GCN250, the first high resolution global data set for runoff curve numbers
In 2018, Scientific Data published the first global soils data set (HYSOGs250m) that can be directly used for curve-number (CN)-based runoff modeling. While the CN method was developed in the United States by the Department of Agriculture (USDA), it’s international adaptation has quickly grown.
The CN method is a semi-empirical engineering approach for hydrologic modeling and design purposes. The curve number is calculated from a combination of soils data, land cover/land use data, and antecedent runoff conditions. Without incorporating this information, the HYSOGs250m (which is at 250m resolution) cannot be directly used to estimate CNs. Within the framework of the Climate Change Initiative by the European Space Agency (ESA), the Land Cover project lead to the development of a global land cover map in raster format for 2015 at the 300m resolution, in addition to a time series of these maps dating back to 1992. The production of the two data sets (the HYSOG250m and the ESA LC map of 2015) inspired me to develop the first global curve number dataset at the 250m resolution, the GCN250.
Our data descriptor published today in Nature’s Scientific Data (https://www.nature.com/articles/s41597-019-0155-x) details the methodology and the validation behind generating curve numbers from these two data sets. The first challenge we faced was in how to map the ESA land cover classification into the Land cover types described by the USDA. While some land cover types existed in both classification systems, many did not exist. We mapped the plant functional types into the USDA classes in order to assign them curve numbers according to the underlying hydrologic soil group. We also developed a weighting function to determine the curve numbers for ESA Land covers that map into several plant functional types. The second challenge was the computation effort required to calculate the curve numbers for 2.4 billion pixels covering the world. We used parallel programming and tiling to speed up the computational time. The third challenge was the validation (which also tackled validating the curve number method itself). We used three years of daily runoff data from the Global Land Data Assimilation System (GLDAS), made available on Google Earth Engine. We compared GLDAS runoff for major watersheds in the world to runoff from our data set in response to daily precipitation from GLDAS. Because runoff response to rainfall is dynamic in nature, we generated three curve number data sets for dry, average, and wet antecedent runoff conditions. It is up to the user to judge which data set (or combination thereof) to choose for the modeling/engineering scenario studied.
While other efforts have been made elsewhere to develop a global curve number data set, our GCN250 differs from all because: 1) it is publicly available 2) highest resolution (250m); 4) consistent 5) incorporates the most recent hydrologic soil group, and 6) validated at the daily and monthly scale.
We are positive that scientists, engineers, and practitioners working in hydrology and floodplain analysis find this data helpful and of value for hydrologic design and modeling.