Inundation observations and inundation model predictions for vernal pools of the northeastern United States Inundation observations, climate data, and landscape attributes for vernal pools of the northeastern United States County-Level Geographic Distributions for 47 Exotic Plant Species in Midwest USA and Central Canada, Compiled 2019 Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data Process-guided deep learning predictions of lake water temperature Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Inundation observations and inundation model predictions for vernal pools of the northeastern United States Inundation observations, climate data, and landscape attributes for vernal pools of the northeastern United States Mapping Climate Change Resistant Vernal Pools in the Northeastern U.S. County-Level Geographic Distributions for 47 Exotic Plant Species in Midwest USA and Central Canada, Compiled 2019