Delivery-Scale Evaluation and Modeling of Nutrient Reduction Practices
Most prior work on practice performance and nutrient loads in Iowa has been done at either plot scale or larger watershed scale. It has been challenging to properly scale up plot-level measurements to the area of practice implementation and larger watersheds in order to better assess water quality impacts across landscapes and with multiple practices. Nutrient loads and load reductions at plot scale can differ substantially from loads actually delivered to surface waters. Likewise, nutrient loads at larger watershed scales can differ substantially due to the effects of in-stream processes. Few studies have focused on delivery-scale water and nutrient monitoring, which hinders model improvement and evaluation.
This project focuses on delivery-scale evaluation and modeling of the effectiveness of in-field and edge-of-field practices including cover crops, fertilizer management, and wetlands in increasing ecosystem nitrogen and phosphorus retention, and reducing downstream nutrient loads. This research will build process-based simulation capacity by integrating a delivery-scale monitoring network, in-field management surveys, and modeling estimation.
Researchers will test, improve and validate the DLEM’s (Dynamic Land Ecosystem Model) performance in estimating delivery-scale water flow and nitrogen and phosphorus loads against long-term monitoring for four watersheds. Two of the watersheds have widespread implementation of cover crops and all four outlet to CREP (Conservation Reserve Enhancement Program) wetlands. The improved and calibrated DLEM will be used to assess the effectiveness of cover crop implementation, combined with various fertilizer management practices on delivery-scale nutrient reduction, and identify the hot spots and moments contributing to high nitrogen and phosphorus loads in these four watersheds. In addition, researchers will quantify how alternative nutrient reduction practices would affect nutrient and phosphorus loads in the future.
During the first half of 2019, work was done on model-data comparison and model implementation at the catchment level. The soil water transportation process in the DLEM model was improved to capture the water table fluctuation between soil layers. By doing this, the water table close can be raised closer to the surface during heavy rainfall, and thus, more water can be drained into the tile lines after heavy rainfall events. In addition, the subsurface hydrological processes in the DLEM were revised to add subsurface lateral flow to better simulate water recession following rainfall events. The simulated baseflow in the original DLEM model can only represent total subsurface water flow. After these modifications, the model is able to represent quick, medium and slow drainage, respectively, from tile lines, lateral flow from multiple soil layers and groundwater flow from the aquifer layer.
The time-series of simulated nitrogen (N) loading was also compared with measured data at the inlet of wetland in RS catchment. The model performance in simulating daily magnitude and variations of N loading is good but some peaks were missed. This may be caused by the underestimation of rainfall intensity, which leads to less rainfall water entering the soil at sub-daily scales. Now, the team is working to improve the model estimates of N loading by allocating precipitation to sub-daily scale based on historical hourly precipitation records from weather stations in Iowa. This version of the model will be set up to run in other three catchments to test model performance, and then investigate the effect of different agricultural management, such as cover cropping, on N loading among the four catchments.
This quarter has been focused on model improvement and model-data comparison. A new snow module was added to the Dynamic Land Ecosystem Model (DLEM) to improve its performance in winter hydrological processes. Model representation of snow formation and melting was improved by 1) introducing snow phase change and compaction processes to track the dynamics of snow depth, density, coverage and temperature, and 2) updating liquid water flow in snow pack regarding capillary force of snow porosity. The model-estimated formation and melting of snow and ice is shown to align well with observations. Specifically, it successfully reduces discharge peak when the snow pack exists and enhances the peak when snow melting occurs. The modeled - to 3-day lag of peak time behind measurement was also fixed.
DLEM is original designed for large scale study, in which the time step of 30 minutes is reasonable. However, it is no longer fitful for small regions, such as RS catchment. So the time step was adjusted to 10 minutes, which solved the lag problem despite more computational costs. In contrast to time lag, the model-estimated magnitude of peak flow is still lower than that of the monitored data. Tile line maps and tile line processes have been incorporated into the model, but it doesn’t raise flow amount effectively. Based on a site-level modeling process check, the lower modeled peak flow has been attributed to underrepresented subsurface hydrological processes in the model. More work will be done on this in the following quarter.
The time-series of simulated nitrogen (N) loading was also compared with measured data at the inlet of the wetland in RS catchment. The model performance in simulating daily magnitude and variations of N loading is generally good but underestimates some peaks and troughs of N load before the growing season. We expect the model estimate could be improved after the subsurface processes modification.
In the next quarter, the same model will be set up to run in the paired catchment to test model performance and investigate the effect of different agricultural management practices, including cover crops, on N loading between two catchments.
During this quarter, the team has been working on primary model setting and model testing. Based on one of the two catchments in Story County, the team has refined the procedure in the development of model input data and cross-checked the whole dataset by using multiple data sources. The team also included gridded maps of nitrogen deposition, tillage and cover crop practices to drive the process-based ecosystem model. The initial and transient model run has been completed, and the preliminary model estimates of magnitude and spatiotemporal patterns of water discharge and in-flow NO3 concentration compared with some long-term delivery-scale monitoring data. Site-level measurement data of surface/sub-surface runoff and N leaching has been used to calibrate the model.
The focus this quarter was on input data development, and making the format compatible to a process-based land ecosystem model. The catchment in Story County, one of four catchments in the study, was chosen as the data development template. Based on the multi-source data from remotely sensed products, national survey and monitoring network, team members developed spatially explicit time-series data of climate, soil properties, land use and land cover maps, flow direction and other ancillary data. These data are used to characterize the conditions of geography, climate, soil, hydrology and land use pattern within the catchment boundary over time. The input data for the other three catchments will be built using this data development template and similar data sources.
Planning and data sharing to facilitate regional data development is underway. Based on the research goal, a set of gridded input data is needed to drive the ecosystem model, and characterize weather, soil properties, DEM (digital elevation model), land cover and land use change, plus agricultural management practices across four catchments in Story and Floyd counties. Various data sources were complied and group meetings held to discuss the best way to produce time-series spatially explicit data during 2000 through to the present at a spatial resolution of 30m × 30m. A shared data folder is accessible to all project investigators. Spatial database, such as catchment boundary, land use history, are uploaded into the geospatial data library.