Understanding Water Quality Ecosystem Services
Water quality ecosystem services (ES) are crucial for maintaining ecological integrity and supporting various human activities. These services are essential for clean drinking water, sustainable fisheries, and recreational opportunities (Olander et al., 2017; Torres et al., 2021; van Wijk et al., 2022). Effective management of these services relies on accurate ES modeling platforms that simulate the biophysical functions underpinning service delivery. These models help inform economic valuations, trade-off analyses, and policy prioritizations aimed at improving water quality management (Meraj et al., 2022; Posner et al., 2016).
The Role of ES Modeling Platforms
Several advanced modeling platforms, such as the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), the Soil and Water Assessment Tool (SWAT), and the SPAtially Referenced Regressions On Watershed attributes (SPARROW), have significantly enhanced our understanding of how environmental features influence economically and socially relevant outcomes (Taylor and Druckenmiller, 2022). These models enable us to examine how policies impact biophysical conditions in service-providing landscapes (Tallis et al., 2012; Wong et al., 2015) and how land-use changes or climate shifts affect the provision of crucial water-related ES (Shi et al., 2013; Yohannes et al., 2021; Zong et al., 2020). Ultimately, these insights guide effective environmental governance, offering science-based guidelines for decision-makers (Cao et al., 2023; Zulian et al., 2018).
Data Limitations and Their Implications
Despite these advancements, modeling efforts face challenges related to data scarcity, which can undermine accuracy and reliability. In the U.S., legislative measures like the Clean Water Act and initiatives like the National Aquatic Resource Surveys (NARS) have improved access to water quality data. However, the availability and quality of this data can vary significantly depending on geographic location, institutional capacity, and the specific parameters being tracked.
Even in relatively data-rich regions, monitoring efforts are often hindered by financial and logistical constraints, resulting in low-frequency or irregular sampling regimes. This inconsistency can lead to gaps in data that fail to capture vital seasonal dynamics or event-driven variability, such as storm runoff (Alilou et al., 2019; Huang et al., 2022). Consequently, the lack of consistent temporal coverage complicates the validation of model estimates against real-world conditions, eroding confidence in the results (Anttila et al., 2012).
Spatial Data Gaps in ES Modeling
Spatial data scarcity poses additional challenges in water quality ES modeling. Uneven distribution of monitoring infrastructures creates gaps, especially in rural and economically disadvantaged areas (Cassidy and Jordan, 2011; Huang et al., 2022). This limited spatial representation collapses the ability to capture heterogeneity in land use, hydrological dynamics, and pollutant transport, all of which are fundamental to understanding how these variables influence ES delivery (Jiang et al., 2021; Xia et al., 2023).
Often, common ES modeling tools operate across limited spatial extents using default parameters and uncalibrated assumptions in data-scarce regions, potentially leading to significant uncertainties in outputs. For regional planning and policy-directed conservation actions, such spatial blind spots can cause mischaracterization of service provision, which could misallocate valuable resources (Lautenbach et al., 2019).
Attempting Solutions for Data Scarcity
Previous efforts to tackle data scarcity in ES modeling have frequently drawn from methodologies used in hydrological and geomorphological applications (Rieb et al., 2017). Techniques such as interpolation and regression have aimed to fill temporal and spatial data gaps. However, these methods often fall short in data-scarce conditions typical of water quality monitoring because they are sensitive to irregular datasets (Lee et al., 2016; Pagliero et al., 2019). Additionally, many water quality parameters display complex, non-linear relationships with environmental drivers, which limits the efficacy of linearity-based techniques (Scowen et al., 2021).
Conversely, machine learning (ML) techniques present a promising avenue for improving data imputation and enhancing ES model performance. Although applications of ML have been established in other fields—such as modeling provisioning of firewood, clustering ES interactions, and extracting biophysical features from remote imagery—the integration of ML into water quality ES modeling remains underexplored (Scowen et al., 2021). This potential for bolstering model generalizability and accuracy in the face of data scarcity remains largely untapped.
A New Methodological Framework
To bridge the gaps created by temporal and spatial data scarcity, we propose a methodological framework that enhances the application of water quality ES models. This framework prioritizes regional-scale validation and facilitates the extrapolation of model parameters from data-rich to data-poor areas by incorporating machine learning techniques.
In our case study using the InVEST Nutrient Delivery Ratio (NDR) model in Puerto Rico, we embark on two key objectives: first, to develop and assess a workflow for retrieving and imputing historical water quality monitoring data through ML techniques; second, to create an automated process for calibration and validation that extrapolates parameters based on hydrogeological similarities across regions. By addressing both temporal and spatial gaps, this approach aims to bolster the accuracy and relevance of water quality ES modeling, ultimately fostering better decision-making in areas where monitoring and data collection efforts are limited.