Subsurface tile drainage (TD) is a dominant agriculture water management practice in the United States (US) to enhance crop production in poorly drained soils. Assessments of field‐level or watershed‐level (<50 km2) hydrologic impacts of TD are becoming common; however, a major gap exists in our understanding of regional (>105 km2) impacts of TD on hydrology. The National Water Model (NWM) is a distributed 1‐km resolution hydrological model designed to provide accurate streamflow forecasts at 2.7 million reaches across the US. The current NWM lacks TD representation which adds considerable uncertainty to streamflow forecasts in heavily tile‐drained areas. In this study, we quantify the performance of the NWM with a newly incorporated tile‐drainage scheme over the heavily tile‐drained Midwestern US. Employing a TD scheme enhanced the uncalibrated NWM performance by about 20–50% of the fully calibrated NWM (
Tile drainage is one of the dominant agricultural management practices in the United States and has greatly expanded since the late 1990s. It has proven effects on land surface water balance and quantity and quality of streamflow at the local scale. The effect of tile drainage on crop production, hydrology, and the environment on a regional scale is elusive due to lack of high-resolution, spatially-explicit tile drainage area information for the Contiguous United States (CONUS). We developed a 30-m resolution tile drainage map of the most-likely tile-drained area of the CONUS (AgTile-US) from county-level tile drainage census using a geospatial model that uses soil drainage information and topographic slope as inputs. Validation of AgTile-US with 16000 ground truth points indicated 86.03% accuracy at the CONUS-scale. Over the heavily tile-drained midwestern regions of the U.S., the accuracy ranges from 82.7% to 93.6%. These data can be used to study and model the hydrologic and water quality responses of tile drainage and to enhance streamflow forecasting in tile drainage dominant regions.
more » « less- Award ID(s):
- 1739705
- NSF-PAR ID:
- 10181145
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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