Water Quality Simulation of Rice/Crawfish Field Ponds within Annualized AGNPS

Abstract

Rice and crawfish are important agricultural commodities in Louisiana and they are often managed as integrated cropping systems. The effluent from rice/crawfish ponds is an environmental concern because of potentially high sediment and nutrient concentrations. However, current available models used for TMDL development such as BASIN, SWAT, and AnnAGNPS do not have the capability of simulating rice/crawfish farming and their impact on water quality. The objective of this study was to develop a rice/crawfish module which can be integrated into the Annualized Agricultural Non-point Source Pollution (AnnAGNPS) model to simulate rice/crawfish farming and its impact on water quality. Both hydrologic and pollutant mass balances are maintained for the crawfish ponds in AnnAGNPS. Inflows, outflows, precipitation, infiltration (or seepage), and evaporation (or evapotranspiration) are considered for hydrologic mass balance. Water release from the pond is added to the receiving reach of the AnnAGNPS cell in which the rice/crawfish pond is located. Pollutant mass balances are maintained based on the inflows and outflows to the crawfish pond and water quality characteristics of the flows. The dynamics of complicated nutrient reactions in the pond are not considered at this development stage. Water quality constituents considered in the model are suspended sediment, total nitrogen, total phosphorus, dissolved nitrogen, and dissolved phosphorus. Based on a detailed review of rice/crawfish management practices and their impact on water quality, a water quality database of rice/crawfish ponds has been built into AnnAGNPS and a user interface was designed to quantify the rice/crawfish farming processes. This water quality database provides an estimate of water quality in the crawfish pond effluent during simulations, and a more accurate quantification of effluent water quality can be achieved through analysis of water samples of the water released from the pond. Model testing of proof of concept showed that the model performed appropriately and provided logical results.