Soil legacy nutrients contribute to the decreasing stoichiometric ratio of N and P loading from the Mississippi River Basin.

Human-induced nitrogen-phosphorus (N, P) imbalance in terrestrial ecosystems can lead to disproportionate N and P loading to aquatic ecosystems, subsequently shifting the elemental ratio in estuaries and coastal oceans and impacting both the structure and functioning of aquatic ecosystems. The N:P ratio of nutrient loading to the Gulf of Mexico from the Mississippi River Basin increased before the late 1980s driven by the enhanced usage of N fertilizer over P fertilizer, whereafter the N:P loading ratio started to decrease although the N:P ratio of fertilizer application did not exhibit a similar trend. Here, we hypothesize that different release rates of soil legacy nutrients might contribute to the decreasing N:P loading ratio. Our study used a data-model integration framework to evaluate N and P dynamics and the potential for long-term accumulation or release of internal soil nutrient legacy stores to alter the ratio of N and P transported down the rivers. We show that the longer residence time of P in terrestrial ecosystems results in a much slower release of P to coastal oceans than N. If contemporary nutrient sources were reduced or suspended, P loading sustained by soil legacy P would decrease much slower than that of N, causing a decrease in the N and P loading ratio. The longer residence time of P in terrestrial ecosystems and the increasingly important role of soil legacy nutrients as a loading source may explain the decreasing N:P loading ratio in the Mississippi River Basin. Our study underscores a promising prospect for N loading control and the urgency to integrate soil P legacy into sustainable nutrient management strategies for aquatic ecosystem health and water security.

Nutrient Removal Potential of Headwater Wetlands in Coastal Plains of Alabama, USA.

Headwater streams drain over 70% of the land in the United States with headwater wetlands covering 6.59 million hectares. These ecosystems are important landscape features in the southeast United States, with underlying effects on ecosystem health, water yield, nutrient cycling, biodiversity, and water quality. However, little is known about the relationship between headwater wetlands' nutrient function (i.e., nutrient load removal (RL) and removal efficiency (ER)) and their physical characteristics. Here, we investigate this relationship for 44 headwater wetlands located within the Upper Fish River watershed (UFRW) in coastal Alabama. To accomplish this objective, we apply the process-based watershed model SWAT (Soil and Water Assessment Tool) to generate flow and nutrient loadings to each study wetland and subsequently quantify the wetland-level nutrient removal efficiencies using the process-based wetland model WetQual. Results show that the calculated removal efficiencies of the headwater wetlands in the UFRW are 75-84% and 27-35% for nitrate (NO3-) and phosphate (PO4+), respectively. The calculated nutrient load removals are highly correlated with the input loads, and the estimated PO4+ ER shows a significant decreasing trend with increased input loadings. The relationship between NO3-ER and wetland physical characteristics such as area, volume, and residence time is statistically insignificant (p > 0.05), while for PO4+, the correlation is positive and statistically significant (p < 0.05). On the other hand, flashiness (flow pulsing) and baseflow index (fraction of inflow that is coming from baseflow) have a strong effect on NO3- removal but not on PO4+ removal. Modeling results and statistical analysis point toward denitrification and plant uptake as major NO3- removal mechanisms, whereas plant uptake, diffusion, and settling of sediment-bound P were the main mechanisms for PO4+ removal. Additionally, the computed nutrient ER is higher during the driest year of the simulated period compared to during the wettest year. Our findings are in line with global-level studies and offer new insights into wetland physical characteristics affecting nutrient removal efficiency and the importance of headwater wetlands in mitigating water quality deterioration in coastal areas. The regression relationships for NO3- and PO4+ load removals in the selected 44 wetlands are then used to extrapolate nutrient load removals to 348 unmodeled non-riverine and non-riparian wetlands in the UFRW (41% of UFRW drains to them). Results show that these wetlands remove 51-61% of the NO3- and 5-10% of the PO4+ loading they receive from their respective drainage areas. Due to geographical proximity and physiographic similarity, these results can be scaled up to the coastal plains of Alabama and Northwest Florida.

Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale.

Rainfall kinetic energy (RKE) constitutes one of the most critical factors that drive rainfall erosivity on surface soil. Direct measurements of RKE are limited, relying instead on the empirical relations between kinetic energy and rainfall intensity (KE-I relation), which have not been well regionalized for data-scarce regions. Here, we present the first global rainfall microphysics-based RKE (RKEMPH) flux retrieved from radar reflectivity at different frequencies. The results suggest that RKEMPH flux outperforms the RKE estimates derived from a widely used empirical KE-I relation (RKEKE-I) validated using ground disdrometers. We found a potentially widespread underestimation of RKEKE-I, which is especially prominent in some low-income countries with ~20% underestimation of RKE and the resultant rainfall erosivity. Given the evidence that these countries are subject to greater rainfall-induced soil erosion, these underestimations would mislead conservation practices for sustainable development of terrestrial ecosystems.

Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China.

Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5-10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies.

Improved forest dynamics leads to better hydrological predictions in watershed modeling.

This study explored how the characterization of forest processes in hydrologic models affects watershed hydrological responses. To that end, we applied the widely used Soil and Water Assessment Tool (SWAT) model to two forested watersheds in the southeastern United States. Although forests can cover a large portion of watersheds, tree attributes such as leaf area index (LAI), biomass accumulation, and processes such as evapotranspiration (ET) are rarely calibrated in hydrological modeling studies. The advent of freely and readily available remote-sensing data, combined with field observations from forestry studies and published literature, allowed us to develop an improved forest parameterization for SWAT. We tested our proposed parameterization at the watershed scale in Florida and Georgia and compared simulated LAI, biomass, and ET with the default model settings. Our results showed major improvements in predicted monthly LAI and ET based on MODIS reference data (NSE > 0.6). Simulated forest biomass also showed better agreement with the USDA forest biomass gridded data. Through a series of modeling experiments, we isolated the benefits of LAI, biomass, and ET in predicting streamflow and baseflow at the watershed level. The combined benefits of improved LAI, biomass, and ET predictions yielded the most optimal model configuration where terrestrial and in-stream processes were simulated reasonably well. We performed automated model calibration using two calibration strategies. In the first calibration scheme (M0), SWAT was calibrated for daily streamflow without adjusting LAI, biomass, and ET. In the second calibration scheme (MLAI+BM+ET), previously calibrated parameters constraining LAI, biomass, and ET were incorporated into the model and daily streamflow was recalibrated. The MLAI+BM+ET model showed superior performance and reduced uncertainties in predicting daily streamflow, with NSE values ranging from 0.52 to 0.8. Our findings highlight the importance of accurately representing forest dynamics in hydrological models.

Two-Layer numerical model of soil moisture dynamics: Model assessment and Bayesian uncertainty estimation.

A two-layer model based on the integrated form of Richards' equation (RE) was recently developed to simulate the soil water movement in the roots layer and the vadose zone with a relatively shallow and dynamic water table. The model simulates thickness-averaged volumetric water content and matric suction as opposed to point values and was numerically verified for three soil textures using HYDRUS as a benchmark. However, the strengths and limitations of the two-layer model and its performance in stratified soils and under actual field conditions have not been tested. This study further examined the two-layer model using two numerical verification experiments and, most importantly, tested its performance at site-level under actual, highly variable hydroclimate conditions. Moreover, model parameters were estimated and uncertainty and sources of errors were quantified using a Bayesian framework. First, the two-layer model was evaluated for 231 soil textures under varying soil layer thicknesses with a uniform soil profile. Second, the two-layer model was assessed for stratified conditions where the top and bottom soil layers have contrasting hydraulic conductivities. The model was evaluated by comparing soil moisture and flux estimates to those from the HYDRUS model. Last, a case study of model application using data from a Soil Climate Analysis Network (SCAN) site was presented. Bayesian Monte Carlo (BMC) method was implemented for model calibration and quantifying sources of uncertainty under real hydroclimate and soil conditions. For a homogeneous soil profile, the two-layer model generally had excellent performance in estimating volumetric water content and fluxes, while the model performance slightly declined with increasing layer thickness and coarser textured soils. The model configurations regarding layer thicknesses and soil textures that generate accurate soil moisture and flux estimations were further suggested. With the two layers of contrasting permeability, model-simulated soil moisture contents and fluxes agreed well with those computed by HYDRUS, indicating that the two-layer model accurately handles the water flow dynamics around the layer interface. In the field application, given the highly variable hydroclimate conditions, the two-layer model combined with the BMC method showed good agreement with the observed average soil moisture of the root zone and the vadose zone below (RMSE <0.021 during calibration and <0.023 during validation periods). The contribution of parametric uncertainty to the total model uncertainty was too small compared to other sources. The numerical tests and the site level application showed that the two-layer model can reliably simulate thickness-averaged soil moisture and estimate fluxes in the vadose zone under various soil and hydroclimate conditions. Results also indicated that the BMC method could be a robust framework for vadose zone hydraulic parameters identification and model uncertainty estimation.

Improving the representation of forests in hydrological models.

Forests play a critical role in the hydrologic cycle, impacting the surface and groundwater dynamics of watersheds through transpiration, interception, shading, and modification of the atmospheric boundary layer. It is therefore critical that forest dynamics are adequately represented in watershed models, such as the widely applied Soil and Water Assessment Tool (SWAT). SWAT's default parameterization generally produces unrealistic forest growth predictions, which we address here through an improved representation of forest dynamics using species-specific re-parameterizations. We applied this methodology to the two dominant pine species in the southeastern U.S., loblolly pine (Pinus taeda L.) and slash pine (Pinus elliotti). Specifically, we replaced unrealistic parameter values related to tree growth with physically meaningful parameters derived from publicly available remote-sensing products, field measurements, published literature, and expert knowledge. Outputs of the default and re-parameterized models were compared at four pine plantation sites across a range of management, soil, and climate conditions. Results were validated against MODIS-derived leaf area index (LAI) and evapotranspiration (ET), as well as field observations of total biomass. The re-parameterized model outperformed the default model in simulating LAI, biomass accumulation, and ET at all sites. The two parametrizations also resulted in substantially different mean annual water budgets for all sites, with reductions in water yield ranging from 13 to 45% under the new parameterization, highlighting the importance of properly parameterizing forest dynamics in watershed models. Importantly, our re-parameterization methodology does not require alteration to the SWAT code, allowing it to be readily adapted and applied in ongoing and future watershed modeling studies.

A secondary assessment of sediment trapping effectiveness by vegetated buffers.

Vegetated buffers and filter strips are a widely used Best Management Practice (BMP) for enhancing streamside ecosystem quality and water quality improvement through nonpoint source pollutant removal. Most existing studies are either site-specific, rely on limited data points, or evaluate buffer width and slope as the only design variables for predicting sediment reduction, not considering other parameters such as soil texture, vegetation types, and runoff loads that can significantly influence the buffer efficiency. In this paper, we carry out a meta-analysis of published studies and fit regression models to explore the sediment removal capacity of riparian buffers. We compiled 905 data points from over 90 studies (including data from an online BMP database) documenting sediment trapping by vegetated buffers and recorded data regarding buffer characteristics such as buffer width, slope, area, vegetation type, sediment loading, water flow rates, and sediment removal efficiency. We found that an exponential regression model describing the relationship between sediment removal efficiency by the buffer and water inflow/outflow volume ratio explained 44% of the variance. Adding the square root of roughness increased the R 2 to 0.50. The model performance was compared with other sediment reduction regression models reported in the literature. The results point towards the importance of considering flow parameters in vegetative buffer design. The improved empirical relationships derived here can be used at local scales to understand sediment trapping potential by vegetated buffers for water quality mitigation purposes and can be built into extant hydrologic models for improved watershed-scale assessments.

A two-layer numerical model of soil moisture dynamics: Model development.

Simulating water moisture flow in variably saturated soils with a relatively shallow water table is challenging due to the high nonlinear behavior of Richards' equation (RE). A two-layer approximation of RE was derived in this paper, which describes vertically-averaged soil moisture content and flow dynamics in the root zone and the unsaturated soil below. To this end, the partial differential equation (PDE) describing RE was converted into two-coupled ordinary differential equations (ODEs) describing dynamic vertically-averaged soil moisture variations in the two soil zones subject to a deep or shallow water table in addition to variable soil moisture flux and pressure conditions at the surface. The coupled ODEs were solved numerically using the iterative Huen's method for a variety of flux and pressure-controlled top and bottom boundary conditions (BCs). The numerical model was evaluated for three typical soil textures with free-drainage and mixed flux-pressure head at the bottom boundary under various atmospheric conditions. The results of soil water contents and fluxes were validated using HYDRUS-1D as a benchmark. Simulated values showed that the new model is numerically stable and generally accurate in simulating vertically-averaged soil moisture in the two layers under various flux and prescribed pressure BCs. A hypothetical simulation scenario involving desaturation of initially saturated soil profile caused by exponentially declining water table demonstrated the robustness of the numerical model in tracking vertically-averaged moisture contents in the roots layer and the lower vadose soil as the water table continued to fall. The two-layer model can be used by researchers to simulate variably saturated soils in wetlands and by water resources planners for efficient coupling of land-surface systems to groundwater and management of conjunctive use of surface and groundwater.

Combined and synergistic effects of climate change and urbanization on water quality in the Wolf Bay watershed, southern Alabama.

This study investigated potential changes in flow, total suspended solid (TSS) and nutrient (nitrogen and phosphorous) loadings under future climate change, land use/cover (LULC) change and combined change scenarios in the Wolf Bay watershed, southern Alabama, USA. Four Global Circulation Models (GCMs) under three Special Report Emission Scenarios (SRES) of greenhouse gas were used to assess the future climate change (2016-2040). Three projected LULC maps (2030) were employed to reflect different extents of urbanization in future. The individual, combined and synergistic impacts of LULC and climate change on water quantity/quality were analyzed by the Soil and Water Assessment Tool (SWAT). Under the "climate change only" scenario, monthly distribution and projected variation of TSS are expected to follow a pattern similar to streamflow. Nutrients are influenced both by flow and management practices. The variation of Total Nitrogen (TN) and Total Phosphorous (TP) generally follow the flow trend as well. No evident difference in the N:P ratio was projected. Under the "LULC change only" scenario, TN was projected to decrease, mainly due to the shrinkage of croplands. TP will increase in fall and winter. The N:P ratio shows a strong decreasing potential. Under the "combined change" scenario, LULC and climate change effect were considered simultaneously. Results indicate that if future loadings are expected to increase/decrease under any individual scenario, then the combined change will intensify that trend. Conversely, if their effects are in opposite directions, an offsetting effect occurs. Science-based management practices are needed to reduce nutrient loadings to the Bay.

Climatic, ecological, and socioeconomic factors associated with West Nile virus incidence in Atlanta, Georgia, U.S.A.

The integrated effects of the many risk factors associated with West Nile virus (WNV) incidence are complex and not well understood. We studied an array of risk factors in and around Atlanta, GA, that have been shown to be linked with WNV in other locations. This array was comprehensive and included climate and meteorological metrics, vegetation characteristics, land use / land cover analyses, and socioeconomic factors. Data on mosquito abundance and WNV mosquito infection rates were obtained for 58 sites and covered 2009-2011, a period following the combined storm water - sewer overflow remediation in that city. Risk factors were compared to mosquito abundance and the WNV vector index (VI) using regression analyses individually and in combination. Lagged climate variables, including soil moisture and temperature, were significantly correlated (positively) with vector index as were forest patch size and percent pine composition of patches (both negatively). Socioeconomic factors that were most highly correlated (positively) with the VI included the proportion of low income households and homes built before 1960 and housing density. The model selected through stepwise regression that related risk factors to the VI included (in the order of decreasing influence) proportion of houses built before 1960, percent of pine in patches, and proportion of low income households.

Larval development of Culex quinquefasciatus in water with low to moderate.

Population growth and urbanization have increased the potential habitats, and consequently the abundance of Culex quinquefasciatus, the southern house mosquito, a vector of West Nile Virus in urban areas. Water quality is critical in larval habitat distribution and in providing microbial food resources for larvae. A mesocosm experiment was designed to demonstrate which specific components of water chemistry are conducive to larval Culex mosquitoes. Dose-response relationships between larval development and NO3 , NH4 , and PO4 concentrations in stream water were developed through this experiment to describe the isolated effects of each nutrient on pre-adult development. The emergence pattern of Culex mosquitoes was found to be strongly related to certain nutrients, and results showed that breeding sites with higher PO4 or NO3 concentrations had higher larval survival rates. High NO3 concentrations favor the development of male mosquitoes and suppress the development of female mosquitoes, but those adult females that do emerge develop faster in containers with high NO3 levels compared to the reference group. The addition of PO4 in the absence of nitrogen sources to the larval habitat slowed larval development, however, it took fewer days for larvae to reach the pupal stage in containers with combinations of NO3 and PO4 or NH4 and PO4 nutrients. Results from this study may bolster efforts to control WNV in urban landscapes by exploring water quality conditions of Culex larval habitats that produce adult mosquitoes.

Watershed-level comparison of predictability and sensitivity of two phosphorus models.

Buildup of phosphorus (P) in agricultural soils and transport of P to nearby surface waters due to excessive, long-term application of poultry litter is an environmental concern in many poultry-producing states. Watershed models are often used to quantify soil and water quality impacts of poultry litter applications. However, depending on how P transport is simulated in watershed models, the anticipated impact could be quite different. The objective of this study was to determine the predictability and sensitivity of the Soil and Water Assessment Tool (SWAT) P model and a newly developed, state-of-the-art manure P model called SurPhos in a poultry litter-applied pasture watershed. A small, predominantly agricultural watershed in Randolph County, Alabama was used for this study. The SWAT model, calibrated for surface runoff and total stream flows (Nash-Sutcliffe coefficient of 0.70 for both), was used to provide runoff inputs to the SurPhos model. Total dissolved P (TDP) exports simulated by the SWAT P and SurPhos models from the hay hydrological response units of the watershed were compared for different poultry litter application rates and different initial soil Solution P levels. Both models showed sensitivity to poultry litter application rates, with SWAT simulating linear and SurPhos simulating nonlinear increases in TDP exports with increase in poultry litter application rates. SWAT showed greater sensitivity to initial soil Solution P levels, which can lead to overestimation of TDP exports, especially at low poultry litter application rates. As opposed to the SurPhos model simulations and contrary to recent studies, SWAT simulated excessive accumulation of Solution P in the top 10 mm of soil. Because SurPhos appears to simulate P transport and build-up processes from manure-applied areas more accurately, this study suggests that SWAT be replaced by SurPhos to more accurately determine watershed-level effectiveness of P management measures.

Water resources and land use and cover in a humid region: the southeastern United States.

It is widely recognized that forest and water resources are intricately linked. Globally, changes in forest cover to accommodate agriculture and urban development introduce additional challenges for water management. The U.S. Southeast typifies this global trend as predictions of land-use change and population growth suggest increased pressure on water resources in coming years. Close attention has long been paid to interactions between people and water in arid regions; however, based on information from regions such as the Southeast, it is evident that much greater focus is required to sustain a high-quality water supply in humid areas as well. To that end, we review hydrological, physicochemical, biological, and human and environmental health responses to conversion of forests to agriculture and urban land uses in the Southeast. Commonly, forest removal leads to increased stream sediment and nutrients, more variable flow, altered habitat and stream and riparian communities, and increased risk of human health effects. Although indicators such as the percentage of impervious cover signify overall watershed alteration, the threshold to disturbance, or the point at which effects can been observed in stream and riparian parameters, can be quite low and often varies with physiographic conditions. In addition to current land use, historical practices can greatly influence current water quality. General inferences of this study may extend to many humid regions concerning climate, environmental thresholds, and the causes and nature of effects.

Predicting water quality in unmonitored watersheds using artificial neural networks.

Land use and land cover (LULC) play a central role in fate and transport of water quality (WQ) parameters in watersheds. Developing relationships between LULC and WQ parameters is essential for evaluating the quality of water resources. In this paper, we present an artificial neural network (ANN)-based methodology to predict WQ parameters in watersheds with no prior WQ data. The model relies on LULC percentages, temperature, and stream discharge as inputs. The approach is applied to 18 watersheds in west Georgia, United States, having a LULC gradient and varying in size from 2.96 to 26.59 km2. Out of 18 watersheds, 12 were used for training, 3 for validation, and 3 for testing the ANN model. The WQ parameters tested are total dissolved solids (TDS), total suspended solids (TSS), chlorine (Cl), nitrate (NO3), sulfate (SO4), sodium (Na), potassium (K), total phosphorus (TP), and dissolved organic carbon (DOC). Model performances are evaluated on the basis of a performance rating system whereby performances are categorized as unsatisfactory, satisfactory, good, or very good. Overall, the ANN models developed using the training data performed quite well in the independent test watersheds. Based on the rating system TDS, Cl, NO3, SO4, Na, K, and DOC had a performance of at least "good" in all three test watersheds. The average performance for TSS and TP in the three test watersheds were "good." Overall the model performed better in the pastoral and forested watersheds with an average rating of "very good." The average model performance at the urban watershed was "good." This study showed that if WQ and LULC data are available from multiple watersheds in an area with relatively similar physiographic properties, then one can successfully predict the impact of LULC changes on WQ in any nearby watershed.

An ecologically-sustainable surface water withdrawal framework for cropland irrigation: a case study in Alabama.

Agricultural production in the state of Alabama, USA, is mostly rain-fed, because of which it is vulnerable to drought during growing season. Since Alabama receives a significant portion of its annual precipitation during winter months, the goal of this study was to evaluate the feasibility of water withdrawal from streams during winter months for irrigation in the growing season. The Soil and Water Assessment Tool (SWAT) was used to estimate the quantity of water that can be sustainably withdrawn from streams during winter high flow periods. The model was successfully calibrated and validated for surface runoff, base flow, and total stream flow. The stream flows generated by the model at several locations within the watershed were then used to examine how much water can be sustainably withdrawn from streams of various orders (first, second and third). Although there was a considerable year-to-year variability in the amount of water that can be withdrawn, a 16-year average showed that first, second, and third order streams can irrigate about 11.6, 10.3, and 10.6% of their drainage areas, respectively. The percentage of drainage area that can be irrigated was not a function of stream order.

An auxiliary method to reduce potential adverse impacts of projected land developments: subwatershed prioritization.

An index based method is developed that ranks the subwatersheds of a watershed based on their relative impacts on watershed response to anticipated land developments, and then applied to an urbanizing watershed in Eastern Pennsylvania. Simulations with a semi-distributed hydrologic model show that computed low- and high-flow frequencies at the main outlet increase significantly with the projected landscape changes in the watershed. The developed index is utilized to prioritize areas in the urbanizing watershed based on their contributions to alterations in the magnitude of selected flow characteristics at two spatial resolutions. The low-flow measure, 7Q10, rankings are shown to mimic the spatial trend of groundwater recharge rates, whereas average annual maximum daily flow, QAMAX, and average monthly median of daily flows, QMMED, rankings are influenced by both recharge and proximity to watershed outlet. Results indicate that, especially with the higher resolution, areas having quicker responses are not necessarily the more critical areas for high-flow scenarios. Subwatershed rankings are shown to vary slightly with the location of water quality/quantity criteria enforcement. It is also found that rankings of subwatersheds upstream from the site of interest, which could be the main outlet or any interior point in the watershed, may be influenced by the time scale of the hydrologic processes.