AgHive announces the GCN250 App
The first global data set of rainfall-runoff relationships for hydrologic modeling and design
AgHive just published a new Hydrologic Curve Number (CN) App in Google Earth Engine.
The GCN250m app presents a global coverage of runoff potential and it is of value and importance to the scientific community because it allows users to visualize the gridded hydrologic curve number dataset at 250 m resolution globally.
The application demonstrates the first global data set of rainfall-runoff relationship based on a recent paper by Dr. Hadi Jaafar and team published in Nature’s Scientific Data.
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 us to develop the first global curve number dataset at the 250m resolution, the GCN250.
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.
Users have the option to choose run-off condition (wet, dry, or average) and a specific country of interest. Once the user clicks on the map, over their area of interest, the CN value is calculated. Hydrologic curve number is a crucial parameter in hydrology as it indicates the runoff potential lands based on land cover, soils, and antecedent runoff conditions. 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. GCN250 can be accessed through this link.