Non-floodplain Wetlands Affect Watershed Nutrient Dynamics: A Critical Review

Diminished storage capacity of ponds caused by sedimentation weakens their nitrogen removal efficiency

Though their small size, ponds play a disproportionately crucial role in eliminating nitrogen (N) transporting to downstream freshwaters. As significant water infrastructures, ponds are non-sustainable due to loss of storage capacity resulting from sedimentation. However, the effects of pond sedimentation on N removal is widely neglected in landscape N processing. The NUFER (Nutrient flows in Food chains, Environment and Resources use) model was employed to estimate N runoff from 1960 to 2018. We reconstructed the dynamic of number and storing capacity of about 14 million ponds due to construction and sedimentation from 1960 to 2018, projecting these trends into the year 2060. Our approach incorporated first-order kinetic reactions, including water residence time (HRT), to estimate N removal of ponds, utilizing data 6 monitoring ponds and 81 ponds from literature studies. Our analysis reveals a fourteen-fold increase in N runoff over the past six decades, rising from 0.8 Mt N in 1960 to 11.4 Mt N in 2018. Due to the initial rapid expansion of ponds, N removal by ponds increased from 6.4 % in 1960 to 13.6 % in 1990. Sedimentation is prevalent in ponds, particularly in small ponds with a sedimentation accumulation rate of 2.96 cm yr-1. Pond sedimentation, which reduces HRT, resulted in a decrease in pond N removal percentage to 11.2 % in 2018 and a projected 7.4 % by the year 2060, assuming similar sediment accumulation rates persist in the future. Overall, our findings underscore the non-negligible role of ponds as landscape nodes in N cycling. Urgent mitigation measures are needed to extend the lifetime of existing ponds and sustain their critical role in water quality management.

Health Risk Assessment of Heavy Metal(loid)s in the Overlying Water of Small Wetlands Based on Monte Carlo Simulation

Heavy metal(loid) (HM) contamination is a significant threat to wetland ecosystem. However, contamination risks of HMs in overlying water of small wetlands, which are easily ignored because of their minor occupancy in an overall area, are nearly unknown. A total of 36 water samples containing six HMs were collected from the urban and rural small wetlands of Urumqi in China, and the contamination levels and probabilistic health risks caused by HMs were assessed using the Nemerow pollution index (NPI) and the health risk assessment model introduced by the US EPA. The results revealed that the average concentration of Hg in the urban and rural small wetlands surpassed the Class II thresholds of the Environmental Quality Standards for Surface Water (GB 3838-2002) by factors of 3.2 and 5.0 times, respectively. The overall contamination levels of HMs in the small wetlands fall into the high contamination level. Results of a health risk assessment indicated that non-carcinogenic health risk of the investigated HMs are found to be lower than the acceptable range for adults, but higher than the acceptable range for children. Meanwhile, As falls into the low carcinogenic risk level, whereas Cd falls into the very low carcinogenic risk level. Overall, HMs in rural small wetlands showed relatively higher contamination levels and probabilistic health risks than that of urban small wetlands. In addition, As was identified as the dominant health risk factor in the overlying water of small wetlands in the study area. Findings of this study provide scientific support needed for the prevention of HM contamination of small wetlands in arid zones.

Wetland water quality patterns and anthropogenic pressure associations across the continental USA

Anthropogenic impacts on lake and stream water quality are well established but have been much less studied in wetlands. Here we use data from the 2016 National Wetland Condition Assessment to characterize water quality and its relationship to anthropogenic pressure for inland wetlands across the conterminous USA. Water samples obtained from 525 inland wetlands spanned pH from <4 to >9 and 3 to 5 orders of magnitude in ionic strength (chloride, sulfate, conductivity), nutrients (total N and P), turbidity, planktonic chlorophyll, and dissolved organic carbon (DOC). Anthropogenic pressure levels were evaluated at two spatial scales - an adjacent scale scored from field checklists, and a catchment scale indicated by percent agricultural plus urban landcover. Pressure at the two spatial scales were uncorrelated and varied considerably across regions and wetland hydrogeomorphic types. Both adjacent- and catchment-scale pressure were associated with elevated ionic-strength metrics; chloride elevation was most evident in road-salt using states, and sulfate was strongly elevated in a few sites with coal mining nearby. Nutrients were elevated in association with catchment-scale pressure but concomitant changes were not seen in planktonic chlorophyll. Acidic pH and high DOC occurred primarily in upper Great Lakes and eastern seaboard sites having low anthropogenic pressure, suggesting natural organic acid sources. Ionic strength and nutrients increased with increasing catchment-scale pressure even in Flats and closed Depression and Lacustrine sites, which indicates connectivity to rather than isolation from upland anthropogenic landuse even for wetlands lacking inflowing streams.

Small ponds have stronger potential for net nitrogen removal: Insight from direct dissolved N2 measurement

Growing demands for watershed nitrogen (N) removal have called attention to abundant small bodies of water such as ponds, which have long been heralded as efficient storage and processing systems. Although pond conservation, restoration, and creation have been widely implemented to mitigate N pollution, information is limited regarding the impact of size-that is, whether N removal potential and efficiency are dependent upon pond size. We investigated the dynamics of N removal rates in 56 ponds from a hilly watershed by studying their bimonthly N2 concentrations and fluxes. Our results showed that smaller ponds performed better in net N removal. This can be discerned from the areal N2 fluxes, which were the highest in small ponds (< 4, 000 m2). The corresponding N2 fluxes (4.73 ± 4.53 mmol N2 m-2 d-1) were 2 to 14 times greater than those observed in larger ponds. The N removal efficiency, a metric used to describe the portions of the substrates released as N2, was also significantly higher in the small ponds (∼8.7 %) than in the larger ponds (∼5.0 %). Further regression analysis showed that both areal N2 flux and N removal efficiency were negatively correlated with pond area. The underlying mechanisms behind the size effects of N removal could be attributed to small ponds having larger sediment contact area to water volume ratios. Thus, smaller ponds allow more opportunities for N to interact with bioactive sediments than larger ponds. Overall, our findings contribute to the understanding of the distal role of pond size in affecting N removal. This research also provides a strong rationale for considering the effects of system size when implementing management practices dedicated to maximizing N removal.

Wetland Flowpaths Mediate Nitrogen and Phosphorus Concentrations across the Upper Mississippi River Basin

Eutrophication, harmful algal blooms, and human health impacts are critical environmental challenges resulting from excess nitrogen and phosphorus in surface waters. Yet we have limited information regarding how wetland characteristics mediate water quality across watershed scales. We developed a large, novel set of spatial variables characterizing hydrological flowpaths from wetlands to streams, that is, "wetland hydrological transport variables," to explore how wetlands statistically explain the variability in total nitrogen (TN) and total phosphorus (TP) concentrations across the Upper Mississippi River Basin (UMRB) in the United States. We found that wetland flowpath variables improved landscape-to-aquatic nutrient multilinear regression models (from R2 = 0.89 to 0.91 for TN; R2 = 0.53 to 0.84 for TP) and provided insights into potential processes governing how wetlands influence watershed-scale TN and TP concentrations. Specifically, flowpath variables describing flow-attenuating environments, for example, subsurface transport compared to overland flowpaths, were related to lower TN and TP concentrations. Frequent hydrological connections from wetlands to streams were also linked to low TP concentrations, which likely suggests a nutrient source limitation in some areas of the UMRB. Consideration of wetland flowpaths could inform management and conservation activities designed to reduce nutrient export to downstream waters.

Improving model capability in simulating spatiotemporal variations and flow contributions of nitrate export in tile-drained catchments

It is essential to identify the dominant flow paths, hot spots and hot periods of hydrological nitrate-nitrogen (NO3-N) losses for developing nitrogen loads reduction strategies in agricultural watersheds. Coupled biogeochemical transformations and hydrological connectivity regulate the spatiotemporal dynamics of water and NO3-N export along surface and subsurface flows. However, modeling performance is usually limited by the oversimplification of natural and human-managed processes and insufficient representation of spatiotemporally varied hydrological and biogeochemical cycles in agricultural watersheds. In this study, we improved a spatially distributed process-based hydro-ecological model (DLEM-catchment) and applied the model to four tile-drained catchments with mixed agricultural management and diverse landscape in Iowa, Midwestern US. The quantitative statistics show that the improved model well reproduced the daily and monthly water discharge, NO3-N concentration and loading measured from 2015 to 2019 in all four catchments. The model estimation shows that subsurface flow (tile flow + lateral flow) dominates the discharge (70-75%) and NO3-N loading (77-82%) over the years. However, the contributions of tile drainage and lateral flow vary remarkably among catchments due to different tile-drained area percentages and the presence of farmed potholes (former depressional wetlands that have been drained for agricultural production). Furthermore, we found that agricultural management (e.g. tillage and fertilizer management) and catchment characteristics (e.g. soil properties, farmed potholes, and tile drainage) play important roles in predicting the spatial distributions of NO3-N leaching and loading. The simulated results reveal that the model improvements in representing water retention capacity (snow processes, soil roughness, and farmed potholes) and tile drainage improved model performance in estimating discharge and NO3-N export at a daily time step, while improvement of agricultural management mainly impacts NO3-N export prediction. This study underlines the necessity of characterizing catchment properties, agricultural management practices, flow-specific NO3-N movement, and spatial heterogeneity of NO3-N fluxes for accurately simulating water quality dynamics and predicting the impacts of agricultural conservation nutrient reduction strategies.

Modeling flood susceptibility zones using hybrid machine learning models of an agricultural dominant landscape of India

Flooding events are determining a significant amount of damages, in terms of economic loss and also casualties in Asia and Pacific areas. Due to complexity and ferocity of severe flooding, predicting flood-prone areas is a difficult task. Thus, creating flood susceptibility maps at local level is though challenging but an inevitable task. In order to implement a flood management plan for the Balrampur district, an agricultural dominant landscape of India, and strengthen its resilience, flood susceptibility modeling and mapping are carried out. In the present study, three hybrid machine learning (ML) models, namely, fuzzy-ANN (artificial neural network), fuzzy-RBF (radial basis function), and fuzzy-SVM (support vector machine) with 12 topographic, hydrological, and other flood influencing factors were used to determine flood-susceptible zones. To ascertain the relationship between the occurrences and flood influencing factors, correlation attribute evaluation (CAE) and multicollinearity diagnostic tests were used. The predictive power of these models was validated and compared using a variety of statistical techniques, including Wilcoxon signed-rank, t-paired tests and receiver operating characteristic (ROC) curves. Results show that fuzzy-RBF model outperformed other hybrid ML models for modeling flood susceptibility, followed by fuzzy-ANN and fuzzy-SVM. Overall, these models have shown promise in identifying flood-prone areas in the basin and other basins around the world. The outcomes of the work would benefit policymakers and government bodies to capture the flood-affected areas for necessary planning, action, and implementation.

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 ( R L ) and removal efficiency ( E R ) ) 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 ( NO 3 - ) and phosphate ( PO 4 + ) , respectively. The calculated nutrient load removals are highly correlated with the input loads, and the estimated PO 4 +   E R shows a significant decreasing trend with increased input loadings. The relationship between NO 3 - E R and wetland physical characteristics such as area, volume, and residence time is statistically insignificant (p > 0.05), while for PO 4 + , 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 NO 3 - removal but not on PO 4 + removal. Modeling results and statistical analysis point toward denitrification and plant uptake as major NO 3 - removal mechanisms, whereas plant uptake, diffusion, and settling of sediment-bound P were the main mechanisms for PO 4 + removal. Additionally, the computed nutrient E R 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 NO 3 - and PO 4 + 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 NO 3 - and 5-10% of the PO 4 + 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.

Mapping global non-floodplain wetlands

Non-floodplain wetlands - those located outside the floodplains - have emerged as integral components to watershed resilience, contributing hydrologic and biogeochemical functions affecting watershed-scale flooding extent, drought magnitude, and water-quality maintenance. However, the absence of a global dataset of non-floodplain wetlands limits their necessary incorporation into water quality and quantity management decisions and affects wetland-focused wildlife habitat conservation outcomes. We addressed this critical need by developing a publicly available "Global NFW" (Non-Floodplain Wetland) dataset, comprised of a global river-floodplain map at 90 m resolution coupled with a global ensemble wetland map incorporating multiple wetland-focused data layers. The floodplain, wetland, and non-floodplain wetland spatial data developed here were successfully validated within 21 large and heterogenous basins across the conterminous United States. We identified nearly 33 million potential non-floodplain wetlands with an estimated global extent of over 16×106 km2. Non-floodplain wetland pixels comprised 53% of globally identified wetland pixels, meaning the majority of the globe's wetlands likely occur external to river floodplains and coastal habitats. The identified global NFWs were typically small (median 0.039 km2), with a global median size ranging from 0.018-0.138 km2. This novel geospatial Global NFW static dataset advances wetland conservation and resource-management goals while providing a foundation for global non-floodplain wetland functional assessments, facilitating non-floodplain wetland inclusion in hydrological, biogeochemical, and biological model development. The data are freely available through the United States Environmental Protection Agency's Environmental Dataset Gateway (https://gaftp.epa.gov/EPADataCommons/ORD/Global_NonFloodplain_Wetlands/, last access: 24 May 2023) and through https://doi.org/10.23719/1528331 (Lane et al., 2023a).

Exploring the canal environment in terms of water, bed sediments and vegetation in a reclaimed floodplain of Northern Italy

The Po plain (Italy) is one of the largest floodplains in Europe that needs environmental restoration. To achieve this goal, the knowledge of the 'environment' (water, bed sediments and vegetation) of the canals crossing such floodplain is necessary. The water flow of the canals was kept low for hydraulic safety purposes from October to March (NIR), and high for irrigation purposes from April to September (IR). Within this framework, this study aimed to assess in 9 sites of the east part of Po plain 1) the canals' environment quality in terms of vegetation diversity, and water and bed sediment physicochemical properties; and 2) how these features are influenced by canal managements and landscape properties. Water was monthly sampled both in NIR and IR periods, the bed sediments were sampled in summer and winter periods, while the vegetation was recorded in spring and autumn. The low water flow during NIR worsened the water quality by increasing the concentrations of nutrients and salts. A higher salt and nutrient concentrations were observed both in water and bed sediments of canals crossing areas with fine texture alluvial deposits than in those flowing through medium texture alluvial deposits. Further, higher nutrient and salt concentrations were observed for the canals used as collectors of the water coming from other canals. Despite the differences observed for the bed sediments and water quality, the vegetation type and biodiversity did not show differences among the study sites probably because affected by the land use of the surrounding landscape. Indeed, the canals cross agricultural land which limit the developments of natural vegetation and do not promote plant biodiversity. Overall, the present study found out the key role of landscape properties and canal managements on 'canal environment' quality which need to be considered to perform an appropriate reclamation of such environments.

Alterations in hydrological variables and substrate qualities and its impacts on a critical conservation reserve in the southwest coast of India

The present study investigated the long-term fluctuation in the hydrological and substrate variables at different habitats of Kadalundi-Vallikkunnu Community Reserve (KVCR) over the last decade. We hypothesize that natural impact represented by climate change and long-term impact from anthropogenic activities including industrialization and intensified agricultural practices have a direct effect on the natural hydrological cycle and the quality of coastal shores and thus can be a reason for coastal habitat and wildlife degradation. Results indicate a significant degradation in nutrient and organic matter concentration in the sediment and dramatic increase in nutrient concentration, salinity, temperature, and pH in the water. Sediment and water degradation can be one of the important factors affecting the structural quality and biodiversity of the region. Therefore, having long-term monitoring data can be useful to plan and design management and conservation strategies to protect local biodiversity and ecosystem.

Headwater streams and inland wetlands: Status and advancements of geospatial datasets and maps across the United States

Headwater streams and inland wetlands provide essential functions that support healthy watersheds and downstream waters. However, scientists and aquatic resource managers lack a comprehensive synthesis of national and state stream and wetland geospatial datasets and emerging technologies that can further improve these data. We conducted a review of existing United States (US) federal and state stream and wetland geospatial datasets, focusing on their spatial extent, permanence classifications, and current limitations. We also examined recent peer-reviewed literature for emerging methods that can potentially improve the estimation, representation, and integration of stream and wetland datasets. We found that federal and state datasets rely heavily on the US Geological Survey's National Hydrography Dataset for stream extent and duration information. Only eleven states (22%) had additional stream extent information and seven states (14%) provided additional duration information. Likewise, federal and state wetland datasets primarily use the US Fish and Wildlife Service's National Wetlands Inventory (NWI) Geospatial Dataset, with only two states using non-NWI datasets. Our synthesis revealed that LiDAR-based technologies hold promise for advancing stream and wetland mapping at limited spatial extents. While machine learning techniques may help to scale-up these LiDAR-derived estimates, challenges related to preprocessing and data workflows remain. High-resolution commercial imagery, supported by public imagery and cloud computing, may further aid characterization of the spatial and temporal dynamics of streams and wetlands, especially using multi-platform and multi-temporal machine learning approaches. Models integrating both stream and wetland dynamics are limited, and field-based efforts must remain a key component in developing improved headwater stream and wetland datasets. Continued financial and partnership support of existing databases is also needed to enhance mapping and inform water resources research and policy decisions.

Strong variability in nitrogen (N) removal rates in typical agricultural pond from hilly catchment: Evidence from diel and monthly dissolved N2 measurement

Ponds, depressional submerged landscapes that can store and process nitrogen (N)-enriched runoff from surrounding uplands, are recognized as biogeochemical hotspots for N removal. Despite their strong potential for N removal, information is limited concerning the specifics of their changing nature. Here, we investigated the dynamics of N removal rate in a typical agricultural pond from a hilly catchment, by unraveling the monthly and diel patterns of N₂ concentrations and fluxes. Our observations showed that the N pollution in the pond was severe. Its averaged total N level reached 3.6 mg L^-1, of which ∼72% consisted of NO₃-N. Meanwhile, the water samples were supersaturated with N₂, demonstrating N removal occurring in the pond. Further estimates of net N₂ fluxes indicated that N removal rates exhibited obvious day-and-night and monthly differences. On the diel scale, N removal rates exhibited a distinct diurnal cycle, with nocturnal rates around 20% higher than during the day. Such a diel pattern can be mainly explained by the fluctuation in DO levels, showing that at nighttime when photosynthesis is absent, low DO environments are conducive to N removal. On a monthly scale, the monthly rates ranged from 0.02 to 0.49 mmol N₂ m^-2 h^-1 (mean: 0.23 mmol N₂ m^-2 h^-1), with generally higher removal rates in the warmer and concurrently rainy months (June-September). N levels in the pond were the corresponding primary explanatory variables. Assembled data from both monthly and hourly scales provided a more complete picture of the changing nature of N removal in ponds. Future work should carefully consider the effects of altered environmental conditions triggered by hydrological events to better reveal the control mechanisms behind the time-immediate N removal from lowland ponds.

Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications

Anthropogenic nutrient inputs have led to nutrient enrichment in many waterbodies worldwide, including Chesapeake Bay (USA). River water quality integrates the spatial and temporal changes of watersheds and forms the foundation for disentangling the effects of anthropogenic inputs. We demonstrate with the Chesapeake Bay Non-Tidal Monitoring Network that machine learning approaches - i.e., hierarchical clustering and random forest (RF) classification - can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks, resulting in information useful for watershed management. Cluster analysis revealed regional patterns of short-term TN trends (2007-2018) and categorized the stations into three distinct trend clusters, namely, V-shape (n = 23), monotonic decline (n = 35), and monotonic increase (n = 26). RF models identified regional drivers of TN trend clusters by quantifying the effects of watershed characteristics (land use, geology, physiography) and major N sources on the trend clusters. Results provide encouraging evidence that improved agricultural nutrient management has resulted in declines in agricultural nonpoint sources, which in turn contributed to water-quality improvement in our period of analysis. Moreover, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport of this terrain. By contrast, water-quality improvements are less likely in Coastal Plain watersheds, reflecting the effect of legacy N in groundwater. Notably, results show degrading trends in forested watersheds, suggesting new and/or remobilized sources that may compromise management efforts. Finally, the developed RF models were used to predict TN trend clusters for the entire Chesapeake Bay watershed at the fine scale of river segments (n = 979), providing fine spatial information that can facilitate targeted watershed management, including unmonitored areas. More broadly, this combined use of clustering and classification approaches can be applied to other regional monitoring networks to address similar water-quality questions.

Vulnerable Waters are Essential to Watershed Resilience

Watershed resilience is the ability of a watershed to maintain its characteristic system state while concurrently resisting, adapting to, and reorganizing after hydrological (for example, drought, flooding) or biogeochemical (for example, excessive nutrient) disturbances. Vulnerable waters include non-floodplain wetlands and headwater streams, abundant watershed components representing the most distal extent of the freshwater aquatic network. Vulnerable waters are hydrologically dynamic and biogeochemically reactive aquatic systems, storing, processing, and releasing water and entrained (that is, dissolved and particulate) materials along expanding and contracting aquatic networks. The hydrological and biogeochemical functions emerging from these processes affect the magnitude, frequency, timing, duration, storage, and rate of change of material and energy fluxes among watershed components and to downstream waters, thereby maintaining watershed states and imparting watershed resilience. We present here a conceptual framework for understanding how vulnerable waters confer watershed resilience. We demonstrate how individual and cumulative vulnerable-water modifications (for example, reduced extent, altered connectivity) affect watershed-scale hydrological and biogeochemical disturbance response and recovery, which decreases watershed resilience and can trigger transitions across thresholds to alternative watershed states (for example, states conducive to increased flood frequency or nutrient concentrations). We subsequently describe how resilient watersheds require spatial heterogeneity and temporal variability in hydrological and biogeochemical interactions between terrestrial systems and down-gradient waters, which necessitates attention to the conservation and restoration of vulnerable waters and their downstream connectivity gradients. To conclude, we provide actionable principles for resilient watersheds and articulate research needs to further watershed resilience science and vulnerable-water management.

Assessment of water quality in ponds across the rural, peri-urban, and urban gradient

The Prairie Pothole Region is one of the most wetland rich areas of the world and has experienced intense disturbance from increased agricultural demands and urban sprawl. This study assessed ponds across the urban gradient for the first time in the region to determine the impacts of urbanization on water quality. Thirty ponds (ten rural, ten peri-urban, and ten urban) were randomly selected and compared based on land use type and the impervious to pervious surface ratio within 1.6 km of each pond. Water quality samples were taken monthly in 2015 and 2016, across 3 and 6 months respectively. Assessment included chemical and physical parameters, which were compared spatially across the gradient and temporally between sampling periods. Results indicate disturbance from urbanization negatively impacts water quality. Spatially across the gradient, rural pond water quality was significantly different from both peri-urban and urban ponds, whereas peri-urban and urban pond water quality was not significantly different. Temporally, differences between water quality parameters and sampling periods indicate that surrounding land use, land cover, and precipitation influence parameter concentrations across the urbanization gradient. Information from this study is useful to water professionals dealing with urban development and sprawl that continue to impact water and natural habitat.

Integrated assessment modeling reveals near-channel management as cost-effective to improve water quality in agricultural watersheds

Despite decades of policy that strives to reduce nutrient and sediment export from agricultural fields, surface water quality in intensively managed agricultural landscapes remains highly degraded. Recent analyses show that current conservation efforts are not sufficient to reverse widespread water degradation in Midwestern agricultural systems. Intensifying row crop agriculture and increasing climate pressure require a more integrated approach to water quality management that addresses diverse sources of nutrients and sediment and off-field mitigation actions. We used multiobjective optimization analysis and integrated three biophysical models to evaluate the cost-effectiveness of alternative portfolios of watershed management practices at achieving nitrate and suspended sediment reduction goals in an agricultural basin of the Upper Midwestern United States. Integrating watershed-scale models enabled the inclusion of near-channel management alongside more typical field management and thus directly the comparison of cost-effectiveness across portfolios. The optimization analysis revealed that fluvial wetlands (i.e., wide, slow-flowing, vegetated water bodies within the riverine corridor) are the single-most cost-effective management action to reduce both nitrate and sediment loads and will be essential for meeting moderate to aggressive water quality targets. Although highly cost-effective, wetland construction was costly compared to other practices, and it was not selected in portfolios at low investment levels. Wetland performance was sensitive to placement, emphasizing the importance of watershed scale planning to realize potential benefits of wetland restorations. We conclude that extensive interagency cooperation and coordination at a watershed scale is required to achieve substantial, economically viable improvements in water quality under intensive row crop agricultural production.

Assessment of extrinsic and intrinsic influences on water quality variation in subtropical agricultural multipond systems

Understanding wetland water quality dynamics and associated influencing factors is important to assess the numerous ecosystem services they provide. We present a combined self-organizing map (SOM) and linear mixed-effects model (LMEM) to relate water quality variation of multipond systems (MPSs, a common type of non-floodplain wetlands in agricultural regions of southern China) to their extrinsic and intrinsic influences for the first time. Across the 6 test MPSs with environmental gradients, ammonium nitrogen (NH₄⁺-N), total nitrogen (TN), and total phosphate (TP) almost always exceeded the surface water quality standard (2.0, 2.0, and 0.4 mg/L, respectively) in the up- and midstream ponds, while chlorophyll-a (Chl-a) exhibited hypertrophic state (≥28 μg/L) in the midstream ponds during the wet season. Synergistic influences explained 69±12% and 73±10% of the water quality variations in the wet and dry season, respectively. The adverse, extrinsic influences were generally 1.4, 6.9, 3.2, and 4.3 times of the beneficial, intrinsic influences for NH₄⁺-N, nitrate nitrogen (NO₃^--N), TP, and potassium permanganate index (COD_Mn), respectively, although the influencing direction and degree of forest and water area proportion were spatiotemporally unstable. While COD_Mn was primarily linked with rural residential areas in the midstream, higher TN and TP concentrations in the up- and midstream were associated with agricultural land, and NH₄⁺-N reflected a small but non-negligible source of free-range poultry feeding. Pond surface sediments exhibited consistent, adverse effects with amplifications during rainfall, while macrophyte biomass can reflect the biological uptake of COD_Mn and Chl-a, especially in the mid- and downstream during the wet season. Our study advances nonpoint source pollution (NPSP) research for small water bodies, explores nutrient "source-sink" dynamics, and provides a timely guide for rural planning and pond management. The modelling procedures and analytical results can inform refined assessment of similar NFWs elsewhere, where restoration efforts are required.

Process-based modeling to assess the nutrient removal efficiency of two endangered hydrophytes: Linking nutrient-cycle with a multiple-quotas approach

Hydrophytes have been widely used to reduce nutrient levels in aquatic ecosystems, but only limited species with high nutrient removal efficiencies have been implemented. Thus, it is necessary to continually explore new candidate species with high nutrient removal efficiencies. To effectively explore the nutrient removal ability of hydrophytes, a new process-based model combining the multiple-quotas approach and nutrient-cycle model was developed. The multiple-quotas approach provides a theoretical framework to conceptually explain the uptake and response of autotrophs to multiple nutrients. The developed process-based model was validated using observational data from microcosm experiments with two emergent hydrophytes, Menyanthes trifoliata and Cicuta virosa. The results showed that both M. trifoliata and C. virosa effectively reduced nitrogen (N) and phosphorus (P) in both water and sediment layers, but M. trifoliata showed a higher removal efficiency for both nutrients than C. virosa, particularly for total ammonia + ammonium-nitrogen (NH_x-N) and nitrate-nitrogen (NO₃-N) in the sediment layer (M. trifoliata: 0.579-0.976 for NH_x-N, 0.567-0.702 for NO₃-N; C. virosa: 0.212-0.501 for NH_x-N, 0.466-0.560 for NO₃-N). In addition, M. trifoliata achieved the maximum removal efficiency for N and P at higher nutrient exposure levels than C. virosa (M. trifoliata: exposure level of 0.725-0.775; C. virosa: exposure level of 0.550-0.575). The developed model well simulated the species-specific growth patterns of hydrophytes depending on the nutrient exposure level as well as the N and P dynamics in the water and sediment layers. The approach adopted in this study provides a useful tool for discovering candidate species to improve hydrophyte diversity and effectively remove nutrients from aquatic ecosystems.

Wetland restoration yields dynamic nitrate responses across the Upper Mississippi river basin

Wetland restoration is a primary management option for removing surplus nitrogen draining from agricultural landscapes. However, wetland capacity to mitigate nitrogen losses at large river-basin scales remains uncertain. This is largely due to a limited number of studies that address the cumulative and dynamic effects of restored wetlands across the landscape on downstream nutrient conditions. We analyzed wetland restoration impacts on modeled nitrate dynamics across 279 subbasins comprising the ∼0.5 million km2 Upper Mississippi River Basin (UMRB), USA, which covers eight states and houses ∼30 million people. Restoring ∼8,000 km2 of wetlands will reduce mean annual nitrate loads to the UMRB outlet by 12%, a substantial improvement over existing conditions but markedly less than widely cited estimates. Our lower wetland efficacy estimates are partly attributed to improved representation of processes not considered by preceding empirical studies - namely the potential for nitrate to bypass wetlands (i.e., via subsurface tile drainage) and be stored or transformed within the river network itself. Our novel findings reveal that wetlands mitigate surplus nitrogen basin-wide, yet they may not be as universally effective in tiled landscapes and because of river network processing.

Maximizing US nitrate removal through wetland protection and restoration

Growing populations and agricultural intensification have led to raised riverine nitrogen (N) loads, widespread oxygen depletion in coastal zones (coastal hypoxia)¹ and increases in the incidence of algal blooms.Although recent work has suggested that individual wetlands have the potential to improve water quality^2-9, little is known about the current magnitude of wetland N removal at the landscape scale. Here we use National Wetland Inventory data and 5-kilometre grid-scale estimates of N inputs and outputs to demonstrate that current N removal by US wetlands (about 860 ± 160 kilotonnes of nitrogen per year) is limited by a spatial disconnect between high-density wetland areas and N hotspots. Our model simulations suggest that a spatially targeted increase in US wetland area by 10 per cent (5.1 million hectares) would double wetland N removal. This increase would provide an estimated 54 per cent decrease in N loading in nitrate-affected watersheds such as the Mississippi River Basin. The costs of this increase in area would be approximately 3.3 billion US dollars annually across the USA-nearly twice the cost of wetland restoration on non-agricultural, undeveloped land-but would provide approximately 40 times more N removal. These results suggest that water quality improvements, as well as other types of ecosystem services such as flood control and fish and wildlife habitat, should be considered when creating policy regarding wetland restoration and protection.

Outsized nutrient contributions from small tributaries to a Great Lake

Excessive nitrogen (N) and phosphorus (P) loading is one of the greatest threats to aquatic ecosystems in the Anthropocene, causing eutrophication of rivers, lakes, and marine coastlines worldwide. For lakes across the United States, eutrophication is driven largely by nonpoint nutrient sources from tributaries that drain surrounding watersheds. Decades of monitoring and regulatory efforts have paid little attention to small tributaries of large water bodies, despite their ubiquity and potential local importance. We used a snapshot of nutrient inputs from nearly all tributaries of Lake Michigan-the world's fifth largest freshwater lake by volume-to determine how land cover and dams alter nutrient inputs across watershed sizes. Loads, concentrations, stoichiometry (N:P), and bioavailability (percentage dissolved inorganic nutrients) varied by orders of magnitude among tributaries, creating a mosaic of coastal nutrient inputs. The 6 largest of 235 tributaries accounted for ∼70% of the daily N and P delivered to Lake Michigan. However, small tributaries exhibited nutrient loads that were high for their size and biased toward dissolved inorganic forms. Higher bioavailability of nutrients from small watersheds suggests greater potential to fuel algal blooms in coastal areas, especially given the likelihood that their plumes become trapped and then overlap in the nearshore zone. Our findings reveal an underappreciated role that small streams may play in driving coastal eutrophication in large water bodies. Although they represent only a modest proportion of lake-wide loads, expanding nutrient management efforts to address smaller watersheds could reduce the ecological impacts of nutrient loading on valuable nearshore ecosystems.

Exploring the multiscale hydrologic regulation of multipond systems in a humid agricultural catchment

Assessing the hydrologic processes over scales ranging from single wetland to regional is critical to understand the hydrologically-driven ecosystem services especially nutrient buffering of wetlands. Here, we present a novel approach to quantify the multiscale hydrologic regulation of multipond systems (MPSs), a common type of small, scattered wetland in humid agricultural regions, because previous studies have stopped in commending the catchment scale flood and drought resilience of these waters, and contemporary models do not adequately represent the corresponding intra-catchment fill-spill relationships. A new version of Soil and Water Assessment Tool (SWAT) was developed to incorporate improved representation of: (1) perennial or intermittent spillage connections of pond-to-pond and pond-to-stream, and (2) bidirectional exchange between pond surface water and shallow groundwater. We present SWAT-MPS, which adopts rule-based artificial intelligence to model the possibilities of different spillage directions and GA-based parameter optimization over the two simulation years (June 2017 to May 2019), with successfully replicated streamflow and pond water-level variations in a 4.8 km² test catchment, southern China. Water balance analysis and scenario simulations were then executed to assess the hydrologic regulation at single pond, single MPS, and entire catchment scales. Results revealed (1) the presence of 9 series- or series-parallel connected MPSs, in which pond overflow accounted for as much as 59% of the catchment water yield; (2) seasonally- and MPS-independent baseflow support and quickflow attenuation, with ranked level of pond water storage for baseflow support across different landuse types: forest > farm > village, and inversed correlation of pond spillage to baseflow and quickflow variations in the farmland; and (3) MPS-aggregated catchment flood peak reduction (>20%) and baseflow increment (26%) in the following dry days. Meteorological data analysis and simulated average daily values indicated these hydrologic patterns are credible even if extending to a 5-year period. As a first modelling attempt to explore the intra-catchment details of MPSs, our study underscores the water storage and connectivity in their hydrologic regulation, and suggests inventories, long-term field monitoring, and several research directions of the new model for integrated pond management in watersheds and river basins. These findings can inform refined assessment of similar small, scattered wetlands elsewhere, where restoration efforts are required.

Surface Depression and Wetland Water Storage Improves Major River Basin Hydrologic Predictions

Surface water storage in small yet abundant landscape depressions-including wetlands and other small waterbodies-is largely disregarded in conventional hydrologic modeling practices. No quantitative evidence exists of how their exclusion may lead to potentially inaccurate model projections and understanding of hydrologic dynamics across the world's major river basins. To fill this knowledge gap, we developed the first-ever major river basin-scale modeling approach integrating surface depressions and focusing on the 450,000-km2 Upper Mississippi River Basin (UMRB) in the United States. We applied a novel topography-based algorithm to estimate areas and volumes of ~455,000 surface depressions (>1 ha) across the UMRB (in addition to lakes and reservoirs) and subsequently aggregated their effects per subbasin. Compared to a "no depression" conventional model, our depression-integrated model (a) improved streamflow simulation accuracy with increasing upstream abundance of depression storage, (b) significantly altered the spatial patterns and magnitudes of water yields across 315,000 km2 (70%) of the basin area, and (c) provided realistic spatial distributions of rootzone wetness conditions corresponding to satellite-based data. Results further suggest that storage capacity (i.e., volume) alone does not fully explain depressions' cumulative effects on landscape hydrologic responses. Local (i.e., subbasin level) climatic and geophysical drivers and downstream flowpath-regulating structures (e.g., reservoirs and dams) influence the extent to which depression storage volume in a subbasin causes hydrologic effects. With these new insights, our study supports the integration of surface depression storage and thereby catalyzes a reassessment of current hydrological modeling and management practices for basin-scale studies.

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km² agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model's built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a "basic" traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model's LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

Land-Cover Changes to Surface-Water Buffers in the Midwestern USA: 25 Years of Landsat Data Analyses (1993-2017)

To understand the timing, extent, and magnitude of land use/land cover (LULC) change in buffer areas surrounding Midwestern US waters, we analyzed the full imagery archive (1982-2017) of three Landsat footprints covering ~100,000 km2. The study area included urbanizing Chicago, Illinois and St. Louis, Missouri regions and agriculturally dominated landscapes (i.e., Peoria, Illinois). The Continuous Change Detection and Classification algorithm identified 1993-2017 LULC change across three Landsat footprints and in 90 m buffers for ~110,000 surface waters; waters were also size-binned into five groups for buffer LULC change analyses. Importantly, buffer-area LULC change magnitude was frequently much greater than footprint-level change. Surface-water extent in buffers increased by 14-35x the footprint rate and forest decreased by 2-9x. Development in buffering areas increased by 2-4x the footprint-rate in Chicago and Peoria area footprints but was similar to the change rate in the St. Louis area footprint. The LULC buffer-area change varied in waterbody size, with the greatest change typically occurring in the smallest waters (e.g., <0.1 ha). These novel analyses suggest that surface-water buffer LULC change is occurring more rapidly than footprint-level change, likely modifying the hydrology, water quality, and biotic integrity of existing water resources, as well as potentially affecting down-gradient, watershed-scale storages and flows of water, solutes, and particulate matter.