Characterizing Spatiotemporal Variability in Phosphorus Export across the United States through Bayesian Hierarchical Modeling.

Phosphorus inputs from anthropogenic activities are subject to hydrologic (riverine) export, causing water quality problems in downstream lakes and coastal systems. Nutrient budgets have been developed to quantify the amount of nutrients imported to and exported from various watersheds. However, at large spatial scales, estimates of hydrologic phosphorus export are usually unavailable. This study develops a Bayesian hierarchical model to estimate annual phosphorus export across the contiguous United States, considering agricultural inputs, urban inputs, and geogenic sources under varying precipitation conditions. The Bayesian framework allows for a systematic updating of prior information on export rates using an extensive calibration data set of riverine loadings. Furthermore, the hierarchical approach allows for spatial variation in export rates across major watersheds and ecoregions. Applying the model, we map hotspots of phosphorus loss across the United States and characterize the primary factors driving these losses. Results emphasize the importance of precipitation in determining hydrologic export rates for various anthropogenic inputs, especially agriculture. Our findings also emphasize the importance of phosphorus from geogenic sources in overall river export.

Advancing Watershed Legacy Nitrogen Modeling to Improve Global Water Quality.

Despite widespread implementation of watershed nitrogen reduction programs across the globe, nitrogen levels in many surface waters remain high. Watershed legacy nitrogen storage, i.e., the long-term retention of nitrogen in soils and groundwater, is one of several explanations for this lack of progress. However scientists and water managers are ill-equipped to estimate how legacy nitrogen moderates in-stream nitrogen responses to land conservation practices, largely because modeling tools and associated long-term monitoring approaches to answering these questions remain inadequate. We demonstrate the need for improved watershed models to simulate legacy nitrogen processes and offer modeling solutions to support long-term nitrogen-based sustainable land management across the globe.

A review of Soil and Water Assessment Tool (SWAT) studies of Mediterranean catchments: Applications, feasibility, and future directions.

The Soil and Water Assessment Tool (SWAT) is a well-established eco-hydrological model that has been extensively applied to watersheds across the globe. This work reviews over two decades (2002-2022) of SWAT studies conducted on Mediterranean watersheds. A total of 260 articles have been identified since the earliest documented use of the model in a Mediterranean catchment back in 2002; of which 62% were carried out in Greece, Italy, or Spain. SWAT applications increased significantly in recent years since 86% of the reviewed papers were published in the past decade. A major objective for most of the reviewed works was to check the applicability of SWAT to specific watersheds. A great number of publications included procedures of calibration and validation and reported performance results. SWAT applications in the Mediterranean region mainly cover water resources quantity and quality assessment and hydrologic and environmental impacts evaluation of land use and climate changes. Nevertheless, a tendency towards a multi-purpose use of SWAT is revealed. The numerous examples of SWAT combined with other tools and techniques outline the model's flexibility. Several studies performed constructive comparisons between Mediterranean watersheds' responses or compared SWAT to other models or methods. The effects of inputs on SWAT outputs and innovative model modifications and improvements were also the focus of some of the surveyed articles. However, a significant number of studies reported difficulties regarding data availability, as these are either scarce, have poor resolution or are not freely available. Therefore, it is highly recommended to identify and develop accurate model inputs and testing data to optimize the SWAT performance.

Pronounced Increases in Future Soil Erosion and Sediment Deposition as Influenced by Freeze-Thaw Cycles in the Upper Mississippi River Basin.

Soil erosion and sediment deposition are relevant to multiple important ecosystem services essential for natural and human systems. The present study aims to project future soil erosion and sediment deposition in the Upper Mississippi River Basin (UMRB) using climate projections by five Global Circulation Models (GCMs) under the Representative Concentrations Pathway (RCP) 8.5 scenario. To understand the importance of freeze-thaw cycles (FTCs) for soil erosion and sediment deposition estimation with climate change, this study compared two Soil and Water Assessment Tool (SWAT) models with different representations of the FTCs, with the standard SWAT using a simple regression method and SWAT-FT employing a physically based method. Modeling results show that future climate change can pronouncedly intensify soil erosion and increase sediment deposition, and the impacts are sensitive to how FTCs are represented in the model. The standard SWAT projected an increase in soil erosion by nearly 40% by the end of the 21st century, which is much lower than the projected over 65% increase in soil erosion by SWAT-FT. For sediment deposition, the projected percent changes by the standard SWAT and SWAT-FT also deviate from each other (i.e., about 70% by the standard SWAT vs about 120% by SWAT-FT). Overall, these results demonstrate the important roles of FTCs in projecting future soil erosion and sediment deposition and underline the need to consider the effects of conservation practices on FTCs to realistically assess the effectiveness of those measures.

Stochastic reliability-based risk evaluation and mapping for watershed systems and sustainability (STREAMS).

Mitigating water contamination, improving water security, and increasing sustainability involve environmental awareness and conscientious decision-making by denizens and stakeholders. Achieving such awareness requires visually compelling geospatial decision-making tools that take into account the probabilistic and spatially distributed nature of water contamination. Inspired by the success of weather maps, this paper presents a novel STochastic Reliability-based Risk Evaluation And Mapping for watershed Systems and Sustainability (STREAMS) tool that produces and effectively communicates the risk of water contamination as maps. STREAMS is integrated with ArcGIS geoprocessing tools and uses physics-based reliability theory to compute the spatial distribution of risk, which is defined as the probability of exceeding a safety threshold of water contamination within a watershed. A quantitative analysis of the efficacy of mitigation strategies is conducted by estimating risk reduction from best management practices throughout the entire watershed. Two case studies at different spatial scales are presented, demonstrating STREAMS application to watersheds with varied properties.

The Chesapeake Bay Program Modeling System: Overview and Recommendations for Future Development.

The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.

Modeling the hydrologic effects of watershed-scale green roof implementation in the Pacific Northwest, United States.

Green roofs are among the most popular type of green infrastructure implemented in highly urbanized watersheds due to their low cost and efficient utilization of unused or under-used space. In this study, we examined the effectiveness of green roofs to attenuate stormwater runoff across a large metropolitan area in the Pacific Northwest, United States. We utilized a spatially explicit ecohydrological watershed model called Visualizing Ecosystem Land Management Assessments (VELMA) to simulate the resulting stormwater hydrology of implementing green roofs over 25%, 50%, 75%, and 100% of existing buildings within four urban watersheds in Seattle, Washington, United States. We simulated the effects of two types of green roofs: extensive green roofs, which are characterized by shallow soil profiles and short vegetative cover, and intensive green roofs, which are characterized by deeper soil profiles and can support larger vegetation. While buildings only comprise approximately 10% of the total area within each of the four watersheds, our simulations showed that 100% implementation of green roofs on these buildings can achieve approximately 10-15% and 20-25% mean annual runoff reductions for extensive and intensive green roofs, respectively, over a 28-year simulation. These results provide an upper limit for volume reductions achievable by green roofs in these urban watersheds. We also showed that stormwater runoff reductions are proportionately smaller during higher flow regimes caused by increased precipitation, likely due to the limited storage capacity of saturated green roofs. In general, green roofs can be effective at reducing stormwater runoff, and their effectiveness is limited by both their areal extent and storage capacity. Our results showed that green roof implementation can be an effective stormwater management tool in highly urban areas, and we demonstrated that our modeling approach can be used to assess the watershed-scale hydrologic impacts of the widespread adoption of green roofs across large metropolitan areas.

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT.

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter-elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network-Daily (GHCN-D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN-D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN-D based SWAT-simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge-based measurements can improve hydrologic model performance, especially for extreme events.

Phenology-adjusted dynamic curve number for improved hydrologic modeling.

The Soil Conservation Service Curve Number (SCS-CN, or CN) is a widely used method to estimate runoff from rainfall events. It has been adapted to many parts of the world with different land uses, land cover types, and climatic conditions and successfully applied to situations ranging from simple runoff calculations and land use change assessment to comprehensive hydrologic/water quality simulations. However, the CN method lacks the ability to incorporate seasonal variations in vegetated surface conditions, and unnoticed landuse/landcover (LULC) change that shape infiltration and storm runoff. Plant phenology is a main determinant of changes in hydrologic processes and water balances across seasons through its influence on surface roughness and evapotranspiration. This study used regression analysis to develop a dynamic CN (CNNDVI) based on seasonal variations in the remotely-sensed Normalized Difference Vegetation Index (NDVI) to monitor intra-annual plant phenological development. A time series of 16-day MODIS NDVI (MOD13Q1 Collection 5) images were used to monitor vegetation development and provide NDVI data necessary for CNNDVI model calibration and validation. Twelve years of rainfall and runoff data (2001-2012) from four small watersheds located in the Konza Prairie Biological Station, Kansas were used to develop, calibrate, and validate the method. Results showed CNNDVI performed significantly better in predicting runoff with calibrated CNNDVI runoff increasing by approximately 0.74 for every unit increase in observed runoff compared to 0.46 for SCS-CN runoff and was more highly correlated to observed runoff (r = 0.78 vs. r = 0.38). In addition, CNNDVI runoff had better NSE (0.53) and PBIAS (4.22) compared to the SCS-CN runoff (-0.87 and -94.86 respectively). In the validated model, CNNDVI runoff increased by approximately 0.96 for every unit of observed runoff, while SCS-CN runoff increased by 0.49. Validated runoff was also better correlated to observed runoff than SCS-CN runoff (r = 0.52 vs. r = 0.33). These findings suggest that the CNNDVI can yield improved estimates of surface runoff from precipitation events, leading to more informed water and land management decisions.

Evaluation of SWAT Impoundment Modeling Methods in Water and Sediment Simulations.

Worldwide studies show that 80-90% of all sediments eroded from watersheds is trapped within river networks such as reservoirs, ponds and wetlands. To represent the impact of impoundments on sediment routing in watershed modeling, Soil and Water Assessment Tool (SWAT) developers recommend to model reservoirs, ponds and wetlands using impoundment tools. This study evaluates performance of SWAT impoundment tools in the modeling of a small, agricultural watershed dominated by lakes and wetlands. The study demonstrates how to incorporate impoundments into the SWAT model and discusses and evaluates involved parameters. The study then recommends an appropriate calibration sequence, i.e., landscape parameters calibration, followed by pond/wetlands calibration, then channel parameter calibrations and, lastly, reservoir parameter calibration. Results of this study demonstrate that not following SWAT recommendation regarding modeling water land-use as an impoundment depreciates SWAT performance and may lead to misplaced calibration efforts and model over-calibration. Further, the chosen method to model impoundments' outflow significantly impacts sediment loads in the watershed, while streamflow simulation is not very sensitive. This study also allowed calculation of mass accumulation rates in modeled impoundments where the annual mass accumulation rate in wetlands (2.3 T/ha/yr) was 39% higher than mass accumulation rate in reservoirs (1.4 T/ha/yr).

Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin-up.

Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady-state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.

Capturing Microbial Sources Distributed in a Mixed-use Watershed within an Integrated Environmental Modeling Workflow.

Many watershed models simulate overland and instream microbial fate and transport, but few provide loading rates on land surfaces and point sources to the waterbody network. This paper describes the underlying equations for microbial loading rates associated with 1) land-applied manure on undeveloped areas from domestic animals; 2) direct shedding (excretion) on undeveloped lands by domestic animals and wildlife; 3) urban or engineered areas; and 4) point sources that directly discharge to streams from septic systems and shedding by domestic animals. A microbial source module, which houses these formulations, is part of a workflow containing multiple models and databases that form a loosely configured modeling infrastructure which supports watershed-scale microbial source-to-receptor modeling by focusing on animal- and human-impacted catchments. A hypothetical application - accessing, retrieving, and using real-world data - demonstrates how the infrastructure can automate many of the manual steps associated with a standard watershed assessment, culminating in calibrated flow and microbial densities at the watershed's pour point.

Riparian erosion vulnerability model based on environmental features.

Riparian erosion is one of the major causes of sediment and contaminant load to streams, degradation of riparian wildlife habitats, and land loss hazards. Land and soil management practices are implemented as conservation and restoration measures to mitigate the environmental problems brought about by riparian erosion. This, however, requires the identification of vulnerable areas to soil erosion. Because of the complex interactions between the different mechanisms that govern soil erosion and the inherent uncertainties involved in quantifying these processes, assessing erosion vulnerability at the watershed scale is challenging. The main objective of this study was to develop a methodology to identify areas along the riparian zone that are susceptible to erosion. The methodology was developed by integrating the physically-based watershed model MIKE-SHE, to simulate water movement, and a habitat suitability model, MaxEnt, to quantify the probability of presences of elevation changes (i.e., erosion) across the watershed. The presences of elevation changes were estimated based on two LiDAR-based elevation datasets taken in 2009 and 2012. The changes in elevation were grouped into four categories: low (0.5 - 0.7 m), medium (0.7 - 1.0 m), high (1.0 - 1.7 m) and very high (1.7 - 5.9 m), considering each category as a studied "species". The categories' locations were then used as "species location" map in MaxEnt. The environmental features used as constraints to the presence of erosion were land cover, soil, stream power index, overland flow, lateral inflow, and discharge. The modeling framework was evaluated in the Fort Cobb Reservoir Experimental watershed in southcentral Oklahoma. Results showed that the most vulnerable areas for erosion were located at the upper riparian zones of the Cobb and Lake sub-watersheds. The main waterways of these sub-watersheds were also found to be prone to streambank erosion. Approximatively 80% of the riparian zone (streambank included) has up to 30% probability to experience erosion greater than 1.0 m. By being able to identify the most vulnerable areas for stream and riparian sediment mobilization, conservation and management practices can be focused on areas needing the most attention and resources.

Development of a HEC-HMS-based watershed modeling system for identification, allocation, and optimization of reservoirs in a river basin.

One of the primary objectives of river basin planning and management is to assess the behavior of the river towards man-made and natural changes. In recent times, the self-purifying capacity of the river is found to be substantially affected because of extensive use of water for agricultural and industrial purposes. Any variation in the flow regime of a river poses a severe impact on the aquatic ecosystem, which affects its self-purifying capacity. Diverting river water for industrial and agricultural uses through dams and barrages reduces the natural flow rate of the river. The present study develops a novel approach by coupling Watershed Modeling System (WMS ver. 10.1) with linear optimization to provide an alternate means of water supply for such users. To explain the effectiveness of the model, a case study on the Ganges river basin of India has been considered. The ecosystem of the Ganges provides such a magnificent biological fabric, that its self-purifying capacity exceeds that of any other river water across the globe. However, the industries found in the river's most polluted stretch consume around 1200 million liters of water every day. In addition, 80% of the river water diverts at Narora barrage for agricultural purposes. As a result, the flow of the river in dry seasons is as less as 300 m3/s. The study suggests the need to develop economically feasible and efficient storage reservoirs to store the rainwater, which can be used to supply industrial and agricultural needs. The WMS software is used to acquire the watershed basin, outlet location, simulated runoff volume, proposed reservoir site, and the hydrograph using the monitored rainfall data of 5 years (2010-2014). The simulated runoff volume is then used to develop an optimization model to determine the required capacity of each reservoir using LINGO software (ver. 16.0). Four different storage reservoirs are proposed in the selected industrial sites of Unnao district, Uttar Pradesh, India. These reservoirs can supply the needs of industries, and thus reducing their dependency on the river Ganges. The model developed herein acts as an effective tool for giving a possible solution to large-scale water supply problems in the river basins, and also guides the decision makers towards restoring the stream flow.

Modeling Agricultural Watersheds with the Soil and Water Assessment Tool (SWAT): Calibration and Validation with a Novel Procedure for Spatially Explicit HRUs.

Applications of the Soil and Water Assessment Tool (SWAT) model typically involve delineation of a watershed into subwatersheds/subbasins that are then further subdivided into hydrologic response units (HRUs) which are homogeneous areas of aggregated soil, landuse, and slope and are the smallest modeling units used within the model. In a given standard SWAT application, multiple potential HRUs (farm fields) in a subbasin are usually aggregated into a single HRU feature. In other words, the standard version of the model combines multiple potential HRUs (farm fields) with the same landuse/landcover, soil, and slope, but located at different places of a subbasin (spatially non-unique), and considers them as one HRU. In this study, ArcGIS pre-processing procedures were developed to spatially define a one-to-one match between farm fields and HRUs (spatially unique HRUs) within a subbasin prior to SWAT simulations to facilitate input processing, input/output mapping, and further analysis at the individual farm field level. Model input data such as landuse/landcover (LULC), soil, crop rotation, and other management data were processed through these HRUs. The SWAT model was then calibrated/validated for Raccoon River watershed in Iowa for 2002-2010 and Big Creek River watershed in Illinois for 2000-2003. SWAT was able to replicate annual, monthly, and daily streamflow, as well as sediment, nitrate and mineral phosphorous within recommended accuracy in most cases. The one-to-one match between farm fields and HRUs created and used in this study is a first step in performing LULC change, climate change impact, and other analyses in a more spatially explicit manner.

Pathogen transport and fate modeling in the Upper Salem River Watershed using SWAT model.

Simulation of the fate and transport of pathogen contamination was conducted with SWAT for the Upper Salem River Watershed, located in Salem County, New Jersey. This watershed is 37 km(2) and land uses are predominantly agricultural. The watershed drains to a 32 km stretch of the Salem River upstream of the head of tide. This strech is identified on the 303(d) list as impaired for pathogens. The overall goal of this research was to use SWAT as a tool to help to better understand how two pathogen indicators (Escherichia coli and fecal coliform) are transported throughout the watershed, by determining the model parameters that control the fate and transport of these two indicator species. This effort was the first watershed modeling attempt with SWAT to successfully simulate E. coli and fecal coliform simultaneously. Sensitivity analysis has been performed for flow as well as fecal coliform and E. coli. Hydrologic calibration at six sampling locations indicate that the model provides a "good" prediction of watershed outlet flow (E = 0.69) while at certain upstream calibration locations predictions are less representative (0.32 < E < 0.70). Monthly calibration and validation of the pathogen transport and fate model was conducted for both fecal coliform (0.07 < E < 0.47 and -0.94 < E < 0.33) and E. coli (0.03 < E < 0.39 and -0.81 < E < 0.31) for the six sampling points. The fit of the model compared favorably with many similar pathogen modeling efforts. The research contributes new knowledge in E. coli and fecal coliform modeling and will help increase the understanding of sensitivity analysis and pathogen modeling with SWAT at the watershed scale.

Inferring land use and land cover impact on stream water quality using a Bayesian hierarchical modeling approach in the Xitiaoxi River Watershed, China.

Lake eutrophication has become a very serious environmental problem in China. If water pollution is to be controlled and ultimately eliminated, it is essential to understand how human activities affect surface water quality. A recently developed technique using the Bayesian hierarchical linear regression model revealed the effects of land use and land cover (LULC) on stream water quality at a watershed scale. Six LULC categories combined with watershed characteristics, including size, slope, and permeability were the variables that were studied. The pollutants of concern were nutrient concentrations of total nitrogen (TN) and total phosphorus (TP), common pollutants found in eutrophication. The monthly monitoring data at 41 sites in the Xitiaoxi Watershed, China during 2009-2010 were used for model demonstration. The results showed that the relationships between LULC and stream water quality are so complicated that the effects are varied over large areas. The models suggested that urban and agricultural land are important sources of TN and TP concentrations, while rural residential land is one of the major sources of TN. Certain agricultural practices (excessive fertilizer application) result in greater concentrations of nutrients in paddy fields, artificial grasslands, and artificial woodlands. This study suggests that Bayesian hierarchical modeling is a powerful tool for examining the complicated relationships between land use and water quality on different scales, and for developing land use and water management policies.

Modeling the impacts of oil palm plantations on water quantity and quality in the Kais River Watershed of Indonesia.

Oil palm plantations can impact hydrological processes in many tropical watersheds. The rapid conversion of tropical rainforests for commercial operations in recent decades has been associated with water scarcity, flooding, and polluted rivers. However, this widespread and emerging issue is less studied and underreported due to limited data availability, modeling complexity, and the remote nature of these landscapes. Ecohydrologic modeling enables us to investigate changes in watershed conditions caused by large-scale land cover changes from plantations. This study examines the impact of oil plantations on water quantity and quality using the SWAT+ model in the Kais River Watershed, West Papua, Indonesia. The objective is to assess the hydrological changes concerning land cover conversion to oil palm plantations. Results show that establishing oil palm plantations increased surface runoff by 21 %, and sediment yields rose by 16.9 % compared to the baseline. There was also a significant increase of 78 % in mean annual total nitrogen and 144 % in total phosphorous after the plantations' establishment. The results show that forest conversion to oil palm plantations in the Kais River watershed is a primary driver of change in hydrological regimes, resulting in the deterioration of water quality. There is a need for conservation strategies to mitigate the impacts of significant landscape changes in watershed ecosystems.

Implementing constructed wetlands for nutrient reduction at watershed scale: Opportunity to link models and real-world execution.

The negative effects of nutrient pollution in streams, rivers, and downstream waterbodies remain widespread global problems. Understanding the cost-effectiveness of different strategies for mitigating nutrient pollution is critical to making informed decisions and defining expectations that best utilize limited resources, which is a research priority for the US Environmental Protection Agency. To this end, we modeled nutrient management practices including residue management, cover crops, filter strips, grassed waterways, constructed wetlands, and reducing fertilizer in the upper East Fork of the Little Miami River, an 892 km2 watershed in southwestern Ohio, United States. The watershed is 64% agriculture with 422 km2 of row crops contributing an estimated 71% of the system's nutrient load. The six practices were modeled to treat row crop area, and among them, constructed wetlands ranked highest for their low costs per kilogram of nutrient removed. To meet a 42% phosphorus (P) reduction target for row crops, the model results suggested that the runoff from 85.5% of the row crop area would need to be treated by the equivalent of 3.61 km2 of constructed wetlands at an estimated cost of US$2.4 million annually (or US$48.5 million over a 20-year life cycle). This prompted a series of projects designed to understand the feasibility (defined in terms of build, treatment, and cost potential) of retrofitting the system with the necessary extent of constructed wetlands. The practicalities of building this wetland coverage into the system, while leading to innovation in unit-level design, has highlighted the difficulty of achieving the nutrient reduction target with wetlands alone. Approximately US$1.2 million have been spent on constructing 0.032 km2 of wetlands thus far and a feasibility analysis suggests a cost of US$38 million for an additional 0.409 km2. However, the combined expenditures would only achieve an estimated 13% of the required treatment. The results highlight the potential effectiveness of innovative design strategies for nutrient reduction and the importance of considering realistic field-scale build opportunities, which include accounting for acceptance among landowners, in watershed-scale nutrient reduction simulations using constructed wetlands.

Agricultural watershed conservation and optimization using a participatory hydrological approach.

Maximizing the impact of agricultural wastewater conservation practices (CP) to achieve total maximum daily load (TMDL) scenarios in agricultural watersheds is a challenge for the practitioners. The complex modeling requirements of sophisticated hydrologic models make their use and interpretation difficult, preventing the inclusion of local watershed stakeholders' knowledge in the development of optimal TMDL scenarios. The present study develops a seamless modeling approach to transform the complex modeling outcomes of Hydrologic Simulation Program Fortran (HSPF) into a simplified participatory framework for developing optimized management scenarios. The study evaluates seven conservation practices in the Pomme de Terre watershed in Minnesota, USA, focusing on sediment and phosphorus pollutant load reductions incorporating farmers' opinions to guide practitioners toward implementing cost-effective CPs. Results show reduced tillage and filter strips are the most cost-effective practices for non-point source pollution reduction, followed by conservation cover perennials. The integration of SAM with HSPF is crucial for sustainable field-scale implementation of conservation practices through enhanced involvement of amateur-modeling stakeholders and farmers directly connected to fields.