Before the Streams: Modeling the Effectiveness of In-Field and Edge-of-Field Practices in Reducing Nitrogen Loads
A wide variety of in-field and edge-of-field practices have been developed and implemented to reduce nitrogen (N) loads in Iowa and other Corn Belt states. Despite progress in both measurement and modeling, one of the bottlenecks in obtaining reliable modeling estimates is improper scaling up from field-level measurements to the watershed scale.
This project focuses on scaling up monitoring data at delivery scale and riparian buffers from a single catchment to the entire Bear Creek Watershed. The research team will assess and predict the relative contributions of in-field and edge-of- field practices to reduce NO3 loads. This will reduce scaling uncertainty in water quality models, and promote accurate modeling assessments and predictions of nutrient reduction practices across agricultural landscapes.
Some team members were involved in the earlier development of a coupled hydrological-biogeochemical model, Dynamic Land Ecosystem Model (DLEM). In this model, river network and in-stream processes have been incorporated into a terrestrial modeling scheme, and the design of grid-to-grid connection allows researchers to track lateral water flow and nutrient movement from land to aquatic systems. In this project, model performance will be tested against the long-term monitoring of water flow and NO3 loads in the tile outlets, diverted into the buffers and NO3 concentration changes within the buffers. The in-situ measurements in crop fields, at delivery scales, in tiles, and riparian buffers will constitute long-term benchmark data along hydrological flow routing for the team to calibrate and validate DLEM’s estimates of N dynamics before entering streams. The improved model will distinguish and quantify the impacts of individual and combined in-field and edge-of-field practices on N load reduction.
To incorporate the effect of the saturated riparian buffer practice in DLEM and apply it in the Bear Creek Watershed, researchers modified the DLEM to incorporate the tile drainage process in the model based on water table level and the parameters of tile lines (e.g. depth, width, space between lines). The incorporated tile drainage calculates the volume of water entering the pipes and the flow rate. A pool in each grid cell was created to store water and nutrient drained by tile lines and transport the mass through pipes to the stream along the flow direction. This technique allows water with high N concentration in the tile lines to spread over the riparian buffer. Instead of flowing the water and leached N in tile lines to surface drainage in each grid cell, tile lines are connected between upstream and downstream grid cells, allowing water and dissolved N to drain to the river channel through the continuous tile lines. At the outlet of tile lines, if there is a saturated riparian buffer, the water is spread out to irrigate the vegetation on the buffer.
A saturated riparian buffer map has been generated based on satellite images.
Outreach during this period included 1 field day, 4 presentations and 1 workshop.
The team received the NSF Early Career Award to support its water quality related research for the next five years.
To gain the DLEM’s representation in prairie potholes regions, the research team introduced the impact of potholes in DLEM by obtaining the area and depth maps of potholes and resampling them into 30 meter resolution as DLEM’s input. DLEM's structure was modified by delivering surface flow into potholes and trapping the water in them. Instead of combining surface and subsurface flows in every grid cell, these two components flow separately to the downstream grid cells based on flow direction. When the potholes exist in the downstream grid cell, the water of surface flow is distributed to and stored in the pothole temporarily. The potholes also collect water from rainfalls on the grid cell. Water stored in the pothole can be lost as evaporation, infiltration and overland flow when the water depth exceeds the maximum depth.
Nitrogen biogeochemical processes were also included, related to pothole distributions, to improve the estimation of nitrogen leaching. The performance of DLEM simulation before and after modification shows that the modified DLEM improved the peak estimation. For example, small peaks during non-rainy days, such as in fall and spring, are eliminated due to the pothole absorption, which matches well with observations. By using the original version of the DLEM model, annual N yield, N loading and N input was quantified for the Bear Creek Watershed from 1979 to 2015. The modeled results represent the baseline scenario of the N budget during the study period. The total N inputs, including N fertilizer, N fixation and atmospheric N deposition, increased from 926 tons year-1 in 1979 to 1100 tons year-1 in 2015, with an average of 1041 tons year-1. Compared to the N input, both the N leaching and N loading from the watershed showed a larger inter-annual variation, due to the year-to-year corn-soybean rotation. Annual N loading ranged from 232 to 932 tons year-1 from 1979 to 2015, with an average of 632 tons year-1, accounting for 61% of the annual N input.
These modeling results together suggest that the total N input and N loss from the Bear Creek Watershed increased over time and the inter-annual variations in N loading were larger than the N input. Future work will include updating DLEM to fully incorporate surface runoff, lateral flow, tile drainage and baseflow to estimate the contributions of these water flows to the N loading given different N management practices.
During January-June 2019, geospatial data at a 30-m resolution was developed to characterize key environmental changes and drive ecosystem modeling in the Bear Creek watershed. These datasets include gridded input data of climate, soil properties, DEM (digital elevation model), land cover and land-use change (LULC), river network, spatial nitrogen (N) deposition and agricultural management practices across the entire study region. The daily spatial weather data from 1979 to 2018, including temperature, precipitation and shortwave solar radiation, were interpolated from six weather stations in the Iowa Environmental Mesonet. The Inverse Distance Weighting (IDW) method was used to interpolate station-derived weather data to gridded maps. 1979 was used as the starting year because of the radiation data availability. The LULC data were extracted from the 30-m resolution Cropland Data Layer (CDL) dataset from 2000 to 2018 (pre-2000 LULC were assumed the same as in 2000). The river network was constructed based on a recent 30-m DEM dataset. The 30-m gridded N deposition input data were resampled from the NADP database with a ~4 km resolution. Tile-drained soil for Bear Creek was created based on the SURRGO soil dataset. Because there is no available surveyed N fertilizer data of corn (the major N consumer) in Bear Creek watershed, six corn N fertilizer input scenarios were developed from the surveyed N fertilizer information in the RS catchment. First, the maximum, mean, and minimum values of the N fertilizer input in the RS catchment was calculated for 2016, assuming the 2016 N fertilizer input in the Bear Creek watershed has a similar amount as in the RS catchment. Using the county-level NuGIS N database (www.ipni.net/NuGIS), the average N fertilizer amount of the Story and Hamilton counties was then calculated (from 1987 to 2013, and N fertilizer from 2014-2016 was assumed the same as in 2013). The trend of the averaged NuGIS N fertilizer use rates from 1987-2016 was used to back-calculate three time series by using the maximum, mean and minimum values of 2016 N fertilizer amount in the RS catchment. Since there were two N fertilizer application timings in the RS catchment, the three N fertilizer time series were partitioned based on these two timings, before-planting vs. after-planting, to create the six N fertilizer scenarios. The ratio between ammonia and nitrite N in Bear Creek was kept the same as the ratio in the RS catchment. Using these input data, initial model runs were completed (to get initial status of ecosystem carbon, nutrient and water storage) in the Bear Creek Watershed. The first transient model simulation has been carried out to validate model performance against the field observations.
During the reporting period, one project-related workshop was presented and one paper was accepted:
Lu, C., J. Zhang, P. Cao, J. Hatfield. Are we getting better in using nitrogen? — Variations in nitrogen use efficiency of two cereal crops across the United States. Earth’s Future DOI: 10.1029/2019EF001155
During this quarter, available data sources were collected to develop geospatial data that can drive ecosystem modeling in the Bear Creek watershed. Based on the research goal, a set of gridded input data is needed to characterize weather, soil properties, digital elevation model (DEM), land cover and land use change (LULC), and agricultural management practices. Data sources were compiled and group meetings held to discuss ways to produce time-series spatially explicit data during 2000 to present. Land cover and cropping system data from 2000 to 2015 will be developed from maps from the USDA National Agricultural Statistics Service (NASS) cropland data layer. Data to characterize agricultural management practices is based on the field- and county-surveys, literature and farmer-interviews. Daily weather data will be interpolated from the US-Historic Climatology Network (HCND) database. Topographic information will be derived from the Shuttle Radar Topography Mission (SRTM, Farr et al., 2007). County-level Soil Survey Geographic Database (SSURGO) data from USDA Geospatial Data Gateway will be resampled. We will finish data preparation and pre-processing by the end of February 2019.
During the first quarter, a team met to kick off the project. Team members who conducted field monitoring at tile outlet and riparian buffers have shared the published data and relevant records in the Bear Creek Watershed. The data sets include monitored discharge and nitrate (NO3) concentration at each outlet, water table depth (from groundwater wells), and denitrification and nitrous oxide (N2O) emissions at the riparian zone. The data have been collected and compiled into excel files that will support HUC 12-level model test and calibration. In addition to these measurement data, digital maps of tile lines and unified riparian buffer zones developed by Co-PI Isenhart’s team were also shared. The new data will be used to characterize distribution of saturated and traditional riparian buffers, riparian vegetation cover and tile lines across the Bear Creek Watershed.