Author(s): Samaneh Alighanbari; David Styles; Eoghan Clifford
Linked Author(s): Eoghan Clifford
Keywords: Land use nutrient loss modelling water quality modelling
Abstract: Changes in land use, driven by urban development, economic growth, and transportation, are placing considerable pressure on the environment. In Ireland, the agriculture, forestry, and other land use (AFOLU) sector accounts for over 40% of the country’s greenhouse gas emissions and significantly impacts water quality. Various models, such as SPARROW, SWAT, and MARINA and other nutrient export models, have been developed and used to model the effects of human activities on water quality. In Ireland, The GOBLIN model, has been developed to explore land-use scenarios that align with climate neutrality goals. However, to date these models are not linked and there is a significant gap in relation to validating such models using water quality data within catchments. This study combines water quality modelling at sub-catchment and catchment scales with land-use modelling systems to improve estimates of nutrient emissions from different land uses. Using Ireland as a case study, this research refines how nutrient losses from sources, including septic tanks, agriculture, urban wastewater and forestry, are evaluated within broader land-use scenarios that could deliver climate neutrality. A framework is being developed to validate these integrated models, enabling their application in land-use and land-management scenario modelling. The insights aim to guide sustainable land-use decisions that reduce nutrient loading in water bodies, supporting both rural development and environmental protection. Keywords: land use, nutrient loss modelling, water quality modelling. 1. Introduction Land use and environmental impacts The rapid growth of urban areas, driven by economic development and improved transportation systems, is significantly transforming landscapes globally and placing immense pressure on natural resources and ecosystems (Foley et al., 2011). Intensive farming methods, alongside deforestation and land conversion, have caused widespread habitat degradation, biodiversity loss, and a decline in water quality across rivers, lakes, and coastal regions (European Commission, 2019). In Ireland, as in many countries, the agriculture, forestry, and other land use (AFOLU) sector plays a vital role in both the economy and the rural community, contributing significantly to global food security (DAFM, 2020). However, the AFOLU sector contributes over 40% of Ireland’s territorial greenhouse gas emissions, largely in the form of methane from livestock and nitrous oxide from agricultural soils (EPA, 2020). Urban land use must also be considered when analysing land use changes. For example, Tavares et al. (2019) examined the spatial and temporal trajectories of land use in three small to medium-sized cities in central Portugal -- Viseu, Leiria, and Castelo Branco -- over the period from 1958 to 2011. The study identifies significant trends of peri-urbanization and urbanization, characterized by a decrease in agricultural areas and an increase in artificial land use. It emphasizes the influence of local planning frameworks and highlights the distinct dynamics of land-use change in each city, reflecting broader European trends in urban development and environmental sustainability. Models quantifying land use impacts. The Paris Agreement aims to cap global temperature rise, with countries committing to nationally determined contributions (NDCs). Many nations are striving for climate neutrality by 2050, though uncertainties remain about the necessary adaptations within the AFOLU sector. To support policy-making around land use, several models have been developed to provide a solid evidence base for future land-use policies. For instance, GOBLIN, in Ireland, evaluates detailed AFOLU scenarios nationally, taking into account all major greenhouse gas emissions and carbon sinks. It identifies the biophysical limits related to climate neutrality objectives, offering evidence for NDCs (Duffy et al., 2022). Stepanov et al. (2020) investigates the complexities of land-use change (LUC) in Brazil by coupling two distinct models: the GLOBIOM-Brazil partial equilibrium model and the demand-driven spatially explicit model PLUC. The research focuses on the period from 2007 to 2030 and aims to enhance the accuracy of LUC projections through model harmonization and integration. The authors assess the performance of the coupled model against the individual GLOBIOM-Brazil model, particularly in regions experiencing significant agricultural expansion, such as Mato Grosso and Para. Their findings reveal that the coupled model outperforms GLOBIOM-Brazil, with reductions in root mean squared error (RMSE) ranging from 31% to 80% across various land-use classes. This improvement underscores the potential benefits of model collaboration in understanding and projecting land-use dynamics, which is crucial for informing environmental policies and addressing challenges related to deforestation and biodiversity loss. Investigating future land change dynamics through land use models is critical for understanding the interplay between human and natural systems. However, these models (ie, SLAM, GOBLIN, etc) often face limitations, which restrict their ability to accurately capture complex land use and environmental interactions. For example, many models fail to account for the complexities of feedback loops between land use, policy interventions, and environmental changes. Improving their effectiveness requires integrating multi-scalar dynamics, such as local land-use decisions and their broader regional impacts, alongside incorporating demand-supply relationships influenced by economic and demographic factors. Additionally, learning from past model evaluations through validation with historical data and scenario testing can enhance their predictive capacity, making them more robust tools for guiding sustainable land management practices. Impacts of land use on water quality. In Ireland, the Source Load Apportionment Model (SLAM) was developed to support the Water Framework Directive by predicting phosphorus and nitrogen sources in water bodies. It integrates catchment data and pressure information from both point and diffuse sources, including hydrogeological factors. Although some agri-environmental policies have curtailed nutrient emissions, countries such as Ireland may require additional measures to sever the link between agricultural land use and water pollution. The SLAM model plays a critical role in identifying phosphorus and nitrogen sources in water, highlighting significant contributions from municipal wastewater treatment plants and agriculture throughout Ireland. This information is essential for policymakers to address regional and local nutrient risks in water bodies (Mockler et al., 2017). Huttunen et al. (2021) developed a framework for investigating agricultural nutrient pollution at a catchment scale, using global climate and socioeconomic scenarios. Focusing on the Aura River catchment in South-West Finland, the study assessed farmer adaptations like optimizing fertilization and implementing manure recycling to mitigate nutrient overloading. It projected an 18% increase in phosphorus loads by 2100, with a 9% decrease in nitrogen. The study links nutrient loads to water quality impacts, particularly eutrophication in the Baltic Sea, highlighting the need for better management practices to reduce phosphorus. However, it lacks an exploration of the long-term effects on soil health and the social factors affecting sustainable farming practices. A study conducted in China in 2019 addressed three challenges in nutrient modelling for rivers (Chen et al., 2019). These challenges were (1) the difficulty in transferring modelling results across biophysical and administrative scales, (2) inadequate representation of point source locations, and (3) limited incorporation of direct manure discharge into rivers. A multi-scale modelling approach was developed, including a detailed representation of point nutrient sources in rivers. The results indicated significant variations in river pollution and source attribution across different spatial scales. Point sources accounted for 75% of total dissolved phosphorus (TDP) inputs to rivers in China in 2012, while diffuse sources contributed 72% of total dissolved nitrogen (TDN) inputs. A third of sub-basins contributed over half of the pollution. Downscaling to the smallest scale (polygons) revealed that 14% and 9% of the area contributed to over half of the calculated TDN and TDP pollution, respectively. Pollution sources showed considerable variation among and within counties. The study concluded that multi-scale models can assist in developing effective water pollution policies. Considering these challenges and opportunities, this study aims to bridge the knowledge gap by integrating water quality modelling at finer spatial scales, such as sub-catchment and catchment levels, into existing land use models, with a particular focus on enhancing the GOBLIN model. By building on previous research and methodological advancements, this study seeks to refine nutrient loss and transport modelling, improve the accuracy of land use scenario analyses, and facilitate comprehensive, multidimensional evaluations of sustainable land management strategies. 2. Methodology This research comprises three main phases. The first phase, which is the focus of this work, assesses nutrient loss from various land uses, including agriculture, urban wastewater, and forestry, using updated data on fertilizer use, livestock numbers, forestry, and wastewater outputs. Initially focusing on four case-study catchments, the methodology is expanded nationally. The second phase examines how soil types and topography influence nutrient movement. Using GIS tools, this phase will integrate soil maps and topographic data into nutrient models, addressing gaps in model validation by leveraging water quality data. The final phase will apply validated models to scenario analyses, predicting the impact of land-use changes on water quality and supporting policy development by combining these findings with parallel GHG emissions studies. Nutrient export rate for Forestry Nutrient loads, particularly nitrogen (N) and phosphorus (P), from forested catchments can significantly impact water quality, making accurate load estimation essential for effective environmental management. To date this study has analysed the total N and P loads for five catchments in Ireland. The studied catchments are located the west (Corrib), northwest (Moy and Killala Bay), northeast (Newry, Fane, Glyde, and Dee), southeast (Owenavorragh), and southwest (Tralee Bay-Feale), providing a broad representation of the country's hydrological contexts, forest types and soil characteristics. Table 1 summarizes the total nitrogen, and phosphorus loads for each Irish catchment, calculated based on export rate coefficients from previous relevant studies. The range of coefficients applied in this table was taken from De Melo et al. (2022), Finer et al. (2021), Deval et al. (2021), Ouyang et al. (2022), and Raty et al. (2020). In these studies, export coefficients for forestry were given for mineral Table1. Summary of estimated nutrient export from five catchments in Ireland due to forestry. The minimum and maximum values result from the variation in export coefficients found in literature. Location N Load in Corrib P Load in Corrib N Load in Moy & Killala Bay P Load in Moy & Killala Bay N Load in Newry, Fane, Glyde and Dee P Load in Newry, Fane, Glyde and Dee N Load in Owenavorragh P Load in Owenavorragh N Load in Tralee Bay-Feale P Load in Tralee Bay-Feale Min (kg y-1) 42510 3932 35881 4200 14126 1501 6114 618 29526 2923 Max (kg y-1) 167781 13520 185732 15721 47593 3891 16056 1208 117817 13224 In Table 1 export rate coefficients specific to miscellaneous soil types and scrub forest cover were unavailable. However, this had a minimal impact on the calculations related to forestry, as these categories represent only a small portion of the catchment area. For instance, in the Corrib, Moy & Killala Bay, Newry, Fane, Glyde and Dee, Owenavorragh, and Tralee Bay-Feale catchments, miscellaneous soils make up 4%, 1.5%, 3.5%, 5%, and 0.7% of the total area, respectively. Table 2 summarises potential nutrient exports based on SLAM (which has previously been used for estimating nutrient export from various activities in Ireland). Location N Load in Corrib P Load in Corrib N Load in Moy & Killala Bay P Load in Moy & Killala Bay N Load in Newry, Fane, Glyde and Dee P Load in Newry, Fane, Glyde and Dee N Load in Owenavorragh P Load in Owenavorragh N Load in Tralee Bay-Feale P Load in Tralee Bay-Feale SLAM (kg y-1) 214094 16351 225574 17031 77607 7021 26397 2263 214128 15188 Table2. Summary of estimated nutrient export from five catchments in Ireland due to forestry based on SLAM. The SLAM values are those resulting from applying the coefficients applied by Mockler et al (2017) to all soil types. The export coefficients for forestry derived from SLAM are noticeably higher compared to those reported in other literature. This difference may stem from differences in methodologies, as SLAM employs a national-scale approach. In contrast, literature values are often based on localized measurements under specific environmental and management conditions. This indicates that there can be wide variation in modelled nutrient exports depending on what loss coefficients are applied; thus, care must be taken in applying context specific coefficients where possible. This study will analyze nutrient exports from various land use types across the country at the catchment scale accounting for localized conditions related to fertilizer use, animal stocking density, septic tank density etc. The hydrological model will consider the effects of soil types and topography on nutrient transport to waterbodies. In future work, a comprehensive scenario analysis will encompass the potential changes in water quality resulting from land use policy and projected changes to climate to mid and end century. 3. Conclusion This study seeks to fill a gap in current research by integrating water quality modeling at finer scales -- such as sub-catchment and catchment levels -- within existing land use models that emphasize GHG emissions, specifically enhancing models like SLAM and GOBLIN. Building on previous research, it aims to advance nutrient loss and transport modeling and enable scenario analysis for informed land use planning. By incorporating water quality into land use modeling frameworks, the study can provide the framework, with national and international relevance, for a more holistic, multidimensional evaluation of land use strategies. Ultimately, these efforts will support the development of policies and practices that encourage sustainable land management and protect water resources. 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Year: 2025