Targeting priority management areas for multiple pollutants from non-point sources

Towards implementing precision conservation practices in agricultural watersheds: A review of the use and prospects of spatial decision support systems and tools

Agricultural nonpoint source (NPS) pollution leads to water quality degradation. While agriculture is faced with the challenge of feeding a growing population in a changing climate, farmers must also strive to minimize adverse impacts of agriculture on the environment. As a result, policies, and agri-environmental programs to promote agricultural conservation practices for controlling NPS pollution have been emerging. Despite progress, reducing NPS is a complex challenge that requires ongoing innovation and investment. A major challenge is to achieve an optimal spatial trade-off between the economic costs and positive environmental outcomes of conservation practices on complex agricultural landscapes. Geospatial systems and tools can help to address this challenge and enhance the effectiveness and efficiency of conservation efforts. However, using these tools for precision conservation is underexamined. This review paper aims to address this gap through a critical exploration of spatial decision support systems and tools to provide synthesized knowledge for implementing precision conservation practices. This paper proposes a conceptual framework to guide the implementation of precision conservation and identifies areas for further development of geospatial systems and tools on planning and assessment of precision conservation efforts. All of which will be helpful for decision-makers and watershed managers in determining the most effective approaches for precision conservation. Furthermore, this review highlights the need for further research and development towards establishing an integrated spatial decision support system framework, which can improve socio-economic, environmental, and ecological outcomes.

Accounting for the annual variability when assessing non-point source pollution potential in Mediterranean regulated watersheds

The characterization of non-point source pollution at the watershed scale difficult owing to its distributed nature combined with the lack of suitable measurements for validation. This study proposes the classification of land within a Mediterranean watershed according to its potential source of non-point pollution, considering interannual precipitation variability and dam regulation effects. For this purpose, the potential non-point pollution index (PNPI) developed by the Italian Environmental Protection Agency was modified to include annual local precipitation behavior, named local annual PNPI (APNPI). PNPI and APNPI were computed for the Guadalquivir River (Spain), which has a drainage surface of 57,500 km2 and is highly regulated by >60 reservoirs. The results reflect the vulnerability along the Guadalquivir River in terms of the spatially variable non-point pollutant nature of its contributing watersheds. The annual average nitrate concentration values on the southern side exceeded the average value on the northern side by almost five times and showed a statistically significant power fit with the PNPI, with an R2 of 0.65. Long-term available nitrate data (1981/82-2006/07) on a monthly scale at the outlets of some watersheds allowed us to rank priority pollutant source areas within the watershed. The power fits between the annual average nitrate loads and the APNPI (R2 = 0.51-0.99) were statistically significant, which validated the utility of adding the variability of precipitation at an annual scale as a dynamic factor in the index. The APNPI can constitute a simple dynamic classification index for assessing the relative risk of non-point source pollution across a large area, especially in data-scarce situations.

Evaluation of management practices on agricultural nonpoint source pollution discharges into the rivers under climate change effects

In recent years, agricultural non-point source pollution (ANPSP) has become the biggest threat to Aras River water quality by completing the Mughan irrigation and drainage network. Nutrient pollutants, including nitrate and phosphate, released into the river through drains have created a range of obstacles for locals living around the river. Agricultural activities are generally considered the largest source of non-point pollution. They have no complex and uniform impact along the river. Thus, the spatial distribution of ANPS and highly polluted areas should be identified to manage watershed management. This study proposes a simple framework for identifying pollutant-sensitive areas along the river and management strategies to improve water quality. To this aim, the main factors affecting ANPSP were identified, and the effectiveness of the scenarios selected to comply with water quality regulations for drinking and environment during 1993-2007 were simulated. Based on the sensitivity analysis, land use and fertilizer are the main factors affecting river ANPSP. Thus, their changes were modeled in different scenarios. Based on the results, the ANPSP load was higher downstream. The agricultural lands in region 3 were considered the main source of pollution. Comparing the management scenarios showed that the amount of nitrate and phosphate leaching into the river decreased to 18.1 and 8.35 %, respectively, by reducing the consumption of urea and phosphate fertilizers by 50 %. The results help watershed managers implement eco-friendly land use and nutrient management programs at specific locations during specific periods to control ANPSP along the rivers.

Best Management Practices for Diffuse Nutrient Pollution: Wicked Problems Across Urban and Agricultural Watersheds

Extensive time and financial resources have been dedicated to address nonpoint sources of nitrogen and phosphorus in watersheds. Despite these efforts, many watersheds have not seen substantial improvement in water quality. The objective of this study is to review the literature and investigate key factors affecting the lack of improvement in nutrient levels in waterways in urban and agricultural regions. From 94 studies identified in the academic literature, we found that, although 60% of studies found improvements in water quality after implementation of Best Management Practices (BMPs) within the watershed, these studies were mostly modeling studies rather than field monitoring studies. For studies that were unable to find improvements in water quality after the implementation of BMPs, the lack of improvement was attributed to lack of knowledge about BMP functioning, lag times, nonoptimal placement and distribution of BMPs in the watershed, postimplementation BMP failure, and socio-political and economic challenges. We refer to these limiting factors as known unknowns. We also acknowledge the existence of unknown unknowns that hinder further improvement in BMP effectiveness and suggest that machine learning, approaches from the field of business and operations management, and long-term convergent studies could be used to resolve these unknown unknowns.