Engineering topics
Cromwell, Erol
Publications and source records attributed to Cromwell, Erol.
Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks, Frontiers in Earth Science: Dataset
This data package contains watershed modeling inputs and outputs as well as deep neural networks training and testing results used in "Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks" (Cromwell et al., 2021). We train various deep neural network models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models. For detailed information regarding watershed model description and DNNs setup, please refer to Cromwell et al., 2021. The deep_learning.zip file contains the inputs and outputs for the different deep neural networks. The watershed_model.zip file contains the inputs and outputs for watershed simulation using ATS. The figures.zip file contains raw figures and their corresponding scripts. For a more detailed description, please see the README.md file within each compressed file.
aodbe2ch.c1
The Aerosol Optical Depth is a Best Estimate (AODBE) value-added product (VAP) provides important quality control data on aerosol optical depth (AOD) VAP. This VAP focuses on several important aerosol radiative properties including aerosol optical depth and particle size. Improved knowledge of these basic aerosol properties will help reduce the uncertainties associated with aerosol effects in climate models. This VAP generates a combined AOD across 5 wavelengths (415, 500, 615, 673, 870 nanometers) under 5 and 2 channel versions, with high-quality, enhanced continuity and fine (1-minute) temporal resolution.