Using AnnAGNPS to Evaluate On-Farm Water Storage Systems (OFWS) as a BMP for Nutrient Loading Control in a Small Watershed in East Mississippi

Using AnnAGNPS to Evaluate On-Farm Water Storage Systems (OFWS) as a BMP for Nutrient Loading Control in a Small Watershed in East Mississippi Ritesh Karki Graduate Student, Mississippi State University Mary Love M. Tagert Assistant Extension Professor, Mississippi State University Joel O. Paz Associate Professor, Mississippi State University Ronald L. Bingner Agricultural Engineer, USDA-Agricultural Research Service, National Sedimentation Laboratory, Oxford, MS

Outline Background/Motivation On-Farm Water Storage (OFWS) System Objectives AnnAGNPS Site Description Model Setup/Input Parameters Preliminary Results Future Works

Background/Motivation Nutrient enrichment to downstream rivers and streams from agricultural fields is a major concern. Irrigation problems o Declining water source for irrigation o Lack of water source for irrigation o Mississippi rainfall pattern OFWS systems are fairly new in East MS, primarily established for supplemental irrigation. Figure 1: State of Mississippi with different soil types (source: msucares.com)

An On-Farm Water Storage (OFWS) System is a planned irrigation system consisting of collection, storage, and distribution systems for irrigation tail water and/or runoff events. Field Water directed through terraces and levees Pump On-Site Water Storage Overflow Spillway Figure 2: General OFWS system designs

Objectives 1. Estimate nitrogen and phosphorus load captured by the OFWS system using AnnAGNPS to determine nutrient loading reduction to streams and rivers downstream. 2. Evaluate alternative scenarios (management practices) to evaluate the reduction of nutrient loading from the agricultural watershed.

Annualized Agricultural Non-Point Source Pollution Loading Model (AnnAGNPS) Developed through the partnership between USDA-ARS and NRCS. A daily time step, continuous simulation, watershed-scale model Field 2 used to evaluate pollution loading from non-point sources. It simulates surface water, sediments, nutrients, and pesticides. Field 3 Required input parameters: climate data, watershed physical information, and management information.

Study Site (Brooksville, MS) Noxubee County, East Mississippi. Located in the Middle Tombigbee- Lubbub watershed. OFWS Pond: 17 acres and 25 feet deep. Total drainage area to pond : about 111 acres. Runoff and nutrient monitoring from only Drainage 1 About 70 acres. Figure 3: OFWS System Study Site, Brooksville, MS

Constructed levees to direct water from the field to the pond. A 6712 ISCO portable sampler was installed near the inlet of the pond to collect runoff. A weather station was installed to gather climate information. Study Site Contd. Figure 4: Instruments for data collection

Monitored Data Data Monitoring: a. Storm Runoff Samples: Nitrate Total Nitrogen Dissolved Orthophosphate Total Phosphorus b. 750 Area Velocity Module: Level Velocity c. Weather Station: Various climate data for model input Figure 5: Nitrate concentration in captured storm events

Input Parameters Digital Elevation Model (DEM) Data: Sub-meter LIDAR Data was obtained from the USDA:NRCS Geospatial Data Gateway. The LIDAR was transformed to a 1m x 1m DEM for model input. Maximum elevation difference of 5m. Critical to match observed drainage network with model generated network. Figure 7: Brooksville DEM

Input Parameters Contd.. Digital Elevation Model (DEM) Data: TOPAGNPS was used to generate the watershed, AnnAGNPS cell, and reach data section of the AnnAGNPS Input Editor. The CSA and MSCL values are critical to determine the extent of the stream network and resulting AnnAGNPS cell. Figure 8: Watershed, cell and stream network

Input Parameters Contd.. Soil Data: Soil data was acquired from the SSURGO data base. The soils have calcareous sub-soils with high content of clay. 3 different soil series but dominated by Broadband Silt Loam (BrA). Hydrologic Condition: D Figure 9: Soil Distribution in the Watershed

Input Parameters Contd.. Climate Data: AnnAGNPS required climate information: daily precipitation, maximum and minimum temperature, sky cover, dew point temperature, and wind speed. Climate input file was prepared by combining observed and generated data. Average annual rainfall: 56 inches. Most rainfall during the winter and early spring months. Gathered from Weather Station Daily Precipitation Generated using aggem Sky Cover Maximum and Minimum Temperature Dew Point Temperature Wind Speed

Input Parameters Contd.. Management Information: Main crops grown in the study site are: Corn and Soybean Detailed management information for Corn the site including tillage, planting, harvest, fertilizer application etc. was obtained for the year 2014 and 2015. Corn- Soybean Rotation Figure 10: Agriculture practice during the modeling period

Input Parameters Contd.. Important Management Events Field Date Action Corn Field April 10, 2014 Plantation April 15, 2014 Fertilizer Application May 20, 2014 June 28, 2014 August 15, 2014 September 16, 2014 September 21, 2014 September 26, 2014 September 30, 2014 Fertilizer Application Fertilizer Application Harvest Fertilizer Application Disking Subsoiling Bedding

Input Parameters Contd.. Important Management Events Field Date Action Corn - Soybean Field April 15, 2014 Disking May 1, 2014 Disking May 15, 2014 May 16, 2014 June 15, 2014 July 1, 2014 August 1, 2014 September 1, 2014 October 5, 2014 October 15, 2014 October 21, 2014 October 26, 2014 Pre-Emergence Sprayer Plantation Cultivator Insecticides Application Insecticides Application Insecticides Application Harvest Fall Fertilizer Application Disking Bedding

Other Important Model Input SCS Curve Number: The SCS Curve Number is a very important input in predicting accurate runoff and sediment. SCS Curve Numbers Used for the initial Model Setup: Cover type and hydrologic condition Curve Number for hydrologic soil group A B C D Row Crop (Straight Row - Poor) 72 81 88 91 Fallow (Bare Soil) 77 86 91 94

Model Analysis Runoff Volume (Event Based): 3 Comparison Between Predicted and Observed Volume Predicted Runoff Observed Runoff Volume (Million Gallons) 2.5 2 1.5 1 0.5 0 Figure 11: Comparison between observed and predicted runoff volume

Model Analysis Runoff Volume (Event Based): Predicted Runoff (Million Gallons) 3 2.5 2 1.5 1 0.5 0 y = 0.2262x + 1.1132 R² = 0.0688 0 0.5 1 1.5 2 2.5 3 Observed Runoff (Million Gallons) Data Linear (Data) Figure 12: Regression analysis for observed and predicted runoff

Model Analysis Peak Discharge (Event Based): 25 Comparison Between Predicted and Observed Peak Discharge Predicted Peak Discharge Observed Peak Discharge Peak Discharge (CFS) 20 15 10 5 0 Figure 13: Comparison between observed and predicted peak runoff

Model Analysis Peak Discharge (Peak Discharge): 25 Predicted Peak Discharge (CFS) 20 15 10 5 y = 0.2502x + 8.981 R² = 0.1094 Data Linear (Data) 0 0 5 10 15 20 25 Observed Peak Discharge (CFS) Figure 14: Regression analysis for observed and predicted peak runoff

Model Analysis Model prediction needs to be improved before further analysis can be conducted. Model calibration using SCS curve number method for runoff has not shown improvement in model prediction. Model calibration using Global Storm Type ID for peak runoff also did not show improvement.

Future Work Improve model prediction for runoff and peak discharge. Calibrate and validate the model for nitrogen and phosphorus Conduct a model performance analysis Estimate nutrient load from the agricultural watershed Evaluate alternative management practice for reducing agricultural nutrient runoff.

Acknowledgement Farmers: Mr. Dale Weaver and Mr. Paul Good Dr. Dennis Reginelli, Regional Extension Specialist II Mississippi Soybean Promotion Board (MSPB)