Projection of Sediment Loading from Pearl River Basin, Mississippi into Gulf of Mexico under a Future Climate with Afforestation

Sediment load in rivers is recognized as both a carrier and a potential source of contaminants. Sediment deposition significantly changes river flow and morphology, thereby affecting stream hydrology and aquatic life. We projected sediment load from the Pearl River basin (PRB), Mississippi into the northern Gulf of Mexico under a future climate with afforestation using the SWAT (Soil and Water Assessment Tool)-based HAWQS (Hydrologic and Water Quality System) model. Three simulation scenarios were developed in this study: (1) the past scenario for estimating the 40-year sediment load from 1981 to 2020; (2) the future scenario for projecting the 40-year sediment load from 2025 to 2064, and (3) the future afforestation scenario that was the same as the future scenario, except for converting the rangeland located in the middle section of the Pearl River watershed of the PRB into the mixed forest land cover. Simulations showed a 16% decrease in sediment load for the future scenario in comparison to the past scenario due to the decrease in future surface runoff. Over both the past and future 40 years, the monthly maximum and minimum sediment loads occurred, respectively, in April and August; whereas the seasonal sediment load followed the order: spring > winter > summer > fall. Among the four seasons, winter and spring accounted for about 86% of sediment load for both scenarios. Under the future 40-year climate conditions, a 10% reduction in annual average sediment load with afforestation was observed in comparison to without afforestation. This study provides new insights into how a future climate with afforestation would affect sediment load into the northern Gulf of Mexico.

Quantification and simulation of nutrient sources at watershed scale in Mississippi

Excessive quantity of nutrient produced from various point and non-point source deteriorates the quality of water. For reduction of nutrient from a watershed, it is needed to be quantified followed by implementation of appropriate management practices. In this study, major sources of nutrient in Big Sunflower River Watershed (BSRW) were identified, quantified, and Soil and Water Assessment Tool (SWAT) model was applied for assessment of flow, sediment and nutrient. SWAT was calibrated and validated for streamflow, sediment, total nitrogen (TN), and total phosphorus (TP) for three United States Geological Survey (USGS) gauge stations within BSRW. Moreover, different scenarios, based on agricultural operation and best management practices, were developed. Performance of SWAT was evaluated using Nash-Sutcliffe Efficiency (NSE) and coefficient of determination (R²). The performance was good for streamflow during calibration and validation with R² and NSE ranging from 0.74 to 0.90 and 0.70 to 0.82 respectively. SWAT performed satisfactorily for sediment, TN and TP except in few extreme conditions, where animal waste was mixed with farm runoff. This study has provided a suitable crop rotation and management practice for efficient management of nutrient in BSRW. Manure applied on field cultivated entirely with soybean was the best practice for reduction of TN with R² and NSE ranging from 0.70 to 0.90 and 0.58 to 0.76 respectively. Application of manure only on existing soybean crop-land was the best practice for reduction of TP with R² and NSE ranging from 0.51 to 0.69 and 0.41 to 0.60 respectively. Soybean was effective in accumulating both nitrogen and phosphorus from soil. This study will be helpful for efficient planning and management of nutrient through suitable crop rotation and management practice.

Application of Climate Assessment Tool (CAT) to Estimate Climate Variability Impacts on Nutrient Loading from Local Watersheds

A vast amount of future climate scenario datasets, created by climate models such as general circulation models (GCMs), have been used in conjunction with watershed models to project future climate variability impact on hydrological processes and water quality. However, these low spatial-temporal resolution datasets are often difficult to downscale spatially and disaggregate temporarily, and they may not be accurate for local watersheds (i.e., state level or smaller watersheds). This study applied the US-EPA (Environmental Protection Agency)'s Climate Assessment Tool (CAT) to create future climate variability scenarios based on historical measured data for local watersheds. As a case demonstration, CAT was employed in conjunction with HSPF (Hydrological Simulation Program-FORTRAN) model to assess the impacts of the potential future extreme rainfall events and air temperature increases upon nitrate-nitrogen (NO₃-N) and orthophosphate (PO₄) loads in the Lower Yazoo River Watershed (LYRW), a local watershed in Mississippi, USA. Results showed that the 10 and 20% increases in rainfall rate, respectively, increased NO₃-N load by 9.1 and 18% and PO₄ load by 12 and 24% over a 10-year simulation period. In contrast, simultaneous increases in air temperature by 1.0 ^oC and rainfall rate by 10% as well as air temperature by 2.0 ^oC and rainfall rate by 20% increased NO₃-N load by 12% and 20%%, and PO₄ load by 14 and 26 %, respectively. A summer extreme rainfall scenario was created if a 10% increase in rainfall rate increased the total volume of rainwater for that summer by 10% or more. When this event occurred, it could increase the monthly loads of NO₃-N and PO₄, by 31 and 41%, respectively, for that summer. Therefore, the extreme rainfall events had tremendous impacts on the NO₃-N and PO₄ loads. It is apparent that CAT is a flexible and useful tool to modify historical rainfall and air temperature data to predict climate variability impacts on water quality for local watersheds.

Identify temporal trend of air temperature and its impact on forest stream flow in Lower Mississippi River Alluvial Valley using wavelet analysis

Characterization of stream flow is essential to water resource management, water supply planning, environmental protection, and ecological restoration; while air temperature variation due to climate change can exacerbate stream flow and add instability to the flow. In this study, the wavelet analysis technique was employed to identify temporal trend of air temperature and its impact upon forest stream flows in Lower Mississippi River Alluvial Valley (LMRAV). Four surface water monitoring stations, which locate near the headwater areas with very few land use disturbances and the long-term data records (60-90 years) in the LMRAV, were selected to obtain stream discharge and air temperature data. The wavelet analysis showed that air temperature had an increasing temporal trend around its mean value during the past several decades in the LMRAV, whereas stream flow had a decreasing temporal trend around its average value at the same time period in the same region. Results of this study demonstrated that the climate in the LMRAV did get warmer as time elapsed and the streams were drier as a result of warmer air temperature. This study further revealed that the best way to estimate the temporal trends of air temperature and stream flow was to perform the wavelet transformation around their mean values.

Relationships between water table and model simulated ET

This research was conducted to develop relationships among evapotranspiration (ET), percolation (PERC), groundwater discharge to the stream (GWQ), and water table fluctuations through a modeling approach. The Soil and Water Assessment Tool (SWAT) hydrologic and crop models were applied in the Big Sunflower River watershed (BSRW; 7660 km(2) ) within the Yazoo River Basin of the Lower Mississippi River alluvial plain. Results of this study showed good to very good model performances with the coefficient of determination (R(2) ) and Nash-Sutcliffe efficiency (NSE) index from 0.4 to 0.9, respectively, during both hydrologic and crop model calibration and validation. An empirical relationship between ET, PERC, GWQ, and water table fluctuations was able to predict 64% of the water table variation of the alluvial plain in this study. Thematic maps were developed to identify areas with overuse of groundwater, which can help watershed managers to develop water resource programs.

Source specific fecal bacteria modeling using soil and water assessment tool model

Fecal bacteria can contaminate water and result in illness or death. It is often difficult to accurately determine sources of fecal bacteria contamination, but bacteria source tracking can help identify non-point sources of fecal bacteria such as livestock, humans and wildlife. The Soil and Water Assessment Tool (SWAT) microbial sub-model 2005 was used to evaluate source-specific fecal bacteria using three years (2004-2006) of observed modified deterministic probability of bacteria source tracking data, as well as measure hydrologic and water quality data. This study modeled source-specific bacteria using a model previously calibrated for flow, sediment and total fecal coliform bacteria (FCB) concentration. The SWAT model was calibrated at the Rock Creek sub-watershed, validated at the Deer Creek sub-watershed, and verified at the Auburn sub-watershed and then at the entire Upper Wakarusa watershed for predicting daily flow, sediment, nutrients, total fecal bacteria, and source-specific fecal bacteria. Watershed characteristics for livestock, humans, and wildlife fecal bacterial sources were first modeled together then with three separate sources and combinations of source-specific FCB concentration: livestock and human, livestock and wildlife and human and wildlife. Model results indicated both coefficient of determination (R(2)) and Nash-Sutcliffe Efficiency Index (E) parameters ranging from 0.52 to 0.84 for daily flow and 0.50-0.87 for sediment (good to very good agreement); 0.14-0.85 for total phosphorus (poor to very good agreement); -3.55 to 0.79 for total nitrogen (unsatisfactory to very good agreement) and -2.2 to 0.52 for total fecal bacteria (unsatisfactory to good agreement). Model results generally determined decreased agreement for each single source of bacteria (R(2) and E range from -5.03 to 0.39), potentially due to bacteria source tracking (BST) uncertainty and spatial variability. This study contributes to new knowledge in bacteria modeling and will help further understanding of uncertainty that exists in source-specific bacteria modeling.