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The objective of this study was to investigate the ability of on-farm water storage (OFWS) systems to mitigate off-site nutrient movement in a south-central U.S. agricultural watershed. We examined the seasonal water quality changes in an OFWS system by measuring several physical and chemical constituents at multiple sampling points throughout the system. Water quality sampling occurred every three weeks during the growing season and every six weeks during the dormant season from February 2012 to December 2014. The collected data were grouped into four seasons and then analyzed using boxplots along with the Wilcoxon and Kruskal-Wallis rank-sum tests for detecting changes in nutrient concentrations. Significant water quality changes were observed in the OFWS system by season and nutrient species, indicating a variation in downstream nutrient reduction with season. The in-ditch median removal efficiency, from the center of the tailwater recovery ditch to the outlet, was 54% during winter and 50% during spring for NO3-N; 60% during spring for NH3-N; 26% during autumn and 65% during winter for ortho-P; and 31% during winter and 10% during spring for TP. The in-pond median concentration removal efficiency was ∼77% during summer for NO3-N, while the concentration remained stable during winter, spring and autumn; 53% from winter to spring and 58% from spring to summer for NH3-N; 70% from winter to spring for ortho-P, while remaining stable during the other seasons; and 28% from winter to spring and 55% from spring to summer for TP. Our results support the hypothesis that OFWS systems could mitigate downstream nutrient-enrichment pollution, especially during spring. The results obtained from this study offer a better insight into the behavior of OFWS systems and help enhance the management of agroecosystems from an ecological and hydrological perspective for water quality pollution control and water resource conservation.

The lack of information to manage groundwater for irrigation is one of the biggest concerns for farmers and stakeholders in agricultural areas of Mississippi. In this study, we present a novel implementation of a nonlinear autoregressive with exogenous inputs (NARX) network to simulate daily groundwater levels at a local scale in the Mississippi River Valley Alluvial (MRVA) aquifer, located in the southeastern United States. The NARX network was trained using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms, and the results were compared to identify an optimal architecture for the forecasting of daily groundwater levels over time. The training algorithms were implemented using different hidden node combinations and delays (5, 25, 50, 75, and 100) until the optimal network was found. Eight years of daily historical input time series including precipitation and groundwater levels were used to forecast groundwater levels up to three months ahead. The comparison between LM and BR showed that NARX-BR is superior in forecasting daily levels based on the Mean Squared Error (MSE), coefficient of determination (R2), and Nash-Sutcliffe coefficient of efficiency. The results showed that BR with two hidden nodes and 100 time delays provided the most accurate prediction of groundwater levels with an error of ± 0.00119 m. This innovative study is the first of its kind and will provide significant contributions for the implementation of data-based models (DBMs) in the prediction and management of groundwater for agricultural use.

On-farm water storage (OFWS) systems are best management practices that consist of a tailwater recovery (TWR) ditch used with a storage pond to provide irrigation water and improve downstream water quality. These systems have been increasingly implemented in the southeastern US, but the individual and cumulative effects of these systems on a watershed scale are unknown. In this study, the runoff, nutrient, and sediment loads entering a TWR ditch in an agricultural catchment were quantified, and contributing sources were identified using the annualized agricultural non-point source (AnnAGNPS) model. Fields with larger areas and soils with a high runoff potential produced more runoff. The volume of runoff exceeded the TWR ditch storage volume approximately 110 times, mostly during the winter and spring seasons. During years when corn and winter wheat were planted, NO3&ndash, N loads increased because these crops need nitrogen fertilization to grow. Planting winter wheat in priority subwatersheds reduced the total phosphorous (TP) and sediment loads by about 19% and 13%, respectively, at the TWR ditch inlet. Planting winter wheat can reduce runoff, TP, and sediment loads but also result in higher NO3&ndash, N loads. AnnAGNPS simulations quantified the benefits of an OFWS system to advance the understanding of their impact on water availability and quality at a watershed scale.

Rainfall characteristics can be a major factor influencing the ability of best management practices to reduce nutrient loss from agricultural lands to receiving waterbodies. The goal of this study was to examine how rainfall characteristics impacted NO3 – N concentrations in a tailwater recovery (TWR) ditch implemented at a farm within the Porter Bayou watershed in Mississippi, USA. We used a methodology that correlates rainfall characteristics (i.e., a combination of variables such as depth, intensity, frequency, duration, and antecedent hydrological conditions before water sampling) and NO3 – N levels measured in the ditch. Subsequently, a hierarchical clustering approach was implemented to classify rainfall events in the context of the NO3 – N concentrations. Results indicate that NO3 – N concentrations observed in the ditch were strongly dependent on antecedent hydrological conditions within the study area, specifically on the (1) duration of rainfall events before sampling and (2) characteristics of next-to-last rainfall events that occurred prior to sampling. Combined variables of total rainfall depth and frequency showed that rainfall classes I and III were likely to have the most impact on in-ditch NO3 – N concentration. Effects of class I rainfall events on NO3 – N levels appear to be magnified under higher depth, intensity, duration, and a shorter time before next-to-last rainfall events. Also, the influence of class III rainfall events on NO3 – N concentrations in the ditch was driven mainly by high-frequency, low magnitude, and dry antecedent conditions. Results suggest that during periods with frequent, consecutive rainfall events, the nutrient reduction potential of TWR ditches may be reduced. Data can be used to improve management of TWR ditches with more frequent pumping when possible, to increase retention times, particularly during the spring and late fall periods.

The overuse of fertilizers in supporting increased agricultural production to feed a growing population can contribute to a significant release of nutrients to groundwater via percolation, and adjacent waterbodies via surface and irrigation runoff. This excess of nutrients could be detrimental to aquatic ecosystems by altering the nutrients’ natural cycle. This environmental issue is of special interest in the Mississippi River Basin, as the downstream movement of nutrients from croplands stimulates the development of the hypoxia zone in the northern Gulf of Mexico. To address this regional concern, several types of Best Management Practices (BMPs) have been implemented with the fundamental aim of reducing nutrient loading from agricultural lands in the Mississippi Delta Region. In recent years, On-Farm Water Storage (OFWS) systems have been attracting much attention because of their potential benefits to the environment, farmers, and landowners. However, little is known about the watershed-scale impacts of these systems, as well as their effectiveness in reducing nutrient loading downstream. This study describes the seasonal variability of nitrate nitrogen and total phosphorus concentrations measured in the OFWS system at a farm in Porter Bayou watershed, Mississippi. We collected water samples from February 2012 to December 2014 and used boxplots along with the Wilcoxon rank-sum test to analyze water quality changes in the system. Our initial assessment of nutrient changes through the system showed a reduction of 67% in nitrate nitrogen concentration during winter, 50% during spring, and 32% during fall. Total phosphorus was reduced by 22% during the fall season. Preliminary results of this study will help improve the understanding of OFWS systems for water quality pollution control and water conservation.

Groundwater from the Mississippi River Valley Alluvial Aquifer (MRVA) is the primary source of water for irrigation in the Mississippi Delta. While most of East Mississippi is still in dryland production, there is an increasing interest in irrigation in this region, where surface water is the main source of irrigation. Over the last ten years, excessive water withdrawal from the MRVA has led to a net loss of about 4,500,000 acre-ft of water just in the Mississippi Delta. Nutrient loading to local streams and rivers from agricultural fields is another major concern in both Mississippi Delta and in East Mississippi. An On-Farm Water Storage (OFWS) system is a constructed BMP consisting of a tail water recovery ditch and a water storage pond that has the primary goal of reducing downstream nutrient loading by capturing storm runoff and irrigation tailwater. These systems also supply water for irrigation by capturing surface runoff and irrigation tailwater. This paper presents the preliminary findings on the effectiveness of OFWS system in reducing nutrient loading from the study area in East Mississippi. Analysis of storm runoff captured by the OFWS system showed very high nitrate, TN, and TP concentrations. Concentration of nitrate in the storm runoff ranged up to 80 mg/l. Preliminary results conclude that OFWS systems can be an important BMP for reducing downstream nutrient loading to streams and rivers. Analysis of grab samples collected from the storage pond has shown that it can act as a sink to reduce nutrient concentrations in water.

Groundwater is an important source of irrigation water in the Mississippi Delta region where pumping rates have steadily increased over the past two decades. Groundwater withdrawals have exceeded aquifer recharge rates causing a decline in groundwater levels in the Mississippi River Valley Shallow Alluvial (MRVA) aquifer. In this study, we present the results of an artificial neural network (ANN) technique used to estimate groundwater levels of two wells in Sunflower County and Leflore County, Mississippi that are within the MRVA aquifer. The performance of two neural network learning algorithms, Levenberg-Marquardt and Bayesian Regularization, was examined in order to identify an optimal ANN architecture that can simulate the changes in daily groundwater level and provide acceptable predictions up to three months ahead. The two algorithms were trained using different hidden layer combinations and delays (5, 25, 50, 75, and 100) in order to establish the best network model. We discuss the modeling process and accuracy of these two methods for training and prediction stages based on mean square error (MSE). A recurrent neural network trained with the Bayesian Regularization algorithm, with 2 hidden layers and a delay of 100, had the lowest MSE (0.0005612) and produced the best forecast of daily groundwater levels for a lead time of up to three months. Groundwater level forecasting with adequate lead time will help resource managers identify areas where groundwater levels may reach a critical threshold and implement appropriate groundwater conservation policies and practices.

Since the 1970’s, groundwater levels in the Mississippi Alluvial Aquifer have decreased at a rate of approximately 100,000 acre-feet per year due to increased irrigated acres. There are roughly 13,000 permitted irrigation wells dependent on water from the Mississippi Alluvial Aquifer. Farmers and landowners are faced with two major issues with regard to sustainably managing agroecosystems in the Mississippi Delta region, namely, the declining groundwater levels in the Mississippi Delta Shallow Alluvial Aquifer, and nutrient loads into the Mississippi River and the Gulf of Mexico. Due to concerns over groundwater declines and increasing fuel costs to run irrigation pumps, farmers in the Mississippi Delta region have begun implementing irrigation conservation measures, such as tailwater recovery (TWR) ditches and storage ponds to capture irrigation and surface water runoff from the field for later use. Monitoring of two on-farm water storage (OFWS) systems at Pitts and Metcalf farms in Porter Bayou Water shed was initiated in February 2012. Water samples from storage ponds and TWR ditches were analyzed for different water quality parameters. Additional information such as water level in the TWR ditch and water weather station were recorded at 10-minute and 15 minute intervals. An inventory of installed and pending OFWS systems in Porter Bayou Watershed will be conducted as part of this study.

The objective of this research was to evaluate spatially and temporally variable phosphorus loading to the Ross Barnet Reservoir in east-central Mississippi using a modeling approach. Modeling methods were developed to model livestock, poultry, and human sources of nutrients from the Upper Pearl River watershed (UPRW-7,885 km2).The Soil and Water Assessment Tool (SWAT) model was applied to evaluate average monthly flow, sediment, total nitrogen (N), and total phosphorus (P) loading to the Ross Barnett Reservoir inlet.