Quantifying the impacts of inundated land area on streamflow and crop development
The presented work quantifies the impacts of inundated land area (ILA) on streamflow and crop development in the Upper Midwest, which is experiencing a changing climate with observed increases in temperature and precipitation. Quantitative information is needed to understand how upland and downstream stakeholders are impacted by ILA; yet the temporal and spatial extent of ILA and the impact of water storage on flood propagation is poorly understood. Excess water in low gradient agricultural landscapes resulting in ILA can have opposing impacts. The ILA can negatively impact crop development causing financial loss from a reduction or total loss in yield while conversely, ILA can also benefit downstream stakeholders by preventing flood damage from the temporary surface storage that slows water movement into channels. This research evaluates the effects of ILA on streamflow and crop development by leveraging the utility of remotely sensed observations and models.
The influence of ILA on streamflow is investigated in the Red River basin, a predominantly agricultural basin with a history of damaging flood events. An inundation depth-area (IDA) parameterization was developed to parameterize the ILA in a hydrologic model, the Variable Infiltration Capacity (VIC) model, using remotely sensed observations from the MODIS Near Real-Time Global Flood Mapping product and discharge data. The IDA parameterization was developed in a subcatchment of the Red River basin and compared with simulation scenarios that did and did not represent ILA. The model performance of simulated discharge and ILA were evaluated, where the IDA parameterization outperformed the control scenarios. In addition, the simulation results using the IDA parameterization were able to explain the dominant runoff generation mechanism during the winter-spring and summer-fall seasons. The IDA parameterization was extended to the Red River basin to analyze the effects of ILA on the timing and magnitude of peak flow events where observed discharge revealed an increasing trend and magnitude of summer peak flow events. The results also showed that the occurrence of peak flow events is shifting from unimodal to bimodal structure, where peak flow events are dominant in the spring and summer seasons. By simulating ILA in the VIC model, the shift in occurrence of peak flow events and magnitude are better represented compared to simulations not representing ILA.
The impacts of ILA on crop development are investigated on soybean fields in west-central Indiana using proximal remote sensing from unmanned aerial systems (UASs). Models sensitive to ILA were developed from the in-situ and UAS data at the plot scale to estimate biomass and percent of expected yield between the R4-R6 stages at the field scale. Low estimates of biomass and percent of expected yield were associated with mapped observations of ILA. The estimated biomass and percent of expected yield were useful early indicators to identify soybean impacted by excess water at the field scale. The models were applied to satellite imagery to quantify the impacts of ILA on soybean development over larger areas and multiple years. The estimated biomass and percent of expected yield correlated well with the observed data, where low model estimates were also associated with mapped observations of ILA and periods of excessive rainfall. The results of the work link the impacts of ILA on streamflow and crop development, and why it is important to quantify both in a changing climate. By representing ILA in hydrologic models, we can improve simulated streamflow and ILA and represent dominant physical process that influence hydrologic responses and represent shift and seasonal occurrence of peak flow events. In the summer season, where there is an increased occurrence of peak flow events, it is important to understand the impacts of ILA on crop development. By quantifying the impacts of ILA on soybean development we can analyze the spatiotemporal impacts of excess water on soybean development and provide stakeholders with early assessments of expected yield which can help improvement management decisions.