Upper Midwest Environmental Sciences Center
UMESC - Laboratory/Office - #1
2630 Fanta Reed Road
Jeff N Houser
This data release contains the climate change model inputs and Soil and Water Assessment Tool (SWAT) model outputs from 360 HUC-8 watersheds in the Midwest United States (Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio, and Wisconsin), that were generated using the HAWQS (Hydrologic and Water Quality System) platform (https://hawqs.tamu.edu). The summarized data for a watershed-based climate change vulnerability assessment for U.S. Fish and Wildlife Service is also provided, along with the R code used to summarize the raw outputs.
Future climate conditions in the Upper Mississippi River Basin are projected to include many more extreme precipitation events. These intense periods of rain can lead to flooding of the Mississippi River itself, as well the small streams and rivers that feed it. This flooding presents a challenge for local communities, farmers, small businesses, river users, and the ecosystems and wildlife in the area. To reduce the damage done by these extreme rainfall events, ‘natural solutions’ are often helpful. This might include preserving forests and grasslands to absorb rainwater before it arrives at streams or restoring wetlands to slow and clean runoff water. For river and natural resource managers to adapt to future climate...
Types: Map Service
OGC WFS Layer
OGC WMS Layer
OGC WMS Service
Drought, Fire and Extreme Weather
Drought, Fire and Extreme Weather
This dataset contains the input (temperature and precipitation from climate models) and output from the Soil and Water Assessment Tool (SWAT) model runs using the Hydrologic and Water Quality System (HAWQS) platform (https://hawqs.tamu.edu/). The HAWQS platform is an online tool developed by Texas A&M and US EPA to allow scientists and decision-makers to run large scale watershed simulation models using the Soil & Water Assessment Tool (SWAT) model without the need to download/install software, gather input data, perform initialization steps, or use up local computer resources. We ran the model at the Hydrologic Unit Code-8 scale over Region 3 of the United States Fish and Wildlife Service (Illinois, Indiana, Iowa,...
The datasets are to accompany a manuscript describing the prediction of submersed aquatic vegetation presence and its potential vulnerability and recovery potential. The data and accompanying analysis scripts allow users to run the final random forests predictive model and reproduce the figures reported in the manuscript. Files from several data sources (aqa_2010_lvl3_pct_oute_joined_VEG_BARCODE.csv, eco_states_near_SAV.csv, ltrm_vegsrs_thru2019_GEOMORPHIC_METRICS_final.csv, vegetation_data.csv, and water_full.csv) were combined into a single .csv file (analysis_data_for_SAV_RandomForest.csv) used as the input for the random forest model. When intersecting points with geomorphic metrics some sites were moved slightly...
This file contains five metrics that were selected to collectively represent the adaptive capacity of each of the 360 HUC-8 watersheds in US Fish and Wildlife Service Region 3 (Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, and Wisconsin). The metrics were: percent cultivated, density of dams, projected increase in developed land cover, landscape diversity and local connectedness. Percent cultivated land cover was obtained from the National Agricultural Statistics Services 2018 Cultivated layer and was calculated by dividing the number of cultivated grid cells by the total number of grid cells in each watershed. Density of dams was calculated as the number of dams per area of the watershed using the...