MULTI-ORDER HYDROLOGIC POSITION: A HIGH-RESOLUTION DATASET FOR THE CONTERMINOUS UNITED STATES

The location of a point within a stream network can be an important measure in hydrology. Hydrologic position (HP) is defined here by two metrics: lateral position (LP) and distance from stream to divide (DSD). LP is the relative position of a point between the stream and its watershed divide. DSD for a point is the sum of the shortest distance to the stream (DS) and the shortest distance to the divide. LP is the quotient of DS divided by DSD. In addition, HP is specified relative to a stream network of a given order, and hence each point is assigned multiple values of HP. The highest stream order in the conterminous U.S. is ten (Mississippi River below Missouri River).

Hydrologic order “n” is defined here as the network of streams of order n and higher. For example, hydrologic order 1 is the network of all streams, and hydrologic order 2 consists only of streams of order 2 and higher. For a given hydrologic order, watershed divides are defined by Thiessen polygons, rather than by topographic divides. A Thiessen watershed consists of all points that are closer to a given stream than to any other stream. Thiessen watershed divides can change from one hydrologic order to the next. The term multi-order hydrologic position (MOHP) is used to acknowledge that HP for a point is defined for each of the different hydrologic orders. MOHP was developed for nine hydrologic orders.

MOHP for the conterminous U.S. was developed using the National Hydrography Dataset Version 2 (NHDPlusV2). The NHDplusV2 represents streams and waterbodies as flowlines, and includes an attribute equivalent to stream order. In addition, it was necessary to identify streams in Canada and Mexico that are close enough to the U.S. to influence computations. Values of MOHP were identified using a raster approach with a 30-meter cell size. The file sizes for the resulting geodatabases – LP and DSD for 9 hydrologic orders for about 8.7 billion cells – were large. Consequently, an additional dataset was developed for 90-m cells by subsampling the 30-m datasets.

Two case studies, using random forest models, are presented that demonstrate the utility of MOHP as predictor variables: geomorphic province in California’s Central Valley, and depth to the water table for the Fox-Wolf-Peshtigo drainages in Wisconsin.