Kathryn Lawson

Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 3 Model inputs

This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge.

Categories: Data; Tags: AL, Alabama, DE, Delaware, GA, All tags... Georgia, IA, ID, Idaho, Iowa, KS, Kansas, MA, MD, ME, MI, MS, Maine, Maryland, Massachusetts, Michigan, Mississippi, NC, NJ, NM, NV, NY, Nevada, New Jersey, New Mexico, New York, North Carolina, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, South Carolina, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags

4. Figure code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling

This section provides code for reproducing the figures in Rahmani et al. (2023b). The full model archive is organized into these four child items: 1. Model code - Python files and README for reproducing model training and evaluation 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations 3. Simulations - Simulation descriptions, configurations, and outputs [THIS ITEM] 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b) The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling....

Tags: US, United States, deep learning, environment, inlandWaters, All tags... machine learning, modeling, streams, water resources, water temperature, Fewer tags

Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 5 Model predictions

This data release component contains water temperature predictions in 118 river catchments across the U.S. Predictions are from the four models described by Rahmani et al. (2020): locally-fitted linear regression, LSTM-noQ, LSTM-obsQ, and LSTM-simQ.

Categories: Data; Tags: AL, Alabama, DE, Delaware, GA, All tags... Georgia, IA, ID, Idaho, Iowa, KS, Kansas, MA, MD, ME, MI, MS, Maine, Maryland, Massachusetts, Michigan, Mississippi, NC, NJ, NM, NV, NY, Nevada, New Jersey, New Mexico, New York, North Carolina, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, South Carolina, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags

2 Observations: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30.

Categories: Data; Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags

4 Model Code: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

This data release component contains model code and configurations for the LSTM models used to predict stream temperature.

Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags