Identification

Title

Edge-of-field runoff prediction by a hybrid modeling approach using causal inference

Abstract

Unforeseen runoff events cause nutrient losses that affect crop production, revenue, and contribute to deteriorated water quality, leading to harmful algal blooms and hypoxia in receiving water bodies in the Great Lakes region. To mitigate the negative impacts caused by runoff events, we developed a hybrid modeling approach by combining physics-based and statistical models to predict the occurrence and level of severity of daily runoff events, supporting agricultural producers to avoid nutrient application before significant runoff events. We chose to use the National Oceanic and Atmospheric Administration's National Water Model (NWM) as the physical model given its flexible architecture design, technical robustness, model resolution, data availability, and wide application scale. For the statistical model, we developed a data-driven tool built from Directed Information and eXtreme Gradient Boosting (XGBoost) to estimate the occurrence and the level of severity of daily edge-of-eld (EOF) runoff events. This data-driven tool ingests a large variety of variables from NWM operational runs and translates them into the EOF runoff predictions on a daily scale in the Great Lakes region. Without calibrating the large-scale NWM for the local runoff prediction, the results show large improvements in the prediction of the occurrence and level of severity of daily EOF runoff using the hybrid physical-statistical modeling approach. Ultimately, the hybrid approach, when integrated into runoff risk decision support tools, is expected to provide dual benefits to agricultural producers and water quality, retaining more nutrients on their fields and lowering nutrient loads to water bodies during runoff events.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d70z76pw

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2021-07-01T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact