Assessment of Implementing Land Use/Land Cover LULC 2020-ESRI Global Maps in 2D Flood Modeling Application

Floods are one of the most dangerous water-related risks. Numerous sources of uncertainty affect flood modeling. High-resolution land-cover maps along with appropriate Manning's roughness values are the most significant parameters for building an accurate 2D flood model. Two land-cover datasets are available: the National Land Cover Database (NLCD 2019) and the Land Use/Land Cover for Environmental Systems Research Institute (LULC 2020-ESRI). The NLCD 2019 dataset has national coverage but includes references to Manning's roughness values for each class obtained from earlier studies, in contrast to the LULC 2020-ESRI dataset, which has global coverage but without an identified reference to Manning's roughness values yet. The main objectives of this study are to assess the accuracy of using the LULC 2020-ESRI dataset compared with the NLCD 2019 dataset and propose a standard reference to Manning's roughness values for the classes in the LULC 2020-ESRI dataset. To achieve the research objectives, a confusion matrix using 548,117 test points in the conterminous United States was prepared to assess the accuracy by quantifying the cross-correspondence between the two datasets. Then statistical analyses were applied to the global maps to detect the appropriate Manning's roughness values associated with the LULC 2020-ESRI map. Compared to the NLCD 2019 dataset, the proposed Manning's roughness values for the LULC 2020-ESRI dataset were calibrated and validated using 2D flood modeling software (HEC-RAS V6.2) on nine randomly chosen catchments in the conterminous United States. This research's main results show that the LULC 2020-ESRI dataset achieves an overall accuracy of 72% compared to the NLCD 2019 dataset. The findings demonstrate that, when determining the appropriate Manning's roughness values for the LULC 2020-ESRI dataset, the weighted average technique performs better than the average method. The calibration and validation results of the proposed Manning's roughness values show that the overall Root Mean Square Error (RMSE) in depth was 2.7 cm, and the Mean Absolute Error (MAE) in depth was 5.32 cm. The accuracy of the computed peak flow value using LULC 2020-ESRI was with an average error of 5.22% (2.0% min. to 8.8% max.) compared to the computed peak flow values using the NLCD 2019 dataset. Finally, a reference to Manning's roughness values for the LULC 2020-ESRI dataset was developed to help use the globally available land-use/land-cover dataset to build 2D flood models with an acceptable accuracy worldwide.

Automated summary

Flood modeling requires high-resolution land-cover maps and appropriate Manning's roughness values. This study evaluates the accuracy of the LULC 2020-ESRI dataset compared to the NLCD 2019 dataset and proposes a standard reference for Manning's roughness values in the LULC 2020-ESRI dataset.