Water Erosion Surrogate Modeling for Sustainability Assessments

Abstract

Abstract Online tools such as the Truterra Insights Engine (IE) provide key performance indicator (KPI) assessments supporting farmer decisions to sustainably produce and deliver crops to market (<underline>https://www.truterraag.com/</underline>). They enable farmers to analyze and improve the long-term health of their ag-operations and participate in resilient supply chains. For example, IE leverages four physically-based models and algorithms for creating data used to assess cropping system KPIs for water and wind soil erosion, soil organic matter trend, and greenhouse gas emissions. Its scope spans 19 states from Minnesota to Louisiana, and from Kansas to Pennsylvania. Currently, IE supports assessments of six crop rotations (corn, soybeans, wheat), four tillage regimes, six cover crop options, six nitrogen fertilization methods, and six conservation practice combinations across the soils and climates of the 19 states. The existing models used to create the KPI dataset require relatively extensive data input and take significant time to finish a simulation, thus they are not suitable for direct real-time use. By contrast, surrogate models (SM) require many fewer inputs and finish quickly, but are constrained by a defined spatial coverage and providing less precise results. The challenge is to achieve a solution balancing rapid, scientifically valid, sufficiently precise, and applicable results for the regions of interest. Recent advances in machine learning combined with a streamlined SM delineation (Serafin, 2019; Serafin et al., 2021) offer potential for significantly improving surrogate model availability, precision, and applicability. For estimating water induced sheet and rill erosion for KPI assessments we employed the WEPP (Water Erosion Prediction Project) model as the process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. The overall objective of this ongoing modeling effort is the creation of a surrogate model for WEPP to produce erosion estimates at the field scale within a given region while: 1) creating surrogate erosion estimates within the range of 10-20% relative RMSE of WEPP results, 2) minimizing the number of required surrogate model inputs, and 3) consistently creating SM erosion results within seconds. We focused our study on rotations of Corn-Soybean, Continuous Corn, Corn-Winter Wheat, Corn-Sorghum in the Central Great Plains (LRR H), creating 5M WEPP simulation scenarios, permuting the inputs for yield, slope steepness and length as well as soil properties. All simulations were executed on a 1200 core Kubernetes cluster, hosting the WEPP CSIP service and supporting Conservation Resources CSIP data services for SSURGO soils, climate and DEM data. Using the CSIP Publish/Subscribe approach (David et al., 2013) for high performance model executions, 2.7M result sets were generated and analyzed. Those data sets were used for ML training with LightGBM, a Light Gradient Boosting Machine open-source framework based on decision tree algorithms for ranking and classification. Exploring various approaches for selecting sensitive and representative input data we generated a surrogate model for 16 sensitive SM inputs for precipitation, yield, crop, slope and soil properties to estimate sheet and rill erosion. Results from SM testing are shown in Figure 1. <fig><graphic xlink:href=23102_files/23102-00.jpg id=E412E382-9B1C-4A55-A8A7-9CDF01C8C2D0></graphic></fig> Using ~1.9M training and ~820K test samples (70/30%), selected using a Kolmogorov–Smirnov test, we achieved a RRMSE of 6.4%, KGE of 0.999, and NSE of 0.998 for training and a RRMSE of 8.1%, KGE of 0.998, and NSE of 0.997 for testing. The generated SM was further integrated into an SM KPI service in CSIP, producing erosion estimates for fields within LRR H in 2.8 seconds of SM runtime, adding time to obtain needed inputs. This work is ongoing and the presented results represent a research snapshot. Currently, the authors are exploring supplementing the current method with an ensemble approach to further align the SM output accuracy with the original WEPP output.