the datasets include: Land surface model (VIC) simulation of historical and future streamflow data. This dataset is associated with the following publication: Reeder, W., F. Gariglio, R.S. Carnie, C. Tang, D. Isaak, C. Qiuwen , Y. Zhongbo, J.A. McKean, and D. Tonina. Some (Fish Might) Like It Hot: Climate and Habitat Quality Variability from Past to Future Climates. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 787: 147532, (2021).

There are soil moisture, ET, and meteorological data which I developed for the Salmon River Basin. This dataset is associated with the following publication: Tonina, D., J.A. McKean, D. Isaak, R. Benjankar, C. Tang, and C. Qiuwen. Climate Change Shrinks and Fragments Habitats for Salmon in a Snow-Dependent Region. GEOPHYSICAL RESEARCH LETTERS. American Geophysical Union, Washington, DC, USA, 49(12): e2022GL098552, (2022).

There are soil moisture, ET, and meteorological data which I developed for the Salmon River Basin. This dataset is associated with the following publication: Tonina, D., J.A. McKean, D. Isaak, R. Benjankar, C. Tang, and C. Qiuwen. Climate Change Shrinks and Fragments Habitats for Salmon in a Snow-Dependent Region. GEOPHYSICAL RESEARCH LETTERS. American Geophysical Union, Washington, DC, USA, 49(12): e2022GL098552, (2022).

The dataset contains long-term and short-term summaries of streamflow alteration and measures of biological condition (fish multi-metric index). Streamflow alteration metrics include the magnitude, duration, frequency, and seasonality of high and low flow streamflow. Biological condition was estimated from the National Rivers and Streams Assessment and National Water Quality Assessment fish sampling programs. Using fish samples, a fish multi-metric index was calculated and categorized into altered versus non-altered fish communities.

The dataset contains long-term and short-term summaries of streamflow alteration and measures of biological condition (fish multi-metric index). Streamflow alteration metrics include the magnitude, duration, frequency, and seasonality of high and low flow streamflow. Biological condition was estimated from the National Rivers and Streams Assessment and National Water Quality Assessment fish sampling programs. Using fish samples, a fish multi-metric index was calculated and categorized into altered versus non-altered fish communities.

The U.S. Geological Survey (USGS) in cooperation with NOAA, developed a regression model for estimating August mean baseflow per square mile of drainage area to help resource managers assess relative amounts of baseflow in streams with Maine Atlantic Salmon habitat. The model was derived from August mean baseflows computed at 31 USGS streamgages in and near the Gulf of Maine Atlantic Salmon Habitat Recovery Units. An ordinary least squares regression model estimates August mean baseflow per unit drainage area using two explanatory variables: percentage of the basin underlain by sand and gravel aquifers, and the basin mean July precipitation. This model provides the means to estimate August mean baseflow in cubic feet per second per square mile of basin area on user-selected ungaged sites throughout Maine south of 46º 21′55″ N latitude. Estimates will support prioritization of habitat conservation and restoration work for those reaches that offer baseflow refugia during summer low-flow periods, and thus have the potential to be high-quality Atlantic Salmon habitat. This data release includes 3 excel tables of data used in these analyses.

The U.S. Geological Survey (USGS) in cooperation with NOAA, developed a regression model for estimating August mean baseflow per square mile of drainage area to help resource managers assess relative amounts of baseflow in streams with Maine Atlantic Salmon habitat. The model was derived from August mean baseflows computed at 31 USGS streamgages in and near the Gulf of Maine Atlantic Salmon Habitat Recovery Units. An ordinary least squares regression model estimates August mean baseflow per unit drainage area using two explanatory variables: percentage of the basin underlain by sand and gravel aquifers, and the basin mean July precipitation. This model provides the means to estimate August mean baseflow in cubic feet per second per square mile of basin area on user-selected ungaged sites throughout Maine south of 46º 21′55″ N latitude. Estimates will support prioritization of habitat conservation and restoration work for those reaches that offer baseflow refugia during summer low-flow periods, and thus have the potential to be high-quality Atlantic Salmon habitat. This data release includes 3 excel tables of data used in these analyses.

The use of streamflow simulations from the Vflo model and subsequent calculation of streamflow metrics to investigate flow-ecology relationships may be hindered by our inability to accurately model flow variability and extreme flows of the arid Great Plains. The Canadian River and other rivers in the Great Plains tend to have highly variable flows and harsh environmental conditions. The combination of these environmental conditions makes semi-arid and arid regions difficult to represent with a hydrologic model, especially extreme events. In some cases, overestimating flows may be acceptable to water managers (e.g., vulnerability of infrastructures), but could greatly affect estimates of fish species persistence. To address incidences where poor model performance affected metrics derived from Vflo simulations, we suggest three possible options. 1) Restrict flow-ecology relationships to the mainstem of the Canadian River below Lake Meredith, 2) Restrict assessments to streamflow data aggregated at a monthly time step (although typically, this does not match ecological processes well); 3) Focus on streamflow metrics with a high prediction accuracy (e.g., magnitude, timing and duration at some locations). To maximize the number of potential explanatory variables and survey locations available in the Canadian River basin for the development of flow-ecology response models and minimize bias and uncertainty, a combination of these approaches is likely warranted. To move forward on flow-ecology relationships with valid statistical power, the compiled fish data (see processing steps) is best combined with available gage data to improve the development of ecological relationships.

The use of streamflow simulations from the Vflo model and subsequent calculation of streamflow metrics to investigate flow-ecology relationships may be hindered by our inability to accurately model flow variability and extreme flows of the arid Great Plains. The Canadian River and other rivers in the Great Plains tend to have highly variable flows and harsh environmental conditions. The combination of these environmental conditions makes semi-arid and arid regions difficult to represent with a hydrologic model, especially extreme events. In some cases, overestimating flows may be acceptable to water managers (e.g., vulnerability of infrastructures), but could greatly affect estimates of fish species persistence. To address incidences where poor model performance affected metrics derived from Vflo simulations, we suggest three possible options. 1) Restrict flow-ecology relationships to the mainstem of the Canadian River below Lake Meredith, 2) Restrict assessments to streamflow data aggregated at a monthly time step (although typically, this does not match ecological processes well); 3) Focus on streamflow metrics with a high prediction accuracy (e.g., magnitude, timing and duration at some locations). To maximize the number of potential explanatory variables and survey locations available in the Canadian River basin for the development of flow-ecology response models and minimize bias and uncertainty, a combination of these approaches is likely warranted. To move forward on flow-ecology relationships with valid statistical power, the compiled fish data (see processing steps) is best combined with available gage data to improve the development of ecological relationships.

The use of streamflow simulations from the Vflo model and subsequent calculation of streamflow metrics to investigate flow-ecology relationships may be hindered by our inability to accurately model flow variability and extreme flows of the arid Great Plains. The Canadian River and other rivers in the Great Plains tend to have highly variable flows and harsh environmental conditions. The combination of these environmental conditions makes semi-arid and arid regions difficult to represent with a hydrologic model, especially extreme events. In some cases, overestimating flows may be acceptable to water managers (e.g., vulnerability of infrastructures), but could greatly affect estimates of fish species persistence. To address incidences where poor model performance affected metrics derived from Vflo simulations, we suggest three possible options. 1) Restrict flow-ecology relationships to the mainstem of the Canadian River below Lake Meredith, 2) Restrict assessments to streamflow data aggregated at a monthly time step (although typically, this does not match ecological processes well); 3) Focus on streamflow metrics with a high prediction accuracy (e.g., magnitude, timing and duration at some locations). To maximize the number of potential explanatory variables and survey locations available in the Canadian River basin for the development of flow-ecology response models and minimize bias and uncertainty, a combination of these approaches is likely warranted. To move forward on flow-ecology relationships with valid statistical power, the compiled fish data (see processing steps) is best combined with available gage data to improve the development of ecological relationships.