Water quality trading opportunities in two sub-watersheds in the northern Lake Okeechobee watershed

Assessing and improving the outcomes of nonpoint source water quality trading policies in urban areas: A case study in Virginia

Nonpoint source (NPS) water quality trading (WQT) is a market-based approach to improving water quality. Past work has shown that these programs could increase localized pollutant loadings, in part by exporting water quality controls from urban to rural areas. Virginia's NPS WQT program has enabled thousands of transactions and may provide a model for other programs, but its impacts on urban water quality have not been thoroughly assessed. We quantify the impact of NPS WQT purchases in Virginia on water quality and hydrology in an urban catchment. We go on to assess outcomes of a policy alternative where buyers and sellers are collocated in the urban catchment. Simulation results show that NPS WQT increased total phosphorus (TP) loading by an average of 0.8 lbs TP/year for each 1.0 offsite credits purchased in the analyzed catchment. The TP loading increased in years with greater rainfall, such that TP loads were increased by up to 1.2 lbs TP/year for each offsite credit purchased. These loading increases may or may not be acceptable, depending on the cumulative number of purchases within an urban catchment and existing local water quality issues. In our policy alternative with buyers and sellers collocated in the catchment, we found that the TP increase from development was completely offset at the catchment scale, with a decrease of 4.3 lbs TP/year for each 1.0 credits purchased. This suggests that credits awarded for urban mitigation practices are undervalued compared with water quality requirements for credit purchasers. This undervaluation is a result of the Virginia trading program using one approach to compute the credit value for buyers and a different approach to compute the credit value for sellers. We demonstrate how using a single model to determine both buyer and seller credit values in urban areas could provide greater transparency and mitigate the risk of urban pollution hot spots. This work demonstrates the importance of consistency in the scale of pollutant load calculations between buyers and sellers for NPS WQT, and contributes novel insight into the implications of WQT for urban NPS pollution.

Optimal allocation and transaction of waste load permits for transboundary basin: A Bi-level programming approach based on node-arc

Inadequate water quality exacerbates global water resources scarcity. Hence, water quality of the river basin is increasingly perceived as a global obstacle to sustainable development because of the limited water carrying capacity. Efficient waste load permits (WLPs) allocation plays a critical role in enhancing water quality by controlling the emission cap. Considering transboundary water pollution and transaction among regions, a bi-level objective model is proposed to analyze the WLPs allocation based on the node-arc method. Motivated by alleviating regional development differences, the watershed management committee concentrates on equitable distribution of WLPs to regions. Furthermore, regional authorities focus on how to guarantee the maximum economic development and balance the WLPs emissions from the municipal, industrial, and agricultural sectors. Practicality and efficiency of the constructed model is demonstrated by applying it to Tuojiang River Basin. Through the analysis of the results, three management recommendations are proposed for Tuojiang River: strengthening the prevention of agricultural non-point source pollution, sticking to the cooperation between upstream and downstream regions, and speeding up the construction of sewage environmental tax system. The results illustrate that as the proposed method can control the total amount of sewage, it could provide decision-making references for the amelioration of water environment.

Nonpoint Source Water Quality Trading outcomes: Landscape-scale patterns and integration with watershed management priorities

Nonpoint source (NPS) water quality trading (WQT) has been lauded as a way to reduce water pollution while mitigating costs, but NPS WQT programs often do not account for cumulative landscape-scale impacts to hydrological and ecological processes. In this work, we parameterize the landscape-scale patterns of an emerging NPS WQT market in Virginia (n = 606 transactions) and describe potential tradeoffs and synergies. We also examine program outcomes in the context of Virginia's spatially-explicit conservation and restoration priorities, and discuss ways in which NPS WQT integrates or fails to integrate with these state-level watershed management goals. These spatial and policy analyses demonstrate novel ways to evaluate NPS WQT programs. Our results reveal how NPS WQT has influenced Virginia land management patterns in practice. Specifically, we show that this program has encouraged the transfer of water quality Best Management Practices (BMPs) from urban to rural areas. Impact sites are often far from mitigation sites, at an average of 164.6 km apart measured along the stream network and most often migrated outside the 8-digit Hydrologic Unit Code watershed boundaries. We also find opportunity for improved integration with the state-level management priorities, including that an estimated 22% of the NPS WQT mitigation site area works against state priorities (for example by converting prime farmland to forest), 9% supports state priorities, and 69% neither negates nor supports state priorities. We suggest policy and management actions that can increase the integration of NPS WQT with statewide watershed management goals, and could ultimately improve environmental returns from this fast-growing program.

Review of Watershed-Scale Water Quality and Nonpoint Source Pollution Models

Watershed-scale nonpoint source (NPS) pollution models have become important tools to understand, evaluate, and predict the negative impacts of NPS pollution on water quality. Today, there are many NPS models available for users. However, different types of models possess different form and structure as well as complexity of computation. It is difficult for users to select an appropriate model for a specific application without a clear understanding of the limitations or strengths for each model or tool. This review evaluates 14 more commonly used watershed-scale NPS pollution models to explain how and when the application of these different models are appropriate for a given effort. The models that are assessed have a wide range of capacities that include simple models used as rapid screening tools (e.g., Long-Term Hydrologic Impact Assessment (L-THIA) and Nonpoint Source Pollution and Erosion Comparison Tool (N-SPECT/OpenNSPECT)), medium-complexity models that require detail data input and limited calibration (e.g., Generalized Watershed Loading Function (GWLF), Loading Simulation Program C (LSPC), Source Loading and Management Model (SLAMM), and Watershed Analysis Risk Management Frame (WARMF)), complex models that provide sophisticated simulation for NPS pollution processes with intensive data and rigorous calibration (e.g., Agricultural Nonpoint Source pollution model (AGNPS/AnnAGNPS), Soil and Water Assessment Tool (SWAT), Stormwater Management Model (SWMM), and Hydrologic Simulation Program Fortran (HSPF)), and modeling systems that integrate various sub-models and tools, and contain the highest complexity to solve all phases of hydrologic, hydraulic, and chemical dynamic processes (e.g., Automated Geospatial Watershed Assessment Tool (AGWA), Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) and Watershed Modeling System (WMS)). This assessment includes model intended use, components or capabilities, suitable land-use type, input parameter type, spatial and temporal scale, simulated pollutants, strengths and limitations, and software availability. Understanding the strengths and weaknesses of each watershed-scale NPS model will lead to better model selection for suitability and help to avoid misinterpretation or misapplication in practice. The article further explains the crucial criteria for model selection, including spatial and temporal considerations, calibration and validation, uncertainty analysis, and future research direction of NPS pollution models. The goal of this work is to provide accurate and concise insight for watershed managers and planners to select the best-suited model to reduce the harm of NPS pollution to watershed ecosystems.

Giving credit to reforestation for water quality benefits

While there is a general belief that reforesting marginal, often unprofitable, croplands can result in water quality benefits, to date there have been very few studies that have attempted to quantify the magnitude of the reductions in nutrient (N and P) and sediment export. In order to determine the magnitude of a credit for water quality trading, there is a need to develop quantitative approaches to estimate the benefits from forest planting in terms of load reductions. Here we first evaluate the availability of marginal croplands (i.e. those with low infiltration capacity and high slopes) within a large section of the Ohio River Basin (ORB) to assess the magnitude of the land that could be reforested. Next, we employ the Nutrient Tracking Tool (NTT) to study the reduction in N, P and sediment losses from converting corn or corn/soy rotations to forested lands, first in a case study and then for a large region within the ORB. We find that after reforestation, N losses can decrease by 40 to 80 kg/ha-yr (95–97% reduction), while P losses decrease by 1 to 4 kg/ha-yr (96–99% reduction). There is a significant influence of local conditions (soils, previous crop management practices, meteorology), which can be considered with NTT and must be taken into consideration for specific projects. There is also considerable interannual and monthly variability, which highlights the need to take the longer view into account in nutrient credit considerations for water quality trading, as well as in monitoring programs. Overall, there is the potential for avoiding 60 million kg N and 2 million kg P from reaching the streams and rivers of the northern ORB as a result of conversion of marginal farmland to tree planting, which is on the order of 12% decrease for TN and 5% for TP, for the entire basin. Accounting for attenuation, this represents a significant fraction of the goal of the USEPA Gulf of Mexico Hypoxia Task Force to reduce TN and TP reaching the dead zone in the Gulf of Mexico, the second largest dead zone in the world. More broadly, the potential for targeted forest planting to reduce nutrient loading demonstrated in this study suggests further consideration of this approach for managing water quality in waterways throughout the world. The study was conducted using computational models and there is a need to evaluate the results with empirical observations.

A Phased Assessment of Restoration Alternatives to Achieve Phosphorus Water Quality Targets for Lake Okeechobee, Florida, USA

Achieving total phosphorus (TP) total maximum daily loads (TMDL) for Lake Okeechobee (Florida, FL, USA), a large freshwater lake, is a key component of the greater Everglades ecosystem restoration and sustainability of south Florida. This study was aimed at identification of a cost-effective restoration alternative using four TP control strategies—Best Management Practices (BMPs), Dispersed Water Management (DWM), Wetland Restoration, and Stormwater Treatment Areas (STAs)—to achieve a flow-weighted mean TP concentration of 40 µg/L at lake inflow points, through a phased scenario analysis approach. The Watershed Assessment Model was used to simulate flow and phosphorus dynamics. The 10-year (1998–2007) ‘Base’ scenario calibration indicated ‘acceptable’ to ‘good’ performance with simulated annual average flows and TP load of 2.64 × 109 m3 and 428.6 metric tons, respectively. Scenario results showed that TP load reduction without STAs would be around 11–40% with respect to Base compared to over 75% reduction requirement to achieve TMDL, indicating STAs as a necessary component to achieve restoration. The most cost-effective alternative to achieve TP target consisted of implementation of nutrient management BMPs, continuation of existing DWM projects, and the construction of ~200 km2 of STAs for a total project cost of US $4.26 billion.

Effluent trading planning and its application in water quality management: A factor-interaction perspective

In this study, a Bayesian risk-induced interval stochastic modeling framework (BRISF) is proposed for planning effluent trading program among point and nonpoint sources as well as identifying interactions of important trading factors under system risk. BRISF incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian inference with random walk Metropolis algorithm (RWM), and constraint-violation risk-based two-stage stochastic programming (CRTSP) within a general framework. Bayesian inference is employed for uncertainty analysis of SWAT model parameters and uncertain prediction of nutrient loadings; this process provides the random inputs for optimization process. CRTSP is capable of dealing with multiple uncertainties in modeling effluent trading program as well as system risk of environmental allowance violation. BRISF is applied to a real case of Xiangxihe watershed in China for water quality management. Solutions for optimal trading scheme corresponding to different risk levels are generated. Thousands of scenarios are examined to analyze the individual and interactive effects of trading ratios and treatment rates on trading system. Comparison between cross-industry and intra-industry effluent trading scheme is also conducted. It is proved that cross-industry trading would bring about higher benefit with reduced pollution loading; cross-industry effluent trading scheme would be recommended to achieve optimal water quality management and system benefit.

Effluent trading in river systems through stochastic decision-making process: a case study

The objective of this paper is to provide an efficient framework for effluent trading in river systems. The proposed framework consists of two pessimistic and optimistic decision-making models to increase the executability of river water quality trading programs. The models used for this purpose are (1) stochastic fallback bargaining (SFB) to reach an agreement among wastewater dischargers and (2) stochastic multi-criteria decision-making (SMCDM) to determine the optimal treatment strategy. The Monte-Carlo simulation method is used to incorporate the uncertainty into analysis. This uncertainty arises from stochastic nature and the errors in the calculation of wastewater treatment costs. The results of river water quality simulation model are used as the inputs of models. The proposed models are used in a case study on the Zarjoub River in northern Iran to determine the best solution for the pollution load allocation. The best treatment alternatives selected by each model are imported, as the initial pollution discharge permits, into an optimization model developed for trading of pollution discharge permits among pollutant sources. The results show that the SFB-based water pollution trading approach reduces the costs by US$ 14,834 while providing a relative consensus among pollutant sources. Meanwhile, the SMCDM-based water pollution trading approach reduces the costs by US$ 218,852, but it is less acceptable by pollutant sources. Therefore, it appears that giving due attention to stability, or in other words acceptability of pollution trading programs for all pollutant sources, is an essential element of their success.